The problem of dissipative stabilization for a class of T-S fuzzy systems (TSFS) is studied by generalized memory sampled-data control (SDC). A novel Lyapunov–Krasovskii functional (LKF) is developed, which extends existing looped-functional approaches by integrating augmented terms, triple integral functionals, and, notably, membership function information of the fuzzy system. This enables the LKF to more accurately capture sampling pattern characteristics, thereby reducing conservatism and enhancing dissipative performance of the closed-loop system. Based on a switching strategy and generalized free-matrix-based integral inequality (GFMBII), new criteria are established to ensure asymptotic stability and strict(Q,S,R)-ρ-dissipativity of the resulting fuzzy sampled-data system. Furthermore, the generalized SDC is specifically designed to ensure dissipativity of the considered system. Finally, the obtained dissipative criterion is applied to the truck-trailer system to verify the effectiveness and superiority of the proposed method.

This article is concerned with the stochastic finite-timeL1control problem of multi-loop networked control systems (NCSs) with network-induced delay, random packet loss, and external interference. Firstly, considering the data processing mode jumping, data transmission channel switching, and positive total amount of data, the multi-loop NCSs with network-induced delay, random packet loss, and external interference are modeled as a more general class of variable dual switching positive time-delay systems (VDSPTDSs) for the first time. Secondly, a new scheduling strategy that fully considers the random packet loss and the total amount of data, named positive minimum state expectation (PMSE), is proposed. Under this scheduling strategy, the channel with the smaller expectation of the total amount of data is selected to reduce the communication overhead. Subsequently, a stochastic multiple co-positive Lyapunov-Krasovskii functional (SMCPLKF) is constructed to establish the criteria of stochastic finite-time bounded (SFTB) and finite-timeL1-gain performance. A mode-dependent finite-timeL1-gain state feedback controller is further designed such that the closed-loop VDSPTDSs are positive and SFTB withL1-gain characterization. Finally, a multi-loop data communication NCS model is given to demonstrate the validity and generality of the proposed methods.

Natural fractures significantly influence the production performance of volcanic hydrocarbon reservoirs. However, the complex nature of fracture networks affects the elastic behavior and seismic responses of these reservoirs, creating substantial challenges for seismic-based fracture characterization. This study proposes a method for quantitatively estimating fracture parameters in volcanic formations by integrating a rock physics model with the Bayesian optimized bidirectional long short-term memory (BO-BiLSTM) network architecture. Following the established model for volcanic formations, a model-based inversion approach is introduced to calculate fracture parameters using well log data, incorporating the simulated annealing particle swarm optimization (SA-PSO) algorithm for reliable multi-parameter simultaneous estimation. Fracture density is derived from these parameters, enabling a comprehensive assessment of fracture parameters. The validity of the volcanic model and inversion framework is confirmed by the strong correlation between predicted and observed velocities during the inversion process. Subsequently, the BO-BiLSTM architecture is used to build a predictive framework that captures the intricate relationships between fracture parameters and elastic properties. This predictive model is then applied to estimate fracture parameters from seismically-derived elastic properties. The seismic-based predictions exhibit strong agreement with results obtained from well log data, demonstrating the applicability of the proposed method. The predicted fracture parameters reflect micro-scale fracture characteristics and exhibit a structure-related distribution when visualized alongside macro-scale fractures, revealing patterns of fracture networks and potential connectivity between larger fractures. These insights enhance the understanding of multi-scale fracture systems, providing critical information for volcanic reservoir characterization.

Localized water-rich areas in aquifers can cause severe accidents during coal mining, including property damage and casualties. Traditional ground-based geophysical methods often struggle in the mountainous terrains where coal mines are typically located. To address this, we applied a high-resolution exploration and interpretation strategy based on advancements in semi-airborne transient electromagnetic (SATEM) technology, integrating drilling and logging to detect water-rich areas in the Huangling coal mine. Our approach involved acquiring high signal-to-noise ratio electromagnetic (EM) data near the transmitting line source, using unstructured tetrahedral meshes to simulate complex topography, and employing a quasi-Newton optimization algorithm for detailed 3-D inversion. Synthetic tests demonstrate the accuracy of this method in resolving underground conductivity structures, even in challenging terrains. Field data inversion showed a good fit with observed data and good agreement with resistivity logging, confirming the reliability of the results. This study not only addresses the critical need for efficient water hazard detection in coal mining but also offers significant potential for mineral exploration, geological surveying, and disaster prevention.

Magnetotelluric (MT) natural signals are characterized by randomness, nonstationarity, and nonlinearity. At low frequencies, long-duration noise frequently reduces the signal-to-noise ratio (SNR). Especially around the dead band below 1 Hz, the data quality is poor due to the low energy of the natural MT field. This study presents a novel approach using adaptive multiscale sample entropy (AMSE) to identify noisy segments, mainly targeting highly predictable noise types such as square wave, impulse, and triangular-wave interference in low frequency. The primary method employs a robust empirical wavelet transform (REWT) for effective noise suppression. To enhance time–frequency resolution and improve the constraints of direct spectral segmentation in traditional empirical wavelet transform (EWT), the short-time Fourier transform (STFT) is applied to REWT components for enhanced signal-to-noise separation. In addition, Gaussian white noise is introduced to mitigate MT noise effects further. Results show that AMSE effectively identifies noisy segments, and the proposed REWT method successfully retains valuable low-frequency information while significantly suppressing square wave, triangular wave, and impulse noise. Field data show that this method enhances the quality of MT responses, resulting in smoother, more continuous apparent resistivity-phase curves with reduced errors, which improves the accuracy of inversion interpretation and provides a reliable dataset for subsequent calculation of inversion profiles.

