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 …

Despite exhibiting excellent inversion results for synthetic data in magnetotelluric (MT)
inversion, applying deep learning (DL) to directly inverting MT field data remains …

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

This paper develops a probabilistic Bayesian approach to the problem of inferring the
spatiotemporal evolution of earthquake rupture on a fault surface from seismic data with …

This paper develops an efficient hierarchical trans-dimensional (trans-D) Bayesian algorithm
to invert magnetotelluric (MT) data for subsurface geoelectrical structure, with unknown …

The most computationally intensive step in 3D magnetotelluric (MT) inversion is the
calculation of the forward response. This fact makes any modelling which requires many …

高效高精度的反演算法在重力大数据时代背景下显得尤为重要, 受深度学**卓越的非线性映射
能力的启发, 本文提出了一种基于深度学**的重力异常及重力梯度异常的联合反演方法 …

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are
well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet …

Edge detection is a fundamental technique in the potential field data processing. The current
methodology for edge detection belongs to unsupervised machine operation, whose …