When seismic waves propagate through horizontal transversely isotropic (HTI) media, the variation of acoustic impedance with azimuth can effectively reflect the fracture density and direction information. The key to high-precision fracture parameter prediction lies in improving the accuracy of azimuthal impedance inversion and elliptical analysis. This study proposes an integrated solution: First, the total-variation multiplicative regularization multichannel impedance inversion technique is employed to obtain azimuthal impedance information. By incorporating formation dip constraints and an improved Polak-Ribière-Polyak-Hestenes-Stiefel (PRP-HS) hybrid conjugate gradient algorithm, the inversion accuracy and lateral resolution are significantly enhanced. Second, to address the small-sample problem in azimuthal impedance elliptical analysis, we developed a hybrid elliptical analysis method incorporating probability distribution and uncertainty estimation. The methodology involves: (1) applying the adaptive random sample consensus (RANSAC) algorithm to screen high-quality data points, (2) utilizing Bayesian regression for probabilistic modeling of the data, and (3) implementing Bayesian inference through Markov chain Monte Carlo (MCMC) methods. This combined strategy of random sampling and iterative optimization ensures the stability and reliability of fitting results. Furthermore, a planar transformation matrix is introduced to correct the rotation angle, thereby optimizing the prediction accuracy of fracture direction. Application results from actual well logging and seismic data demonstrate that this method exhibits superior performance in predicting both fracture direction and density, providing a reliable technical approach for fractured reservoir characterization.

The conventional seismic strong and weak reflection separation method (SRSM) encounters significant challenges due to the complexity of seismic data, the horizon time (or depth) accuracy of the target horizon, and the space-variant seismic wavelet, resulting in undesired seismic strong and weak reflection separation results. The popular seismic facies-guided trace-by-trace high-precision seismic SRSM can address the abovementioned issues and obtain high-precision seismic strong and weak reflection separation results; however, it still requires the horizon time of the target horizon, and its computational efficiency needs to be improved. In this article, in order to obtain high-efficiency high-precision seismic strong and weak reflection separation results without any horizon time, we propose a multitask two-stage deep learning seismic strong and weak reflection separation method (MTSM) based on the SRSM and the multitask deep learning network framework. MTSM consists of the SRSM-based seismic strong and weak reflection label automatic generation (SLG), the multitask two-stage seismic strong and weak reflection separation network (MTSN), and the energy balance loss function of MTSN. SLG aims to use SRSM and data augmentation to generate massive high-precision seismic strong and weak reflection labels, thereby providing sufficient high-precision training datasets for MTSN; MTSN aims to simultaneously output high-precision seismic strong and weak reflections, and the energy balance loss function of MTSN aims to address the imbalance between multiple loss functions resulting from the energy disparity between the seismic strong and weak reflections. An actual 3-D seismic dataset example demonstrates that MTSM has great potential as a technique for high-precision seismic strong and weak reflection separation.

Multihorizon tracking deep learning methods have higher efficiency than single-horizon tracking deep learning methods, but they still have a great challenge to adapt to complex seismic data, resulting in undesired horizon tracking results. To achieve high-precision horizon tracking results, we propose a boundary-constrained multitask multihorizon tracking (BMTM) based on semantic segmentation, instance segmentation, and multitask learning (ML). The core idea of BMTM is to transform multihorizon tracking into strata recognition, using where the strata boundaries serve as the tracking results. BMTM comprises three components: strata label automatic generation, strata boundary-constrained multihorizon tracking network (BMTN), and strata boundary-constrained loss function. Strata label automatic generation automatically generates strata labels based on the input horizon labels. BMTN consists of a shared layer, an auxiliary task, and a main task. The auxiliary task takes the input horizon labels as its labels and employs semantic segmentation to directly output multihorizon tracking results. Main task takes the generated strata labels as its labels and employs instance segmentation with the outputs of auxiliary task to achieve high-precision strata (or horizon) tracking results. The strata boundary-constrained loss function aims to pay more attention to the strata boundary and ultimately improve the horizon tracking precision. One public 3-D synthetic seismic dataset study demonstrated the performance of BMTM, and one field 3-D seismic dataset application demonstrated that BMTM can be used for high-precision multihorizon tracking.

Strong and weak reflection separation is crucial for seismic interpretation. The conventional strong and weak reflection separation method (CRSM) faces great challenges, due to the complex seismic data, the target horizon accuracy and the space-variant wavelet, resulting in undesired strong and weak reflection separation results. In this paper, in order to minimize the impact of the complex seismic data, the target horizon accuracy and the space-variant wavelet, we propose a seismic facies-guided trace-by-trace high-precision strong and weak reflection separation method (SRSM), which is based on the CRSM, the seismic facies and the concept of trace-by trace processing. SRSM includes the flowchart of SRSM, the seismic facies-guided target trace two-dimensional (2D) sub-seismic dataset automatic generation (SDG), the seismic facies-guided target trace 2D sub-seismic dataset optimization (SDO), and the strong and weak reflection separation result optimization (RSO). SDG aims to automatically generate the 2D sub-seismic dataset corresponding to the target trace to reduce the impact of the complex seismic data, the target horizon accuracy and space-variant wavelet based on the seismic facies, thereby providing high-consistency 2D sub-seismic dataset. SDO aims to use the correlation algorithm to automatically optimize the SDG result to minimize the impact of the complex seismic data, the target horizon accuracy and space-variant wavelet based on the seismic facies, ultimately providing high-consistency and high-continuity 2D sub-seismic dataset for wavefield separation. RSO aims to optimize the 2D strong and weak reflection datasets obtained by wavefield separation, ultimately providing 1D high-precision strong and weak reflection seismic data corresponding to the target trace. An actual 3D seismic dataset example demonstrates that SRSM has great potential as a technique for high-precision strong and weak reflection separation.