The detection of vertical fractures using seismic methods is crucial for characterizing shale gas reservoirs. When vertical fractures are aligned within a vertically transverse isotropic (VTI) shale formation, the medium behaves as an orthorhombic system. Seismic waves propagating through such fluid-saturated fractured shales exhibit anisotropic dispersion and attenuation, effects often not adequately considered by conventional fracture detection methods. This paper addresses this limitation by proposing an azimuthal frequency-dependent inversion method to compute anisotropic dispersion attributes for fracture characterization in orthorhombic shales. The method employs an azimuthal amplitude difference scheme to highlight anisotropy’s influence on the reflection coefficient by removing the VTI background effects, enhancing the robustness in anisotropic parameter estimation. A frequency-dependent inversion framework fully utilizes seismic reflection frequency information, while a frequency-scanning scheme improves mathematical rigor over traditional approaches. Numerical examples based on a viscoelastic orthorhombic shale model demonstrate the feasibility of the proposed dispersion attributes for fracture characterization. Synthetic data tests validate their effectiveness in assessing vertical fracture density, and field data applications confirm their reliability through good correlations with FMI images. Finally, a fracture indicator integrating the anisotropic dispersion attributes is developed to characterize vertical fractures in the studied shale reservoirs. This methodology holds potential for advancing the detecting of more complex fracture systems in hydrocarbon reservoirs.

Since the applications of the full-polarimetric ground penetrating radar (FP-GPR) technique have evolved from the detection of isolated targets to the interpretation of regional geologic structures, the study of the FP-GPR data fusion imaging method has become an urgent issue. Current data fusion methods for FP-GPR are all mathematical algorithm-based, aiming to obtain optimal weights for stacking. However, these stacking methods will produce wrong results if there are phase oppositions between different channels of FP-GPR data. Therefore, this article proposes a physical model-based fusion method called predictive rotation fusion (PRF). The new method aims to pursue special angles between the antenna and the surveying line for every scattering point in the subsurface, at which the amplitudes of acquired ground penetrating radar (GPR) data will be maximum. Besides, a new predictive filter is established and embedded into the PRF method to suppress the distortions and noises in the fusion results. The effectiveness of the proposed method is verified through laboratory experiments and its superiorities in fusion and denoising are also discussed by comparing it with a currently best weight-based fusion method. Finally, two real experiments are introduced to show the abilities of the PRF method in urban underground space imaging and field loess structure imaging.

Forests are vital components of the ecosystem of the Earth, regulating climate, conserving soil and water, and fostering biodiversity. Sustainable forest management offers crucial long-term benefits, but challenges such as disease threaten tree health. Disease-infected trees in both forests and urban areas pose risks to safety, property, and culture, leading to economic losses. Traditional inspection methods, such as resistance drilling, are invasive and laborious, while nondestructive techniques such as ground penetrating radar (GPR), offer promising alternatives. Although GPR shows potential, complexities such as tree structure and electromagnetic properties hinder accurate disease detection. In order to address these challenges, a new approach integrates structure-from-motion (SfM) photogrammetry with GPR measurement, and a novel holographic radar tomography processing approach has been proposed in this article. These methodologies accurately reconstruct tree trunks in 3-D, enabling precise GPR positioning and the obtaining of trunk permittivity. An arc-shaped Kirchhoff migration algorithm, moreover, helps mitigate irregular trunk shapes, enhancing data accuracy. The proposed framework demonstrates efficacy in real-tree measurement, offering high-precision disease monitoring. This innovation not only aids in preserving biodiversity but also enhances ecosystem services by promoting sustainable forest management. Effective disease monitoring ensures timely intervention, safeguarding valuable old trees and ecosystems while minimizing economic and cultural losses.

Lacus Mortis has been clarified as the landing site of the Astrobotic Mission One lander mission. In this work, the Chang’E-2 microwave radiometer data was first introduced to evaluate thermophysical features of surface deposits in Lacus Mortis. Several findings are noted as follows. (1) Microwave radiometer data shows abnormal thermophysical parameters existing in shallow layer of mare surface. (2) In crater unit, a strong relationship between microwave performances and other parameters including rock abundance and elevation is shown.

This article presents a bio-inspired safety control scheme for unmanned aerial vehicles (UAVs) in confined environments with disturbance. Although there has been some existing research on the effect of disturbance for a single UAV, multi-UAV formation under external wind disturbances remains challenging, especially in a tight and confined environment. Inspired by nature, this study concentrates on an anti-disturbance mechanism for safe multi-UAV formation in a tight environment. The presented safety control system combines disturbance observer-based control (DOBC), bionic formation switching (BFS) strategy, and safety evaluation. Two safety issues are considered in this article. For a single UAV, the estimated disturbance is compensated in the inner-loop controller. While for multi-UAV formation, the BFS strategy attenuates the effect of external wind disturbance leveraging the formation configuration. The so-called group perturbation immune factor (GPIF) is designed to analyze and evaluate the safety of the overall formation. The experimental results validate the comprehensiveness and anti-disturbance capability of the system.

The stability and stabilization problems for T–S fuzzy systems with a periodic variable delay are analyzed in this article. First, an improved delay-dependent reciprocally convex inequality is presented to deal with the periodic variable delay, which contains some existing results. Second, according to the monotonicity of the delay interval, the interval of each period is divided into a monotonically increasing interval and a monotonically decreasing interval, and different looped functionals are constructed on the two intervals. A new monotone delay-interval-based functional is presented to introduce more delay information and system state information on the basis of augmented functional and looped functional methods. Then, a generalized memory controller is designed to assure robust stabilization of the system by considering the variable delay and its bounds, which is more general than conventional controllers. Finally, some examples are shown to elaborate on the feasibility and validity of the obtained results.

Fractures with fluid flow can lead to the damage of rock carving relics. During the detection of fractures, millimeter-scale fractures are usually difficult to determine due to their small apertures. Considering the rapid variation of water content in the fracture seepage zone can lead to anisotropy, this article proposes a new methodology to detect these millimeter-scale fractures with fluid flow using a time-lapse full-polarimetric ground penetrating radar (FP-GPR) scheme and an anisotropy analysis method. The time-lapse FP-GPR detection can monitor the water flow in the fracture and the infiltration in the rock, and the Freeman decomposition, H-Alpha decomposition, and a polarimetric phase (PP) feature are adopted to quantify and analyze the anisotropic effects over time. In the numerical test, we adopt hydrological modeling to build realistic dielectric models for time-lapse FP-GPR simulations. The results indicate that the variations of water contents and several polarimetric features, i.e., the surface-like scattering power, the double-bounce scattering power, and the averaged scattering angle, are consistent and are essentially related to the anisotropy of the seepage zone. Finally, we introduce the field tests performed at the experimental station of the Dazu Rock Carvings in Chongqing, China, which contain two cases I and II. Case I is an experiment on a surface fracture of a cliff, whereas case II is a detection test of a buried fracture. The results verify the effectiveness of the proposed methodology.

