Work

Pre-stack seismic inversion based on one-dimensional GRU combined with two-dimensional improved ASPP

Year: 2024

Type: article

Abstract: Pre-stack seismic inversion is essential to detailed stratigraphic interpretation of seismic data. Recently, various deep learning methods have been introduced into pre-stack inversion, effectively capturing the vertical correlations of seismic data. However, existing deep learning methods face challenges such as insufficient feature extraction, poor lateral continuity, and unclear inversion details. We introduce the atrous spatial pyramid pooling (ASPP) module into the pre-stack inversion process, modifying the connection order and mode of its three components. Additionally, we incorporate a triplet attention module to extract features at different scales and utilize a gate recurrent unit (GRU) module to extract global information. During the network training stage, we employ a multi-gather simultaneous inversion method, combining one- and two-dimensional inversions. The proposed method is named IGIT (I for improved ASPP, G for GRU, I for initial model, and T for triplet attention). To verify the feasibility of this network model, we evaluate it using the Marmousi2 model, SEAM model, and field data, comparing the results with other deep learning methods. Experimental results demonstrate that the IGIT not only improves lateral continuity but also delivers accurate and clear inversion details. Notably, the inversion effect for density parameters shows significant enhancement.

Source: Journal of Geophysics and Engineering

DOI: 10.1093/jge/gxae106

Authors: Xiao Chen, Shu Li, Zong Wei, Juan Ning, Xi Yang

Institutions: Jishou University, Guangzhou Medical University

Language: English

Topic: Seismic Imaging and Inversion Techniques

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,600

Funder: National Natural Science Foundation of China