The popular deep-learning-based horizon tracking methods heavily relies on large volumes of well-labeled horizon data, which face significant challenges in achieving high-precision horizon tracking with limited label (or few sample), especially when encountering complex seismic data and geological structures with 1-D limited label. In this article, we propose a limited-label multiscale multihorizon tracking method (LMMT) based on the multimodal deep learning and (1-D limited label. In this method, the horizon is characterized in the seismic trace (1-D), the seismic profile (2-D), and the horizon slice (3-D). LMMT is comprised of the flowchart of LMMT, the 1-D convolution kernel single-modal multihorizon tracking method (OMT), the high-precision high-continuity horizon and strata optimization (HHO), and the 2-D (or 3-D) convolution kernel multimodal multihorizon tracking method (TMT). OMT takes the input 1-D limited horizon labels as its labels and utilizes a 1-D convolution kernel for strata division and multihorizon tracking. HHO aims to generate the 2-D (or 3-D) high-precision and high-continuity horizon and strata based on the 3-D horizon tracking results obtained by OMT or TMT, thereby providing high-precision high-continuity horizon labels and strata for TMT. TMT incorporates the 2-D (or 3-D) high-precision high-continuity horizon obtained by HHO as its labels, integrates the random masking result of the high-precision high-continuity strata obtained by HHO as the reference information, and utilizes a 2-D (or 3-D) convolution kernel for high-precision multihorizon tracking. Two 3-D seismic dataset applications demonstrate that LMMT achieves high-precision multihorizon tracking results with limited labels.

Due to the precision of the first break, time-variant wavelet, and strata dip angle, the popular iterative vertical seismic profiling (VSP) wavefield separation method may not yield high-precision wavefield separation results. The single-stage multi-task U-Network VSP wavefield separation method can avoid the impact of the first break, time-variant wavelet, the strata dip angle, but it faces challenge in complex VSP wavefield due to its network performance. In this paper, based on the iterative VSP wavefield separation method, the U-Network and multi-task deep learning, we propose a two-stage multi-task U-Network VSP wavefield separation method. The two-stage multi-task U-Network VSP wavefield separation method comprises the two-stage multi-task U-Network, the loss function, and the synthetic VSP training data automatic generation. The two-stage multi-task U-Network aims to simultaneously output high-precision downgoing and upgoing wavefield, as well as the residual wavefield, while the synthetic VSP training data automatic generation aims to automatically generate numerous and various VSP training data. Applications of both synthetic and actual VSP data demonstrate that the two-stage multi-task U-Network VSP wavefield separation method can be widely used for high-precision VSP wavefield separation.

Synthetic Aperture Radar is an active remote sensing system that possesses characteristics such as all-weather capability and strong penetrability. In ship detection, SAR is widely used in military and civilian applications due to its excellent properties. However, in practical applications, SAR often faces challenges in achieving ideal imaging results due to factors such as height and sea conditions. Although deep learning-based object detection methods have achieved promising results in SAR ship detection, the small size of ship targets and complex sea clutter make it difficult to further improve the precision and recall rates of such algorithms. To address the challenge of balancing global and local features using convolution-based algorithms, this paper proposes a detection algorithm that integrates convolution and transformers. Firstly, an improved transformer module is designed, which fuses the output feature maps of the traditional convolution module and the improved transformer module through a parallel structure. Then, a novel backbone structure is developed by stacking the aforementioned parallel structures, enabling the integration of global and local features as well as the detection of multi-scale features. Experimental results demonstrate that compared to other models, the proposed method achieves more effective SAR detection in SAR images.

To reduce the impact of nontarget horizon regions and improve horizon tracking precision, we propose an attention based two-stage U-net horizon tracking method (ATUM). The ATUM consists of the horizon region label automatic generation and the attention module based two-stage U-Net (ATUN). Horizon region label automatic generation aims to automatically generate the target horizon region label of the target horizon label. In ATUN, the two stages (stages Ⅰ and Ⅱ) consist of the conventional encoder-decoder U-Net, and the two decoder parts are connected by the attention module. Stage Ⅰ treats horizon tracking as an objection detection problem. It takes the seismic data as its input and the automatically generated target horizon region label as its label, and finally obtains the target horizon region. Stage Ⅱ takes the results of stage Ⅰ with the corresponding seismic data as its input, and finally obtains the precise horizon. Two field three-dimensional seismic dataset studies demonstrated the performance of the ATUM for high-precision horizon tracking.