Estimation of groundwater storage from seismic data using deep learning

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Fältnamn	Indikatorer	Metadata
000		03010naa a2200409 4500
001		oai:DiVA.org:uu-396134
003		SwePub
008		191104s2019 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3961342 URI
024	7	a https://doi.org/10.1111/1365-2478.128312 DOI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Lahivaara, Timou Univ Eastern Finland, Dept Appl Phys, Kuopio, Finland4 aut
245	1 0	a Estimation of groundwater storage from seismic data using deep learning
264		c 2019-07-22
264	1	b WILEY,c 2019
338		a print2 rdacarrier
520		a 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.
650	7	a NATURVETENSKAPx Geovetenskap och miljövetenskapx Geofysik0 (SwePub)105052 hsv//swe
650	7	a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Geophysics0 (SwePub)105052 hsv//eng
653		a Modelling
653		a Wave
653		a Monitoring
653		a Inverse problem
700	1	a Malehmir, Alireza,d 1977-u Uppsala universitet,Geofysik4 aut0 (Swepub:uu)almle363
700	1	a Pasanen, Anttiu Geol Survey Finland, Kuopio, Finland4 aut
700	1	a Karkkainen, Leou Nokia Bell Labs, Espoo, Finland;Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland4 aut
700	1	a Huttunen, Janne M. J.u Nokia Bell Labs, Espoo, Finland4 aut
700	1	a Hesthaven, Jan S.u Ecole Polytech Fed Lausanne, Computat Math & Simulat Sci, Lausanne, Switzerland4 aut
710	2	a Univ Eastern Finland, Dept Appl Phys, Kuopio, Finlandb Geofysik4 org
773	0	t Geophysical Prospectingd : WILEYg 67:8, s. 2115-2126q 67:8<2115-2126x 0016-8025x 1365-2478
856	4	u http://arxiv.org/pdf/1806.08375
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396134
856	4 8	u https://doi.org/10.1111/1365-2478.12831

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Av författaren/redakt...: Lahivaara, Timo; Malehmir, Alirez ...; Pasanen, Antti; Karkkainen, Leo; Huttunen, Janne ...; Hesthaven, Jan S ...

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