Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing Data
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66965-1_31
Reference21 articles.
1. Shiloh, L., Eyal, A., Giryes, R.: Efficient processing of distributed acoustic sensing data using a deep learning approach. J. Lightwave Technol. 37(18), 4755–4762 (2019)
2. Spikes, K.T., et al.: Comparison of geophone and surface-deployed distributed acoustic sensing seismic data. Geophysics 84(2), A25–A29 (2019)
3. Ma, Y., et al.: Machine learning-assisted processing workflow for multi-fiber DAS microseismic data. Front. Earth Sci. 11, 1096212 (2023)
4. Stork, A.L., et al.: Application of machine learning to microseismic event detection in distributed acoustic sensing data. Geophysics 85(5), KS149–KS160 (2020)
5. Binder, G., Chakraborty, D.: Detecting microseismic events in downhole distributed acoustic sensing data using convolutional neural networks. In: SEG International Exposition and Annual Meeting (2019)
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