Array-based convolutional neural networks for automatic earthquake
detection and 4D localization
Abstract
The growing amounts of seismic data necessitates efficient and effective
methods to monitor earthquakes. Current methods are computationally
expensive, ineffective under noisy environments, or labor-intensive. We
leverage advances in machine learning to propose an improved solution -
a convolutional neural network that uses array data to seamlessly detect
and localize events. When testing this methodology with events at
Hawai‘i, we achieve 99.4% accuracy and predict hypocenter locations
within a few kilometers of the U.S. Geological Survey catalog. We
demonstrate that training with relocated earthquakes reduces
localization errors significantly. We outline several ways to improve
the model, including enhanced data augmentation and use of relocated
offshore earthquakes recorded by ocean bottom seismometers. Application
to continuous records shows that our algorithm detects 6 times as many
earthquakes as the published catalog. Due to the enhanced detection
sensitivity, localization granularity, and minimal computation costs,
our solution is valuable, particularly for real-time earthquake
monitoring.