Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/77341
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Type: Conference paper
Title: Application of evolutionary methods to 3D geoscience modelling
Author: Alexander, B.
Peacock, J.
Thiel, S.
Citation: Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, held in Philadelphia, Pennsylvania, 7-11 July, 2012 / T. Soule (ed.): pp.1039-1046
Publisher: ACM
Publisher Place: New York
Issue Date: 2012
ISBN: 9781450311779
Conference Name: Genetic and Evolutionary Computation Conference (14th : 2012 : Philadelphia, Pennsylvania)
Statement of
Responsibility: 
Brad Alexander, Jared Peacock and Stephan Thiel
Abstract: Geoscience modelling plays a vital role in mapping and tracking Earth’s resources. Magnetotellurics, which maps the electrical resistivity of the subsurface, is a useful and costeffective technique for sensing large areas at depth. However, due to the inherent difficulty in sensing deep strata, models produced using MT have a degree of uncertainty. Geoscientists can reduce this uncertainty by producing multiple alternative models, and using multiple modelling techniques and settings, to correlate robust model features with field data responses. Population-based evolutionary search techniques are of interest to MT modelling because they offer an alternative to deterministic techniques, and are able to produce multiple models for analysis. Unfortunately, evolutionary techniques have not been successfully applied to 3D MT modelling. In this work we describe a new, more compact, representation of MT models using volumetric functions. Using this representation we successfully apply evolutionary search techniques to 3D MT modelling for both artificial and real models and show how the development of large scale features during modelling can be correlated with the model’s fit to field data.
Keywords: Modelling, geoscience; evolutionary search; magnetotelurics
Rights: Copyright 2012 ACM
RMID: 0020121662
DOI: 10.1145/2330163.2330308
Appears in Collections:Computer Science publications

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