Global Storm Resolving Models (GSRMs) is crucial for understanding extreme weather events under the climate change background. In this study, we optimize Global-Regional Integrated Forecast System (GRIST), which is a unified weather-climate modeling system designed for research and operation, for the next-generation Sunway supercomputer, incorporating AI-enhanced physics suite, OpenMP-based parallelization, and mixed-precision optimizations to enhance both efficiency and performance portability, as well as the unified modeling capability. Our experiments successfully capture significant events during the "23.7" extreme rainfall over northern China influenced by super Typhoon Doksuri, at 1km resolution. Notably, our work scales to 34 million cores, enabling simulation speeds at 491 SDPD (3km) and 181 SDPD (1km).

Kilometer-scale Earth system models (ESMs) necessitate exascale supercomputers to facilitate realistic simulations of weather phenomena and climate variability over a time span ranging from days to decades. We present AP3ESM, an ultra‑high‑resolution, AI‑Powered, Performance‑Portable ESM coupling atmosphere, land surface, ocean, and sea ice components. By leveraging the performance portability features of Kokkos and OpenMP, the AP3ESM operates efficiently on two heterogeneous systems while incurring minimal development overhead. Advanced optimization techniques, such as adaptive parallel algorithms, AI-enhanced physical parameterizations, and mixed-precision computations, have been implemented to further boost the computational efficiency. Breaking the 1-km resolution barrier, AP3ESM delivers 0.85 and 1.98 simulated-years-per-day (SYPD) for the standalone atmosphere and ocean components on 34.1 million Sunway cores and 16085 GPUs, respectively; the holistic AP3ESM achieves 0.54 SYPD on 37.2 million Sunway cores. Notably, the forecast experiment successfully captures Super Typhoon Doksuri in 2023 and its associated extreme rainfall across China.

The accurate measurement of total electron content (TEC) is vital for ionospheric research and satellite navigation services. Nowadays, the advancement of multiple Global Navigation Satellite System (multi-GNSS) has resulted in over 100 seamlessly spaced satellites and thousands of publicly available GNSS stations globally, which enables precise estimation of global ionospheric TEC and differential code biases (DCBs). However, challenges persist in eliminating system differences and leveraging the advantages of multi-GNSS big data due to frequency variances and hardware delays. Here, we propose a novel estimation method that incorporates satellites, constellations, stations, and time constraints for joint global ionospheric TEC and DCB estimation to improve the consistency and reliability of the usage of global observation data from multi-GNSS systems. The results show that the global vertical TEC map derived from approximately 5000 global multi-GNSS sites significantly reduces discrepancies between different satellites, constellations, and receivers, enhancing temporal and spatial consistency, with a standard deviation improvement of over 20%. The long-term stability of satellite DCBs shows tiny fluctuations for the Global Positioning System (GPS), GALILEO, and BeiDou Navigation Satellite System (BDS) systems within ±0.5 ns, with slightly larger fluctuations for the GLONASS system within ±0.6 ns. The long-term stability of receiver DCBs shows that the standard deviation in mid- and high-latitude regions ranges from 0.1 to 0.2 ns, while in low-latitude regions ranging from 0.3 to 0.6 ns. The proposed method leverages multi-GNSS big data and pronouncedly improves the accuracy of global ionospheric TEC and DCB estimation, which provides a valuable tool for high-precision global ionosphere monitoring and related applications.

Accurately identifying rock mass discontinuities and understanding distribution, characteristics, and properties are crucial for assessing slope stability and mitigating the risk of collapse or sliding. The automated identification of discontinuities efficiently provides valuable information such as spacing and volumetric joint count. However, determining the optimal number of clusters automatically for complex rock mass discontinuities remains a technical bottleneck. To address this difficulty, this study introduces an approach based on the fast search and find density peaks (3D-SFDP) algorithm, which autonomously determines the number of clusters by analysing spatial density distributions. Furthermore, traditional clustering algorithms struggle with nonspherical clusters when identifying rock discontinuities using 3D point clouds. In this study, a 3D Gaussian mixture model (3D-GMM) based on 3D pole projection density mapping is presented. This model is designed for handling non-spherical clustering scenarios. Combined with the DBSCAN algorithm, this approach enables precise identification of individual discontinuities. More importantly, the study innovatively introduces discontinuity density cloud maps. Building upon this identification methodology and integrating it with the geological strength index (GSI) for open-pit mine slopes, we analyse high-risk areas prone to rock sliding from a global perspective. This research provides effective data support for slope stability analysis in mining operations.

Active microwave remote sensing data, such as Sentinel-1 Synthetic Aperture Radar (SAR), is indispensable for flooding process monitoring and emergency response, as it possesses the all-weather Earth's surface imaging capabilities and provides global coverage freely. Nevertheless, flood monitoring based on SAR still faces great challenges stemming from the inherent noise and interference present in background, which are further complicated by the dynamic and urgent nature of floods. To address these challenges, this study utilizes Sentinel-1 SAR data, complemented by Sentinel-2 multispectral data and Digital Elevation Model (DEM), to construct a knowledge-driven flood intelligent monitoring (KDFIM) method consisting of three progressive modules. Firstly, KDFIM employs a spatiotemporal SAR feature fusion module, designed to mitigate the noise and interference present in background, effectively extracting multi-temporal SAR features for flood detection. Subsequently, a flood knowledge implanting module is constructed to facilitate the adaptive extraction of flood extent by integrating flood-related knowledge extracted into multi-temporal SAR features. Finally, a knowledge-driven flood multi-parameters calculating module is developed to enhance three-dimensional dynamic flood analysis by integrating multiple remote sensing data. The KDFIM is validated with the flood event caused by the destruction of the Kahovka Dam being a case study, which presented that the flood inundation extraction accuracy reaching 98.46 ± 0.39% and a Kappa coefficient of 0.9691 ± 0.08. Additionally, analysis of flood inundation extent, water depth, and inundation duration based on KDFIM reveals the peak flood on June 9, 2023, with water depth exceeding 6 meters and inundation lasting 8 days in certain areas. Overall, following a comprehensive multi-parameters analysis to the flooding process reveals a significant and far-reaching impact on both the watershed and local cities.

Damage to terrain and the collapse of buildings present significant challenges to human society due to earthquakes, floods, and other natural disasters. Rapid and reliable remote sensing identification of disaster area damage and building collapses is crucial for emergency response and disaster mitigation. However, disaster areas are often affected by clouds, rain, fog, smoke, etc., making SAR-based remote sensing an essential tool for ground observation. Leveraging the scale invariance of texture information features in SAR images, the SAR Texture Fractal Change-Based Model (STFCM) enables a swift evaluation of post-disaster surface damage conditions. To address the challenge of coordinating the use of pre- and post-disaster images, the proposed SAR image preprocessing of STFCM effectively overcomes the difficulties arising from significant differences between pre- and post-disaster SAR images. Furthermore, considering the issue of weak SAR image texture information and severe noise interference, STFCM introduces the concept of a neighborhood and proposes a multi-scale multi-order neighborhood SAR texture fractal dimension calculation module based on nested windows. This module effectively enhances the texture information of buildings in SAR images and reduces the interference of noise in SAR image information extraction. Finally, to address the uncertainty caused by chaotic post-disaster surface structures, STFCM proposes a multiscale fractal dimensional consistency assessment module. The consistency analysis based on multiscale fractal change monotonicity provides more accurate and reliable results. The STFCM operates independently on computers, conducts automatic computations, and efficiently identifies surface damage across disaster-stricken areas as well as critical local regions with speed and accuracy, providing technical support for remote sensing mapping of damaged areas, rapid assessment of disaster situations, and decision-making in emergency response and relief efforts.

The experimental detection of the changes in microwave radiation of rocks under pressure is key to identifying earthquake anomalies through satellite passive microwave remote sensing. However, such changes have not been comprehensively characterized due to the considerable differences in crustal lithology, weak microwave radiation signals, and strong environmental noise. Considering the intrinsic and significant diversity of different polarized microwave radiations of any materials, this study investigated the responses of different polarized microwave radiations during loading the rock materials. Thus, a synchronized detection system including multiple sensors was constructed at outdoor to reveal the stress-induced changes in C-band microwave brightness temperature (MBT) of diorite specimen. Experimental results show that both the horizontal and vertical MBT varied regularly with the changes of pressure; however, the trends of changes of MBT were greatly influenced by the polarization modes. Specifically, a positive correlation was illustrated between the change in vertical polarization MBT and cyclically varied pressure, during which the MBT changed with a rate of 0.033 K/MPa about. In contrast, the changes in horizontal polarization MBT exhibited a negative correlation with the varied pressure, and the MBT change rate was approximately −0.031 K/MPa. Based on the radiative transfer theory, it was found that the opposite MBT changes with respect to h- and v-polarizations are supposed to be caused by the dielectric anisotropy under uniaxial compression conditions. This study illustrates the significant and discernible MBT changes of diorite induced by the stress, which is helpful to identify the detectable microwave radiation anomalies before large earthquake occurrence.

The spatio-temporal difference in infrared radiation from partly loaded rock to fracturing was not investigated before, and the detailed mechanisms are unknown. With particularly produced battery-shaped rock specimen made of diorite being example, and a cooled long-wave infrared thermographic camera with high sensitivity and high frequency applying together with an acoustic emission (AE) system and an electrical potential signal acquisition system, the changes in infrared brightness temperature (IBT), AE and electrical potential of rock partly loaded to fracturing were detected simultaneously. The detected sequence of IBT, AE and electrical potential were carefully investigated and contrasted, and the characteristic variation of positive IBT anomalies were systematically classified and attributively analyzed. Results show that: 1) the IBT changes at different parts of the rock specimen behave various characteristics and different physical mechanisms; 2) The IBT of the loaded part changed a little before the macroscopic fracturing, which was attributed to the rise in physical temperature from the thermoelastic effect and the friction of shearing microfracture surface; while the IBT rose significantly at the moment of specimen failure, which was mainly attributed to the frictional heating on macroscopic fracture surface and the lift in rock surface equivalent emissivity; 3) The IBT of the free part rose also slightly in the pre-failure stage but not respond to stress increment in the initial loading stage, which could be attributed to the infrared radiation excitation effect of P-holes activated by pressure in the loaded part and transferred upward to the free part where they get recombination.

