Real Time Estimation of Earthquake Location and Magnitude Using Large Language Models

Estimation of earthquake parameters has always been a focus for seismologists. Efficient and rapid determination of earthquake location and magnitude is essential for mitigating the potential hazards associated with seismic shaking. Nowadays, Earthquake Early Warning Systems (EEWS) are implemented in most earthquake-prone areas, with the system varying according to the specific needs. Although methods for their estimation exist, many still lack a fast enough process, which is crucial for reducing the waiting time before issuing a warning.

Here, we propose a novel model to enhance multi-station EEWS using Large Language Models (LLM). We adopt a pre-trained LLM and fine-tune it on a customized version of INSTANCE (The Italian Seismic Dataset for Machine Learning), thus eliminating the need to develop and train a tailor-made architecture. The model uses stations with P-wave arrival times up to 5 s apart from the first recorded one, and, for each seismic trace, it exploits a very small time window around the P-wave arrival time (0.21 s), thus effectively reducing warning latency.

Comparative analysis against the automatic method employed by the Italian National Institute of Geophysics and Volcanology (INGV) demonstrates that our model achieves comparable performance in magnitude estimation and superior accuracy in epicenter, hypocenter and origin time prediction. For instance, the LLM-based model achieves average errors of 6.3 km, 11.1 km, and 1.1 s for epicenter, hypocenter, and origin time estimation, respectively, in contrast to 8.6 km, 15.0 km, and 1.8 s for the INGV automatic solution resulting in an average improvement of more than 26% for all parameters.

We study the validity of our model by assessing its ability using P- and S-waves to predict magnitude, and show that in this case study the S-waves are not strictly necessary for accurate predictions.