中圖分類號(hào)：P631.4 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.256258
中文引用格式： 劉順強(qiáng). 基于無(wú)監(jiān)督機(jī)器學(xué)習(xí)的地質(zhì)斷層識(shí)別與深度估算[J]. 電子技術(shù)應(yīng)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