Bayesian estimation and reconstruction of marine surface contaminant dispersion
Bayesian estimation and reconstruction of marine surface contaminant dispersion
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DOI：
10.1016/j.scitotenv.2023.167973
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发表时间：
2023-11-07
影响因子：
9.8
通讯作者：
Liu，Cunjia
中科院分区：
文献类型：
--
作者：
Liu，Yang;Harvey，Christopher M.;Liu，Cunjia
向海洋环境排放有害物质对公众健康和生态系统构成重大风险。在此类事件中，必须准确估计源的释放强度并根据收集的测量数据重建物质的时空扩散。在本研究中，我们提出了一个综合估计框架来应对这一挑战，该框架可以与传感器网络或移动传感器结合使用以进行环境监测。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色散。使用动态瞬态有限元法 (FEM) 将偏微分方程模型在空间上离散为线性状态空间模型，以便将时变色散的表征转化为从传感器测量推断模型状态的问题。我们还考虑了使用传感器网络时经常遇到的不完美传感现象，包括误检测和信号量化。这种复杂的传感器过程将非线性引入贝叶斯估计过程。 Rao-Blackwellized 粒子滤波器 (RBPF) 旨在通过利用状态空间模型的线性结构提供有效的解决方案，而测量模型的非线性可以通过粒子的蒙特卡洛近似来处理。使用波罗的海的模拟漏油事件和真实的洋流数据对所提出的框架进行了验证。结果表明，在存在不完美测量的情况下，所开发的时空色散模型和估计方案的有效性。此外，还讨论了参数选择过程，并进行了一些比较研究，以说明所提出的算法相对于现有方法的优点。
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.