Predicting microcracks is crucial for identifying tight sandstones with high permeability, which is essential for economic gas production. However, effective methods for directly detecting microcracks using prestack seismic data are currently lacking. Rock physics modeling and experiments suggest that microcracks can cause frequency-dependence of elastic properties and seismic responses, which are not fully utilized in existing seismic methods for microcrack prediction. This article addresses this gap by introducing an approach for identifying microcracks using seismic dispersion attributes derived from a new frequency-dependent amplitude variation with offset (AVO) inversion. A new AVO equation is derived, parameterized by a proposed microcrack indicator and relevant elastic properties, with its accuracy verified against exact solutions from the Zoeppritz equations. A frequency-dependent AVO inversion approach is then developed to compute the microcrack-related dispersion attribute from prestack seismic data. The effectiveness of the proposed dispersion attribute for robust microcrack prediction is validated using synthetic data. The results suggest that the proposed dispersion attribute increases with microcrack density and is unaffected by varying gas saturation. In real seismic data applications, high-value anomalies of the proposed dispersion attribute indicate tight sandstones with high permeability. Given the geological understanding that microcracks significantly enhance the permeability of tight sandstones, the reliability of the proposed dispersion attribute for detecting microcracks is further confirmed by this result. The presented method provides valuable information for the comprehensive characterization of prospective areas in tight sandstone gas reservoirs.

Prediction of fluid-saturated fractures is crucial for characterizing tight hydrocarbon reservoirs. Elastic amplitude variation versus azimuth (AVAz) methods are commonly employed for fracture prediction. Although frequency-dependent anisotropy has been explored using various experiments and numerical modeling, there is a lack of applicable methods for fracture prediction based on seismic anisotropy dispersion. To address this gap, we propose a frequency-scanning AVAz (FS-AVAz) method to predict fluid-saturated fractures by extracting anisotropy dispersion properties associated with fluid flow in fractured porous rocks from wide-azimuth seismic data. In this context, the presented FS-AVAz method offers a new perspective for fracture prediction that differs from elastic AVAz approaches. In the FS-AVAz method, the reflectivity equation formulated by azimuthal amplitude differences enables a robust estimation of the anisotropy dispersion inversion by eliminating the effect of the isotropic host rock. Meanwhile, as a critical inversion framework, the proposed frequency-scanning scheme ensures a reliable calculation of anisotropy dispersion attributes. Synthetic tests validate the capability of the FS-AVAz method in hydrocarbon-saturated fracture prediction. The effectiveness of the presented method for fracture prediction is further confirmed by field data applications using wide-azimuth seismic data. The predicted anisotropy dispersion attribute shows a good agreement with logging permeability and can serve as an indicator of fluid-saturated fractures. Meanwhile, incorporating the results of FS-AVAz and frequency-dependent amplitude variation versus offset can achieve a comprehensive characterization of fluids and fractures in tight rocks.

Elastic full-waveform inversion (EFWI) can retrieve multiple subsurface elastic parameters beyond the capabilities of the simple acoustic assumption. Compared to acoustic FWI (AFWI), EFWI faces complexities in dealing with multiple parameters and high nonlinearity in elastic inversion. Wavefield reconstruction inversion (WRI) was proposed to mitigate the cycle skipping and improve the computational efficiency of AFWI. WRI uses the wave equation as a regularization term for the objective of data fitting. By controlling the weight factor for the regularization term, the accuracy of the wave equation is relaxed and the data fitting term is enhanced. Thus, the cycle-skipping issue is reduced. WRI requires wavefield reconstruction in the calculation of the model gradient, which is the key step in WRI. The success of this wavefield reconstruction step highly depends on the accuracy of the source wavelet. In this paper, we propose an elastic WRI (EWRI) with source estimation (SE) method for multiple elastic parameters inversion. In the proposed method, we first reconstruct multicomponent wavefields (vertical and horizontal displacements) and estimate the source wavelet, simultaneously. Then, we formulate the elastic waveform inversion problem into a linear inversion system to mitigate the nonlinearity in EFWI. Applications on synthetic data generated from a modified Overthrust model and a Section of the Sigsbee2A model show the effectiveness of the proposed method in inverting P- and S-wave velocity models with an unknown source wavelet.

Seismic traveltime is critical information conveyed by seismic waves, widely used in various geophysical applications. Conventionally, the simulation of seismic traveltime involves solving the eikonal equation. However, the efficiency of traditional numerical solvers is hindered, as they are typically capable of simulating seismic traveltime for only a single source at a time. Recently, deep learning tools, particularly physics-informed neural networks (PINNs), have proven effective in simulating seismic traveltimes for multiple sources. Nonetheless, PINNs face challenges such as limited generalization capabilities across different models and difficulties in training convergence. To address these issues, we have developed a method for simulating multisource seismic traveltimes in variable velocity models using a deep learning technique, known as the physics-informed Fourier neural operator (PIFNO). The PIFNO-based method for seismic traveltime generator takes both velocity and background traveltime as inputs, generating the perturbation traveltime as the output. This method incorporates a factored eikonal equation as the loss function and relies solely on physical laws, eliminating the need for labeled training data. We demonstrate that our proposed method is not only effective in calculating seismic traveltimes for velocity models used during training but also shows promising prediction capabilities for test velocity models. We validate these features using velocity models from the Sibsbee2A velocity and OpenFWI dataset.

Cavity detection is vital to homeland security, civil engineering, and geological surveys. However, the limited target classification ability of traditional ground penetrating radar (GPR) may lead to its inability to identify cavities in complex environments with multiple anomalies, and the resolution of traditional GPR may not be enough to distinguish the internal structure of cavities. To address this issue, we have developed the dominant scattering mechanism analysis (DSMA) technique of full polarimetric GPR (FP-GPR). This technique calculates the scattering power of three physical models and determines the dominant scattering mechanism of subsurface targets. Cavity identification coefficients (CIC) are then utilized to enhance cavity detection and evaluation accuracy. Numerical simulation experiments have validated the workflow of this cutting-edge method. Scaled model experiments have depicted the polarization characteristics of the scattering field of typical tunnel structures within a laboratory setting. Field experiments have demonstrated that the novel technique can accurately identify and assess tunnels in complex environments with multiple anomalies, providing a foundation for determining regional function.

