The popular deep learning-based karst caves recognition methods have higher precision than the traditional karst caves recognition approaches; however, they still face great challenges in processing complex seismic data and areas with strong reflection shielding. In this paper, to achieve high-efficiency and high-precision karst caves recognition results with the strong reflection shielding, we propose a wavefield separation-driven high-precision deep-learning karst caves recognition method (WCRM). WCRM is composed of the multitask two-stage seismic strong and weak reflection separation method (MTSM)-based high-precision karst caves recognition training data generation (MKCG), the high-precision deep-learning karst caves recognition network (HCRN), the loss function of WCRM, and the karst caves recognition result optimization (KCRO). MKCG aims to use the MTSM results to generate sufficient training data for HCRN; HCRN takes three-dimensional (3D) synthetic seismic data and the data augmentation results obtained by MKCG as its inputs, the corresponding karst caves recognition labels as its labels, and uses the 3D convolution kernel for high-precision karst caves recognition; The loss function of WCRM aims to calculate the loss function which focuses on the karst caves; KCRO aims to optimize the karst caves recognition results obtained by HCRN to obtain high-precision karst cave recognition results. One public synthetic 3D seismic dataset and one field 3D seismic dataset applications demonstrate that WCRM achieves high-precision karst caves recognition results.

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.

The popular iterative Vertical seismic profiling (VSP) wavefield separation method has higher precision wavefield separation results than the conventional VSP scalar wavefield separation method, but it still faces challenges in achieving desirable high-precision wavefield separation results due to the time-variant wavelet, the complex wavefield, and the precision of wavefield flattening. In this paper, in order to minimize the impact of the factors mentioned above, we propose a trace-by-trace iterative VSP wavefield separation method (TISM) based on the gradually changing characteristics of VSP data in adjacent traces, the cross-correlation, and the iterative VSP wavefield separation method. TISM includes the flowchart of TISM, the target trace guided sub dataset automatic generation (TDG), the cross-correlation guided sub dataset optimization (CDO), and the cross-correlation guided wavefield separation result optimization (CWO). TDG aims to automatically generate the sub VSP dataset corresponding to the target trace and minimize the impact of the time-variant wavelet and complex wavefield. CDO aims to minimize the effect of wavefield flattening and form the high-precision wavefield flattened sub dataset for scalar wavefield separation. CWO aims to obtain the high-precision wavefield separation result. Synthetic and actual VSP data applications demonstrate that TISM can minimize the impact of the time-variant wavelet, the complex wavefield and wavefield flattening, thereby obtaining high-precision VSP wavefield separation results.

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 geological anomaly (such as fault, river course, cave, and crack) identification methods, such as coherence cube, semblance, likelihood, and others, usually can achieve higher precision geological anomaly identification results when applied to the target horizon flattened seismic data, comparing to their counterparts using the target horizon-unflattened seismic data. However, these methods still face great challenges in achieving high-precision geological anomaly identification results, due to the complexity of the geological structure (or the seismic data) and the horizon tracking accuracy of the target horizon. To minimize the impact of the complexity of geological structure and the horizon tracking accuracy of the target horizon in geological anomaly identification, thereby obtaining high-precision geological anomaly identification results and providing precise labels for deep-learning-based geological anomaly identification methods, we propose a seismic facies-guided high-precision geological anomaly identification method (FHGI), basing on the concept of seismic facies and the cross-correlation algorithm. FHGI contains the flowchart of FHGI, and the seismic facies-guided trace-by-trace high-precision geological anomaly identification factor calculation (FTGC); in which FTGC consists of the target horizon-based seismic data flattening (THF), the seismic facies-guided target trace 2-D subseismic dataset generation (FTG), the cross-correlation algorithm-based target horizon further flattening (CFA), and the cross-correlation coefficient-based high-precision geological anomaly identification factor calculation (CGC). The THF aims to reduce the impact of the complexity of the geological structure and provide the input 3-D seismic data for the FTG. FTG aims to automatically generate the 2-D subseismic dataset corresponding to the target trace, thereby further reducing the impact of the complexity of the geological structure and providing the input 2-D subseismic dataset for CFA. CFA takes the target trace in the result of FTG as the reference for cross-correlation functions calculation and then uses them to further flatten the target horizon in the result of FTG, thereby minimizing the impact of the horizon tracking accuracy of the target horizon and providing the input 2-D subseismic dataset for CGC. CGC takes the target trace in the result of CFA as the reference for cross-correlation coefficient calculation and then uses them for high-precision geological anomaly identification factor calculation, thereby providing high-precision geological anomaly identification results. A public synthetic seismic dataset and actual 3-D seismic dataset examples demonstrate that FHGI has great potential as a technique for geological anomaly identification.

Accurate seismic velocity inversion is crucial for oil and gas exploration. The popular fixed-scale regular-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (FFAST) (or adjoint-state first-arrival slope tomography seismic velocity inversion method with fixed-scale regular-grid) can obtain good seismic velocity inversion results, but it still faces great challenges in achieving desirable high-precision seismic velocity inversion results due to its fixed-scale regular-grid. In this article, we use the multiscale staggered grid to replace the fixed-scale regular-grid in FFAST for model parametrization and propose the multiscale staggered-grid adjoint-state first-arrival (or first-arrival travel-time) slope tomography seismic velocity inversion method (MFAST), thereby obtaining high-precision seismic velocity inversion result. The staggered-grid is composed of a finite set of fixed-scale regular-grids with spatially staggered (or overlapped) relationships, which aims to change the grid coordinate to fully sample the structure information in the velocity model space with multiple fixed-scale regular-grids. The multiscale staggered-grid is composed of multiple staggered-grids with different fixed scales, which aims to adapt to the different scale complex structures in the velocity model space; in which, the large-scale staggered-grid based MFAST aims to reconstruct the large-scale background structures, thereby providing the essential guidance (or prior) information for the small-scale staggered-grid based MFAST which aims to obtain the detailed structural information. The model parametrization with multiscale staggered-grid is achieved by performing the model parametrization with regular-grid multiple times; the mean or median value of the outputs of multiple model parametrizations with regular-grid is considered the output of MFAST in the current iteration, and used to iteratively update the velocity model obtained by MFAST in the previous iteration. The checkboard and Marmousi model testing validate the effectiveness of MFAST.

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.

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.