School of GeoSciences

School of GeoSciences


3D Monte Carlo tomography using both body and surface wave data

This code implements a fully 3D Monte Carlo Tomography method using both surface and body wave data. It employs Voronoi tessellation to parameterize the subsurface, and the reversible jump Markov chain Monte Carlo method to sample the parameter space. It supports 3D travel time tomography using body wave arrival (or travel) times, surface wave frequency-dependent travel times, as well as joint inversion using both body and surface wave data. The package can be accessed HERE.

Non-linear transdimensional electrical resistivity tomography

These Fortran 90 codes can be used to perform non-linear transdimensional electrical resistivity tomography with the reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm. The codes combine the rj-McMC algorithm (Bodin & Sambridge (2009)) with a 2.5D forward solver (Pidlisecky & Knight (2008)) to make the inversion fully non-linear, and make use of parallel tempering (Sambridge, 2014) to accelerate convergence. A user guide and examples are provided within the package, HERE.

Non-linear transdimensional travel-time tomography using reversible-jump Markov chain Monte Carlo

This Fortran 90 code, available HERE, can be used to perform non-linear transdimensional seismic traveltime tomography with the reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm. The code combines the rj-McMC algorithm (Bodin & Sambridge (2009), Bodin et al. (2012)) with a Fast Marching eikonal raytracer (Rawlinson et al. (2004)) to make the inversion fully non-linear. A user guide and a synthetic example are provided within the package.

Relative source location using coda wave interferometry

The matlab code package for coda wave interferometry can be found HERE called CWIcluster. A user manual accompanies the coda wave interferometry relative source location MATLAB code package (CWIcluster). The code estimates the relative location of a cluster of events of similar source mechanisms using inter-source separations estimated with CWI (Snieder, 2006). The advantage of this location technique is that the location result is insensitive to the number and distribution of seismic stations. The method is particularly useful where there are not a large number of seismic stations with a good event-sensor azimuthal coverage so that the performance of the conventional double-difference relative source location method of Waldhauser & Ellsworth (2000) deteriorates. It also provides an alternative, entirely independent method with which conventional methods can be compared in order to assess relative location uncertainties.

This manual provides a brief introduction of CWI and the location algorithm. This is sufficient to run the code. More details are given in the accompanying paper. The package consists of three sections, each of which contains codes that conduct one step of the location method: 1) classifying events into different clusters with given waveforms recorded by one or multiple seismic station channels, 2) estimating inter-source separations with the CWI method, and 3) estimating the relative event locations from the separation data. The code solves for the event locations as an optimization problem, where the most likely set of event locations are found where an objective function attains its minimum. This algorithm takes account of the known biases of CWI-estimated source separations, and is able to correct for them to a significant extent in location results. Examples on synthetic and real data are presented in this manual to demonstrate how to use the codes, and test-data is included in the package, HERE.

Simple and exact modelling of scattered wavefields using the Foldy method

A MATLAB code which implements the method of Foldy has been developed in Edinburgh by Erica Galetti and David Halliday (a previous version was developed by Dirk-Jan van Manen), and is made available HERE. In 1945, Foldy published a method to model multiply-scattered wavefields exactly, including all orders of inter-scatterer interactions. A manual and several examples applying the method to interferometric methods are included within that zip file.

The codes use analytical Green’s function formulae to compute the impulse response of a medium of constant background velocity with embedded scatterers. The analytical nature of the solution makes it an  ideal tool to test interferometric theoretical results as it does not suffer from the limitations associated with finite difference codes (dispersion, spurious reflections from absorbing boundaries etc.).