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Deep Learning Framework for Multi-Physics Joint Inversion and its Application in the Decorah Area | IEEE Conference Publication | IEEE Xplore

Deep Learning Framework for Multi-Physics Joint Inversion and its Application in the Decorah Area


Abstract:

In this abstract, we introduce a deep learning enhanced (DLE) framework for solving multi-physics joint inversion. For the inceptive DLE joint inversion scheme, a well-tr...Show More

Abstract:

In this abstract, we introduce a deep learning enhanced (DLE) framework for solving multi-physics joint inversion. For the inceptive DLE joint inversion scheme, a well-trained neural network based on structural similarity is used to provide better initials for the separate inversions. The preservation of the separate inversions makes the framework flexible to deal with different sensing configurations, nonconforming discretization, as well as joint inversion of multiple data types. Further, deep perceptual losses (DPL) derived from a pre-trained edge detection network are introduced to enforce structural constraints. Synthetic examples demonstrate improved inversion results from the DLE framework compared to the separate inversions and cross-gradient-based joint inversion. In addition, the DLE framework is simplified to solve 3D joint inversion of the airborne magnetic and gravity gradient data collected from the Decorah area. The inversion results verify the effectiveness and higher efficiency of the DL-based method compared to the cross-gradient-based joint inversion.
Date of Conference: 07-12 July 2024
Date Added to IEEE Xplore: 05 September 2024
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Conference Location: Athens, Greece

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