Use of convolutional neural networks with encoder-decoder structure for predicting the inverse operator in hydraulic tomography

•A deep learning algorithm has been used for the approximation of the inverse operator in HT.•The algorithm is based on convolutional neural networks.•The approach has been successfully applied on the synthetic transmissivity fields. In this manuscript, we discuss the capabilities of a deep learning...

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Veröffentlicht in:	Journal of hydrology (Amsterdam) Jg. 604; S. 127233
Hauptverfasser:	Jardani, A., Vu, T.M., Fischer, P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2022 Elsevier
Schlagworte:	algorithms Convolutional neural networks data collection Deep learning equations groundwater flow Hydraulic tomography image analysis Inverse problem Sciences of the Universe tomography Deep learning Convolutional neural networks Hydraulic tomography Inverse problem
ISSN:	0022-1694, 1879-2707
Online-Zugang:	Volltext
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