Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion

TL;DR

This paper introduces a probabilistic framework combining multi-objective Bayesian optimization and joint inversion to efficiently guide sensor placement in geophysical exploration, reducing costs and improving data integration.

Contribution

It presents a novel method that jointly solves multi-linear inverse problems using Gaussian Process kernels, accounting for cross-parameter variances, and evaluates multiple strategies on synthetic and real data.

Findings

Enhanced sensor placement recommendations for geophysical surveys

Effective joint inversion of 2D sensor data and 3D properties

Applicable to various remote sensing and data acquisition scenarios

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 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.