In this letter, we investigated the potential spatiotemporal characteristics of ionospheric anomalies before the Taiwan earthquake occurred on April 2, 2024, by utilizing the total electron content (TEC) and electron density (${N}_{e}$) observations. These anomalies of global ionospheric map (GIM)-TEC, global positioning system (GPS)-TEC, tomographic${N}_{e}$, and Swarm${N}_{e}$were identified with the GIM, the GPS, and Swarm satellites, respectively. After eliminating the influence of solar and geomagnetic activities, positive anomalies were exhibited by both GIM-TEC and GPS-TEC parameters on March 27 and 29, followed by negative anomalies from April 1 to 3, indicating a noticeable spatiotemporal overlap. The negative${N}_{e}$anomaly 2 h before the shock at the height of 450 km was consistently confirmed by tomographic${N}_{e}$and Swarm${N}_{e}$. The spatiotemporal features of increased intensity and expanded range of ionospheric anomalies on April 2 coincided precisely with the meta-instability phase before the mainshock.

The building contours, as one of the most important features for representing geometry, are widely used in various applications including urban modeling and reconstruction. Automatic extraction of high-fidelity compact contours from unstructured point clouds is rather challenging, and the existing methods are limited in generating global-aware and artifact-free building contours. Here, we approach contour extraction as a Bayesian inference problem. Two ideas are proposed to obtain building contour points and edges directly from unstructured point clouds. First, we construct a point-edge collaborative (PECo) Bayesian framework to couple the information of contour points and contour edges. The developed model fully takes local contour features, global edge structures, and global geometric priors into account. Second, given the Bayesian framework for building contouring, we leverage an expectation maximization (EM) algorithm to iteratively infer the contour edges in a maximum posteriori manner. The alternate EM iterations between point and edge domains progressively refine the local pointwise information to a global representation of contour edges. As a result, a synergistic effect between the point features and edge structures for global-aware building contouring is attained. Our approach outperforms the state-of-the-art methods in terms of geometric accuracy and structural compactness in modeling buildings with various complexities. Furthermore, the compactness of the contouring results can be more flexible and easily controlled.

Vegetation indices (VIs) are valuable in numerous remote sensing fields. Nevertheless, it is difficult to accurately discern vegetation using VIs, causing by the effects of the shaded vegetation, saturation vegetation, synthetic turf stadiums, and color steel tile. Measuring the spectral curves of shadows, synthetic turf stadiums, and color steel tiles, we found that the interfering features can be suppressed by a formula composition, named the Original Multiple Spectrum-based Vegetation Index (OMSVI). In addition, we integrated the anti-saturation function into OMSVI to solve the band saturation issue, resulting in the Improved Multiple Spectrum-based Vegetation Index (IMSVI). To address the structure saturation issue, the blue and red edge 1 bands could be used to alter the denominator structure of IMSVI, thus obtaining the final Modified Multiple Spectrum-based Vegetation Index (MMSVI). To demonstrate the generalization of the MMSVI, nine common VIs were compared in three large cities with diverse environments. The results of MMSVI showed that the vegetation extraction accuracy was stabilized at more than 90%, and the saturated LAI position continued to exceed 5.0, both of which were superior to the current VIs. In combination with a conventional optical sensor, it provided an innovative solution for monitoring vegetation in high-dynamic spaces, particularly in subtropical cities where saturation issues are more prevalent.

High-resolution (HR) digital bathymetric models (DBMs) are essential for marine science studies. Currently, there are very few areas where detailed seafloor surveys have been conducted, which is insufficient to provide insight into the geological and oceanographic processes of our planet. In recent years, HR DBMs have been successfully reconstructed from low-resolution (LR) DBMs using deep learning. However, HR DBM reconstruction has high uncertainties due to the complexity of seafloor topography. Meanwhile, deep learning-based methods do not provide uncertainty in the HR DBM reconstruction results. Here, based on a deep convolutional neural network with skip connection and network in network (DCSCN) we propose a modified model, DCSCN-DBM, to reconstruct HR DBMs from LR DBMs and evaluate reconstruction uncertainties using the dropout technique. DCSCN-DBM is a stacked convolutional layer in the original DCSCN replaced by several residual blocks, which effectively improves the performance of the network. However, the structure of network in network (NIN) in them is not favorable for reconstruction. Furthermore, dropout with appropriate probabilities at the right positions is also found to be not only beneficial or non-influential for deep learning-based HR DBM reconstruction but also to provide reliable reconstruction uncertainty. In regions with large errors, the DCSCN-DBM without NIN can provide more reliable uncertainty and accurate reconstruction. These results can offer HR DBMs with confidence level reconstructions for other areas where DBMs are needed to better address downstream tasks, such as habitat mapping, climate modeling, hazard prediction, resource assessment and many other field applications.

Pressure is a critical factor affecting the dielectric properties of rock materials. Previous studies have shown that the microwave dielectric properties of mineral rocks undergo regular changes under uniaxial pressure. In this study, diorite was selected as the specimen, and an open coaxial resonant microwave dielectric testing system was employed to investigate the microwave dielectric (2–18.3 GHz) properties of rock specimens under uniaxial and biaxial loading modes, respectively. Experimental results indicate that under uniaxial pressure, the dielectric constant at all frequency bands decreased gradually with the increase of compressive stress, with an average decrease amplitude of 4.5%; nevertheless, under the biaxial loading process, the change in dielectric constant exhibited a more complex pattern, which was closely related to the relationship between the axial stress and the lateral stress. When the axial stress was lower than the lateral stress, the dielectric constant increased significantly with the rise of axial stress; however, when the axial stress increased and got greater than the lateral stress, the dielectric constant began to decrease. The theoretical analysis based on dielectric physics indicates that both the changes in the number of polarization units and the polarization abilities of these polarization units influenced the dielectric properties of rocks under different loading conditions. This study is greatly important for revealing the change patterns and mechanisms of microwave dielectric properties of rocks, which implies that stress-induced disturbances are an important factor to be considered in applying radar investigation and microwave remote sensing for geological exploration and geohazard perception.

Although thermal infrared imaging has been developed as an important tool of remote sensing rock mechanics (RSRM) since the 1990s, the relationship between infrared emissivity and rock fragments, which is essential for interpreting positive infrared abnormity, has not been investigated. In this letter, the infrared brightness temperature (IBT) ($T_{b}$) of sandstone, marble, and granite, including an intact rock specimen and its fragments of six-level sizes, was experimentally detected with an infrared imaging system indoor. The relationship between the detected$T_{b}$and the measured size of rock fragments is investigated, and the equivalent emissivity$\varepsilon _{\!\!f}$of rock fragments of different sizes is obtained. It was discovered that$\varepsilon _{\!\!f}$of rock fragments rose up to 8.43% compared to that of intact rock and behaved the maximum as the fragment sizes get close to the mineral particle scale (MPS) of the rock.$T_{b}$is hence locally lifted by 6.16 K. This letter revealed that the rise of$\varepsilon _{\!\!f}$is a third remote sensing mechanism, besides the known thermoelastic and friction thermal effects, of local$T_{b}$enhancement in process of rock loaded to fracturing, which provides new experimental supports to infrared imaging detection on rock fracturing and IBT-based stability analysis in rock engineering and geostructures.

Microwave radiations from ground objects are capable of penetrating the shallow surface, vegetation, and atmosphere, but they are also contaminated by many natural factors. Microwave brightness temperature (MBT) data at different channels (frequencies and polarizations) have been used for studying seismic anomalies for decades. However, there lacks an in-depth research on how MBT responds to the natural but nonseismic (NbNS) factors, and the microwave channels used might not be optimal for earthquake study, which might bias the reliability of the retrieved MBT anomalies. In this study, the monthly mean MBT backgrounds (MMMBs) in and around the Qinghai-Tibet Plateau (QTP) were retrieved by using MBT data from the AMSR-2 sensors. The spatiotemporal characteristics of the MMMBs were carefully investigated and found to behave a significant spatiotemporal differentiation. The NbNS factors with profound influences on MMMBs were theoretically analyzed, and the sensitive microwave channels of these factors affecting MBT were revealed. Furthermore, the set of sensitive channels of different NbNS factors was used to determine reversely the optimal channels for searching weak seismic MBT anomalies in the QTP. The distribution map of the optimal channels has been then employed to explore MBT anomalies associated with ten large earthquakes (EQs) in the QTP, since 1997. The case studies demonstrated the good performances of the suggested channels and well verified their applicability. This study is of scientific meanings for understanding the MBT background in and around the QTP and has guiding significances for microwave channel selection of global seismic MBT anomaly study.

