Recovering 3D Salt Dome by Using Gravity Data Inversion Using ResU-Net++ ...
In myriad disciplines such as mineral exploration, geological survey, groundwater resource inspection, and environmental surveillance, gravity inversion is a method ubiquitously employed. However, conventional gravity inversion approaches grapple with formidable challenges, including heightened susc...
| Main Authors: | , , , , , |
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| Format: | Software |
| Language: | unknown |
| Published: |
Zenodo
2023
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.8279517 https://zenodo.org/record/8279517 |
| Summary: | In myriad disciplines such as mineral exploration, geological survey, groundwater resource inspection, and environmental surveillance, gravity inversion is a method ubiquitously employed. However, conventional gravity inversion approaches grapple with formidable challenges, including heightened susceptibility to minute data variations and the propensity for descent into numerous local minima of the error function. To mitigate these conundrums, we explore deep learning methodologies, specifically presenting the ResU-Net++, a network that synergistically integrates residual connectivity and deep feature fusion tactics. We scrutinize the efficacy of this novel model via an array of simulation experiments encompassing four distinct networks: the AttU-Net, the R2U-Net, the Nested U-Net, and the ResU-Net++ that we utilized. The empirical evidence reveals the superior performance of ResU-Net++ relative to its counterparts in computational proficiency, feature discernment capability, and inversion precision, thereby ... |
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