Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

  title={Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data},
  author={Marina Rosas‐Carbajal and Niklas Linde and Thomas Kalscheuer and Jasper A. Vrugt},
  journal={Geophysical Journal International},
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, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model… 

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes.

A fully 2-D, trans-dimensional Bayesian inversion of magnetotelluric (MT) data is demonstrated, for the first time, by using a stochastic interpolation algorithm known as a Gaussian process (GP) to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modelling codes.

Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data

This review tries to cover linearised model analysis tools such as the sensitivity matrix, the model resolution matrix and the model covariance matrix also providing a partially nonlinear description of the equivalent model domain based on pseudo-hyperellipsoids and emphasises linearisedmodel analysis, as efficient computation of nonlinear model uncertainty and resolution estimates is mainly determined by fast forward and inversion solvers.

Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted

Trans-dimensional Bayesian inversion of airborne electromagnetic data for 2D conductivity profiles

A novel trans-dimensional Bayesian approach using a wavelet parameterisation to airborne electromagnetic (AEM) inversions using data from the Broken Hill region allows exploration of a range of plausible subsurface conductivity models and provides more robust uncertainty estimates while accounting for potential non-uniqueness.

On structure-based priors in Bayesian geophysical inversion

This study introduces a new approach for structure-based prior sampling with Markov chain Monte Carlo that is suitable when only limited prior information is available and provides posterior model realizations and statistics that are significantly more satisfactory than those based on underlying assumptions of uncorrelated model parameters or on explicit penalties on model structure within an empirical Bayes framework.

Erratum to “Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study”

Low-frequency loop–loop electromagnetic induction (EMI) offers several key advantages over many other geophysical techniques for proximal soil sensing. Yet, because of problems with the inversion of

Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data

A probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium.

Bayesian full-waveform tomography with application to crosshole ground penetrating radar data

This study clearly demonstrates the feasibility of probabilistic FWI and highlights the advantages and disadvantages of the approach.

Evaluation of a Straight-Ray Forward Model for Bayesian Inversion of Crosshole Ground Penetrating Radar Data

Bayesian inversion of crosshole ground penetrating radar (GPR) data is capable of characterizing the subsurface dielectric properties and qualifying the associated uncertainties. Markov chain Monte

Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

SUMMARY We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled



Bayesian inversion of marine CSEM data with a trans‐dimensional self parametrizing algorithm

SUMMARY The posterior distribution of earth models that fit observed geophysical data convey information on the uncertainty with which they are resolved. From another perspective, the

A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data

SUMMARY A meaningful interpretation of geophysical measurements requires an assessment of the space of models that are consistent with the data, rather than just a single, ‘best’ model which does not

Thin-sheet electromagnetic inversion modeling using Monte Carlo Markov Chain (MCMC) algorithm

The well-known thin-sheet modeling has become a very useful interpretation tool in electromagnetic (EM) methods. The thin-sheet model approximates fairly well 3-D heterogeneities having a limited

Non-linear model error and resolution properties from two-dimensional single and joint inversions of direct current resistivity and radiomagnetotelluric data

SUMMARY For the first time, a comparative analysis of the resolution and variance properties of 2-D models of electrical resistivity derived from single and joint inversions of dc resistivity

Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem

Summary A key element in the solution of a geophysical inverse problem is the quantification of non-uniqueness, that is, how much parameters of an inferred earth model can vary while fitting a set

Seismic tomography with the reversible jump algorithm

SUMMARY The reversible jump algorithm is a statistical method for Bayesian inference with a variable number of unknowns. Here, we apply this method to the seismic tomography problem. The approach

Stochastic inversion of magnetotelluric data using a sharp boundary parameterization and application to a geothermal site

We developed a Bayesian model to invert magnetotelluric (MT) data using a 2D sharp boundary parameterization. We divided the 2D cross section into layers and considered the locations of interfaces

Bayesian inversion of CSEM and magnetotelluric data

We have developed a Bayesian methodology for inversion of controlled source electromagnetic (CSEM) data and magnetotelluric (MT) data. The inversion method provided optimal solutions and also the

Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data

A stochastic joint-inversion approach for estimating reservoir-fluid saturations and porosity is proposed. The approach couples seismic amplitude variation with angle (AVA) and marine

Mass conservative three‐dimensional water tracer distribution from Markov chain Monte Carlo inversion of time‐lapse ground‐penetrating radar data

Time‐lapse geophysical measurements are widely used to monitor the movement of water and solutes through the subsurface. Yet commonly used deterministic least squares inversions typically suffer from