Seismic phase classification method, device, electronic equipment and medium

By combining two-dimensional scattering transform and densely connected convolutional network, the problems of low efficiency and insufficient accuracy in seismic phase identification are solved, and efficient and stable seismic phase classification is achieved.

CN117251674BActive Publication Date: 2025-09-26XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202311188166.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-09-14
Publication Date
2025-09-26
Estimated Expiration
2043-09-14

AI Technical Summary

Technical Problem

The existing seismic phase identification technology is inefficient and relies on manual experience. Machine learning methods require manual selection of sensitive attributes, and time-frequency transformation does not have local deformation stability and translation invariance, resulting in insufficient accuracy in seismic phase classification.

Method used

Two-dimensional scattering transform is used to extract multi-scale feature information, and multi-resolution processing and feature fusion are performed through densely connected convolutional networks to construct a seismic phase classification model. Seismic phase classification is performed by combining scattering networks and densely connected convolutional networks.

Benefits of technology

The accuracy and stability of seismic facies classification are improved, the workload of manual attribute selection is reduced, and classification accuracy is ensured under local deformation and translation.

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Abstract

The present application provides a seismic phase classification method, device, electronic device and medium. First, a two-dimensional scattering transform is used to extract multi-scale feature information with local deformation stability and translation invariance from two-dimensional seismic data. That is, when the seismic signal is affected by underground fault activity and produces local micro-deformations and time shifts, the extracted multi-scale features will not change significantly, which is conducive to the accurate classification of seismic data. Then, a densely connected convolutional network is constructed based on DenseBlock, and convolutional neural networks of different depths are used to further process and fuse feature information of different scales, that is, multi-resolution processing, and finally realize seismic phase classification of seismic data. Since DenseBlock adopts a fully interconnected structure, feature reuse can be achieved, so that feature information can be better retained during the layer-by-layer processing, further improving the accuracy of seismic phase classification results.
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