Accelerating Bayesian microseismic event location with deep learning
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Published:2021-07-29
Issue:7
Volume:12
Page:1683-1705
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ISSN:1869-9529
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Container-title:Solid Earth
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language:en
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Short-container-title:Solid Earth
Author:
Spurio Mancini AlessioORCID, Piras DavideORCID, Ferreira Ana Margarida Godinho, Hobson Michael Paul, Joachimi Benjamin
Abstract
Abstract. We present a series of new open-source deep-learning algorithms to accelerate Bayesian full-waveform point source inversion of microseismic
events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty
quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling
algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep-learning
models to learn the mapping between source location and seismic traces for a given 3D heterogeneous velocity model and a fixed isotropic moment
tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process. We compare our results with a previous study that used emulators based on Gaussian processes to invert microseismic events. For fairness of
comparison, we train our emulators on the same microseismic traces and using the same geophysical setting. We show that all of our models provide
more accurate predictions, ∼ 100 times faster predictions than the method based on Gaussian processes, and a 𝒪(105) speed-up
factor over a pseudo-spectral method for waveform generation. For example, a 2 s long synthetic trace can be generated in ∼ 10 ms on a
common laptop processor, instead of ∼ 1 h using a pseudo-spectral method on a high-profile graphics processing unit card. We also
show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The
speed, accuracy, and scalability of our open-source deep-learning models pave the way for extensions of these emulators to generic source mechanisms
and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.
Publisher
Copernicus GmbH
Subject
Paleontology,Stratigraphy,Earth-Surface Processes,Geochemistry and Petrology,Geology,Geophysics,Soil Science
Reference107 articles.
1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,
available at: https://www.tensorflow.org/ (last access: 20 July 2021),
software available from tensorflow.org, 2015. a 2. Arjovsky, M., Chintala, S., and Bottou, L.:
Wasserstein GAN,
arXiv [preprint], arXiv:1701.07875, 2017. a, b, c, d 3. Arridge, S., Maass, P., Ozan, O., and Schönlieb, C.-B.:
Solving inverse problems using data-driven models,
Acta Numer.,
28, 1–174, https://doi.org/10.1017/S0962492919000059, 2019. a 4. Auld, T., Bridges, M., Hobson, M., and Gull, S.:
Fast cosmological parameter estimation using neural networks,
Mon. Not. R. Astron. Soc.,
376, L11–L15, https://doi.org/10.1111/j.1745-3933.2006.00276.x, 2007. a 5. Auld, T., Bridges, M., and Hobson, M. P.:
cosmonet: fast cosmological parameter estimation in non-flat models using neural networks,
Mon. Not. R. Astron. Soc.,
387, 1575–1582, https://doi.org/10.1111/j.1365-2966.2008.13279.x, 2008. a
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