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.

In the classic local window filtering seismic data denoising methods, the target sample is usually placed at the center of the given fixed-scale filter kernel. When the target sample is located on the structure edges, the filter kernel will cross the structure edges and leads to blurry structure edges. Multiscale adaptive right-angle side window filtering (MRSF) has better edge preservation capability. However, its 2-D filter kernel and eight right-angle side windows cannot better adapt to complex data, limiting its denoising capability. We extend the 2-D multiscale filter kernel in MRSF with the 3-D multiscale filter kernel. Meanwhile, we extend the eight 2-D right-angle side windows in MRSF with multiple 2-D and 3-D trapezoidal side windows. Finally, we propose the high-dimensional multiscale adaptive trapezoidal side window filtering (HMTSF). Synthetic and field 3-D seismic data examples demonstrate the good denoising capability of HMTSF.

Side window filtering (SWF) can effectively capture detailed image edges and is widely applied in image processing. However, its fixed-scale (or fixed-size) filter kernel cannot adapt to complex images, and the final output at the target pixel is only determined by the side window output with the minimum error functional, limiting its filtering capability. To further enhance the filtering capability of SWF, we first extend the traditional side windows with fixed-scale filter kernel to multiscale side windows by introducing the multiscale filter kernels, which leads to better complex image matching. Then, we further introduce an adaptively weighted parameter, which is inversely proportional to the error functional, to fully consider the contributions of all multiscale side windows to the final output. We finally propose the multiscale adaptive SWF (MASWF). Synthetic and field seismic data examples demonstrate that MASWF is a good potential technique for seismic data random noise attenuation and can be widely used in digital signal processing fields.

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.

High-precision seismic fault detection and fault surface extraction (or grouping) are critical steps in reservoir characterization. In this paper, basing on the gradually changing characteristics of the target faults in the adjacent 2D seismic fault profiles, we propose an efficient and high-precision automatic fault surface grouping method (EHFG) for complex 3D seismic fault data; and it is realized using pairs of adjacent 2D seismic fault profiles without human intervention. EHFG comprises of high-precision fault separation (HFS) and high-precision fault labeling (or naming) (HFL); in which, HFS aims to separate the positive-slope and negative-slope faults in the seismic fault detection result, and ultimately obtain the high-precision positive-slope and negative-slope faults; HFL aims to label the separated positive-slope and negative-slope faults, and ultimately obtain the corresponding high-precision fault surface grouping results. An actual 3D seismic fault data example demonstrates that EHFG is a good potential technique for fault surface grouping.