Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network

TL;DR

This paper introduces a transformer-based deep learning model for real-time earthquake magnitude and location estimation that outperforms classical methods and previous deep learning approaches, especially when trained on large datasets.

Contribution

The study presents a novel attention-based transformer network capable of dynamically incorporating seismic data from varying station sets, improving accuracy over existing models and classical algorithms.

Findings

Transformer model outperforms deep learning baselines.

Large training datasets significantly reduce assessment time.

Model underestimates large magnitude events, mitigated by transfer learning.

Abstract

Precise real time estimates of earthquake magnitude and location are essential for early warning and rapid response. While recently multiple deep learning approaches for fast assessment of earthquakes have been proposed, they usually rely on either seismic records from a single station or from a fixed set of seismic stations. Here we introduce a new model for real-time magnitude and location estimation using the attention based transformer networks. Our approach incorporates waveforms from a dynamically varying set of stations and outperforms deep learning baselines in both magnitude and location estimation performance. Furthermore, it outperforms a classical magnitude estimation algorithm considerably and shows promising performance in comparison to a classical localization algorithm. In this work, we furthermore conduct a comprehensive study of the requirements on training data, the training procedures and the typical failure modes using three diverse and large scale data sets. Our analysis gives several key insights. First, we can precisely pinpoint the effect of large training data; for example, a four times larger training set reduces the required time for real time assessment by a factor of four. Second, the basic model systematically underestimates large magnitude events. This issue can be mitigated by incorporating events from other regions into the training through transfer learning. Third, location estimation is highly precise in areas with sufficient training data, but is strongly degraded for events outside the training distribution. Our analysis suggests that these characteristics are not only present for our model, but for most deep learning models for fast assessment published so far. They result from the black box modeling and their mitigation will likely require imposing physics derived constraints on the neural network.