Natural fractures play a crucial role in shale reservoir characterization. While vertical fractures can be estimated using amplitude variation with azimuth (AVAz) inversion methods, predicting horizontal fractures remains limited due to their intricate seismic responses. This article addresses this gap by introducing a rock-physics-based method for predicting horizontal fracture parameters using well-log and seismic data. A shale model is developed using rock physics methods to quantify elastic properties associated with horizontal fractures. The sensitivity of the elastic properties to horizontal fracture parameters is analyzed to validate their potential for fracture prediction. Subsequently, a model-based inversion approach is proposed to extract horizontal fracture parameters from logging data. This method integrates a simulated annealing particle swarm optimization (SA-PSO) algorithm to ensure robustness and convergence in calculations. The results confirm the efficacy of the proposed method in estimating horizontal fracture parameters in boreholes. Based on the results obtained using the proposed method and well-log data, a prediction model is constructed to capture complex correlations between horizontal fracture density and elastic properties by employing a convolutional neural network (CNN) architecture. Following successful training and validation, the established model predicts horizontal fracture density in shales using elastic properties obtained from seismic inversion. The results align closely with estimates derived from logging data and are consistent with the geological characteristics of the studied area. This study presents a valuable method for predicting horizontal fractures using geophysical logging and seismic data, offering valuable insights into natural fracture characterization in shales.

The Zhurong rover of the Chinese Tianwen-1 mission is equipped with a Mars rover penetrating radar (RoPeR). The high-frequency CH-2 antenna adopts a full-polarimetric (FP) acquisition mode to map the fine structure of the Martian regolith. However, the data from different polarimetric channels characterize different features of the subsurface. The accurate interpretation of regolith structure relies on a clear and comprehensive imaging result. This article proposes a new data fusion algorithm to achieve this goal, which is called the maximum likelihood method with optimal similarity constraints (ML-OSC). A unique loss function is constructed to guarantee the similarity between the fusion result and source radargrams and simultaneously consider the amplitude difference in different channel data. FP ground penetrating radar (FP-GPR) experiments on three typical targets are performed in the laboratory. The comparisons with three normal methods validate the effectiveness and superiority of the proposed method. Finally, the proposed method is applied to the field FP-RoPeR data fusion imaging. Several subsurface structures are analyzed in detail to verify its effectiveness in Martian regolith imaging. The proposed method provides new and complete imaging results of Martian regolith, which will lead to a more accurate interpretation of it in the future.

Seismic modeling is crucial for tackling waveform-based inverse problems in geophysics. Physics-informed neural networks (PINNs) have become a popular tool for simulating seismic waves. Their ability to incorporate partial differential equations (PDEs), initial conditions (ICs), and boundary conditions directly into the loss function allows for physically accurate modeling. The prevalent approach in the current literature treats the wave equation as a parametric PDE. However, the majority of the existing studies simulate wavefields for a specific velocity model, necessitating network retraining for different models, thereby diminishing modeling efficiency. In response, we present a velocity-encoded (VE) PINN (VE-PINN) that introduces feature parameters to represent various layered velocity models, integrating them into the network. Drawing inspiration from supervised learning, our approach employs a VE method to compute initial wavefields for variable layered models. Remarkably, our proposed VE-PINN demonstrates the ability to generalize across different ICs within the dataset. This eliminates the need to retrain the network for each new solution, offering significant efficiency gains. Numerical results show that the VE-PINN significantly enhances efficiency in solving the acoustic wave equation for various layered velocity models compared with finite-difference methods (FDMs). Subsequently, we extend the application of our method to time-domain simulation for variable source locations, demonstrating that the VE-PINN yields the results that are consistent with numerical wavefields.

Almost all Android users have ever experienced poor responsiveness, including the common frame dropping events—slow rendering (SR) and frozen frames (FF), as well as the uncommon Application Not Responding (ANR) and System Not Responding (SNR) that directly disrupt user experience. This work takes two complementary approaches,controlled benchmarkingandin-the-wild crowdsourcing, to comprehensively understand their prevalence, characteristics, and root causes, which turn out to be significantly different from common understandings and prior studies. We find that SR, FF, ANR, and SNR all occur prevalently on all the studied hardware models of Android phones, and better hardware does not seem to relieve ANR/SNR. Most surprisingly, they are oftentimes ascribed to defective software design that incurs substantial resource overuse—lightweight apps can experience severe SR/FF events due toredundant UI rendering, and the most ANR/SNR events stem from Android's aggressive implementation ofwrite amplification mitigation. In fact, the former can be effectively overcome by simplifying the apps' UI hierarchy, and we design a practical approach to address almost all ($>$99%) of the latter while only decreasing 3% of the data write speed with large-scale deployment. We have released our measurement code/data to the research community.

Mare Smythii is a special basin, which is one of the oldest mare basins with the considerably young mare basalts and the high density of floor-fractured craters. In this paper, the Chang’E-2 (CE) lunar microwave radiometer (MRM) data was used to evaluate the thermophysical features of the floor deposits in mare Smythii. Based on the theoretical simulation and the previous geological results, several special findings are noted as follows. (1) The substrate temperature in mare Smythii is likely fairly high. (2) The regolith thermophysical parameters change greatly with depth in the northeast unit, and there probably exists a special material in the shallow layer of the lunar regolith with strong thermal absorption ability. These special findings will be of fundamental significance to improve understanding the thermal evolution of the Moon.

Simulating seismic wave propagation by solving the wave equation is one of the most fundamental topics in applied geophysics. Considering the elastic nature of the Earth, it is important to simulate the elastic behavior of seismic waves. Compared with solving the acoustic wave equation, it often requires a larger computational cost to solve the elastic wave equation. For the finite-difference method, the computational cost for simulating elastic wavefields increases greatly to include multiple wavefield components. We propose to solve the scattered form of the frequency-domain elastic wave equation using a deep learning framework, called physics-informed neural networks (PINNs). PINNs use the physics principles (scattered elastic wave equations in our case) as the loss function. By inputting the spatial model coordinates and source locations into the network, we can evaluate the wavefield solutions of vertical and horizontal displacements in the domain of interest for arbitrary source locations. We demonstrate that this newly developed deep-learning-based method can simulate multicomponent elastic wavefields with reasonable accuracy.

