Bayesian joint inversions for the exploration of earth resources

Authors:
Alistair Reid;Simon O'Callaghan;Edwin V. Bonilla;Lachlan McCalman;Tim Rawling;Fabio Ramos
Affiliations:
NICTA;NICTA;NICTA;NICTA;University of Melbourne;University of Sydney
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 2
Cited 0

Inverse Problem Theory and Methods for Model Parameter Estimation

Inverse Problem Theory and Methods for Model Parameter Estimation
A Unifying View of Sparse Approximate Gaussian Process Regression

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a machine learning approach to geophysical inversion problems for the exploration of earth resources. Our approach is based on nonparametric Bayesian methods, specifically, Gaussian processes, and provides a full distribution over the predicted geophysical properties whilst enabling the incorporation of data from different modalities. We assess our method both qualitatively and quantitatively using a real dataset from South Australia containing gravity and drill-hole data and through simulated experiments involving gravity, drill-holes and magnetics, with the goal of characterizing rock densities. The significance of our probabilistic inversion extends to general exploration problems with potential to dramatically benefit the industry.