Introduction
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*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
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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
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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
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Bayesian1DEM is made freely available under the `GNU GPLv3 license `_.