Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent adaptative MCMC.
Uncertainty quantification and atmospheric source estimation with a discrepancy-based and a state-dependent adaptative MCMC.
复制标题
使用基于差异和状态相关的自适应 MCMC 进行不确定性量化和大气源估计。
DOI：
10.1016/j.envpol.2021.118039
复制
发表时间：
2021
影响因子：
8.9
通讯作者：
A. S. Silva Neto
中科院分区：
文献类型：
--
作者：
R. Albani;V. Albani;H. Migon;A. S. Silva Neto
我们使用贝叶斯推理中的自适应策略，结合稳定有限元法对色散问题的数值解和测量中的不确定性量化来解决大气释放的源表征。自适应技术加快了蒙特卡罗马尔可夫链（MCMC）算法的收敛速度，从而实现了源参数的精确重建。与以往工作结果的比较说明了这种准确性。此外，用于模拟相应色散问题的技术使我们能够引入相关的气象信息。不确定度的量化也提高了重建的质量。使用哥本哈根实验活动数据的数值示例说明了所提出方法的有效性。我们发现重建的误差范围在搜索区域大小的0.11%到8.67%之间，这与之前使用确定性技术的工作结果相似，计算时间相当。
We address the source characterization of atmospheric releases using adaptive strategies in Bayesian inference in combination with the numerical solution of the dispersion problem by a stabilized finite element method and uncertainty quantification in the measurements. The adaptive techniques accelerate the convergence of Monte Carlo Markov Chain (MCMC) algorithms, leading to accurate reconstructions of the source parameters. Such accuracy is illustrated by the comparison with results from previous works. Moreover, the technique used to simulate the corresponding dispersion problem allowed us to introduce relevant meteorological information. The uncertainty quantification also improves the quality of reconstructions. Numerical examples using data from the Copenhagen experimental campaign illustrate the effectiveness of the proposed methodology. We found errors in reconstructions ranging from 0.11% to 8.67% of the size of the search region, which is similar to results found in previous works using deterministic techniques, with comparable computational time.