OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for
Seismic Monitoringin Costa Rica
- URL: http://arxiv.org/abs/2109.02723v1
- Date: Mon, 6 Sep 2021 20:24:49 GMT
- Title: OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for
Seismic Monitoringin Costa Rica
- Authors: Leonardo van der Laat, Ronald J.L. Baldares, Esteban J. Chaves,
Esteban Meneses
- Abstract summary: We introduce OKSP, a novel automatic earthquake detection pipeline for seismic monitoring in Costa Rica.
OKSP is 100% exhaustive and 82% precise, resulting in an F1 score of 0.90.
This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.
- Score: 0.0938460348620674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Small magnitude earthquakes are the most abundant but the most difficult to
locate robustly and well due to their low amplitudes and high frequencies
usually obscured by heterogeneous noise sources. They highlight crucial
information about the stress state and the spatio-temporal behavior of fault
systems during the earthquake cycle, therefore, its full characterization is
then crucial for improving earthquake hazard assessment. Modern DL algorithms
along with the increasing computational power are exploiting the continuously
growing seismological databases, allowing scientists to improve the
completeness for earthquake catalogs, systematically detecting smaller
magnitude earthquakes and reducing the errors introduced mainly by human
intervention. In this work, we introduce OKSP, a novel automatic earthquake
detection pipeline for seismic monitoring in Costa Rica. Using Kabre
supercomputer from the Costa Rica High Technology Center, we applied OKSP to
the day before and the first 5 days following the Puerto Armuelles, M6.5,
earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border
and found 1100 more earthquakes previously unidentified by the Volcanological
and Seismological Observatory of Costa Rica. From these events, a total of 23
earthquakes with magnitudes below 1.0 occurred a day to hours prior to the
mainshock, shedding light about the rupture initiation and earthquake
interaction leading to the occurrence of this productive seismic sequence. Our
observations show that for the study period, the model was 100% exhaustive and
82% precise, resulting in an F1 score of 0.90. This effort represents the very
first attempt for automatically detecting earthquakes in Costa Rica using deep
learning methods and demonstrates that, in the near future, earthquake
monitoring routines will be carried out entirely by AI algorithms.
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