Abstract
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 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.
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1 articles.
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