论文标题
多目标贝叶斯优化和主动传感器融合的联合反转
Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
论文作者
论文摘要
矿产和能源资源探索数据获取的关键决策过程是如何有效地结合各种传感器类型并最大程度地降低总成本。我们为多目标优化和反问题提出了一个概率框架，鉴于分配新测量的昂贵成本功能。这种新方法设计用于使用稀疏的高斯工艺内核共同求解2D传感器数据和3D地球物理特性的多线性正向模型，同时考虑到不同参数的跨海。对一组合成和真实的地球物理数据进行了多种优化策略的测试和评估。我们证明了关节反问题的特定示例的优势，建议给定2D重力和磁性传感器数据，将新的钻核测量值放置在何处，可以将相同的方法应用于线性向前模型的各种遥感问题 - 从约束限制表面访问以限制数据采集到适应性多发镜的位置。
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.
