Preprints
https://doi.org/10.5194/gmd-2022-166
https://doi.org/10.5194/gmd-2022-166
Submitted as: model description paper
08 Aug 2022
Submitted as: model description paper | 08 Aug 2022

Intelligent prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

John Mern1 and Jef Caers2 John Mern and Jef Caers
  • 1Kobold Metals, USA
  • 2Stanford University, Department of Geological Sciences, USA

Abstract. Geoscientific models are based on geoscientific data, hence building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory questions of what additional data is expected to best improve predictions/decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, possibly a large sequence of data acquisition campaigns need to be planned. Geoscientific data acquisition can be expensive and time consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements, however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a Partially Observable Markov Decision Process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

Journal article(s) based on this preprint

John Mern and Jef Caers

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-166', Anonymous Referee #1, 03 Oct 2022
  • RC2: 'Comment on gmd-2022-166', Anonymous Referee #2, 09 Oct 2022
  • AC1: 'Author Reply Letter', Jef Caers, 17 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jef Caers on behalf of the Authors (11 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Nov 2022) by Thomas Poulet
RR by Anonymous Referee #1 (13 Nov 2022)
RR by Anonymous Referee #2 (29 Nov 2022)
ED: Publish subject to minor revisions (review by editor) (01 Dec 2022) by Thomas Poulet
AR by Jef Caers on behalf of the Authors (02 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Dec 2022) by Thomas Poulet

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-166', Anonymous Referee #1, 03 Oct 2022
  • RC2: 'Comment on gmd-2022-166', Anonymous Referee #2, 09 Oct 2022
  • AC1: 'Author Reply Letter', Jef Caers, 17 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jef Caers on behalf of the Authors (11 Nov 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (12 Nov 2022) by Thomas Poulet
RR by Anonymous Referee #1 (13 Nov 2022)
RR by Anonymous Referee #2 (29 Nov 2022)
ED: Publish subject to minor revisions (review by editor) (01 Dec 2022) by Thomas Poulet
AR by Jef Caers on behalf of the Authors (02 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Dec 2022) by Thomas Poulet

Journal article(s) based on this preprint

John Mern and Jef Caers

John Mern and Jef Caers

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
In this work, we formulate sequential geoscientific data acquisition problem as problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI, and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.