.png){kind=icon}
返回
A deep learning-enhanced framework for multiphysics joint inversion
.png){kind=icon}
PRE
AI
DOI：10.1190/geo2021-0589.1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algo-rithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning -en-hanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysi-cal data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a tradi-tional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numeri-cal experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configu-rations and nonconforming discretization.
Keyword:
WAVE-FORM INVERSION
MARINE SEISMIC AVA
PARAMETER-ESTIMATION
GRAVITY-DATA
MODEL
FUSION
期刊
IF:
3.2
论文数:
8.4K
被引数:
3.3W
机构
引用论文
Calculation of the critical currents of Bi(2223)/Ag tapes and coils with reduced anisotropy in Ic(B) characteristic — effect of different proportional representations of the filaments oriented parallel and perpendicularly to the tape surfaceBi(2223)/Ag带材和线圈临界电流的计算及其Ic(B)特性中各向异性降低的影响——平行和垂直于带材表面取向的细丝的不同比例表示的影响
PREAI
分享收藏The 2.6 Angstrom Crystal Structure of a Human A2AAdenosine Receptor Bound to an Antagonist.png)
Science
OAAI
ScienceIF0
OAAI分享收藏
查看更多内容