Bayesian Joint Inversions for the Exploration of Earth Resources

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

Bayesian Joint Inversions for the Exploration of Earth Resources

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

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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.

Alistair Reid, Simon Timothy O'Callaghan, Edwin V. Bonilla, Lachlan McCalman, Tim Rawling, and Fabio Ramos. 2013. Bayesian Joint Inversions for the Exploration of Earth Resources. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2877–2883. https://ijcai.org/proceedings/2013/423

@inproceedings{ijcai2013p423,
  title     = {Bayesian Joint Inversions for the Exploration of Earth Resources},
  author    = {Reid, Alistair and O&apos;Callaghan, Simon Timothy and Bonilla, Edwin V. and McCalman, Lachlan and Rawling, Tim and Ramos, Fabio},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-13}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Francesca Rossi},
  pages     = {2877--2883},
  year      = {2013},
  month     = {8},
  url       = {https://ijcai.org/proceedings/2013/423},
}

TY  - CPAPER
TI  - Bayesian Joint Inversions for the Exploration of Earth Resources
AU  - Reid, Alistair
AU  - O&apos;Callaghan, Simon Timothy
AU  - Bonilla, Edwin V.
AU  - McCalman, Lachlan
AU  - Rawling, Tim
AU  - Ramos, Fabio
T2  - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2013
SP  - 2877
EP  - 2883
UR  - https://ijcai.org/proceedings/2013/423
ER  -