The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …

Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution
velocity maps from seismic data. The recent success of data-driven FWI methods results in a …

Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …

Seismic inversion is one of the most commonly used methods in the oil and gas industry for
reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a …

Machine learning, and specifically deep-learning (DL) techniques applied to geophysical
inverse problems, is an attractive subject, which has promising potential and, at the same …

Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic
travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) …

Neural networks (NNs) are efficient tools for rapidly obtaining geoelectric models to solve
magnetotelluric (MT) inversion problems. Training an NN with strong predictive power …

In the era of big data, high-efficient and high-precise inversion algorithms of gravity data
become particularly important. Inspired by the excellent nonlinear map** capability of …

The present study integrates various local and global optimization techniques together to
estimate subsurface properties from post-stack seismic data and compare their efficacy …