Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine,
geophysics, and various industries. It is an ill-posed inverse problem whose solution is …

Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …

The difficulty of 3-D magnetic inversion is to use 2-D magnetic anomaly data to obtain 3-D
magnetic susceptibility structure. The contribution of the underground medium to the …

Joint inversion has drawn considerable attention due to the availability of multiple
geophysical data sets, ever-increasing computational resources, the development of …

We have implemented multiparameter full-waveform inversions (FWIs) in the framework of
recurrent neural networks in elastic isotropic and transversely isotropic media. A staggered …

We developed an effective U-Net based deep learning (DL) model for inversion of surface
gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution …

Multiphysics inversion exploits different types of geophysical data that often complement
each other and aims to improve overall imaging resolution and reduce uncertainties in …

Full-waveform inversion (FWI) is a powerful technique for building high-quality subsurface
geological structures. It is known to suffer from local minima problems when a good starting …

In this paper, we design a framework to combine deep neural network (DNN) and the
traditional separate inversion workflow together and improve the joint inversion result …

Using multiple geophysical methods has become a prevailing approach in numerous
geophysical applications to investigate subsurface structures and parameters. These …