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Arrival times by Recurrent Neural Network for induced seismic events from a permanent network
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SYSNO ASEP 0574165 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Arrival times by Recurrent Neural Network for induced seismic events from a permanent network Author(s) Kolář, Petr (GFU-E) ORCID, RID
Waheed, U. B. (SA)
Eisner, L. (CZ)
Matoušek, P. (CZ)Article number 1174478 Source Title Frontiers in Big Data
Roč. 6, August (2023)Number of pages 12 s. Publication form Online - E Language eng - English Country CH - Switzerland Keywords Recurrent Neural Network ; automatic arrival time detection ; location ; magnitude ; hydraulic fracturing ; induced seismicity ; traffic light system Subject RIV DC - Siesmology, Volcanology, Earth Structure OECD category Volcanology Method of publishing Open access Institutional support GFU-E - RVO:67985530 UT WOS 001049567200001 EID SCOPUS 85168363271 DOI 10.3389/fdata.2023.1174478 Annotation 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. Workplace Geophysical Institute Contact Hana Krejzlíková, kniha@ig.cas.cz, Tel.: 267 103 028 Year of Publishing 2024 Electronic address https://www.frontiersin.org/articles/10.3389/fdata.2023.1174478/full
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