Quantifying uncertainty of salt body shapes recovered from gravity data using trans-dimensional Markov chain Monte Carlo sampling
Accurate delineation of salt body shapes is critical for hydrocarbon exploration. Various
imaging methods based on seismic data have been developed. Due to the density contrast …
imaging methods based on seismic data have been developed. Due to the density contrast …
A deep Gaussian process model for seismicity background rates
The spatio-temporal properties of seismicity give us incisive insight into the stress state
evolution and fault structures of the crust. Empirical models based on self-exciting point …
evolution and fault structures of the crust. Empirical models based on self-exciting point …
Physically structured variational inference for Bayesian full waveform inversion
Full waveform inversion (FWI) creates high resolution models of the Earth's subsurface
structures from seismic waveform data. Due to the non‐linearity and non‐uniqueness of FWI …
structures from seismic waveform data. Due to the non‐linearity and non‐uniqueness of FWI …
Bayesian inversion, uncertainty analysis and interrogation using boosting variational inference
Geoscientists use observed data to estimate properties of the Earth's interior. This often
requires non‐linear inverse problems to be solved and uncertainties to be estimated …
requires non‐linear inverse problems to be solved and uncertainties to be estimated …
Uncertainty quantification for regularized inversion of electromagnetic geophysical data—part I: motivation and theory
D Blatter, M Morzfeld, K Key… - Geophysical Journal …, 2022 - academic.oup.com
We present a method for computing a meaningful uncertainty quantification (UQ) for
regularized inversion of electromagnetic (EM) geophysical data that combines the …
regularized inversion of electromagnetic (EM) geophysical data that combines the …
Calculating sensitivity or gradient for geophysical inverse problems using automatic and implicit differentiation
Automatic differentiation (AD) is a valuable computing technique that can automatically
calculate the derivative of a function. Using the chain rule and algebraic manipulations, AD …
calculate the derivative of a function. Using the chain rule and algebraic manipulations, AD …
Appropriate reduction of the posterior distribution in fully Bayesian inversions
Bayesian inversion generates a posterior distribution of model parameters from an
observation equation and prior information both weighted by hyperparameters. The prior is …
observation equation and prior information both weighted by hyperparameters. The prior is …
Reconstruction of multiple target bodies using trans-dimensional Bayesian inversion with different constraints
Geophysical geometry inversion aims to reconstruct the geometrical characteristics of
subsurface target bodies, which is different from conventional inversion techniques that …
subsurface target bodies, which is different from conventional inversion techniques that …
A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network
Magnetotelluric (MT) inversion is an important means of MT data interpretation. The use of
deep learning technology for MT inversion has attracted much attention because it is not …
deep learning technology for MT inversion has attracted much attention because it is not …
2-D probabilistic inversion of MT data and uncertainty quantification using the Hamiltonian Monte Carlo method
Bayesian methods provide a valuable framework for rigorously quantifying the model
uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion …
uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion …