Arrival times by Recurrent Neural Network for induced seismic events from a permanent network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F23%3A00574165" target="_blank" >RIV/67985530:_____/23:00574165 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fdata.2023.1174478/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fdata.2023.1174478/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fdata.2023.1174478" target="_blank" >10.3389/fdata.2023.1174478</a>

Result language

angličtina

Original language name

Arrival times by Recurrent Neural Network for induced seismic events from a permanent network

Original language description

We have developed a Recurrent Neural Network (RNN)-based phase picker for data obtained from a local seismic monitoring array specifically designated for induced seismicity analysis. The proposed algorithm was rigorously tested using real-world data from a network encompassing nine three-component stations. The algorithm is designed for multiple monitoring of repeated injection within the permanent array. For such an array, the RNN is initially trained on a foundational dataset, enabling the trained algorithm to accurately identify other induced events even if they occur in different regions of the array. Our RNN-based phase picker achieved an accuracy exceeding 80% for arrival time picking when compared to precise manual picking techniques. However, the event locations (based on the arrival picking) had to be further constrained to avoid false arrival picks. By utilizing these refined arrival times, we were able to locate seismic events and assess their magnitudes. The magnitudes of events processed automatically exhibited a discrepancy of up to 0.3 when juxtaposed with those derived from manual processing. Importantly, the efficacy of our results remains consistent irrespective of the specific training dataset employed, provided that the dataset originates from within the network.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10507 - Volcanology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Big Data

ISSN

2624-909X

e-ISSN

2624-909X

Volume of the periodical

6

Issue of the periodical within the volume

August

Country of publishing house

CH - SWITZERLAND

Number of pages

12

Pages from-to

1174478

UT code for WoS article

001049567200001

EID of the result in the Scopus database

2-s2.0-85168363271