Ionospheric tomography is a popular technique for ionosphere imaging. Though dozens of models have been proposed in the past decades, their inflexibilities in constraints, dependencies on background and lacks of constraint on dark voxels remain headache. Several means were taken in our developed model, named EOF-based multiscale tomographic model (EMST). In EMST, a series of sub-models characterized by different scales are established. A new scale is continuously added to the previous model to capture the structures of different scales by taking each EOF as a new scale. The captured structures are then consolidated by using them to restrain the subsequent models. This ensures a rigid but also a flexible model. Then, the background dependency problem is alleviated by an initial model that can give a rough estimation on the ionosphere without any background. Finally, the VTEC map is used to additionally restrain the voxels, especially the dark voxels, in EMST. A few tests were conducted to validate our model, compare it with the Frazaneh model, and discuss the means. Results show that our model outperforms the reference model in vertical profile, F2 layer peak density (NmF2), slant total electron content (STEC) and vertical total electron content (VTEC) comparisons. Its average improvements over the Farzaneh model reach to 4.93%, 28.69% and 40.89% in NmF2, STEC and VTEC, respectively. Besides, evidences also show that the EMST model is less dominated by the prior model and can be benefited from the multiscale strategy and the VTEC constraint.

Phase unwrapping (PU) is a key step in InSAR surface monitoring, which is directly related to the accuracy of the LOS displacement observations. Underground coal mining generally results in large gradient displacements, producing dense and aliasing fringes in interferograms. In this case it is challenging to accurately unwrap interferometric phases using traditional methods. This letter proposes an iterative algorithm to unwrap interferometric phases over underground coal mining areas. Firstly, differential interferogram is unwrapped by the minimum cost flow (MCF) method to obtain unwrapped phases at the edge of the mining subsidence basin. Then, a 2D elliptical Gaussian function, which could approximately describe the spatial shape of mining subsidence basin, is used to iteratively reduce the phase gradients of interferograms, so that residual wrapped phases at more pixel in subsidence basin can be unwrapped. The presented method was tested over Datong coal mine, China, based on 16 TerraSAR-X acquisitions. The result suggests that the accuracy of time-series displacements along the line-of-sight direction solved from the unwrapped phases using the new PU method is 1.1cm, indicating an improvement of 73.2% compared with that of using the traditional MCF method.

Many studies have confirmed that the microwave radiation of rock materials will change with compressive stress and pore water. However, how the presence of pore water in rock volume influences the stress-induced change in microwave radiation ($\Delta T_{B}$) is not clear. In this study, both the air-dry and humid (partially saturated) sandstone specimens were loaded compressively and the C-band (6.9 GHz) microwave radiometer was used to investigate the change of$T_{B}$and the differences of$\Delta T_{B}$. The experimental results illustrated that the presence of pore water in sandstone promotes significantly the relationship between the positive$\Delta T_{B}$and the applied compressive load. On the basis of radiative transfer theory and effective dielectric constant model of multiple-phase media, we found that$\Delta T_{B}$of sandstone mainly results from the compressive stress distributed on mineral grains, which is able to reduce the dielectric constant of minerals and lead to the increase of microwave radiation. For dry sandstone, the distribution of compressive stress on minerals is mainly controlled by grain contact, while for humid sandstone, the pore water plays an important role in applying uniform pressure on grain surface and reducing the dielectric constant of grains; thus, the effective dielectric constant of rock volume is sensitive to the applied loads on partially saturated specimen. This study is important for understanding the positive$T_{B}$anomalies prior to large tectonic earthquakes in condition of ground surface rock partially saturated and is valuable to uncover potential$T_{B}$anomaly related to seismic activities.

The application of compressive stress on minerals and rocks can reduce dielectric constant ($\varepsilon '$) at microwave frequency, which is of considerable significance to geophysics, mineralogy, petrology, and microwave remote sensing. However, the stress near the surface (e.g., Earth surface) is generally on the order of several MPa only, and its increment is supposed to be excessively slight to influence rock$\varepsilon '$. The cubic- and conical-shaped diorite specimens were specially produced in this study to investigate experimentally the variations of rock$\varepsilon '$and tan$\boldsymbol {\delta }$at the stress-free end as the other end is subjected axially to compressive stress by using an open coaxial resonator probe working at 2.0–18.3 GHz. Surprisingly, the rock$\varepsilon '$at the free end decreased by approximately −13% until loading to fracturing, and rock tan$\boldsymbol {\delta }$demonstrated frequency-dependent variations. This particular phenomenon is supposed to be induced by stress-activated positive holes (h•) that are produced inside the loaded rock volume bearing with mineral peroxy defects, flown out to slightly stressed regions, and, finally, accumulated beneath the free rock surface. Consequently, these phenomena lead to the decrease in the electronic and ionic polarizability and the increase in the local field around dipoles, eventually changing the rock$\varepsilon '$and tan$\boldsymbol {\delta }$at the stress-free end. This study implies that the microwave dielectric property of the surface rock might decrease accordingly with deep stress enhancement, which is an important factor to be considered in the application of radar investigation and microwave remote sensing.

Two catastrophic earthquakes (EQs) with magnitudes of Mw 7.8 and 7.3 occurred in Gorkha and Dolakha, Nepal on April 25 and May 12, 2015, respectively. By employing the spatio-temporally weighted two-step method (STW-TSM), significant positive and negative microwave brightness temperature (MBT) anomalies preceding the two EQs were uncovered with satellite microwave data from FY-3B. Two strip-shaped positive MBT anomalies appeared in the Higher Himalayan 1 day before the Gorkha shock and the Dolakha shock, respectively, which were parallel to the thrust belt and whose intensity distributions exhibited good topographic consistency with the elevation profiles of the central Himalayas. Referring to the P-hole theory and seismogenic mechanism of the Nepal EQs, seismically activated mobile positive charges are presumed to have caused the surface positive MBT anomalies by reducing the superficial dielectric constant and increasing the local temperature of the Himalayan cliffs. A significant negative MBT anomaly occurred plausibly as early as March 2015 in the southern Gangetic Plain and developed into a stripe parallel to the thrust belt on the days of the Gorkha and Dolakha events. The negative MBT anomalies are suggested to result from soil moisture increases due to local synchronous precipitation. This work presents the first case study to use data from the FY-3B microwave radiation imager (MWRI) to investigate seismic anomalies, and discriminate both positive and negative MBT anomalies preceding an EQ for the first time.

Accurately predicting ground surface deformation is a crucial task in mining-related geohazards control. Interferometric synthetic aperture radar (InSAR) technique can widely detect historical line-of-sight (LOS) displacements with a high spatial resolution. By incorporating with spatio-temporal deformation models, InSAR can predict kinematic 3-D displacements due to underground mining. However, this method depends on the geometric parameters (at least seven generally) of underground mined-out areas, hindering its practical applications especially over a large area. To circumvent this, we proposed a new method for predicting kinematic 3-D mining displacements by incorporating InSAR with a temporal model, rather than spatio-temporal models used before, in this article. In doing so, much less prior parameters (only three and can be empirically given) are required, with respect to the previous InSAR-based methods. To achieve this, we first revealed that InSAR LOS displacements caused by underground longwall mining at a single point temporally follow an S-shaped growth pattern. Meanwhile, we also showed that a Weibull model can describe the temporal evolution well. Based on these findings, the proposed method first predicts, in a point-wise manner, the kinematic LOS mining displacements from historical InSAR measurements using the Weibull model and a Kalman filter. The kinematic 3-D displacements are then resolved from the predicted LOS displacements with the help of a common prior information relating to mining deformation. The proposed method was tested in the Datong coal mining area of north China. The results show an averaged accuracy of about 0.007 m of the resolved kinematic 3-D displacements.

It is a common method to resolve three-dimensional (3-D) deformation components associated with underground mining by incorporating Single-track interferometric synthetic aperture radar (InSAR) with a prior deformation model termed linear proportion model (LPM) (hereinafter referred to as Sin-LPM). Nevertheless, the Sin-LPM method relies on three model parameters that are needed to bein situcollected, and it neglects their dynamic changes during the period of underground extraction, narrowing the practical applications of the Sin-LPM method, and degrading the accuracy of the estimated 3-D displacements. In this article we propose a new method to resolve 3-D mining displacements from multi-track InSAR observations by incorporating with the LPM. In which, the model parameters are first considered as dynamic and further adaptively estimated from the multi-track InSAR observations using a robust solver. Following that, 3-D mining displacements are resolved from the multi-track InSAR using the conjugate gradient method (CGM). The proposed method was tested in Datong coalfield, China. The results suggest that the proposed method can well estimate 3-D mining displacements with a mean error of about 1.8 cm. Compared with the previous Sin-LPM, the proposed method can effectively improve the accuracy of the estimated 3-D displacements (e.g., 69% in this study), and can work well even over a large area where the model parameters are unknown. The proposed method offers a new insight to improve the InSAR-based retrieval of 3-D displacements induced by other anthropologic or geophysical activities.

Extra constraints are always brought into an ionosphere tomographic model to strengthen its robustness. However, existent models using the constraints either show a weak restriction, amplifying the model’s uncertainties, or demonstrate a strong restriction, decreasing the model’s flexibility. Empirical Orthogonal Function (EOF), as a kind of basis function and generally being extracted from a prior dataset, is an ideal constraint for the tomographic model. By using EOFs as the vertical and slant constraints of the tomographic model, a dual-EOFs restrained tomographic model (DERT) is proposed. In DERT, a smooth constraint, conducted by a moving window process, is firstly imposed, and then a vertical constraint and a slant constraint, which are carried out by fitting the estimated vertical and slant profiles with their dominant EOFs via a least square method, are appended. To reduce the impacts of the errors of the slant total electron content (STEC) measurements on the processes of moving window and least square fit, an algorithm that can reject abnormal STEC values is employed before the two processes. Validations Tests are conducted to validate the DERT and compare it with the Slepian method and the IRI-2016 model. Results show that DERT outperforms the reference methods in aspects of vertical profiles, F2 layer peak density (foF2), STEC measurement and vertical total electron content (VTEC). The root mean square errors (RMSE) improvements over the Slepian method are 14.17%, 0.47%, 16.24% and 40.46% (IGS map as the truth), respectively.

