Amplitude-preserving depth-domain prestack inversion using the point-spread function (PSF) has emerged as a powerful approach for quantitative reservoir characterization. This technique employs the PSF to approximate the Hessian operator, illumination-induced blurring is compensated during imaging, thereby improving amplitude fidelity and spatial resolution. However, most existing depth-domain inversion studies have focused on poststack applications. Systematic investigations of prestack inversion—particularly for quantitative fluid characterization—remain limited. To address this limitation, we develop a depth-domain prestack inversion framework that leverages an angle-domain Gaussian-beam PSF. Under high-frequency asymptotic assumptions, we derive an analytical expression for the angle-domain Gaussian-beam PSF and compute PP-wave PSFs to approximate the local Hessian, which we incorporate directly into the inversion operator as a local-Hessian preconditioner. The framework combines a nonstationary convolution model with the Aki–Richards approximation to simultaneously invert for elastic parameters in the depth domain. Applied to a 2D marine streamer line from the Northern Viking Graben in the North Sea, the method reveals multiple low-V_p/V_s anomalies within Paleocene and Jurassic sandstones that correspond to hydrocarbon-bearing intervals identified from well data. A depth-domain ϕ_w (water-filled porosity) section is then constructed through a well-log-calibrated V_p/V_s–ϕ_w relationship established for this study area, which effectively discriminates potential hydrocarbon reservoirs. These results demonstrate that the angle-domain PSF–based depthdomain prestack inversion exhibits robust applicability and geological consistency in structurally complex rift basins, providing a novel pathway for quantitative depth-domain reservoir characterization.

Distributed acoustic sensing (DAS) has rapidly emerged as a transformative technology in seismic exploration, particularly in vertical seismic profiles (VSP). However, the acquired VSP data suffer from strong coherent DAS coupling noise and random noise. Current deep learning denoising methods, dependent on noise labels derived from conventional denoising techniques, fall short in addressing the unique noise properties inherent in DAS data. To address this challenge, we propose an exponential decay curve-constrained empirical mode decomposition (EDCC-EMD) analysis-based supervised denoising network. Our method begins with extracting the initial noise from the field DAS VSP data through the traditional EMD method. Despite containing some signal leakage, this noise is further processed through EMD to derive intrinsic mode functions (IMFs). We, then, analyze the correlation coefficients between these IMFs and the initial noise, applying an exponential decay curve (EDC) law to isolate pure noise. This refined noise data serves as accurate labels, enhancing the denoising network's precision. Meanwhile, most of the methods usually consider the t-x domain features and ignore the important frequency-domain features. Consequently, we train our network with frequency-domain data instead of time domain data, capitalizing on the more distinct separation of noise and signal characteristics, thereby facilitating more effective noise-signal discrimination. The experimental results demonstrate that our method significantly enhances the denoising performance and successfully recovers weak signals.

Current data-driven deep learning（DL）methods typically reconstruct subsurface velocity models directly from pre-stack seismic records. However, these purely data-driven methods are often less robust and produce results that are less physically interpretative. Here, the authors propose a new method that uses migration images as input, combined with convolutional neural networks to construct high-resolution velocity models. Compared to directly using pre-stack seismic records as input, the nonlinearity between migration images and velocity models is significantly reduced. Additionally, the advantage of using migration images lies in its ability to more comprehensively capture the reflective properties of the subsurface medium, including amplitude and phase information, thereby to provide richer physical information in guiding the reconstruction of the velocity model. This approach not only improves the accuracy and resolution of the reconstructed velocity models, but also enhances the physical interpretability and robustness. Numerical experiments on synthetic data show that the proposed method has superior reconstruction performance and strong generalization capability when dealing with complex geological structures, and shows great potential in providing efficient solutions for the task of reconstructing high-wavenumber components.

The southern Sichuan Basin in China holds abundant shale gas resources; however, the shale gas bearing property shows great differences due to the multiple stages of tectonic transformation. The key to revealing the shale gas differential enrichment mechanism is to explore the thermal evolution characteristics during tectonic evolution. Therefore, taking the Luzhou and Changning blocks as an example, which have obvious differences in tectonic evolution, the organic geochemical conditions of Longmaxi shale were firstly compared with the test data. Then, the thermal evolution characteristics under the background differential tectonic uplift-erosion were recovered using basin modeling techniques. The results showed that the two blocks contain similar organic geochemical conditions of the Longmaxi shale. Moreover, the hydrocarbon generation condition in Luzhou Block is greater than that in the Changning Block. Influenced by the differential tectonic evolution, the study area experienced a complex burial history and the formation of multiple unconformities. As a result, the present burial depth of Longmaxi Formation in the Luzhou Block is significantly greater than that in the Changning Block. The thermal evolution history of Longmaxi shale in the study area could be divided into three stages, including a low-temperature stage from Caledonian to Hercynian, a middle-temperature stage from Hercynian to Indosinian, and a high-temperature stage from Yanshanian to Himalayan. In addition, it was found that the Himalayan period is the main stage resulting in the differential gas bearing property of Longmaxi shale in the southern Sichuan area. Under the differential structural modification, the peak time of hydrocarbon generation in the Luzhou Block occurred earlier and the conversion rate was slightly higher than that in the Changning Block.

Considerable attempts have been made on removing the crosstalk noise in a simultaneous source data using the popular K-means Singular Value Decomposition algorithm(KSVD). Several hybrids of this method have been designed and successfully deployed, but the complex nature of blending noise makes it difficult to manipulate easily. One of the challenges of the K-means Singular Value Decomposition approach is the challenge to obtain an exact KSVD for each data patch which is believed to result in a better output. In this work, we propose a learnable architecture capable of data training while retaining the K-means Singular Value Decomposition essence to deblend simultaneous source data.