Solving the wave equation is an essential step in the simulation of seismic wavefields. Physics-informed neural networks (PINNs) have been widely applied in geophysics. However, there are still some challenges in solving the time-domain wave equation due to the complexity of seismic wavefields and the point source singularity. Numerical solutions can be used as initial conditions to constrain the network training. However, numerical solver-assisted methods have their own accuracy and stability limitations. We propose to use the analytical solutions of the wave equation as prior knowledge. This method does not rely on numerical solvers of the wave equation. It avoids the point source singularity by using analytical wavefields as initial conditions. In addition, we tackle the issue of balancing different terms in the loss function by proposing task-decomposed PINNs (TD-PINNs). TD-PINNs divide the network training into three steps, including pre-training, full-learning, and the physics-enhanced training. The performance of TD-PINNs to solve the wave equation has been tested in different models, and the results show that it can simulate seismic wave propagation with reasonable accuracy.

The polarimetric response of full-polarimetric ground penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this paper, we focus on the FP-GPR signals from uniaxial bi-anisotropic media, i.e. the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation(PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that εz/εx, σz/σx, and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3D FP-GPR simulations on a series of uniaxial bi-anisotropic models verified the results.

As one of the most important geophysical methods for detecting the lunar underground structure of the Moon, the lunar penetrating radar (LPR) is applied to the Chang’e-4 (CE-4) mission to explore the Von Kármán crater on the far side of the Moon. With the Yutu-2 rover continues to move towards the Zhinyu crater at the west, radar will likely detect the fractures created by the impact that formed the Zhinyu Crater. Therefore, the establishment of a comprehensive lunar subsurface structure model with fracture, random media and fractal terrain is crucial for the LPR data processing and the understanding of the lunar geological impact process. In addition, the method that can image the radar data with fractures well has great significance to the interpretation of the LPR data. In this paper, we establish a comprehensive lunar subsurface structure model firstly, the considering factors including fractures, random media, fractal terrain, and the radar response are calculated through forward modeling. Secondly, according to the high resolution and the sensitive to inhomogeneities of LPR data, we design a set of processing flow including plane-wave destruction, velocity analysis based on the focusing analysis method and migration imaging based on the velocity continuation. Finally, the results including simulation data and Antarctic fracture data are used to verify the effectiveness of imaging method.

Full-waveform inversion (FWI) is a popularly used high-resolution seismic inversion method. It relies on the measure of the misfit between observed data and predicted data. Due to the sinusoidal nature of seismic waves, a direct comparison of observed data and predicted data using thel2norm may cause cycle skipping. A variety of objective functions for FWI have been proposed to resolve this issue over the years. Based on the gradient optimization method, an explicit expression of the model gradient of the defined objective function is needed to be derived and calculated. This complicated step can be circumvented by using an automatic gradient calculation technique, called automatic differentiation (AD). AD allows calculation the gradients of the model parameters, as well as those of the inputs using the chain rule. Taking advantage of the deep-learning framework, FWI with different objective functions can be automatically optimized using AD. To improve the accuracy and applicability of FWI on real data, we propose a new objective function that we refer to as the weighted envelope-correlation inversion (WECI), which combines two correlation-based waveform inversions. The weights imposed on these two terms in this new objective function can be dynamically adjusted by the sigmoid function during the optimization process. We show the versatility and effectiveness of AD-based waveform inversions using different objective functions through numerical tests. We also demonstrate the superiority of the proposed WECI method on synthetic data and real data.

Three-dimensional gravity inversion is an effective method for extracting underground density distribution from gravity data. However, traditional deterministic gravity inversion methods suffer from problems such as skin effect, low computational accuracy, and poor efficiency. Therefore, we propose a three-dimensional gravity data focusing inversion algorithm based on the supervised descent method. Supervised descent method (SDM) is a non-linear optimization method based on the combination of machine learning and gradient descent method. In the offline phase, we construct a training set based on a priori information and iteratively learn a set of average descent directions between the initial model and the training model. In the online phase, we introduce a focused regularization into the prediction objective function. This addition aims to obtain a sharp boundary density model that conforms to the physical distribution. Additionally, we incorporate property boundary constraints in both the offline and online phases to control the upper and lower bounds of the density values to ensure consistency with reality. Model tests show that the proposed method can effectively overcome skin effect, improve the resolution of gravity inversion. Moreover, the construction of the training set of the proposed method is less affected by prior information, and it has strong generalization ability. Furthermore, the method does not require solving large-scale linear equations, accelerating the inversion computation speed and having strong noise resistance. Field examples demonstrate that this method has good potential for improving the accuracy and efficiency of actual gravity data inversion.

Fluid identification using seismic data is critical for the characterization of hydrocarbon reservoirs. Poroelastic behaviors associated with wave-induced fluid flow allow for hydrocarbon detection using seismic attributes derived from frequency-dependent information. However, the conventional inversion methods usually estimate the frequency-dependent seismic reflectivity across subsurface interfaces, which may lead to ambiguities in the interpretation of the obtained results. A novel frequency-dependent elastic impedance inversion approach is proposed in the present study by extending the elastic impedance equation, as represented by the elastic modulus, to the form in the frequency domain. The proposed method has the advantage of calculating the interlayer dispersion properties, instead of the frequency-dependent interface reflectivity, for fluid identification. The estimated interlayer dispersion attributes show more apparent physical meanings than the traditional frequency-dependent interface properties. It can thereby provide intuitive interpretations for hydrocarbon identifications. Synthetic examples show that the interlayer dispersion attribute estimated by the proposed method can reliably indicate tight sandstone targets with less ambiguity than a traditional interface frequency-dependent attribute. Real data applications of tight sandstone reservoirs further validate the effectiveness of the here proposed frequency-dependent elastic impedance inversion method. For gas-bearing tight sandstones, the obtained interlayer dispersion attribute can be used as a fluid identification factor with improved accuracy. By using appropriate elastic impedance representations, the proposed method provides a valuable tool for fluid detection in various hydrocarbon resources by extending the method to the estimation of other interlayer dispersion attributes.

