Overview

Abstract

BayHunter is an open source Python tool to perform an McMC transdimensional Bayesian inversion of surface wave dispersion and/or receiver functions. The algorithm follows a data-driven strategy and solves for the velocity-depth structure, the number of layers, \(\mathrm{V_P}\)/\(\mathrm{V_S}\) ratio and noise parameters, i.e., data noise correlation and amplitude. The forward modeling codes are provided within the package, but are easily replaceable with own codes. It is also possible to add (completely different) data sets.

The BayWatch module can be used to live-stream the inversion while it is running (see animation below). This makes it easy to see how each chain is exploring the parameter space, how the data fits and models change and in which direction the inversion progresses.

Available on GitHub: https://github.com/jenndrei/BayHunter

Citation

Dreiling, J., Tilmann, F. BayHunter – McMC transdimensional Bayesian inversion of receiver functions and surface wave dispersion. GFZ Data Services, 2019. https://doi.org/10.5880/GFZ.2.4.2019.001.

Application examples

  • Dreiling et al., 2020: Crustal structure of Sri Lanka derived from joint inversion of surface wave dispersion and receiver functions using a Bayesian approach. Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/2019JB018688.

  • Green et al., 2020: Magmatic and sedimentary structure beneath the Klyuchevskoy Volcanic Group, Kamchatka, from ambient noise tomography. Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/2019JB018900.

  • Mauerberger et al., n.a.: The multifaceted Scandinavian lithosphere imaged by surface waves and ambient noise. In preparation.

Documentation

The documentation to BayHunter contains three chapters, accessible through the navigation menu, and downloadable as PDF.

  • Introduction: Bayes theorem, McMC inversion approach

  • Algorithm: Python framework behind BayHunter, including optimizer and chain modules, saving and plotting options, and Baywatch

  • Tutorial: Setting up and running an inversion, targets and parameters, example inversion using synthetic data (minimalistic working example of the code in the appendix)

Comments and Feedback

BayHunter is ready to use. It is quick and efficient and I am happy with the performance. Still, there are always things that can be improved to make it even faster and more efficient, and user friendlier.

Although we tested the software with a variety of synthetic and real data, each data set is still unique and shows own characteristics. If you observe any unforeseen behavior, please share it with me to wipe out possible problems we haven’t considered.

I am happy to share my experience with you and also if you share your thoughts with me. I am looking forward to your feedback.

Quickstart

Requirements

  • matplotlib

  • numpy

  • pyPdf

  • configobj

  • zmq

  • Cython

Installation

(compatible with Python 2 and 3)

git clone https://github.com/jenndrei/BayHunter.git
cd BayHunter
sudo python setup.py install

Resources