Multiobjective Bayesian optimization and joint inversion for active sensor fusion

Abstract

A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and how to minimize the total cost. We have developed a probabilistic framework for multiobjective optimization and inverse problems given an expensive cost function for allocating new measurements. This new method is devised to jointly solve multilinear forward models of 2D sensor data and 3D geophysical properties using sparse Gaussian process kernels while taking into account the cross-variances of different parameters. Multiple optimization strategies are tested and evaluated on a set of synthetic and real geophysical data. We determine the advantages on a specific example of a joint inverse problem, recommending where to place new drill-core measurements given 2D gravity and magnetic sensor data; the same approach can be applied to a variety of remote sensing problems with linear forward models — ranging from constraints limiting surface access for data acquisition to adaptive multisensor positioning.