This study presents a spectral–spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods.

Passive microwave remote sensing is a feasible and promising way to reveal seismic thermal anomaly during the seismogenic phase. However, at present, the commonness study and mechanism exploration of MBT anomalies of different earthquake cases are still in urgent need of advancement. Based on the current microwave satellite observations with long temporal coverage, this research exhibited the MBT anomalies associated with some typical earthquakes by using STW-TSM approach. The results were classified as terrestrial anomalies and aquatic anomalies, and the respective principle model was proposed according to the characteristics of seismogenic environment, P-hole theory, and remote sensing physics.

Incidence angles ( θ) and polarization modes ( p) are the essential factors for detecting earthquake (EQ)-related microwave radiation anomalies. However, their influences are still not well studied. In this article, research was carried out to simulate the observation of satellite on EQ under different θ and p. The microwave observation experiments were performed on granite samples pressed with θ from 0° to 30°, as well as vertical (v) to horizontal (h) polarizations under weak radiation environment. It was found that the stress-induced changes ( ΔTB) in the samples' microwave brightness temperature ( TB) are anisotropic and polarized. It was observed that the trends of ΔTBmay be reversed under different θ ( p), with a probability of 57.14% (14.29%). The magnitude of ΔTBat h is also prone to greater changes than that at v, with a probability of 67.86%. Theoretical analysis was performed based on crystal optics, Fresnel formulas, and dielectric physics. This phenomenon may relate to the combined action of piezoelectric effect, extension and contraction of ionic bonds, and turning-direction polarization. The sensitivity of TBto the change in permittivity was analyzed by theoretical simulation in order to reveal the effects of modification in θ and p on the TBchange in stress of rock. For rocks with permittivity ε = 7 - i0.3, the most and least sensitive θ are near 52° at h polarization and 69° at v polarization, respectively. Finally, the significance of these results was discussed.

Generally, ground stress accumulates in the process of earthquake (EQ) preparation. The change trend of microwave brightness temperature (TB) of rock with stress change is an important factor for the understanding of the microwave anomalies associated with EQs. However, it is not yet clear whether the downtrend of rocks' TB is associated with increased stress. To confirm this, in this article, the instantaneous field of view of the microwave radiometer was identified first. Then, the microwave observation experiments were conducted on granite samples at 6.6 GHz under cyclic loading and outdoor conditions with weak background radiation. It was found that besides uptrend and fluctuation, the downtrend of granite samples' TB also correlates with stress, with an occurrence probability of 47.62% and a maximum rate of -0.038 K/MPa. The variation trends of TB with stress are not uniform across different areas of the same sample. To reveal the cause of this phenomenon, the permittivity of single-crystal quartz, one of the main mineral compositions of granite, was measured when it was under compassion loading at the direction perpendicular or parallel to the optical axis. For quartz, the real part of the permittivity rises (falls) when the two directions are perpendicular (parallel), causing the TB to fall (rise). The optic axes of minerals are randomly distributed in granite samples, which make the variation in the permittivity of minerals also random, thereby resulting in the nonuniformity of stress-induced TB variation in granite samples. Finally, the implications of these results were discussed.

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.9 Wenchuan earthquake on May 12, 2008 are revealed by satellite data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor. Two significant MBT positive anomalies are found to be exactly in spatial accordance with two geological Quaternary zones, and the detailed geometric information of the MBT positive anomaly is found to be correlated with the microwave frequencies. After eliminating other possible influential factors, including surface temperature, vegetation index, land-surface roughness, and surface soil moisture under the conditions of space, time, and magnitude, and according to the microwave radiative transfer model, the dielectric variation in the ground surface is suggested to be the primary contributor of the MBT positive anomaly. The positive-hole (P-hole) theory is applied to interpret the geological preference of the MBT positive anomalies through a chain process: crustal stress enhancing-P-hole producing and flowing down stress gradients-surface P-hole accumulating-dielectric constant decreasing-and microwave radiation increasing. The stress-resulting effect of the dielectric decrease on MBT the increase provides a novel mechanism for microwave remote-sensing monitoring of crustal stress field alteration, earthquake preparation, and upcoming shocks. This research has a particular significance for searching potential georelations between the tectonic earthquake preparation and the abnormal satellite MBT.

The additional microwave radiation from underground rock mass (lithosphere) that is caused by tectonic activity and alternating crustal stress has been shown to be a detectable electromagnetic signal via satellite remote sensing. However, the emission of microwave radiation produced inside the lithosphere will be affected by its overburden matter, such as sand, soil, water, and vegetation, and this effect is unknown. In this article, we use a C-band microwave radiometer to detect the variations in the microwave brightness temperature (TB) of rock samples in the process of axial loading. For the rock samples, the surface was bare and covered with dry sand or humid sand. The experimental detection illustrates that the dry sand unexpectedly allows more stress-associated additional radiation (ATB) to be received by the radiometer, while humid sand shows significant extinction of ATB. Both the radiative transfer theory and the stress-activated positive hole hypothesis are applied to interpret the mechanisms of the effect of dry and humid sand layers on ATB. The outflow of stress-activated positive holes is from inside the loaded rock, and hence their accumulation beneath the surface of the sand layer is expected to reduce the local dielectric permittivity of sand, thereby making the detected ATB by the microwave radiometer to be larger than that extrapolated theoretically in accordance with radiative transfer theory. This article is valuable for understanding the diverse TB anomalies that have been observed prior to tectonic earthquakes, and it facilitates the evaluation of the potential application of ATB to seismic monitoring and earthquake prediction.

As one of the most important electrical properties, the dielectric permittivity of minerals and rocks has been studied with respect to its influencing factors, such as texture, density, moisture, frequency, pressure, etc., and has aroused great interest in geophysics, mineralogy, petrology, and microwave remote sensing. However, the studies on the effect of stress on the dielectric property of rock are limited to lower frequencies, and the related mechanism is not yet clear. Considering the limitations of traditional testing methods and the complexities of compositions and structures of rock specimens, in this article, we choose the minerals of the feldspar group as specimens to investigate the impact of compressive stress on their dielectric property at a higher frequency range from 2 to 18.3 GHz. For this purpose, an open coaxial resonator probe is applied to measuring the alteration of the dielectric permittivity of the feldspar specimen in the process of increasing compressive stress using a specially designed loading device. The dielectric constants of the feldspar specimen at all of the five high-frequency points demonstrate obviously decreasing trends with increasing compressive stress. The particular discovery is that the ionic and electronic polarizations are much sensitive to the increasing compressive stress in the microwave frequency band, where the minerals behave like ionic crystals. This article implies that the variation of the microwave dielectric permittivity of rock mass under altering crustal stress due to tectonic plate movements and engineering disturbances is an important factor to be considered in applying radar investigation and microwave remote sensing for mineral exploration, geological exploration, and geo-hazard perception.

A large number of distributed photovoltaic power generations that are connected to the distribution network will have a serious impact on the power quality of the distribution network and load power security. Integrating energy storage and photovoltaic cells into the micro-grid can effectively improve the power quality of distribution network and the reliability of load consumption. This paper is a research on the seamless switching control strategy for micro-grid integrating photovoltaic and energy storage (MIPES). Firstly, it analyzes the boundary conditions of MIPES seamless switching control of grid-connected/islanded modes and studies the output characteristics of photovoltaic cells over a day. The DC bus voltage can be stabilized within a certain range to meet the requirements for MIPES seamless switching of grid-connected/islanded mode. Secondly, this paper analyzes the working characteristics of silicon controlled rectifier (SCR). The results show that when SCR is turned off naturally, the load voltage will rise sharply, affecting the safety of local load. Therefore, a control strategy for SCR switch forced shutdown is proposed to solve the problem of sharp rise of load voltage during grid-connected/islanded conversion of MIPES and to ensure the load security. Finally, relevant simulation studies are carried out to verify the rationality and effectiveness of the proposed control technology.

The 4-points congruent sets (4PCS) techniques have widely been used for global registration of point clouds in terrestrial laser scanning (TLS) applications. Nevertheless, due to many 4PCS methods adopt a downsampling strategy in the collection of correspondences; it is challenging to obtain a real congruent set of tuples in different point clouds with varying density. This letter embeds multiscale sparse features (MSSF) into 4PCS to enable efficient global registration of TLS clouds. Specifically, multiscale clusters are used to extract point features, among which a sparse coding is performed to obtain the representative MSSF. The obtained MSSF are then embedded into the 4PCS variants, taking both geometrical structure and representative feature similarity into account when performing a four-point congruent tuples matching. Moreover, a normal constraint is considered in the selection of noncoplanar four-point bases rather than coplanar ones in the source cloud. This configuration decreases the number of four-point bases and thus improves the processing efficiency and registration accuracy. The proposed method was applied to two experiments with TLS point clouds from building and hillslope scenarios, respectively. In addition, the proposed MSSF configuration was embedded in two 4PCS variants, and compared with the original methods for global registration. The comparison shows evident improvements in terms of the registration accuracy and efficiency if our embedment is used.

At present, the earth surface stress monitoring mainly relies on the ground drilling method. Affected by test conditions and cost, the large-scale and regional stress measurement cannot be realized by traditional methods in the actual measurement. As the ground information at the large scale can be observation by satellite in all-weather condition, it is possible for detecting the stress information via the satellite remote sensing. In this paper, the experimental studies on the microwave and thermal infrared spectrum of loaded rock were carried out. Based on the experimental results, the possibility for detecting the earth surface stress condition via remote sensing was discussed. This study demonstrates the potential ability to use satellite remote sensing to observe ground stress condition and earthquakes.

