2022/02/03 by Shihang Feng, Feng, Shihang, Peng Jin +11
Earth and Planetary Sciences · Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #Geophysical and Geoelectrical Methods #Geophysics (physics.geo-ph) #Hydraulic Fracturing and Reservoir Analysis #Machine Learning (cs.LG) #Seismic Imaging and Inversion Techniques
paper · pdf · doi:10.48550/arxiv.2202.01770
openalex publication_date 2022/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-physical inversion plays a critical role in geophysics. It has been widely used to infer various physical properties~(such as velocity and conductivity). Among those inversion problems, some are explicitly governed by partial differential equations~(PDEs), while others are not. Without explicit governing equations, conventional multi-physical inversion techniques will not be feasible and data-driven inversion requires expensive full labels. To overcome this issue, we develop a new data-driven multi-physics inversion technique with extremely weak supervision. Our key finding is that the pseudo labels can be constructed by learning the local relationship among geophysical properties at very sparse well-logging locations. We explore a multi-physics inversion problem from two distinct measurements~(seismic and EM data) to three geophysical properties~(velocity, conductivity, and CO2 saturation). Our results show that we are able to invert for properties without explicit governing equations. Moreover, the label data on three geophysical properties can be significantly reduced by 50 times~(from 100 down to only 2 locations).