Fault identification method based on tensor sparse optimization analysis

There are still unresolved problems regarding the accuracy and reliability of existing seismic fault identification methods. To explore the three-dimensional spatial structural information of seismic data, enhance the clarity of fuzzy and weak faults, and improve fault 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 of fault 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 effectiveness in weak fault identification and fault continuity enhancement. This method has good reference significance for fault-developed areas.