Discharge of mine tailings significantly impacts the ecological status of the sea. Methods to efficiently monitor the extent of dispersion is essential to protect sensitive areas. By combining underwater robotic sampling with ocean models, we can choose informative sampling sites and adaptively change the robot’s path based on in situ measurements to optimally map the tailings distribution near a seafill. This paper creates a stochastic spatio-temporal proxy model of dispersal dynamics using training data from complex numerical models. The proxy model consists of a spatio-temporal Gaussian process model based on an advection–diffusion stochastic partial differential equation. Informative sampling sites are chosen based on predictions from the proxy model using an objective function favoring areas with high uncertainty and high expected tailings concentrations. A simulation study and data from real-life experiments are presented.

矿山尾矿的排放显着影响海洋生态状况。有效监测扩散范围的方法对于保护敏感区域至关重要。通过将水下机器人采样与海洋模型相结合，我们可以选择信息丰富的采样点，并根据现场测量自适应地改变机器人的路径，以最佳地绘制填海附近的尾矿分布图。本文使用来自复杂数值模型的训练数据创建了扩散动力学的随机时空代理模型。代理模型由基于平流扩散随机偏微分方程的时空高斯过程模型组成。根据代理模型的预测选择信息丰富的采样点，该模型使用有利于具有高不确定性和高预期尾矿浓度的区域的目标函数。介绍了模拟研究和来自真实实验的数据。