A general lightweight global modeling framework for three-dimensional seismic exploration
Abstract
In seismic exploration, the propagation of seismic waves naturally gives rise to long-range dependencies in seismic data. Capturing such global correlations can significantly improve the accuracies of seismic signal processing, inversion, and interpretation. Global modeling (GM) methods have therefore emerged as an effective paradigm for seismic exploration, offering a powerful means of exploiting the intrinsic global relationships within seismic data. However, the mainstream GM approaches, particularly Transformers, incur prohibitively high computational costs for their inherent quadratic complexity, which restricts the scalability of such methods to higher-dimensional data. To achieve efficient GM for three-dimensional (3D) seismic exploration, we propose a general framework, named lightweight Mamba-based global modeling (LMGM), to establish long-range dependencies at a relatively low computational cost. Specifically, LMGM fully leverages the linear complexity of Mamba. A three-directional scanning Mamba block is designed to exploit spatial correlations along the three dimensions of 3D seismic data. Meanwhile, a dual-domain-aware block is further developed to integrate time- and frequency-domain features for enhanced feature representation. In addition, a two-stage plug-and-play nonlinear normalization strategy is proposed to enhance the adaptability of LMGM to diverse seismic records while preserving signal characteristics. We employ 3D seismic data interpolation as a representative task to comprehensively evaluate LMGM. The results demonstrate that LMGM achieves superior processing performance while substantially reducing computational cost compared with U-Net-based and Transformer-based methods. Furthermore, experiments on random noise removal demonstrate the applicability of LMGM beyond interpolation, supporting its potential as a general GM framework.
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BibTeX
@misc{wei2026general,
title = {{A general lightweight global modeling framework for three-dimensional seismic exploration}},
author = {Changxin Wei and Jun Ma and Xintong Dong},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.18294},
eprint = {2609.18294},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.18294},
}
RIS
TY - UNPB
TI - A general lightweight global modeling framework for three-dimensional seismic exploration
AU - Changxin Wei
AU - Jun Ma
AU - Xintong Dong
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2609.18294
UR - https://arxiv.org/abs/2609.18294
AB - In seismic exploration, the propagation of seismic waves naturally gives rise to long-range dependencies in seismic data. Capturing such global correlations can significantly improve the accuracies of seismic signal processing, inversion, and interpretation. Global modeling (GM) methods have therefore emerged as an effective paradigm for seismic exploration, offering a powerful means of exploiting the intrinsic global relationships within seismic data. However, the mainstream GM approaches, particularly Transformers, incur prohibitively high computational costs for their inherent quadratic complexity, which restricts the scalability of such methods to higher-dimensional data. To achieve efficient GM for three-dimensional (3D) seismic exploration, we propose a general framework, named lightweight Mamba-based global modeling (LMGM), to establish long-range dependencies at a relatively low computational cost. Specifically, LMGM fully leverages the linear complexity of Mamba. A three-directional scanning Mamba block is designed to exploit spatial correlations along the three dimensions of 3D seismic data. Meanwhile, a dual-domain-aware block is further developed to integrate time- and frequency-domain features for enhanced feature representation. In addition, a two-stage plug-and-play nonlinear normalization strategy is proposed to enhance the adaptability of LMGM to diverse seismic records while preserving signal characteristics. We employ 3D seismic data interpolation as a representative task to comprehensively evaluate LMGM. The results demonstrate that LMGM achieves superior processing performance while substantially reducing computational cost compared with U-Net-based and Transformer-based methods. Furthermore, experiments on random noise removal demonstrate the applicability of LMGM beyond interpolation, supporting its potential as a general GM framework.
ER -