| Title | Combining deep learning and the source-scanning algorithm for improved seismic monitoring |
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| Author | Dokht, R M H ;
Kao, H; Ghofrani, H; Visser, R |
| Source | Bulletin of the Seismological Society of America 2022 p. 1-15, https://doi.org/10.1785/0120220007 |
| Image |  |
| Year | 2022 |
| Alt Series | Natural Resources Canada, Contribution Series 20220075 |
| Publisher | Seismological Society of America |
| Document | serial |
| Lang. | English |
| Media | paper; digital; on-line |
| File format | pdf; html |
| Subjects | Science and Technology; Nature and Environment; seismology; seismic data; earthquakes |
| Illustrations | graphs; diagrams; charts |
| Program | Environmental Geoscience Shale Gas - induced seismicity |
| Released | 2022 06 23 |
| Abstract | (unpublished)
In the present 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. |
| Summary | (Plain Language Summary, not published)
Due to the successful application of machine learning techniques in the previous seismic studies, we designed and trained an Artificial Neural Network to
identify the major seismic arrivals from earthquakes in the time-frequency domain. The proposed model overcomes the challenges of traditional earthquake detection techniques, especially in identifying small-magnitude earthquakes, and enables us to
obtain reliable earthquake locations without user interaction. The parallel implementation of the proposed framework allows us to tackle the large dataset problems, which is necessary for real-time seismic monitoring. |
| GEOSCAN ID | 330077 |
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