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Estimation of groundwater storage from seismic data using deep learning
Univ Eastern Finland, Dept Appl Phys, Kuopio, Finland.ORCID iD: 0000-0003-0719-1973
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Geophysics.ORCID iD: 0000-0003-1241-2988
Geol Survey Finland, Kuopio, Finland.
Nokia Bell Labs, Espoo, Finland;Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland.
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2019 (English)In: Geophysical Prospecting, ISSN 0016-8025, E-ISSN 1365-2478, Vol. 67, no 8, p. 2115-2126Article in journal (Refereed) Published
Abstract [en]

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic-elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.

Place, publisher, year, edition, pages
WILEY , 2019. Vol. 67, no 8, p. 2115-2126
Keywords [en]
Modelling, Wave, Monitoring, Inverse problem
National Category
Geophysics
Identifiers
URN: urn:nbn:se:uu:diva-396134DOI: 10.1111/1365-2478.12831ISI: 000477189800001OAI: oai:DiVA.org:uu-396134DiVA, id: diva2:1367663
Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-04Bibliographically approved

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Malehmir, Alireza

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Lahivaara, TimoMalehmir, AlirezaHuttunen, Janne M. J.
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