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Efficient Greenfield Mineral Exploration.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficient Greenfield Mineral Exploration./
作者:	Hall, Tyler.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	123 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Geology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30941224
ISBN:	9798380864572

Efficient Greenfield Mineral Exploration.
Hall, Tyler.

Efficient Greenfield Mineral Exploration. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 123 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

A growing population will require more metal in order to sustainably build a high quality of life. However, there have been fewer discoveries of mineral deposits due to constrained exploration budgets and confounding geology. There are few practical tools for decision-support in greenfield mineral exploration. This dissertation focuses on developing and testing methods for increasing the efficiency of mineral exploration, with a focus on optimal planning of boreholes. The global context of mineral supply and an overview for mineral exploration is provided in Chapter 1. The necessary elements for modelling decision-making problems are defined and formulations are explained in the context of mineral exploration. Reward functions such as those based on hypothesis falsification or efficacy of information are demonstrated on illustrative cases. High dimensional optimization problems, such as those in the subsurface, are oftentimes intractable. To address this issue, approximations are introduced and demonstrated. Chapter 3 presents a decision-making problem on a real case in Western Australia, formulated as a partially-observable Markov decision process (POMDP) and solved using Monte Carlo tree search (MCTS) with a belief-based reward function. Belief-based rewards are demonstrated to be more performant than the state-of-the-art solvers, and a sensitivity analysis of performance to the belief-based reward is performed. Furthermore, a sensitivity analysis of the optimal plan to a decision-maker's input preference demonstrates how the optimal first decision changes based on a volume threshold. Chapter 4 formulates greenfield exploration drilling using sequential value-of-information, and shows that the selection of a first borehole changes if the decision-maker directly encodes the option of continuing to a second borehole. Discussion of future research directions conclude the dissertation.

ISBN: 9798380864572Subjects--Topical Terms:

516570
Geology.
Subjects--Index Terms:

Metal

Efficient Greenfield Mineral Exploration.
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245 1 0 $a Efficient Greenfield Mineral Exploration.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 123 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Kochenderfer, Mykel;Mukerji, Tapan.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
520 $a A growing population will require more metal in order to sustainably build a high quality of life. However, there have been fewer discoveries of mineral deposits due to constrained exploration budgets and confounding geology. There are few practical tools for decision-support in greenfield mineral exploration. This dissertation focuses on developing and testing methods for increasing the efficiency of mineral exploration, with a focus on optimal planning of boreholes. The global context of mineral supply and an overview for mineral exploration is provided in Chapter 1. The necessary elements for modelling decision-making problems are defined and formulations are explained in the context of mineral exploration. Reward functions such as those based on hypothesis falsification or efficacy of information are demonstrated on illustrative cases. High dimensional optimization problems, such as those in the subsurface, are oftentimes intractable. To address this issue, approximations are introduced and demonstrated. Chapter 3 presents a decision-making problem on a real case in Western Australia, formulated as a partially-observable Markov decision process (POMDP) and solved using Monte Carlo tree search (MCTS) with a belief-based reward function. Belief-based rewards are demonstrated to be more performant than the state-of-the-art solvers, and a sensitivity analysis of performance to the belief-based reward is performed. Furthermore, a sensitivity analysis of the optimal plan to a decision-maker's input preference demonstrates how the optimal first decision changes based on a volume threshold. Chapter 4 formulates greenfield exploration drilling using sequential value-of-information, and shows that the selection of a first borehole changes if the decision-maker directly encodes the option of continuing to a second borehole. Discussion of future research directions conclude the dissertation.
590 $a School code: 0212.
650 4 $a Geology. $3 516570
650 4 $a Sedimentary geology. $3 3173828
650 4 $a Mineralogy. $3 516743
653 $a Metal
653 $a Sustainably build
653 $a Mineral deposits
653 $a Mineral exploration
653 $a Drilling
690 $a 0372
690 $a 0411
690 $a 0594
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0212
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30941224