With rapid developments of China's economy and industrial modernization, the increasing consumption of coal, oil and other fossil fuels have led to rapidly growing of abnormal aerosols, causing frequently regional haze episodes and having a significant negative impact on human health [1, 2]. As we all know, coal is the main fuel used for the government advocated centralized heating system throughout our northern cities, which is driven by large-capacity boilers in heating stations and power plants [3-5].

For the mine roadway surface reconstruction based on laser scanning point cloud, the cylinder projection method and Poisson reconstruction method were adopted to build the roadway model in this paper. The two methods were introduced in detail including the three modeling parts of the cylinder projection method and the five parts of the Poisson equation reconstruction method. On this basis, two methods were applied to construct approximately real 3D roadway model by processing laser point cloud data and modeling the model of the roadway based on laser scanning point cloud automatically. Finally, the projection method was compared with Poisson reconstruction method in modeling time and modeling effect based on the actual roadway laser scanning data, in which the two methods showed their respective advantages in efficiency and precision. The methods proposed in this paper could also provide reference for the adjacent applications, such as underground tunnel modeling, etc.

The mean shift algorithm shows a good performance in optical image segmentation. However, conventional mean shift algorithm performs poorly if it is used directly to synthetic aperture radar (SAR) image due to the large dynamic range and strong speckle noise. Recently, a generalized mean shift (GMS) algorithm with an adaptive variable asymmetric bandwidth was proposed for polarimetric SAR (PolSAR) image filtering. In this paper, it is further developed and extended for PolSAR image segmentation. The proposed algorithm can be used for PolSAR image superpixel segmentation directly without any preprocessing steps. Experiments using AirSAR and ESAR L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm.

Iron stress could pose a great threat to plant physiological processes and food security. Hyperspectral remote sensing is an important technology for stress monitoring of crop because of its rapid, nondestructive and macro-scale detection ability. In this study, maize seeding was taken as an example to study its chlorophyll change and spectral response under different iron concentration. The result showed that (1) with the increase of concentration of iron stress, chlorophyll content increased at first and then decreased and the maximum value appeared at 500mg/kg, and (2) selected spectral indices are positively correlative with chlorophyll content. Among the indices, RVI is the best index to evaluate chlorophyll content and then it is the right index to detect the iron stress of maize seedlings.

It is very important to quantify water content of leaf for monitoring vegetation using hyperspectral remote sensing. Taking maize seedlings as example, spectra in visible near infrared region (VNIR, 0.4-2.5μm) and thermal infrared region (TIR, 8-14μm) were measured simultaneously. Narrow-band simple ratio water index (SRWI), simple difference water index (DWI) and normalized difference indices (NDWI) were calculated respectively for both VNIR and TIR spectra. According to correlation analysis and model validation, it is found that (1) the VNIR proved highly sensitive spectral region and hold promise for the estimation of leaf water content, and (2) SRWIVNIR is the most accurate index for the retrieval of leaf water content of maize seedlings (R2= 0.78, RMSE = 2.52%).

With the rapid industrialization and urbanization in China, the great increases in anthropogenic emissions during the last decades have caused serious air pollution problems greatly influencing public health [1-2]. The Beijing-Tianjin-Hebei (BTH) region, located in central-eastern China, is not only one of China's most economically developed and industrialized regions, but is the area that most frequently experiences haze episodes. In this study, the characteristics of spatial and temporal distribution and variation trends of aerosol optical depth (AOD) on 550 nm, SO2 and NO2 column density during 2002-2013 over BTH region were analyzed by using MODIS, OMI, GOME-2 and SCIAMACHY satellite data [3-5]. With the Geographically Weighted Regression (GWR) model, the driving factors including motor vehicle ownership, electricity output, population and the construction area from the city statistical yearbooks are preliminary analyzed.

Gully erosion removes large volumes of soil and water from complex areas. It's difficult to quantify the loss of soil because the footprint of individual gullies is too complex to be captured by most common measures, such as general available digital elevation models (DEMs). LiDAR (Light Detection and Ranging) is a recent innovation technique widely used in topographic surveying, smart city, historic preservation, revere engineering, etc. Ground-based LiDAR has the potential to provide the required data with non-contacting, convenient, fast and high accuracy at lower cost. In this study, two different Terrestrial Laser scanners (TLS) were used to monitor an individual gully. Terrain complexity and volumetric estimates of the eroded sediment were produced by comparing the LiDAR-derived DEMs under different point cloud resolution. The result showed that the surface roughness and erosion sediment were increased with higher resolution point cloud. However, time-consuming and computational complexity was suddenly raised as dense point cloud provided more details information. This paper provides a new method for how to choose a moderate resolution in dealing with gully erosion in regional area.

In mining areas, wastes are usually piled-up in the dump, making a man-made hill with height of more than tens of meters. Because of the loose structure of dump sites, landslides or debris flow may occur after heavy rainfall, threatening local people's lives and properties. Therefore, dump stability monitoring and early warning for possible hazards is crucial for ensuring local safety. In this paper, a collaborative stability analysis with high-resolution SAR data and ground measurements is conducted over Xudonggou dump of Anqian iron mine. A two-pass DInSAR analysis is carried out to derive the deformation distribution over the whole area. High-precision GPS and robotic measurements on several points are also conducted over the dump site. Based on the measurements, an slope stability analysis is carried out and eventually a landslide hazard zone is outlined.

The spatial features and dynamics of mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is a critical indicator to evaluate regional air quality. In this paper, a geographically and temporally weighted regression (GTWR) model is developed to depict the spatio-temporal dynamics in the PM2.5-AOD relationship and generate ground-level PM2.5concentrations from satellite-derived 500 m AOD. To test the performance of GTWR model, two case studies were carried out both in Central China and North China. Cross validations display that the performance of GTWR model is better than ordinary least squares (OLS) model, GWR model and temporally weighted regression (TWR) model. GTWR model obtains the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE).

This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and optimally planning further aerial surveys. A label propagation mechanism is adopted to reliably propagate the labeled pixels annotated by the user on the aerial image by visual photointerpretation to the (lower resolution) satellite image. The experimental analysis is carried out using post-disaster images of the 2010 Yushu earthquake in China. The obtained results confirm that the proposed method can significantly reduce the user annotation effort and the cost for acquiring additional aerial images leading to more accurate and timely debris detection maps.

Previous studies have shown that thermal infrared anomalies can be detected in the crust rocks in satellite infrared images before an earthquake. However, thermal infrared remote sensing is easily affected by weather conditions because of the short wavelength of infrared radiation. In this paper, instead of infrared radiation, we focus on the microwave radiation characteristics of loaded rock. First, a microwave observation system was built to observe the loading process of rocks in an outdoor environment with a cold sky background. Then, the microwave radiation changes in the loaded granite samples during elastic deformation and fracturing stages were analyzed. The experiments yielded the following results. First, the microwave brightness temperature has a linear positive correlation with the load in the elastic deformation stage of the granite samples, and lateral pressure accelerates the changes in the microwave radiation. Second, the microwave brightness temperature usually decreases as the rock develops layered fractures but increases as the rock develops surface fractures, which significantly alter the surface morphology and roughness. The mechanisms responsible for the changes in microwave radiation during the rock deformation and fracturing processes are discussed. This study demonstrates the potential ability to use microwave-sensing satellites to observe seismogenic processes and earthquakes.

This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching ± 2.2°, several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

This paper proposes a synchronization algorithm for bistatic synthetic aperture radar (BiSAR) imaging in a spaceborne/stationary configuration. In real bistatic systems, synchronization errors are generally introduced into the received data. Additionally, the lack of precise imaging parameters, such as the position of the transmitter and the accurate sampling time, could affect the imaging quality greatly. Fortunately, the image could be well focused by the proposed algorithm in the case of lack of the accurate position of a transmitter and the sampling time. First, a preprocessing step is employed to remove synchronization errors through matching an echo signal with a direct signal. Then, a modified chirp scaling factor containing an error phase term is constructed, and the accurate position of the transmitter and the sampling time can be acquired by the phase extraction of the direct signal and the searching method based on contrast optimization. After that, the corresponding imaging process can be implemented. Finally, the proposed algorithm is validated by the simulation and experimental results, where TerraSAR-X is used as the illuminator.

The emergency supplies in large-scale natural disaster is affected by the dynamics of transportation network. The travel speed and link capacity change over time because of traffic accidents, landslides, and uncertain weather, etc., which block up the processing of emergency material transmission sometimes. Finding out critical links and knowing how these links affect the transportation are benefit to improve dispatching performance. In this paper, the quickest network flow model is utilized to calculate the significance of transportation network links. Not only the critical links could be identified, but also how much decrease of traffic volume will be caused by the decrease in critical link capacity, or travel speed, or both. Different from conventional methods, the proposed significance in this paper is not only for individual link, but also for multi-links with considering time dimension. The experimental results demonstrate that this method is effective in dynamic transportation network and the computational efficiency is acceptable.

Maximum flow is one of the important and classical combinatorial optimization problems. However, the time complexity of sequential maximum flow algorithms remains high. In this paper, we present a two-stage distributed parallel algorithm (TSDPA) with message passing interface to improve the computational performance. The strategy of TSDPA has two stages, which push excess flows separately along cheap and expensive paths identified by a new distance estimate function. In TSDPA, stage 1 enhances the parallel efficiency by omitting high-cost paths and decentralizing calculations, and stage 2 guarantees the achievement of an optimal solution through divide-and-conquer method. The experimental test demonstrates that TSDPA runs 1.2–15.5 times faster than sequential algorithms and is faster than or almost as fast as the H_PRF and Q_PRF codes.

