Combining Deep Learning and the Source-Scanning Algorithm for Improved Seismic Monitoring
| العنوان: | Combining Deep Learning and the Source-Scanning Algorithm for Improved Seismic Monitoring |
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| المؤلفون: | Ramin M. H. Dokht, Honn Kao, Hadi Ghofrani, Ryan Visser |
| المصدر: | Bulletin of the Seismological Society of America. 112:2312-2326 |
| بيانات النشر: | Seismological Society of America (SSA), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Geophysics, Geochemistry and Petrology |
| الوصف: | In this study, we develop an integrated framework for simultaneous detection of seismic events and picking phase arrival times, phase association, and locating earthquakes. The proposed model combines the accuracy of convolutional neural networks for classification tasks and the efficiency of waveform-based algorithms for identifying coherent seismic arrivals. We find that our model strongly dominates the classic techniques, especially in identifying small magnitude earthquakes. We apply our model to one month of continuous seismic data recorded in western Canada for monitoring seismic activity associated with fluid injection operations. In comparison with previously developed deep-learning models, our technique reveals a nearly identical performance without human interaction during the entire process of picking the phase arrival times and locating the associated events. |
| تدمد: | 1943-3573 0037-1106 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::2f412bb28fe19007674d43711bddc74d https://doi.org/10.1785/0120220007 |
| رقم الأكسشن: | edsair.doi...........2f412bb28fe19007674d43711bddc74d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19433573 00371106 |
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