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公开(公告)号:US20200379135A1
公开(公告)日:2020-12-03
申请号:US16738025
申请日:2020-01-09
申请人: CGG SERVICES SAS
发明人: Song HOU , Stefano ANGIO , Henning HOEBER
摘要: Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.