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Monitoring of water...
Monitoring of water table level and volume of water in a porous storage by seismic data
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- Lähivaara, T. (författare)
- University of Eastern Finland, University of Eastern Finland
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- Göransson, Peter, 1959- (författare)
- KTH,Strömningsmekanik och Teknisk Akustik
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- Heinonen, S. (författare)
- Geological Survey of Finland, Geological Survey of Finland
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- Bojan, B. (författare)
- Geological Survey of Finland, Geological Survey of Finland
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- Hesthaven, J. S. (författare)
- Ecole Polytechnique Federale de Lausanne, Ecole Polytechnique Federale de Lausanne
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- Khalili, M. (författare)
- University of Eastern Finland, University of Eastern Finland
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- Pasanen, A. (författare)
- Geological Survey of Finland, Geological Survey of Finland
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- Yadav, R. (författare)
- Nokia Solutions and Network, Nokia Solutions and Network
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- Vauhkonen, M. (författare)
- University of Eastern Finland, University of Eastern Finland
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(creator_code:org_t)
- EAGE Publications bv, 2023
- 2023
- Engelska.
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Ingår i: 29th European Meeting of Environmental and Engineering Geophysics, Held at Near Surface Geoscience Conference and Exhibition 2023, NSG 2023. - : EAGE Publications bv.
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Neural networks provide an attractive framework to monitor the water table level and the volume of stored water in porous media from seismic data in an automated, fast and cost-efficient manner. In this work, a subsurface reservoir is modeled as a coupled three-dimensional poroviscoelastic-viscoelastic medium. The wave propagation from source to receiver(s) is numerically simulated using a nodal discontinuous Galerkin method coupled with an Adams-Bashforth time-stepping scheme on a graphics processing unit cluster. The wave field solver is used to generate databases for the neural network model to estimate the water table level and actual volume of water. We use a deconvolution-based approach to normalize the effect from the source wavelet. The results demonstrate the capacity of the fully connected neural network for estimating both the water table level and the volume of stored water in the porous storage reservoir from both synthetic and measured data.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Geofysik (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Geophysics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)