Diffracted waves on ground penetrating radar (GPR) radargrams will severely affect the positioning of subsurface anomalies and stratigraphic division of subsurface. I this study, we proposed an f-x domain VMD dip filter to suppress the diffracted waves on GPR radargrams. A simple model and complex model tests are performed. The results indicate that the f-x domain VMD dip filter can suppress the interference of diffraction well. The radargrams after processing can highlight the reflected signals generated by the surface of anomalies and subsurface strata, which is beneficial to further object positioning and stratigraphic division.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss–Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

China’s first Mars probe Tianwen-1 has successfully landed on the southern Utopia Planitia of Mars on May 15, 2021. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the subsurface fine structure and to find the potential underground water ice. However, different from the previous water ice detection of orbital radar, the FP-RoPeR signals will be affected by the induced field rotation (IFR) if electromagnetic (EM) waves propagate through rough interfaces. Therefore, in this article, we assess the IFR effects from rough interfaces on the circular polarization ratio (CPR) response of FP-RoPeR data, which is a significant parameter for water ice detection. The theoretical computation and numerical validation indicate that the depth, the number of rough interfaces, and relative permittivity are three vital parameters that affect the IFR effects; depth plays a more important role than the other two for FP-RoPeR system. The CPR estimation result will be with greater error in the shallow region (0–1 m). The relative error in the region of depth greater than 1 m can be guaranteed to be under 10%.

Seismic prediction of natural fractures is essential for the characterization of unconventional reservoirs because fractures provide seepage paths for fluid migration and storage space for hydrocarbon accumulation. However, it remains challenging to robustly estimate anisotropic parameters for fracture characterization based on seismic inversion methods. We propose an improved inversion scheme for the robust and accurate estimation of anisotropic parameters using PP-wave azimuthal amplitude differences incorporated with a hybrid optimization algorithm. Modeling analysis indicates that by removing the effect of isotropic terms, azimuthal amplitude differences are more sensitive to anisotropic parameters than the traditional azimuthal reflection coefficient; this can avoid possible instabilities in anisotropic parameter estimates that may occur in conventional methods owing to unbalanced weighting coefficients between isotropic and anisotropic terms. Meanwhile, the proposed hybrid algorithm takes advantage of the global optimization ability of the simulated annealing algorithm and the fast convergence characteristics of the particle swarm optimization algorithm. Synthetic data tests indicate that the hybrid algorithm achieves reliable and stable estimates of anisotropic parameters with higher computational accuracy and efficiency than traditional methods. The improved inversion method is applied to characterize fractures in volcanic gas reservoirs. Based on azimuthal amplitude differences obtained after estimating fracture orientation using the Fourier series method, anisotropic parameters are computed and converted to weakness properties, which is more meaningful in terms of the fracture properties. Our results indicate that the obtained fracture tangential weakness exhibits an apparent correspondence with well-log permeability, justifying the applicability of the proposed method for fracture prediction.

The joint inversion of structural constraints is a new and rapidly developing detection technology in comprehensive geophysical interpretation. In this article, a new structural constraint 2-D multiphysical parameter joint inversion algorithm for magnetotelluric (MT), gravity, and magnetic data is developed. The structural constraint term is a combination of cosine dot-gradient (CDG) and joint total variation (JTV) constraints, which not only has characteristics of traditional dot product and cross-gradient structure constraints but also avoids the uncertainty of dot product constraints predicting the gradient direction of the model parameters, overcomes the need for high-order differential approximation of the cross-gradient constraint, ignores the influence of the gradient amplitude of different model parameters on the weight of the structural constraint of different regions, and enhances the reconstruction accuracy of the underground discontinuous interface. To more easily combine multiple optimization algorithms to improve the resolution and computational efficiency of joint inversion, an adaptive inexact structural resemblance (IESR) algorithm is developed to minimize numerical solutions to the objective function. Experimental results have demonstrated that the CDG constraint has a wider use range than the traditional structural constraint, the addition of the JTV constraint can recover the underground discontinuous interface, and an inversion result of higher resolution can be obtained using the adaptive IESR algorithm.

Buried craters within tens of meters of lunar regolith are rarely studied but are significant for understanding the evolution of surface processes on the Moon. Here, we first report the evidence of an intact buried crater within the layered strata at Chang’E-4 (CE-4) landing site revealed by the lunar penetrating radar (LPR). The time–frequency comparative analysis method based on the variational mode decomposition (VMD) and the rock quantitative analysis method based on the local unit correlation (LUC) are proposed and applied to the processing and analysis of LPR data within 15 lunar days. The results presented by the two methods provide evidence of a buried crater at the CE-4 landing site and simultaneously reveal the rock-concentrated structure within the buried crater. According to the results, it is considered that the filling materials within the buried crater have survived the impaction and gardening during the formation of the overlying fine-grained regolith. Recent works have proposed that the near-surface material at the CE-4 landing site is mainly the lunar mantle materials excavated from the nearby Finsen crater. Therefore, the buried crater probably preserves the initial lunar mantle materials.

China’s Tianwen-1 probe carrying the Zhurong rover has successfully landed on the southern Utopia Planitia of Mars. The Zhurong rover is first equipped with a full-polarimetric Mars Rover Penetrating Radar (FP-RoPeR) system, aiming to map the fine structure and potential water-ice distribution of Martian regolith. However, the ground experiment on earth indicates that the RoPeR data is severely affected by noises, clutters, and signal misalignment. More importantly, an imbalance between the data from two cross-polarized channels is observed which is considered to be the interference of the rover. The interference prevents the accurate assessment and analysis of field FP-RoPeR data and may even become the traps in the interpretations of future radar data from Mars. In this article, we firstly analyze the interference in detail and achieve the suppressions of noises, clutters, and signal misalignment; subsequently, a polarized orientation calibration method is proposed to calibrate the imbalance of cross-polarized channels. Finally, we introduce a field experiment at the Ulanhada volcanic geopark in Inner Mongolia Autonomous Region, China. Based on the field RoPeR data and the proposed processing methods, we propose three types of data processing strategies for different aims and present how to use field FP-RoPeR data to analyze subsurface structures and properties.

