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

Frontiers in Big Data

Big Data

Umair bin Waheed¹  Petr Kolar²  Leo Eisner³  Petr Matousek³ 

[1] Department of Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia;Institute of Geophysics of the Czech Academy of Sciences, Prague, Czechia;Seismik s.r.o., Prague, Czechia;

关键词: Recurrent Neural Network;    automatic arrival time detection;    location;    magnitude;    hydraulic fracturing;    induced seismicity;    traffic light system;   

DOI  :  10.3389/fdata.2023.1174478

 received in 2023-03-30, accepted in 2023-07-18,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Kolar, Waheed, Eisner and Matousek.