The traffic congestion changes over time in the real transportation network, and the vehicle navigation is a real-time processing. Both the accuracy and time performance of navigation algorithm are important. To respond rapidly to transportation network changes, this paper proposes two new dynamic shortest path algorithms based on the re-optimization method. The algorithms take full advantage of the previous search results, thus they take relatively few computing time to obtain the optimal solution. Experimental results demonstrate that the proposed algorithms can be applied to vehicle navigation in the transportation network with dynamic variation of the travel times on arcs and runs much faster than the traditional Dijkstra algorithm.

This study utilizes the unmanned aerial vehicle (UAV) to acquire high resolution images for feature matching, resulting in a point cloud. A progressive morphological filter is used to filter out nonground object points from point cloud. Multi-scale and different shape filter windows are adopted for the morphological filter to achieve good performance. The results show that multi-scale and multi-shape window can improve the performance of morphological filter compared with single direction filter window, as nonground objects cannot be completely removed with single direction filter window. With multi-shape or 2-D filter window, buildings can be effectively removed and ground points can be reserved.

There is no single sensor can acquire the complete information for disaster monitoring. This study investigates the applicability of registering multiple point clouds obtained from unmanned aerial vehicle (UAV) images and terrestrial laser scanning (TLS). Low attitude images with high overlaps were collected by an eight-rotor UAV platform and image-based 3D modeling techniques are used to generate 3D point cloud, covering most of roof information of the damaged buildings. TLS was used to collect the side information of the damaged buildings with multiple scans. Point clouds from the two platforms are iteratively registered using a method, from coarse to fine, to get complete geometry of the study area. Geometric features are subsequently extracted to help for the identification of damage degree of buildings. Experimental result shows that by analyzing the intersection lines of plane features, we can further detect the building's inclination.

Representation on the variation of Earth system phenomenon is important to Earth System Science research. However, owing to the complexity and unnecessary transformation, mathematic models are not good method to represent the variations of earth system phenomena. Grid-based models can overcome the shortcomings well. But the grid in used is not perfect enough. A three-dimensional (3D) Global Spatial Grid (GSG), named SDOG-ESSG, was adopted to represent the 3D and large-scale earth system phenomena. The models of variation representation was proposed, and the experiment on ionosphere was taken to prove the superiority of the methods.

Triangular Irregular Network (TIN), especially Constrained Delaunay Triangular Irregular Network (CD-TIN), has been proven to be more accurate for the modeling of complex urban terrain, compared with raster represented digital elevation model (DEM). DEM with high precision is of importance to simulate urban flood inundation. In the paper, the triangular-prism sets are introduced to calculate the inundation water surface within each basin. It is worth mentioning that the explored dichotomy numerical solution is efficient on the simulation of the inundation depth. The case of ramp-shape urban terrain validates the correctness of the algorithm. And the application of the “7.21” storm in main campus of Beijing Normal University (BNU) verifies the usability and practicality of the algorithm. The cases demonstrate the feasibility of the proposed algorithm for simulation of urban flood inundation associated with urban surface runoff and drainage system capacity information.

We address the problem of efficient image matching for large, highly redundant photo collections with highly complex topologies, such as photos acquired by unmanned aerial vehicles (UAV), focusing on disaster monitoring. Our approach conducts a skeleton graph which simplifies the image topology with the consideration of image importance and topological relationship. We define the image with the highest importance weight as candidate, adding the remaining images referring to the topology skeleton. To conduct the skeletal graph, the image topology is first computed depending on the overlapping relationships between images. Experimental results show that our technique drastically limits the searching range that is for feature similarity computation, resulting in dramatic speed up. A final bundler adjustment is implemented in the procedure of scene reconstruction, and the completeness and accuracy are far more comparable to the traditional method.

In this letter, the relationship between corn crop progress stages and fractal dimension derived from remote sensing data has been revealed. Due to the effects of soil background and farming practices on emerged and harvested stages of the corn crop, the roughness of their corresponding normalized difference vegetation index (NDVI) images significantly changes. Therefore, image fractals, which normally indicate such roughness, are proposed to detect corn progress stages. There are four steps in the proposed procedure: 1) NDVI maximum composite process is conducted to eliminate the influence of cloud cover or missing data; 2) Cropland Data Layer (CDL) is used to remove noncorn pixels; 3) fractal dimension is calculated to estimate the spatial roughness of NDVI image; and 4) curve fitting is used to detect peaks that infer the certain progress stages. It is worth mentioning that a dimensionality-reduction-based differential box-counting algorithm is developed to estimate the fractal dimension of NDVI image in an irregular region of interest. The algorithm is applicable for masked NDVI image. Experiments based on Moderate Resolution Imaging Spectroradiometer NDVI time series and CDL of the State of Iowa are conducted. Comparison of the results with corn crop statistics from the National Agricultural Statistics Service indicates that the proposed method is able to detect corn emerged and harvested stages with success.

This paper proposes an extended nonlinear chirp scaling (CS) image formation algorithm for the bistatic synthetic aperture radar systems with the squinted transmitter and a fixed receiver. Since the transmitter with the squint mode was adopted in the system, two main problems, i.e., the spatial variance of the frequency-modulation rate and cubic phase terms, were introduced in the image formation algorithm. The former problem was solved by the linearity approximation of parameter$p$and deduced$q$(the second- and third-order coefficients of CS factors in range, which could be used to remove the spatial variation and high-order phase in the range direction) along the range domain while the latter one was compensated by a cubic analytical phase term in the frequency domain. A corresponding experimental hardware system and the bistatic experiments were also described in this paper. Both the simulation and experimental results validated the proposed algorithm.

Solar activity dependency of the ionosphere in local region during a short timescale is of great importance for the analysis of ionospheric anomalies probably caused by local events such as earthquakes and volcanic eruptions. Recently, a nonlinear background removal method based on multiresolution wavelet transform (MWT) for seismo-ionospheric anomaly analysis has been developed by He et al. To further evaluate the method performance, three years of different solar activity levels have been chosen to examine the relationship between the TEC variations and solar activities during 23rd solar cycle. The results showed that the MWT-based method is able to handle well the solar radiation background in the ionospheric TEC data, especially under a complex solar activity situation.

A violent earthquake (Ms 7.1) occurred in Yushu county of China on April 14, 2010, and the epicenter was (33.1°N, 96.7°E). It caused 2689 people death. We use the AMER-E satellite remote sensing data to analyze the variation of microwave radiation around the epicenter. It was found 45 days before the earthquake a high microwave radiation strip appeared in the southwest of the epicenter, and gradually became longer and wider. The largest increment of microwave brightness temperature was up to 12K. 9 days before earthquake another high microwave radiation strip appeared in the northwest of the epicenter and the epicenter was just in the intersection of the two the high microwave radiation strips. 4 days before the earthquake the strength of the microwave anomaly became weak, but one day before the earthquake the strength became increase again. After the main shock the anomaly still existed for about 20days and was finally disappeared. To explore the cause of microwave radiation anomaly the microwave polarization difference index (MPDI) and the air temperature variation was investigated. The final result indicated the microwave anomaly was caused by the temperature increase, i.e. the anomaly should belong to thermal anomaly.

The anomaly of SLHF, which is a key component of the Earth's energy balance and represents the heat flux from the Earth's surface to the atmosphere associated with evaporation or transpiration of water on the surface and subsequent condensation of water vapor in the troposphere, has been widely reported as a possible earthquake precursor. The causes are generally attributed to the increase in infrared thermal (IR) temperature and the air ionization produced by increased emanation of radon from the Earth's crust. In this paper, the theoretical analysis and case study show that there is close relationship between soil moisture and SLHF anomalies. For inland earthquakes, the increase of soil moisture due to the rising of groundwater level will bring with higher potential evaporation, leading to the increase of latent heat flux. Further study with more accurate soil moisture product after the new satellite mission will help us to better understand the influence of soil moisture on SLHF variation and their relations with seismogenic process.

The Chinese geohazards observations and natural disaster assessment system is established so as to make information related to environmental risk and vulnerability easily accessible to decision-makers through this centralized platform. In the case of MS 7.1 Yushu earthquake disaster, for emergence response, we presented a method for generating seismic intensity map based on seismological mechanism solutions. The disaster assessment system automatically drew the distribution map. Referring to usable data, the chains of rapid assessment on earthquake disaster are analyzed, and different models are established for assessing affected population, damaged houses and lifelines and comprehensive earthquake loss evaluation. We also established an assessment model of mud-rock flow disaster in the disaster assessment system. Take the Gansu Province, Zhouqu County 8.8 extra-large-scale mud-rock flow as example, we present the comprehensive evaluation results by using the aerial images and damaged building evaluation models.

The GEOSS under construction is providing space-,aero-,ground/sea-based multiple observations on planet Earth for the seismogenic process monitoring and earthquake precaution. The stress enhancement and energy accumulation in seismic activity area change locally the physical parameters of lithosphere with the developing of a series of effects that can comprise most of the following ones: initial cracks, the fracturing of rockmass, the changing of electromagnetic properties, the decreasing of dielectric constant, the re-activation of P-holes, the leaking of pore-gas, and the rise of water-level. The physical states of coversphere and atmosphere are to be affected due to the lithosphere-coversphere-atmosphere (LCA) coupling, and the signals from the underground, surface, and atmosphere to satellites are to be changed with parameter anomaly. We suggested that the LCA coupling is important for understanding GEOSS observations, especially for earthquake anomaly recognition (EAR). Using deviation-time-space-thermal (DTS-T) method for EAR, three recent major earthquakes (2009 Italy L'Aquila earthquake, 2010 China Yushu earthquake and 2010-2011 New Zealand earthquake sequence) are taken as typical cases for analysis to the multi-parameters anomalies, preceding the shocking, with quasi-synchronism and geo-consistency. The specific LCA coupling effects related with the earthquakes are also discussed in brief.

