Application of evolutionary methods to 3D geoscience modelling

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.