The cold anomaly represents the special thermophysical features of the lunar regolith, which is only studied with the LRO Diviner thermal infrared (TIR) data. In this paper, sampled a typical TIR cold anomaly, the China Chang' E-2 lunar microwave radiometer data are employed to evaluate the regolith thermophysical features in microwave domain. The results indicate that the special material only exists in the shallow layer, and the thickness is larger than 31 cm but less than 77 cm. Moreover, the special material presents a high correlation with the rocks in the microwave domain, opposite to the findings from Diviner TIR data.

Summary In this paper, a dual switching discrete‐time linear system, simultaneously subject to deterministic switching and Markov chain, is considered. This study does not consider the transition probability of the Markov chain as fixed but determined by the current position of deterministic switching. Namely, such dual switching discrete‐time linear system is composed of a family of discrete‐time Markov jump systems and follows a rule that directs the switching sequences between them. The exponentially almost sure stability problem, for dual switching discrete‐time linear system with exponential uncertainty, is addressed by using persistent dwell time and stochastic multi‐Lyapunov function. The sufficient conditions for the exponentially almost sure stability of dual switching discrete‐time linear system are expressed as linear matrix inequalities. Finally, a simulation example demonstrates the validity of the derived results.

The full-polarimetric ground-penetrating radar (FP-GPR) can obtain the polarimetric attributes of targets and achieve more accurate identification compared with traditional GPRs. However, in most cases, the polarimetric signals collected by GPRs are not only from the targets but also from the ground surface. According to the classical Fresnel formulas, the polarized directions of the waves will change after transmissions due to the difference in the transmission coefficients between horizontally and vertically polarized waves. This effect, called induced field rotation (IFR), will interfere with the acquisition of polarimetric attributes and also exists in the field measurements on a rough surface. In this study, we have derived the association between the measured FP-GPR data and transmission coefficients of the rough surface provided by the small perturbation method (SPM). The effects of IFRs from the rough surface on H-Alpha decomposition are analyzed later. The results show that the parameters of the rough surface will affect the values of components in the scattering matrix, but will not change the matrix and H-Alpha decomposition result; the incident angle and relative permittivity play main roles; for the H-Alpha decomposition results of three typical targets, the flat and dihedral are little influenced by IFRs, but the cylinder is seriously affected; simultaneously, a template for discriminating whether the calibration for H-Alpha decomposition is necessary is established. Both numerical and experimental tests validate the conclusions. Finally, a novel application strategy of H-Alpha decomposition is proposed, which has taken IFR from the rough surface into considerations.

Almost every Android user has unsatisfying experiences regarding responsiveness, in particular Application Not Responding (ANR) and System Not Responding (SNR) that directly disrupt user experience. Unfortunately, the community have limited understanding of the prevalence, characteristics, and root causes of unresponsiveness. In this paper, we make an in-depth study of ANR and SNR at scale based on fine-grained system-level traces crowdsourced from 30,000 Android systems. We find that ANR and SNR occur prevalently on all the studied 15 hardware models, and better hardware does not seem to relieve the problem. Moreover, as Android evolves from version 7.0 to 9.0, there are fewer ANR events but more SNR events. Most importantly, we uncover multifold root causes of ANR and SNR and pinpoint the largest inefficiency which roots in Android's flawed implementation of Write Amplification Mitigation (WAM). We design a practical approach to eliminating this largest root cause; after large-scale deployment, it reduces almost all (>99%) ANR and SNR caused by WAM while only decreasing 3% of the data write speed. In addition, we document important lessons we have learned from this study, and have also released our measurement code/data to the research community.

Lunar penetrating radar (LPR) is an important way to evaluate the geological structure of the subsurface of the moon. The Chang'E-3 has utilized LPR, which is equipped on the lunar rover named Yutu, to obtain the shallow lunar regolith structure in Mare Imbrium. The previous result provides a unique opportunity to map the subsurface structure and vertical distribution of the lunar regolith with high resolution. In order to evaluate the LPR data, the study of lunar regolith media is of great significance for understanding the material composition of the lunar regolith structure. In this letter, we focus on the lunar regolith quantitative random model and parameter inversion with LPR synthetic data. First, based on the Apollo drilling core data, we build the lunar regolith quantitative random model with clipped Gaussian random field theory. It can be used to model the discrete-valued random field with a given correlation structure. Then, we combine radar wave impedance and stochastic inversion methods to carry out LPR data inversion and parameter estimation. The results mostly provide reliable information on the lunar regolith layer structure and local details with high resolution. This letter presents a further research strategy for lunar probe and deep-space detection with LPR.

Although fully polarimetric analysis techniques have been applied to remote sensing radar for charactering different surface scattering properties using, these techniques have not yet been widely adopted for Ground Penetrating Radar (GPR) applications. In 2015 we applied the “H-alpha decomposition” technique to polarimetric GPR data for classifying buried metallic targets such as wire branches, a sphere, a plate, and a dihedral. The H-alpha decomposition can separate scatters into four different types according to different depolarization effects: linear polarization, random depolarization (branches, roots, random media), no depolarization (sphere, plates, and horizontal layers), and 90 degrees depolarization (corner reflectors). In this paper, we explore the utilization of H-alpha decomposition method in clutter reduction and linear target detection during the preprocessing stage so that automatic linear target detection can be achieved in poor signal-to-clutter ratio (SCR) and signal-to-noise ratio (SNR) environments.

A proper description of the frequency-dependent seismic amplitude variation versus offset (AVO) responses should consider the effect of both the layered structure of a reservoir and the dispersive and attenuated property of the media in the reservoir. We propose an improved method to seamlessly link the rock physics modeling and the calculation for frequency-dependent reflection coefficients based on propagator matrix method. The improved AVO modeling method is implemented in frequency-wavenumber domain, and can accurately considers dispersion and attenuation that described by complex and frequency-dependent elastic properties predicted by rock physic models. Therefore, the improved method avoids errors resulting from truncating imaginary parts of the elastic properties as adopted by the conventional Zoeppritz-equation-based method. Moreover, the proposed method considers the intrinsic contribution of the layered structure to frequency-dependence of AVO responses, which has been ignored by current conventional methods. In addition, the method provides an efficient way to calculate seismograms for a dispersive and attenuated layered model. Finally, modeling results show the applicability of the improved method for the interpretation of complex frequency-dependent abnormalities, and indicate the potential for fluid detection in a layered reservoir.

