中图分类号：P631.4 文献标志码：A DOI: 10.16157/j.issn.0258-7998.256258
中文引用格式： 刘顺强. 基于无监督机器学习的地质断层识别与深度估算[J]. 电子技术应用，2025，51(9)：44-49.
英文引用格式： Liu Shunqiang. Geological fault identification and depth estimation based on unsupervised machine learning[J]. Application of Electronic Technique，2025，51(9)：44-49.

Geological fault identification and depth estimation based on unsupervised machine learning

Abstract： To address the uncertainty of Werner deconvolution results in geological structure depth estimation, the K-means clustering algorithm in unsupervised machine learning was applied for optimized analysis of Werner solutions. A synthetic magnetic field model containing two dike-like bodies was constructed and tested, with random noise added to increase complexity. Werner solutions from both synthetic and actual data were analyzed using the clustering algorithm. The results showed that the algorithm accurately identified two geological bodies with depths of 5 m and 8 m in the synthetic model and three bodies with depths of 536 m, 635 m, and 530 m in actual data, consistent with previous exploration findings in the region. The study demonstrated that this algorithm effectively determined the number of geological bodies and accurately estimated their depths, maintaining stability even with noise.

Key words : Werner deconvolution；depth estimation；K-means clustering；geological structure；unsupervised learning