Unsupervised Machine Learning for Seismic Anomaly Detection: Isolation Forest Algorithm Application to Indonesian Earthquake Data

Abstract

Indonesia's position on the seismically active Pacific "Ring of Fire" necessitates advanced methods for earthquake detection and preparedness. This paper introduces an application of the Isolation Forest algorithm—an unsupervised machine learning technique—for detecting seismic anomalies in Indonesia's complex geotectonic landscape. Unlike traditional methods that rely on predefined thresholds or patterns, the Isolation Forest algorithm isolates anomalies based on their rarity and distinctness without the need for labeled data. We applied this algorithm to a comprehensive dataset from the Indonesian Meteorology, Climatology, and Geophysical (BMKG) Agency, featuring a decade's worth of seismic events, to identify outliers that may signify potential seismic hazards. Our findings reveal that the Isolation Forest algorithm effectively identifies seismic anomalies, with 874 out of tens of thousands of events flagged as statistically significant outliers. A comparative analysis with traditional seismic anomaly detection models highlights the robustness of the Isolation Forest algorithm in handling the high-dimensional and noisy nature of seismic data, emphasizing its superiority in detecting subtle anomalies