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.

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.

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.