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 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.

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.

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.