Denoising ambient seismic field correlation functions with convolutional autoencoders

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Loic Viens, Chris Van Houtte


Seismic interferomestry is an established method for monitoring the temporal evolution of the Earths physical properties. We introduce a new technique to improve the precision and temporal resolution of seismic monitoring studies based on deep learning. Our method uses a convolutional denoising autoencoder, called ConvDeNoise, to denoise ambient seismic field correlation functions. The technique can be applied to traditional two-station cross-correlation functions but this study focuses on single-station cross-correlation (SC) functions. SC functions are computed by cross correlating the different components of a single seismic station and can be used to monitor the temporal evolution of the Earths near surface. We train and apply our algorithm to SC functions computed with a time resolution of 20 minutes at seismic stations in the Tokyo metropolitan area, Japan. We show that the relative seismic velocity change (dv/v(t)) computed from SC functions denoised with ConvDeNoise has less variability than that calculated from raw SC functions. Compared to other denoising methods such as the SVD-based Wiener Filter method developed by Moreau et al. (2017), the dv/v results obtained after using our algorithm have similar precision. The advantage of our technique is that once the algorithm is trained, it can be apply to denoise near-real-time SC functions. The near-real-time aspect of our denoising algorithm may be useful for operational hazard forecasting models, for example when applying seismic interferometry at an active volcano.



Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


machine learning, seismic noise, Deep learning, Autoencoders, Denoising autoencoders, Seismic monitoring


Published: 2019-07-04 07:13

Last Updated: 2019-11-07 20:20

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Academic Free License (AFL) 3.0

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