The marine vibrators (MVibs) have become indispensable in marine industrial exploration due to its controllable energy output, high repeatability, and environmental compatibility. MVib-based blended acquisition improves exploration efficiency by reducing sampling duration and operational costs, but it introduces significant challenges in source separation due to blending noise. Since the subsequent processing method of MVibs require precorrelation data, this article presents a deblending framework for precorrelation MVib data by integrating a prior network into the inversion framework. To address the scarcity of labeled MVib data for deep learning, a data augmentation strategy is proposed in which natural images are converted into seismic-like data using band-pass filtering to train the prior network. This approach enhances model generalization and alleviates the limitations of MVib datasets. The proposed framework is validated through multi-MVib simulations and large scale open sea trials. Experimental results demonstrate that the method effectively suppresses blending noise while preserving key signal features, outperforming conventional deblending techniques and demonstrating strong practical value in real-world marine exploration.

Marine vibrators have been favored by seismic acquisition in recent years because of their greater waveform control, repeatability, and lower environmental damage. However, it presents a processing challenge not found with airguns: the Doppler effect. The current industry standard method for source motion correction is based on spatiotemporal filtering or frequency–wavenumber (F-K) domain division. However, both correction methods generate spatial aliasing when the shot interval is coarse. The passage presents a deconvolution–interpolation method implemented in the F-K domain to correct moving marine vibrator data. By deploying a linear composite operator within the sparse inversion framework, including a mask function, an F-K domain convolution operator, a sampling matrix, and a dictionary mapping seismic data to a basis function, the method achieves interpolation, correction, and noise attenuation simultaneously of noisy Doppler-shifted marine vibrator data under coarse shot interval in the F-K domain. The power function threshold model is proposed to be deployed in the fast iterative soft-thresholding algorithm (FISTA) for inversion, thus leading to a substantial saving of iterations. Furthermore, the mask function preserves the effective spectrum during beyond-alias interpolation and denoising. Finally, the amount of observed data involved during the inversion process can be halved by utilizing the conjugate symmetry of the real signal Fourier transform. We demonstrate the impact of the Doppler effect and its correction under coarse shot interval on seismic data and structural imaging, while considering the interference of noise. Synthetic and field data examples verify the effectiveness of our method in mitigating the aforementioned disturbances.

In the field of Earth observation and computer vision, oriented object detection for remotesensing images is a crucial task that aims to locate objects more accurately in complex scenes containing a large number of densely arranged, large aspect ratio, and arbitrarily oriented objects. Although recently proposed methods have achieved remarkable performance, there are still several challenges to address: 1) interference from complex backgrounds, 2) imbalanced and mismatched label assignments caused by tiny objects and objects with large aspect ratios, and 3) misalignment between the tasks of classification and localization. In this article, we propose a frequency-assisted dynamic learning network (FADL-Net) to overcome the crucial challenges. Concretely, we introduce a spatial-spectral feature pyramid network to adaptively capture global long-range dependency feature representations containing various frequency domains. Meanwhile, to produce more reliable training samples for objects with extreme shapes, we design a geometric aware dynamic label assignment to dynamically mitigate the imbalance and mismatch in label assignment in a coarse-to-fine manner, thereby achieving more stable optimization during the training process. Moreover, we propose a joint-learning rotated quality loss that addresses the inconsistency between classification and localization by dynamically adapting the weights of different samples in the training stage. Extensive experiments on several public remote sensing datasets demonstrate that our method performs favorably against state-of-the-art detection approaches.

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

Suppressing random noise in seismic data is a significant problem in seismic data processing. Often, there is serious aliasing between the effective signal and random noise, affecting the identification of weak signals, and even resulting in great difficulties in the suppression of conventional seismic signals. We propose an improved attention-guided convolutional neural network (ADNet) to eliminate seismic interference noise. After a sufficient amount of training, the network removes noise by transferring seismic data features learned from a synthetic dataset to tests with complex field data. Our workflow consists of four parts. First, in the model, we improve the feature enhancement module (FEM) and attention module (AM), increase the convergence speed, and enhance the expressive ability. Second, we use 2-D synthetic data to verify the ability of the model to suppress noise in seismic records. Third, we use 2-D real seismic data to further verify the denoising effect of the improved ADNet. Fourth, we convert the 3-D simulated seismic data and field data into 2-D data for processing and reorganize the 2-D denoising results into 3-D data. By comparing the noise suppression outcomes of several classic denoising methods, simulations and actual experiments show that the improved ADNet effectively maintains the signal amplitude, reduces the network depth, and better suppresses seismic noise. Hence, we believe that our model can be widely applied in the field of seismic data processing.

In permafrost regions, the ground surface deformation is closely related to the ice-water phase transition process in active layer and underground ice. It is of great significance to carry out ground surface deformation detection research for understanding the development of permafrost in the Qinghai- Tibet Plateau. The InSAR technology has been proved to be an effective method for monitoring frozen soil deformation. However, due to the limitation of spatial coverage and revisit cycle of SAR data, few scholars had paid attention on the unstable permafrost regions with less ice content in the previous studies. Thus, a less ice content permafrost region loceted in Tuotuohe was taken as the study area to carry out surface deformation detection research based on SBAS-InSAR technology, and the field measurement was carried out to do the verification. The result showed that there was a good agreement between them, and the errors are 0.9mm, 2.6mm and 2.8mm respectively. The deformation detected by SBAS-InSAR method well reflects the frost heaving and thaw subsidence trend along with seasonal changes. Considering the small content of underground ice, this trend mainly reflects the seasonal freezing-thawing variation of the active layer. This study further confirms the applicability of InSAR technology in unstable permafrost regions.

