Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
This work addresses cost minimization in data acquisition for resource exploration, though it appears incremental as it builds on existing optimization and inversion methods.
The paper tackles the problem of efficiently combining multiple sensor types for mineral and energy resource exploration by proposing a probabilistic framework for multi-objective optimization and inverse problems, demonstrating advantages in recommending new drill-core placements using 2D gravity and magnetic sensor data.
A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost. We propose a probabilistic framework for multi-objective optimisation and inverse problems given an expensive cost function for allocating new measurements. This new method is devised to jointly solve multi-linear 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 optimisation strategies are tested and evaluated on a set of synthetic and real geophysical data. We demonstrate 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 multi-sensor positioning.