Introduction ************ *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 Developers ---------- 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 `_. Citation -------- 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 `_ License ------- Bayesian1DEM is made freely available under the `GNU GPLv3 license `_.