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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Groundwater remediation planning and decision problem formulated as a partially observable Markov decision process (POMDP). • Comparison between POMDP and the conventional state-of-the-art optimization approaches, such as one-shot optimization and model predictive control (closed loop) optimization. • DESPOT, a state-of-the-art POMDP solver, outperforms optimization solution methods using PSO and hand-crafted heuristic solutions. • Optimizing the trade-off between information gathering and the performance of possible future scenarios via POMDP formulation. • Importance of belief updates, also known as inversion problems or history matching, in groundwater problems. Groundwater contamination causes significant groundwater supply risks that severely affect humans and livestock, especially in the era of climate change. Consequently, the need to develop and optimize remediation strategies such as pump and treat is indispensable. An effective and efficient strategy can reduce the spread of contaminants and cost of clean-up. However, developing such a strategy is challenging due to the inherent uncertainty and sequential nature of the problem. For example, taking actions such as drilling wells is interwoven with information gathering over time. In this paper, we formulate the groundwater remediation planning and decision problem as a partially observable Markov decision process (POMDP). A POMDP formulation allows planning by optimizing the trade-off between information gathering and the performance of possible future scenarios. We use a state-of-the-art POMDP solver called DESPOT for planning remediation strategies. The results show that DESPOT can yield much better remediation strategies than hand crafted heuristics and optimization methods. The superior performance of DESPOT is due to the incorporation of both previous information and future reward into the current decision. We also demonstrate the robustness of the POMDP formulation to measurement error. [ABSTRACT FROM AUTHOR]