基于张量稀疏优化分析的断层识别方法

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中图分类号：P631 文献标识码：A DOI：10.13810/j.cnki.issn.1000-7210.20250259

Abstract： There are such problems as low recognition accuracy and insuffcient reliability for existing fault identification methods in seismic data，and they cannot satisfy the demands of high-precision exploration.To explore the three-dimensional spatial structural information of seismic data， enhance the clarity of fuzzy and weak faults，and improvefault continuity，a fault identification method based on tensor sparse optimization analysis is proposed. First，based on the three-dimensional spatial distribution characteristics of fault seismic responses， tensor decomposition analysis is performed，combined with compressive sensing theory and matrix low-rank sparsity theory，to analyze the low-rank sparse decomposition characteristics offault information， background information，and noise information. Second，vector sparse representation is combined with matrix sparse representation，and tensor decomposition theory is applied to achieve tensor dimensionality reduction，matricization， and vectorization. Finally， sparse wavelet decomposition orthogonal matching pursuit （OMP） reconstruction is used for vector optimization，and the matrix low-rank sparse method is used for matrix optimization，achieving noise removal and fault enhancement. Model tests and practical applications show that the proposed method has strong noise resistance and high identification accuracy，and demonstrates significant efectiveness in weak fault identification and fault continuity enhancement.This method has good reference significance for fault-developed areas.

Keywords： low-rank sparsity，tensor-matrix decomposition，low-rank optimization，sparse reconstruction，fault identification

0 引言

断层检测是地震数据解释中一个基本且重要的环节，叠后地震数据体中的同相轴错断、扭曲现象是断层解释的关键异常特征，也是断层检测的重要标志。（剩余12842字）