Adapting a Pretrained LLM to Rapidly Recover Earthquake Magnitude and Location
TL;DR
The utility of LLMs for earthquake characterization is assessed and a novel approach based on a small LLM called TinyLlama is proposed, which achieves comparable performance in magnitude estimation and superior accuracy in epicenter, hypocenter and origin time estimation.
Abstract
Determining earthquake magnitude and location through fast, automated methods is fundamental for seismic monitoring. Recent studies have shown that Large Language Models (LLMs) can operate as pattern‐recognition systems capable of handling novel domains and purely numerical tasks. Here, we assess the utility of LLMs for earthquake characterization and propose a novel approach based on a small LLM called TinyLlama. We adapt the LLM for seismology through fine‐tuning on a subset of Italian earthquakes using the LoRA technique, which involves retraining only about of the model parameters. Our method takes station coordinates, P‐wave arrival times, and peak ground velocities in a small window (0.2 s) around the P‐wave and accurately estimates earthquake magnitude, location, and origin time without using a seismic velocity model. To explore the potential for rapid characterization, we consider only stations where the P‐wave arrives within 5 s of the first triggered station. Compared to standard automatic methods currently used by INGV in Italy our model achieves comparable performance in magnitude estimation and superior accuracy in epicenter, hypocenter and origin time estimation. Validation using both P‐ and S‐waves shows only a slight performance improvement that does not justify the additional latency. We explored a range of data splitting strategies and found that the LLM‐based seismic method is robust. Our results highlight the ability of LLMs to function as effective tools in seismology and suggest their potential for advancing data‐driven approaches to rapid earthquake characterization.
