High-Rate Phase Association with Travel Time Neural Fields

TL;DR

HARPA is a novel deep learning framework that improves high-rate seismic phase association by integrating wave physics and travel time neural fields, enabling more accurate earthquake detection in complex, high-rate scenarios.

Contribution

It introduces HARPA, a deep generative model-based association method that incorporates wave physics and travel time neural fields for high-rate seismic event association.

Findings

Outperforms existing methods on real seismic data

Handles complex synthetic models effectively

Establishes a new paradigm for seismic data analysis

Abstract

Earthquake science and seismology rely on the ability to associate seismic waves with their originating earthquakes. Earthquake detection algorithms based on deep learning have progressed rapidly and now routinely detect microearthquakes with unprecedented clarity, providing information about fault dynamics on increasingly finer spatiotemporal scales. However, this densification of detections can overwhelm existing techniques for phase association which rely on fixed wave speed models and associate events one by one. These methods fail when the event rates become high or where the 4D complexity of elastic wave speeds cannot be ignored. Here, we introduce HARPA, a deep learning solution to this problem. HARPA is a high-rate association framework which incorporates wave physics by leveraging deep generative models and travel time neural fields. Instead of associating events one by one, it lifts arrival sequences to probability distributions and compares them using an optimal transport metric. The generative travel time neural fields are used to estimate the wave speed simultaneously with association. HARPA outperforms state-of-the-art association methods for both real seismic data and complex synthetic models and paves the way for improved understanding of seismicity while establishing a new seismic data analysis paradigm.