The surface soil freezing and thawing (F/T) status plays important roles in water and energy exchanges, hydrology cycle and terrestrial ecosystem. Passive microwave remote sensing tends to be one of the most effective ways of monitoring global surface F/T status, due to its strong sensitivity to the changes of liquid water and its short revisiting period. Many surface F/T products have been produced based on the observations from different passive microwave sensors. However, due to the differences in spatial scales, satellite data sources, transit times etc., the accuracy and the consistency of different F/T products need to be well considered in front of the comprehensive utilization. In this study, the surface F/T products with resolutions of 36 km, 25 km, 25 km and 6 km derived from SMAP, SSMIS, AMSR-2, AMSR-2 (downscaling) were validated separately based on the in-situ observations in Ngari prefecture and Maqu on the Qinghai-Tibet Plateau. Then a comparison analysis was conducted on the F/T products derived from SMAP and SSMIS to figure out the consistency between F/T products derived from different sensors. The results show that: 1) the verification accuracy in Maqu is higher than that in Ngari prefecture, which reflects the limited capability of passive microwave in identifying the soil surface F/T status in the arid region. 2) The F/T products with resolutions of 6 km and 25 km, which were both derived from AMSR-2 and have the same transit time, showed similar validation accuracies. 3) There is a good agreement between the F/T products derived from SMAP and that from SSMIS. This study will be conducive to the comprehensive application of multi-scale F/T products in the fields of climate change and ecological environment.

Soil freezing and thawing processes play important roles in water and energy exchanges, weather and climatology. Passive microwave remote sensing tends to be one of the most effective ways of monitoring global surface state of freezing and thawing. However, Due to the complexity and variability of surface environmental factors, the thresholds in many algorithms are not universally suitable and the selection of thresholds mainly depends on the existing ground data. In addition, there is still a lack of comprehensive consideration of the complexity of the real surface in the modeling process. In order to solve these problems, firstly, a comprehensive database which contains complex surface conditions was built based on the data simulated from Cold Area Microwave Radiation Model and observed from satellite and ground sites. In this database, the effect of soil organic matter on microwave radiation was considered, the effective range of forest stock was redefined based on the biomass data, and the long-range satellite observations of brightness temperatures and nationwide ground-based meteorological site data were integrated. Then, the discrimination indexes (Tb36.5v and Qe, Tb36.5v and SDI) of “DFT algorithm” and “standard deviation algorithm” were respectively selected from the comprehensive database and used to establish new discrimination formulas based on the Fisher discrimination method. Through validated with the ground data, the F/T discrimination results based on the new formulas showed better performance than those based on the original algorithms, which demonstrated a better applicability for complicated surface conditions.

Soil moisture plays a crucial role in the terrestrial water cycle. It can be estimated by manual measurements for a small watershed, while this is very time-consuming when applied in the large scale. The remote sensing technology provides a new approach to monitor the soil moisture in a large scale. However, it should be evaluated before being used. In the study, the L-band Microwave Emission of the Biosphere model (L-MEB) model was used to retrieve the soil moisture based on the airborne brightness temperature in the Heihe River Basin, then the retrieved soil moisture was aggregated to 25km based on the area weighting factor method. The aggregated soil moisture was used to validate the two AMSR2 data: the JAXA soil moisture products and the LPRM soil moisture products. The results shows the JAXA SM products has an underestimation compared with the pixel-averaged SM, and the LPRM SM products is higher than the pixel-averaged SM.

The dielectric property of vegetation has a considerable effect on the characteristics of the microwave radiation of vegetation. In frozen environments, when the temperature is colder than normal, changes such as increased soluble sugar and decreased moisture content (MC) can occur in the vegetation. The dielectric property of vegetation, which is almost entirely controlled by its free and bound water content, will also change. To characterize the dielectric behavior of vegetation in frozen regions, a sensitive experiment was conducted on holly leaves with a high-performance coaxial probe over a frequency range from 0.5 to 40 GHz and a temperature range from 0°C to -20°C. Based on the measurements and the physical properties of the constituent substances of vegetation, a semiempirical dielectric model for holly leaves in low temperature environments was developed. In this model, a decrease in MC, which causes a reduction in the complex permittivity, was described as an increase in the ice content. The complex permittivity of bound water was measured using a saturated sucrose solution at -6.5°C. The research will provide a reference for the dielectric property study of the vegetation in frozen environments.

Soil moisture is an important parameter in many fields. Since the dielectric constant of soil is directly related with its moisture content, many soil dielectric constant models have been established and used in the application of soil moisture inversion. As an effective composition of soil, organic matter could increase the adsorption of soil particles and affect the dielectric constant. However, due to its little content, it was seldom considered in soil moisture inversion and brightness simulation. In this study, a semi-empirical organic dielectric model was used in the forward simulation of brightness temperature in Genhe River basin. The results show that it has a higher accuracy about 1.6k~2.4k than using TMD model at C-band and X-band.

The microwave signal at L-band is very sensitive to the soil moisture due to its penetrability. To analyze an algorithm to retrieve soil moisture at L-band, a simulation of microwave brightness temperature is conducted by using the τ-ω model in this study, and two methods of resampling are compared. One of the brightness temperature simulation is based on the point observation on ground, and the other is from the ground observation data of 1km resolution resampled from point. It turns out the latter method has a smaller error. An airborne L-band data from a Polarimetric L-band Microwave Radiometer (PLMR) acquired during the Hiwater experiment held in the Heihe River Basin in 2012 are used to validate the brightness temperature simulation. And the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to 8K at H-polarization respectively at different angles.