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Title: Array-Based Convolutional Neural Networks for Automatic Detection and 4D Localization of Earthquakes in Hawai‘i
Abstract The growing amount 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, ArrayConvNet—a convolutional neural network that uses continuous array data from a seismic network to seamlessly detect and localize events, without the intermediate steps of phase detection, association, travel-time calculation, and inversion. 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 690% as many earthquakes as the published catalog, and 125% as many events than the Hawaiian Volcano Observatory internal catalog. Because of the enhanced detection sensitivity, localization granularity, and minimal computation costs, our solution is valuable, particularly for real-time earthquake monitoring.  more » « less
Award ID(s):
1949620
NSF-PAR ID:
10397286
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Seismological Research Letters
Volume:
92
Issue:
5
ISSN:
0895-0695
Page Range / eLocation ID:
2961 to 2971
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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