In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

Polarimetric technology has been one of the most important advances in microwave remote sensing during recent decades. H-alpha decomposition, which is a type of polarimetric analysis technique, has been common for terrain and land-use classification in polarimetric synthetic aperture radar. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we apply the H-alpha decomposition to analyze the surface GPR data to obtain polarimetric attributes for subsurface target classification. Also, by combining H-alpha decomposition and migration, we can obtain a subsurface H-alpha color-coded reconstructed target image, from which we can use both the polarimetric attributes and the geometrical features of the subsurface targets to enhance the ability of subsurface target classification of surface GPR. A 3-D full polarimetric GPR data set was acquired in a laboratory experiment, in which four targets, a scatterer with many branches, a ball, a plate, and a dihedral scatter, were buried in dry sand under flat ground surface, and used to test these techniques. As results, we obtained the subsurface H-alpha distribution and classified the subsurface targets. Also, we derived a subsurface H-alpha color-coded reconstructed target image and identified all four targets in the laboratory experiment.

Full-polarimetric Ground-penetrating radar (GPR) is considered as a promising sensor for detecting buried targets. However, the polarimetric decomposition technique plays a crucial role in identifying and classifying targets which are buried in the sand under the surface. The decomposition techniques of full-polarimetric Ground-penetrating radar includes four decomposition methods, namely: (1) Pauli decomposition method, (2) H-α decomposition method, (3) Freeman decomposition method and (4) polarimetric anisotropy analysis method .This paper mainly applys Freeman decomposition method to recognition of metal surface plate, dihedral and metal ball. The potential of polarimetric target decomposition techniques to metal surface plate, dihedral and metal ball characterization and classification is shown which provides valuable information.

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. To solve the problem, a modified migration algorithm was proposed to process irregular GPR data, which has both the advantage of migration that can improve signal-clutter ratio and the advantage of interpolation that produces the grid data set for visualization. An application to field data acquired in Afghanistan shows clear landmine image in both vertical profile and horizontal slice.

Polarimetric GPR requires accurate calibration of channel imbalance and crosstalk not only in the amplitude term but also in the phase term. Currently, there have some calibration techniques. Though these techniques are very easy to perform, they provide less accurate calibration results for the crosstalk. To improve on the accuracy of calibration, we have developed a mathematical formulation to calibrate polarimetric GPR data. We measured several scattering matrices to obtain the necessary calibration parameters. The calibration technique was tested from measurements conducted on dihedral corner reflector.

Recent years LiDAR (Light Detection and Ranging) data is widely used for constructing 3D terrain models which provide realistic impressions of the urban environment. This paper presents a hierarchical contour method for building boundary extraction and 3D reconstruction from LiDAR Data. This method provides acceptable visualization for large-scale poor quality LiDAR data reconstruction.

A multisensor system could offer an effective solution to land-mine detection. A handheld dual-sensor system with a position tracking system is developed, which can acquire ground-penetrating radar (GPR) and metal detector (MD) data with 2-D space coordinates. However, as the measurement points are random and data are irregular for the human operator, it is difficult to display subsurface visualization imaging. We develop a set of techniques to achieve visualization imaging of both GPR and MD. A modified migration algorithm with continuous-root-mean-square velocity is proposed to process irregular GPR data, and an interpolation algorithm is recommended to process irregular MD data. In an application to field experiment data, clear subsurface targets imaging was achieved in both GPR slice and MD image.

Generally GPR transmits and receives radio waves with a single polarization using two parallel antennas. But it is possible to improve the GPR ability of discrimination and imaging of subsurface targets by analyzing the backscattered wave with a variety of polarizations. So we are developing a full-polarimetric GPR system, including PC, network analyzer, rectangular coordinates robot, switch driver, and polarimetric antenna array. Polarimetric antenna array is used to transmit and receive both co-polarimetric and cross-polarimetric signals. Currently polarimetric GPR do not execute precise calibration. But good calibration can improve the classification ability of subsurface targets. So we introduced the calibration technique into the polarimetric GPR, and derived a calibration formula.

We have conducted GPR survey at a site which was partly excavated and filled with highly contaminated soils. The electrical properties and TPH concentration of the core samples were measured in the laboratory. It is verified that an inverse relation between TPH concentration and relative dielectric constant, and a direct proportional correlation between TPH concentration and electrical resistivity. LNAPL contamination area is illustrated by GPR data which shows the decreased radar signal amplitude.

Flow control is an important means of avoiding congestion and providing QoS guarantee in networks. In this paper, a differentiated pricing scheme for flow control in General Multiuser Access Networks (GMAN) is proposed. Under this scheme, the problem of optimizing the network performance is formulated as a two-level hierarchical network game. By employing the Gerolamo Cardano Formula, we obtain the Complete Solution (CS) of the optimal prices and users' flow. Simulation results show that the proposed solution can enhance the revenue of Network Service Provider (NSP) and the network performance without significantly affecting user satisfaction, in comparison with the Asymptotical Solutions of Tamer Basar (AS-TB) in linear network systems. Moreover, analytical solution can also be obtained by CS, even with fixed NSP capacity. This feature makes our CS applicable to practical communication networks.

Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.

It is often advantageous to estimate the ground surface topography from radar returns. However, the popular method, searching for the brightest pixel in the ground-penetrating radar profile, cannot achieve accurate surface topography in the sharp variable surface case because of the effects of diffraction waves. In this letter, we propose a method to solve the problem and improve the accuracy of surface topography. A migration technique is introduced to refocus the diffraction waves before searching for the brightest pixel. Experimental data have been used to display the effects of diffraction waves and test the method. The result shows that the method can dramatically estimate accurate surface topography even in the sharp variable surface area.

Ground-penetrating radar (GPR) is recognized as a promising sensor for detecting buried landmines. In this case, the GPR antenna(s) must be elevated above the ground. However, this requirement results in heavy surface clutter. It is therefore necessary to overcome the effect. A commonly used procedure of time gating and background averaging cannot suit to small shallow nonmetallic landmine beneath a rough ground surface. In this letter, we proposed techniques to enhance the target signal through common midpoint (CMP) antenna array and data processing techniques, including velocity spectrum and CMP multifold stacking. The method has been tested using experiment data over a rough ground under which small plastic antipersonnel landmines is shallowly buried. The result shows the signal-to-clutter ratio was dramatically improved.