Research progress of fault identification technology based on seismic data

Lin ZHANG, Yuan MENG, LiSha QI, Abudusalamu ALIMUJIANG, Jun DAI, Ang LI, LiYan ZHANG

Prog Geophy ›› 2025, Vol. 40 ›› Issue (1) : 208-219.

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Prog Geophy ›› 2025, Vol. 40 ›› Issue (1) : 208-219. DOI: 10.6038/pg2025HH0563

Research progress of fault identification technology based on seismic data

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Abstract

With the further evolution of oil and gas exploration and development technology, the traditional artificial fault interpretation has some defects such as strong subjectivity, heavy workload and low efficiency, which cannot meet the needs of efficient identification of faults on seismic data and the exact realization of structural characteristics in the study area interpretation needs. This article explores the process, advantages, application scope, and limitations of various representative fault identification technologies found on a large number of domestic and foreign literature. Based on this, it can be roughly divided into three categories of fault identification technologies represented by single seismic attribute, multi attribute fusion, and artificial intelligence. Single attribute fault interpretation techniques mainly include spectral decomposition, coherence volume, variance volume, etc. These techniques and methods are mainly applied in the early stage of seismic exploration, and are relatively effective for the identification of large faults. In terms of small fault recognition, the seismic multi-attribute fusion technology based on RBG attribute fusion has unique advantages. By changing the weight of different attributes, the structural information of the fault is highlighted, so as to reduce the interference and reduce interference and ambiguity. With the advent of the big data era, fault identification technology based on artificial intelligence has been widely used. Ant body tracking belongs to the early artificial intelligence fault identification technology, which partly improves the accuracy of fault identification, but there are still some problems such as strong multi-solution and low anti-noise ability. Since then, neural networks have been introduced into seismic data processing and interpretation, mainly including image classification and semantic segmentation. In particular, residual neural networks, convolutional neural networks, fully convolutional neural networks and U-Net have been widely used in the research of fault recognition, which promote further development of automation and intelligence in fault recognition. This paper summarizes and compares various fault identification techniques, proposes future development directions, techniques, proposes future development directions, which provides new solutions for the use of seismic data for fault interpretation and identification in oil and gas exploration for further.

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Fault identification / Seismic attribute analysis / Fusion / Artificial intelligence / Neural network

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Lin ZHANG , Yuan MENG , LiSha QI , et al . Research progress of fault identification technology based on seismic data[J]. Progress in Geophysics. 2025, 40(1): 208-219 https://doi.org/10.6038/pg2025HH0563

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