Joint stochastic constraint of a large data set from a salt dome

Alan W Roberts,Marion Jegen,Bjørn Heincke,Richard W Hobbs,Michael Goldstein,Max Moorkamp

doi:10.1190/geo2015-0127.1

Alan W Roberts, Marion Jegen + Show 4 more

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https://doi.org/10.1190/geo2015-0127.1

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Abstract

Understanding the uncertainty associated with large joint geophysical surveys, such as 3D seismic, gravity, and magnetotelluric (MT) studies, is a challenge, conceptually and practically. By demonstrating the use of emulators, we have adopted a Monte Carlo forward screening scheme to globally test a prior model space for plausibility. This methodology means that the incorporation of all types of uncertainty is made conceptually straightforward, by designing an appropriate prior model space, upon which the results are dependent, from which to draw candidate models. We have tested the approach on a salt dome target, over which three data sets had been obtained; wide-angle seismic refraction, MT and gravity data. We have considered the data sets together using an empirically measured uncertain physical relationship connecting the three different model parameters: seismic velocity, density, and resistivity, and we have indicated the value of a joint approach, rather than considering individual parameter models. The results were probability density functions over the model parameters, together with a halite probability map. The emulators give a considerable speed advantage over running the full simulator codes, and we consider their use to have great potential in the development of geophysical statistical constraint methods.

Highlights

To map a region of earth, it is commonplace to use one or more kinds of data sets to constrain structural models parameterized by one or more proxy parameters, such as seismic velocity, density, or resistivity
In the study detailed here, we adopt a multistage approach (Vernon et al, 2009) of seeking to describe the global behavior and as the implausible model space is excluded, to describe increasingly localized behavior as we develop more predictively accurate emulators
We discern the distribution of jointly plausible models with respect to each of the seismic, gravity, and MT data sets, given all of the uncertainties we wish to specify, by generating candidate joint models drawn from a prior model space

Summary

Introduction

To map a region of earth, it is commonplace to use one or more kinds of data sets to constrain structural models parameterized by one or more proxy parameters, such as seismic velocity, density, or resistivity. Deterministic inverse schemes are optimal when the uncertainties in the data and physical system are small, and the aim is to find the optimum model as fast as possible. In many scenarios, there are considerable uncertainties associated with the data and physics concerned In this case, statistical schemes may be adopted. In these methods, the aim is normally to discern the entire plausible model space for the system concerned. The aim is normally to discern the entire plausible model space for the system concerned The character of such statistical schemes varies from the entirely forward-based screening method (Press, 1970), to the more targeted sampling strategy of the MCMC approach (Hastings, 1970; Sambridge and Mosegaard, 2002).

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