Title: Bayesian estimation and reconstruction of marine surface contaminant dispersion Show Chinese title

Title: 贝叶斯估计和海洋表面污染物扩散的重建

Abstract: Discharge of hazardous substances into the marine environment poses a substantial risk to both public health and the ecosystem. In such incidents, it is imperative to accurately estimate the release strength of the source and reconstruct the spatio-temporal dispersion of the substances based on the collected measurements. In this study, we propose an integrated estimation framework to tackle this challenge, which can be used in conjunction with a sensor network or a mobile sensor for environment monitoring. We employ the fundamental convection-diffusion partial differential equation (PDE) to represent the general dispersion of a physical quantity in a non-uniform flow field. The PDE model is spatially discretised into a linear state-space model using the dynamic transient finite-element method (FEM) so that the characterisation of time-varying dispersion can be cast into the problem of inferring the model states from sensor measurements. We also consider imperfect sensing phenomena, including miss-detection and signal quantisation, which are frequently encountered when using a sensor network. This complicated sensor process introduces nonlinearity into the Bayesian estimation process. A Rao-Blackwellised particle filter (RBPF) is designed to provide an effective solution by exploiting the linear structure of the state-space model, whereas the nonlinearity of the measurement model can be handled by Monte Carlo approximation with particles. The proposed framework is validated using a simulated oil spill incident in the Baltic sea with real ocean flow data. The results show the efficacy of the developed spatio-temporal dispersion model and estimation schemes in the presence of imperfect measurements. Moreover, the parameter selection process is discussed, along with some comparison studies to illustrate the advantages of the proposed algorithm over existing methods. Show Chinese abstract

Abstract: 有害物质排入海洋环境对公共健康和生态系统构成重大风险。 在这些事件中，准确估计污染源的释放强度，并根据收集到的测量数据重建物质的时空扩散情况是至关重要的。 在本研究中，我们提出了一种综合估计框架来解决这一挑战，该框架可以与传感器网络或移动传感器结合用于环境监测。 我们采用基本的对流-扩散偏微分方程（PDE）来表示非均匀流场中物理量的一般扩散。 该PDE模型通过动态瞬态有限元法（FEM）在空间上离散化为线性状态空间模型，从而使时变扩散的表征转化为从传感器测量中推断模型状态的问题。 我们还考虑了不完善的传感现象，包括误检和信号量化，这些现象在使用传感器网络时经常遇到。 这种复杂的传感过程会将非线性引入贝叶斯估计过程中。 设计了一个Rao-Blackwellised粒子滤波器（RBPF），通过利用状态空间模型的线性结构提供一种有效的解决方案，而测量模型的非线性则可以通过粒子的蒙特卡洛近似来处理。 所提出的框架通过使用波罗的海的真实海洋流数据进行模拟的石油泄漏事件进行了验证。 结果表明，在存在不完善测量的情况下，所开发的时空扩散模型和估计方案是有效的。 此外，还讨论了参数选择过程，并进行了一些比较研究，以说明所提出算法相对于现有方法的优势。