{"type":"ScholarlyArticle","title":"Bayesian Joint Inversions for the Exploration of Earth Resources","authors":[{"name":"Alistair Reid","slug":"alistair-reid","given":"Alistair","family":"Reid","affiliation":""},{"name":"Simon Timothy O'Callaghan","slug":"simon-timothy-o-apos-callaghan","given":"Simon Timothy","family":"O'Callaghan","affiliation":""},{"name":"Edwin V. Bonilla","slug":"edwin-v-bonilla","given":"Edwin V.","family":"Bonilla","affiliation":""},{"name":"Lachlan McCalman","slug":"lachlan-mccalman","given":"Lachlan","family":"McCalman","affiliation":""},{"name":"Tim Rawling","slug":"tim-rawling","given":"Tim","family":"Rawling","affiliation":""},{"name":"Fabio Ramos","slug":"fabio-ramos","given":"Fabio","family":"Ramos","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/423","pdf":"https://ijcai.org/proceedings/2013/0423.pdf","markdown":"https://ijcai.org/proceedings/2013/423.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/423","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":423,"pages":{"first":2877,"last":2883},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}