Multifractal signatures reveal source-dependent differences between tectonic earthquakes and glacial tremors
Abstract
Abstract Discriminating between local tectonic earthquakes and cryo-seismic events (icequakes)poses a significant challenge for automated seismic monitoring due to the similarity oftheir waveforms in the time and frequency domains. To address this, we present a robustclassification framework utilizing Multifractal Detrended Fluctuation Analysis (MFDFA)combined with machine learning. Unlike traditional methods that rely on linearspectral features, this approach quantifies the intrinsic non-linear scaling properties andstructural complexity of the seismic source. We analyzed seismic data from the ColumbiaGlacier region, Alaska, categorized into station groups based on Signal-to-Noise Ratio(SNR) and data availability. Analysis of a seismic network dataset across diverse SNRconditions reveals that tectonic earthquakes exhibit higher structural complexity andbroader multifractal spectra in contrast to the narrower, quasi-monofractal signaturesof cryo-seismic sources. This physical distinction enables robust discrimination acrossvarying noise regimes. A Support Vector Machine (SVM) classifier achieved a globalaverage accuracy of 93.95% with individual station performance peaking at 97.35%.These results demonstrate that the proposed multifractal framework provides a physicallygrounded and highly accurate tool for purifying seismic catalogs in complex glaciated environments.
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