An efficient data sharing system is vital to Earth System Science (ESS) researches. However, current systems mainly focus on standards, policies and software environments. None of them pay much attention to spatial references. Adaptable Global Spatial Grid (AGSG), was selected as a unified spatial reference for data sharing. Methods of data representation based on AGSG was introduced and the mode of data sharing based on AGSG was presented. Merits of easy reusing of ESS data, uniform requests, and transparent accessing of multi-sources data were shown in the mode. Potential extensions to the volunteer services of data sharing and cloud technology were discussed.

Although earthquakes are difficult to be predicted because of the complexity of the Earth system and the uncertainty of seismogenic processes, GEOSS provides multiple sources of observation data and brings a possibility to extract a thermal anomaly that would have a significant effect on earthquake prediction. Referring to the analysis on the lithosphere-coversphere-atmosphere (LCA) coupling due to stress enhancement in seismogenic zone, six thermal parameters, including surface latent heat flux (SLHF), thermal infrared radiation (TIR), outgoing longwave radiation (OLR), diurnal temperature range (DTR), atmospheric temperature, and skin temperature, are selected for GEOSS-based earthquake anomaly recognition (EAR). The inherent relations between the six thermal parameters are preliminarily introduced in consideration of possible LCA coupling. With overquantity, quasi-synchronism, and geo-consistency being defined as three rules for data mining, a deviation-time-space-thermal (DTS-T) EAR method as well as its procedures are developed in this paper. With 2008 M7.3 Yutian earthquake, China, 2008 M8.0 Wenchuan earthquake, China, and 2010 M7.1 Christchurch earthquake, New Zealand, being examples of tectonic earthquakes, the technical procedures of DTS-T method are demonstrated, which show that the obtained compositive thermal anomaly has a significant effect on earthquake prediction.

With the rapid development of computer technology and spatial information theory, 3D geological modeling and visualization has become a hot spot in geosciences research such as mathematics geology, geotechnical engineering and so on. In recent years, several kinds of modeling methods have been developed for 3D modeling of simple geological structures. However, for complex geological structures, such as stratal pinch-out, fold and fault, there are still not any automatic modeling methods. This paper mainly introduces an effective method to model geological body with stratal pinch-out based on degradated GTP (Generalized Tri-Prism) model. By using inputted parameter, further degradation (parameterized degradation) could be evolved from simple degradated GTP model. The result of further degradation could be divided into two parts, one part belongs to the current strata, and the other part belongs to previous strata or user specified strata. Compared with standard GTP method, the stratum body modeled by this new method is more accurate and more consistent with the real stratal situation.

Geological Section (GS) map is a kind of fundamental material for studying geological stratum, rock mass and geological structures, which can provide the basic information for geological staff to recognize and analyze geological phenomenon. However, it's not so easy to automatically generate 3D geological sections for complex geological stratum, especially geological sections with stratal pinch-out. This paper proposed an automatic method for generating 3D curvilinear geological sections with stratal pinch-out based on virtual boreholes. In this method, virtual boreholes are generated by computing the intersection points between interpolated vertical lines and 3D stratum body model, from which boundary contour polygons of stratum can be extracted. In order to maintain geometric integrity and promote level of automation in section generation process, firstly necessary stratum points were added to boreholes which are lack of corresponding stratum, then the boundary contour polygons were tessellated as triangles for 3D visualization of the curvilinear geological section. The method has been implemented and successfully applied in a sample engineering case, which demonstrated that it is able to automatically and efficiently generate 3D curvilinear geological section for pinch-out strata with this method.

In this paper, we proposed a wavelet-based methodology that enables to detect and diagnose the ionospheric anomalies associated with seismic activity. Wavelet transform and cross-wavelet analysis methods are used to detect and identify the spatio-temporal variability of ionospheric perturbation associated with Wenchuan Ms8.0 earthquake, 12 May 2008 in China, and to gain further insights into the dynamical relations between the ionospheric anomaly variability and the geophysical indices variability in the same span of observations, 12 April ~ 12 May, 2008. The details of the algorithm and the analysis results are introduced in this paper.

Using data from NCEP/NCAR Reanalysis Project, we have comprehensively analyzed multi-parameters including air temperature, daily temperature range and surface latent heat flux (SLHF) changes before the 3 September 2010 New Zealand EQ. Multi-parameters thermal anomalies before New Zealand EQ were revealed as following: 1). Air temperature anomaly appeared on July. 30, 12-14 and 27 Aug.; 2) Daily temperature range anomaly appeared on 28-29 Jul; 3) SLHF anomaly has three stages: the first appeared during July. 31-Aug. 2, the second appeared on Aug. 8, and the third appeared on Sep. 2. Although the mechanism of these anomalies is not clear, we consider multi-parameters it should be a typical Lithosphere-Coversphere-Atmosphere coupling effect in a seismogenic process especially in the impending-earthquake process.

Some authors reported that there were anomalies appearing in the satellite remote sensing information before earthquake, including thermal infrared anomaly, water vapor anomaly and microwave anomaly etc. But how to obtain the actual anomaly from the complicated remote sensing information is very difficult due to the influencing of many factors, such as terrain, physiognomy, geological condition, and weather etc. Meanwhile the explaining of anomaly mechanism is another difficultly. In this paper, a new method is proposed to extract the microwave radiation anomaly before Wenchuan earthquake from the AMER-E satellite remote sensing data. The method can eliminate the influencing of terrain, physiognomy and weather. The result of anomaly analysis shows that there was microwave anomaly before and after the main shock of Wenchuan earthquake. In order to explore the anomaly mechanism a physical simulation experiment was carried out. The experimental result shows that the microwave brightness temperature increases in the elastic stage, decreases in the plastic stage, and increases again in the fracturing stage. Based on the experimental result the anomaly mechanism is discussed.

The satellite remote sensing has become a promising technique for detecting earthquake and fault activities. But the radiation and the reflection of the earth surface detected by remote sensing are influenced not only by earthquake or fault activities but also by many factors such as terrain, vegetation, weather and so on. In order to effectively extract the earthquake anomaly and get rid of the impacts of non-earthquake factors, this paper presented a new method to extract and analyze anomaly caused by earthquakes or fault activities. We applied the method to the data of AMSR-E and MODIS for the earthquake that struck Sichuan, China, on May 12, 2008. As a result, we detected that the thermal infrared (TIR) anomaly information retrieved from MODIS data emerged before the earthquake and the microwave radiation anomaly retrieved from AMSR-E data emerged both before and after the earthquake.

Although thermal infrared (TIR) imaging technology has been successfully applied in the thermographic inspection of engineering defects and landmines, its use for detecting water permeation on field large-scale rock relics is new. Huashan Rock Painting, a world-famous field large-scale rock relic in Guangxi Province, China, was used as a case for TIR imaging detection of water permeation on field large-scale rock relics. This paper introduced detection methods, a key parameter, and a gradation model for TIR imaging. Three detection methods were specially designed and used in two experiments performed during the drought and rainy seasons, respectively. Thermographic features were comprehensively analyzed, and temperature variation curves of water-permeation places were quantitatively compared with those of places unaffected by water permeation. Water-permeation places can be precisely identified based on thermographic features and temperature variation curves. Physical interpretation was conducted based on the heat transfer equation and on the Stefan-Boltzmann law. The minimum temperature of solar irradiated water-permeation places was selected as the key parameter, and a -based model was established for water-permeation gradation. To strengthen the differences between water-permeation places and places unaffected by water permeation, TIR imaging should also be conducted during a sunny day after the rains, particularly during an intermittent sunny day after heavy rains in the rainy season. It is the best time for TIR-imaging-based water-permeation detection and gradation.

3D entity model with the data structure of regular hexahedron, which is usually called as 3D regular block model, is now widely used in many domains such as resources estimation (RE) and finite element analysis (FEA) etc. In these domains, each regular hexahedron element of a 3D entity should be respectively evaluated with spatial related properties. However, 3D entity models are usually constructed from limit and sparse geometrical elements such as feature points and contour line strings, so these models are usually represented only with connected triangles as their surface but nothing in their hollow interior. We need a method to tessellate these models into regular blocks and fill the hollow interior with them. This paper mainly introduces an effective tessellation algorithm for converting 3D wireframe models which are represented as a collection of surface triangles to 3D regular block models which are represented as a collection of regular hexahedrons. With this algorithm, both simple 3D entity model with single outside boundary and complex 3D entity model with inside cavities can be tessellated into a collection of regular hexahedrons, which are constrained to the inside and outside boundaries of 3D entities. We have developed a test application for resources estimation based on this algorithm. A gold ore-body wire-frame model is tessellated into regular hexahedrons. The inverse distance weighting (IDW) interpolation method is used to evaluate each regular hexahedron with gold grade. The 3D block model of this gold ore-body is visualized with Au-grade distribution information.

Fast and reliable building damage analysis is crucial for survivors rescue and disaster management when a destructive natural disaster occurs which helps to limit life losses. For this purpose, a method was developed in this paper to extract building's geometric axis line from aerial LiDAR data which helps to do further building damage analysis like to identify inclined buildings. To extract building's geometric axis line, several steps were carried out such as segmentation of buildings, extraction of roof facets, fitting of mean roof plane and getting main roof. The aerial LiDAR data was captured over the area of Port-au-Prince, Haiti which contains numerous of inclined buildings. The results demonstrate the feasibility and effectiveness of the proposed approach to identify inclined building for damage management.

