- 专利标题: TRAINING A MACHINE LEARNING SYSTEM USING HARD AND SOFT CONSTRAINTS
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申请号: US17595021申请日: 2020-05-11
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公开(公告)号: US20220206175A1公开(公告)日: 2022-06-30
- 发明人: Haibin Di , Cen Li , Aria Abubakar , Stewart Smith
- 申请人: Schlumberger Technology Corporation
- 申请人地址: US TX Sugar Land
- 专利权人: Schlumberger Technology Corporation
- 当前专利权人: Schlumberger Technology Corporation
- 当前专利权人地址: US TX Sugar Land
- 国际申请: PCT/US2020/032334 WO 20200511
- 主分类号: G01V1/30
- IPC分类号: G01V1/30 ; G06N3/08
摘要:
A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.
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