Bayesian1DEM: 1D inversion of electromagnetic data using trans-dimensional Markov chain Monte Carlo

Bayesian1DEM is a suite of codes that collectively invert electromagnetic geophysical data for 1D models of subsurface resistivity using a trans-dimensional Markov chain Monte Carlo (trans-D MCMC) algorithm. Trans-D MCMC is a Bayesian inversion method that draws samples from a posterior probability density function, conditional on prior assumptions and geophysical data. The product of trans-D MCMC inversion is a collection of models referred to as the model ensemble from which statistical properties of the model parameters can be derived. In other words, the model ensemble tells you how well you can know/constrain the model parameters you inverted for, given the data you inverted. For a detailed description of the algorithm that is, I hope, accessible and easy to learn from, see [Blatter19].

The EM data types that are currently supported are

  • Magnetotelluric data, using the recursion method of Ward and Hohman [WardHohmann87]

  • Controlled source electromagnetic data, using Dipole 1D [Key09]. The Dipole 1D CSEM forward code is currently only supported on MacOS

  • DC resistivity


This code was developed by Daniel Blatter and Anandaroop Ray, who grateful acknowledge funding support from the Scripps Seafloor Electromagnetic Methods Consortium and the Electromagnetic Methods Research Consortium at Columbia University.


If you publish results using Bayesian1DEM, please cite:

Blatter, Daniel, et al. “Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey.” Geophysical Journal International 218.3 (2019): 1822-1837. DOI: 10.1093/gji/ggz253


Bayesian1DEM is made freely available under the GNU GPLv3 license.