This paper presents a QEM-based simplification approach for extracting and delineating building footprints from Airborne LiDAR data. Our approach consists of three steps: first of all, Digital Surface Model (DSM) is generated from the raw point cloud by using the interpolation method. Secondly, the potential points on building outlines have to be aggregated to form connected building blobs. Those blobs that exceed a certain size and have certain characteristics (e.g. consisting of planes) are supposed to be building candidates. In a third step these outlines are simplified to building footprints. The focus lies on taking the characteristics of buildings into account to produce a meaningful 2D building shape and simplifying different possibilities to generalize the building footprints.

This paper discusses the topological relations between 3D spatial entities with fuzzy boundaries, and introduces a regular block model-based computable topological analysis approaches. Based on 3D digital topology theory, a regular block is defined with 6-connectiviity and the interior, the fuzzy boundary and the k-order 6 neighborhoods of a spatial entity with fuzzy boundary are defined. Furthermore, a new topological relationship analysis model improved from 9-Intersection Model is presented and named as k-order six neighborhoods 9-Intersection model (K6N9-I Model). In this model, the original interior, boundary and exterior of 9-I Model are respectively replaced with newly defined components for spatial entities with fuzzy boundaries. SQL is introduced to compute the count and content of each intersection element of the new model. The mapping regulation from quantitative model matrix to qualitative topological relations is discussed in details and several refined topological relations such as deeply/shallowly intersect, far/nearly disjoint etc are identified.

Remote sensing data have been used to analyze the changes in column water vapor in response to the Wenchuan Ms 8.0 earthquake, occurred in China on May 12, 2008. Anomalous changes in water vapor content have been found over the epicentral and surrounding regions 11 days before the earthquake. The sudden increase in water vapor content in the atmosphere before the earthquake may be attributed to the increase in evaporation due to increase in land surface temperature. The brightness temperature of land surface around the epicenter is found to increase one day before the increase in the water vapor content.

The environment and vegetation cover in mining area are influenced by the exploitation and utilization of mineral resources. It needs dynamic monitoring and estimation by the help of satellite remote sensing technology. Taking SPOT-VGT NDVI as information source and considering the characteristics of mining areas in China, this paper researched how to monitor the vegetation variation and desertification in mining area. The method includes the composition of time series data, the vegetation variation analysis and the gradation of desertification land. Concretely, the variation of NDVI is divided into 7 levels according to the slope of the fitted line by using NDVI time series data. Besides, the desertification land is divided into 5 levels according to the vegetation coverage, which is transformed from NDVI. The presented method was validated in Ningdong coal mining area, northwest China, and the results showed that the method is valid and effective.

In recent years, with new services expected, such as navigation systems, location based services, and augmented reality, the need for automatically, efficient 3D building reconstruction systems becomes more urgent than ever. As a new spatial information technology, airborne LiDAR is widely used for the acquisition of 3D objects on the earth. The automatic reconstruction of 3D buildings from airborne LiDAR data has been a topic of research for decades. However the lack of 3D building reconstruction methods is still the bottleneck for the further development of airborne LiDAR. Since the 3D geometry feature is extremely important for many applications such as urban planning, car navigation, or environment monitoring etc., this paper introduces an automated method for implementing building geometric features extraction from raw LiDAR data in details. The outlines of 3D buildings are generated by using discrete curvature analysis according to the building geometry features. In the experiment, 3D building models are automatically reconstructed by using a model-driven approach according to their geometry features derived from raw LiDAR data.

The satellite TIR remote sensing has become a promising technique for monitoring tectonic activities and detecting earthquake precursors due to the advantages of large observation area and short observation period. In order to identify and to extract the TIR anomaly, several presented methods are tested and compared in this paper. The comparison results indicate that Robust AVHRR Technology (RAT) is a better method for detecting pre-earthquake thermal anomaly. Anyway RAT is not able to quantitatively analyze the TIR anomaly before shock. RAT method is hence improved for obtaining the value of area and average temperature of TIR anomaly. Based on the analysis of some tectonic earthquakes, an empirical quantitative model for tectonic activity analysis and earthquake magnitude predication is established.

The phenomenon of a satellite thermal infrared (TIR) anomaly before earthquakes has been reported since the late 1980s. The reported increase of surface temperatures reaches 2-4 degC, occasionally higher. Usually, the anomaly appears one month to several days before the earthquake. Several mechanisms of hypothesis have been put forward to interpret the reported temperature increase. In this paper, the satellite TIR anomaly features of four earthquakes in the Taiwan region are first analyzed. To study the mechanisms of infrared anomaly a group of physical simulation experiments are carried out. The mechanism of the satellite Infrared anomaly before an earthquake in the Taiwan region is discussed based on the experimental results. Furthermore, a preliminary model for tectonic activity analysis and for short-term earthquake prediction based on the analysis of the satellite infrared anomaly before an earthquake in the Taiwan region is presented.

This paper briefly introduces the general scientific facts from the reports on satellite thermal infrared (TIR) anomaly before earthquake and the presented mechanism and hypothesis for interpretation the TIR anomaly. The spatio-temporal features of infrared (IR) radiation from loaded rock, stick-sliding rock and simulated active fault motion based on experimental IR detection are also introduced. Especially, the experiments on simulated fault activity shows that the TIR anomaly is likely to develop along the primary fault and the both sides of wedge-shaped acute geo-block in condition of intersected fault system, and that the intersection location is usually the coming epicenter. The theoretical mechanism of TIR anomaly before tectonic earthquake is hence suggested to be stress-thermal effect accompanied and strengthened by hydro-geological effect, greenhouse effect, P-hole effect and so on. As a case, the spatio-temporal features of TIR before Zhangbei Ms6.2 1998 earthquake is theoretically analyzed based on the overlay of active fault system on NOAA-AVHHR satellite TIR images. It was discovered that TIR anomaly is controlled by a potential great deep active fault and that the epicenter is exactly the intersection point of the primary great fault and two secondary faults being tow sides of an acute wedge-shaped active geo-block. It is concluded that although the earthquake is a complex and uncertain process of crust motion, the predication of earthquake is not impossible. Referring to the developing GEOSS and generalized remote sensing (GRS), a preliminary procedure for comprehensive multiple parameters analysis on fault activities and earthquake early warning is presented, which is based on massive data fusion.

3D fault modeling is one of the key issues of three-dimensional geosciences modeling system (3DGMS). Summarized the 3D modeling methods of faults at present, a new method, named as partition recursively modeling of the rock pillar body (RPB), for 3D modeling of faults (3DMF) based on generalized tri-prism (GTP) volume is put forward. This method takes borehole data as its original data source. The process for 3DMF includes: a) to generate the constrained delaunay TIN (CD-TIN) of terrain according to the collar data of borehole and the outcrops of faults; b) to construct the limit triangles in each PRB; c) to create 3D faults models according to the limit triangles. The test shows that the RPB method can accurately describe 3D complicated faults system involving obverse faults and reverse faults.

It is an efficient way of directly measured exterior orientations using integrated GPS/IMU system. The experiment has shown that the accuracy performance of direct orientation measurement is sufficient to make orthophoto and small or medium scale map. Unfortunately, since the GPS/IMU orientation module is physically separated from the camera to be oriented translational offsets and rotations are existent and have to be considered. This paper firstly discusses the integrated GPS/IMU system and the elements of GPS/IMU-based photogrammetry. Finally, the overall system calibration is investigated through the data obtained from Jiuquan city, Gansu Province.

Dongsha Ms5.9 earthquake and Zhangbei Ms6.2 earthquake are used to analyze the spatial-temporal features of satellite positive TIR anomaly before shock. Four experiments with rock blocks to simulate the tectonic earthquakes and TIR anomaly are introduced. It revealed that (1) the positive TIR anomaly usually develops along active faults, especially along chief faults; (2) the spatio-temporal features of TIR anomaly and precursors are tightly related to regional geological structure and local active faults; (3) the intersection position of faults is always the sensitive seismic region with obvious positive TIR anomaly; (4) the satellite TIR monitoring on tectonic earthquake should focus on the seismic area along active faults.

In geology field, the rock attribute is the reference to judge the strata relationship between boreholes. It is the key issues to transfer the qualitative attribute to quantitative data by using of geo-ontology. Being a case of geo-ontology the Stratum-ontology is a formalized representation and an instance to meet the quantitative need. According to the domination features of carbonate rock, the semantic contents of stratum-ontology are defined. Based on the construction of Stratum-ontology, the formal representation of semantic with BNF normal form is developed, which is the criterion for the judgment of stratum relation. According to the judgment and stratum inference rule, a digital three-dimensional geological model of a research district is constructed as a case.

Constrained Delaunay triangular irregular network (CD-TIN) is a basic data structure widely used in GIS, 3D reconstruction, computer geometry and geosciences. Traditional researches on Delaunay triangular irregular networks (D-TIN) paid more attention to the insertion algorithms for points and edges, while little to the deletion algorithms for points and edges. The present algorithms for D-TIN are far insufficient for the dynamic updating of CD-TIN, which demands for not only the insertion for points and edges, but also the deletion for points and constrained edges. Based on the improvements to the present insertion and deletion algorithms for D-TIN, an algorithm for point deletion in CD-TIN, namely integral ear elimination (IEE), which improved from the EE algorithm for D-TIN, is presented. Some examples are demonstrated that the presented algorithms in this paper for the updating of CD-TIN are efficient.

To fully understand the characteristics of subsurface spatial objects and the relationships between them, the integrating multi-source data of geology is demanded. This is made possible to establish the 3D subsurface spatial data integration model: integrated information voxel (IIV), which is established on the basis of the analysis and recognition of the characteristics of subsurface spatial data. The partition of the IIV should be followed some criteria to construct the boundaries. The characteristics of the IIV and the simplified process for the multi-data integration based on IIV is introduced in this paper. As a concept model, some other key problems such as the weight of the boundary and the precision evaluation of the integration should be studied further.