Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings

Volcanic eruptions pose significant risks, demanding precise monitoring for timely hazard mitigation. However, interpreting noisy seismic data for eruptive precursors remains challenging. This study introduces a novel methodology that extends an earlier time-series feature engineering approach to include template matching against prior eruptions. We aim to identify subtle signals within seismic data to enhance our understanding of volcanic activity and future hazards. To do this, we analyze the continuous seismic record at a volcano and identify the time-series elements that regularly precede eruptions and the timescales over which these are observable. We conduct tests across various time lengths, (Russia), and Whakaari (New Zealand) volcanoes, we confirm statistically significant eruption precursors. In particular, a feature named change quantiles (0.2-0.8), which is related to the conditional dynamics of surface acceleration at the volcano, emerges as a key indicator of future eruptions over 14-day timescales. This research offers new methods for real-time seismovolcanic monitoring, minimizing the effects of unknown, spurious noise, and discerning recurrent patterns through template matching. By providing deeper insights into pre-eruptive behavior, it may lead to more effective hazard reduction strategies, enhancing public safety around active volcanoes.