Unsupervised Machine Learning for Detecting Seismic Anomalies: Local Outlier Factor Algorithm on Indonesian Ring Fire Data
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Abstract
The Indonesian Ring of Fire, known for its intense seismic and volcanic activity, poses significant challenges for hazard mitigation and risk management. This study applies an unsupervised machine learning approach using the Local Outlier Factor (LOF) algorithm to detect seismic anomalies in historical earthquake data. The LOF method is advantageous for identifying subtle deviations from typical seismic patterns, making it suitable for complex, multidimensional datasets. The research leverages seismic data collected over a multi-year period, focusing on key parameters such as magnitude, depth, and location. Results indicate that the LOF algorithm effectively identifies anomalous seismic events that could signify potential precursors to larger-scale geological occurrences. The findings highlight the potential of unsupervised machine learning techniques in enhancing earthquake monitoring systems, contributing to more proactive disaster preparedness and response strategies in Indonesia’s Ring of Fire. This study provides insights into the integration of machine learning for real-time seismic anomaly detection, offering an advanced tool for researchers and policymakers.
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