---
title: "Bayesian Joint Inversions for the Exploration of Earth Resources"
authors:
  - "Alistair Reid"
  - "Simon Timothy O'Callaghan"
  - "Edwin V. Bonilla"
  - "Lachlan McCalman"
  - "Tim Rawling"
  - "Fabio Ramos"
doi: ""
url: "https://ijcai.org/proceedings/2013/423"
pdf: "https://ijcai.org/proceedings/2013/0423.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "2877-2883"
track: ""
keywords:
---

# Bayesian Joint Inversions for the Exploration of Earth Resources

Alistair Reid, Simon Timothy O'Callaghan, Edwin V. Bonilla, Lachlan McCalman, Tim Rawling, Fabio Ramos

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 2877-2883.

## 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.