The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration
<p>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 are expected to best improve predictions and decisions is within th...
Main Authors: | , |
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Format: | Article |
Language: | English |
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Copernicus Publications
2023-01-01
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Series: | Geoscientific Model Development |
Online Access: | https://gmd.copernicus.org/articles/16/289/2023/gmd-16-289-2023.pdf |
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author | J. Mern J. Caers |
author_facet | J. Mern J. Caers |
author_sort | J. Mern |
collection | DOAJ |
description | <p>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 are expected to best improve predictions and 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, a large
sequence of data acquisition campaigns possibly needs 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.</p> |
first_indexed | 2024-04-10T23:41:04Z |
format | Article |
id | doaj.art-0109a2a50f37470e9ceb082ee6a58c46 |
institution | Directory Open Access Journal |
issn | 1991-959X 1991-9603 |
language | English |
last_indexed | 2024-04-10T23:41:04Z |
publishDate | 2023-01-01 |
publisher | Copernicus Publications |
record_format | Article |
series | Geoscientific Model Development |
spelling | doaj.art-0109a2a50f37470e9ceb082ee6a58c462023-01-11T07:44:05ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-01-011628931310.5194/gmd-16-289-2023The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral explorationJ. Mern0J. Caers1Kobold Metals, Berkeley, USADepartment of Earth and Planetary Sciences, Stanford University, Stanford, USA<p>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 are expected to best improve predictions and 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, a large sequence of data acquisition campaigns possibly needs 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.</p>https://gmd.copernicus.org/articles/16/289/2023/gmd-16-289-2023.pdf |
spellingShingle | J. Mern J. Caers The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration Geoscientific Model Development |
title | The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
title_full | The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
title_fullStr | The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
title_full_unstemmed | The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
title_short | The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
title_sort | intelligent prospector v1 0 geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration |
url | https://gmd.copernicus.org/articles/16/289/2023/gmd-16-289-2023.pdf |
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