Bidirectional recurrent neural networks for seismic event detection

TL;DR

This paper presents a deep learning method using bidirectional LSTM neural networks for seismic event detection, outperforming traditional STA/LTA triggers in accuracy and real-time processing capability.

Contribution

It introduces a synthetic-trainable neural network approach for seismic detection that is effective across different settings and suitable for real-time applications.

Findings

Outperforms STA/LTA in detection accuracy

Reduces false detections significantly

Processes 600 traces in real time on a single unit

Abstract

Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters. Whilst numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be globally applied, or they are too computationally expensive therefore cannot be run real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bi-directional, long-short-term memory, neural network is trained solely on synthetic traces. Evaluated on synthetic and field data, the neural network approach significantly outperforms the STA/LTA trigger both on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its real time applicability is proven with 600 traces processed in real time on a single processing unit.