Machine Learning of Element Geochemical Anomalies for Adverse Geology Identification in Tunnels

Abstract

Geological analysis, despite being a long-term method for identifying adverse geology in tunnels, has significant limitations due to its reliance on empirical analysis. The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations. However, statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering. In contrast, data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data. Herein, a method for identifying adverse geology using machine learning of geochemical anomalies is proposed. The method was identified geochemical anomalies in tunnel that were not identified by statistical methods. We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination (AEC). Using the AEC sample data, we trained an isolation forest model to identify the multi-element anomalies, successfully. We analyzed the adverse geological features based the multi-element anomalies. This study, therefore, extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis. Correspondingly, the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.

Keywords

adverse geology / tunnels / geochemical anomalies / machine learning / Isolation Forest / dimensional reduction

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