一种基于稀疏正则化的地下水点污染源识别法

杨方浩; 吕中荣; 汪利

doi:10.13471/j.cnki.acta.snus.2019.07.12.2019B082

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一种基于稀疏正则化的地下水点污染源识别法

研究论文 | 更新时间：2023-11-01

- 一种基于稀疏正则化的地下水点污染源识别法
- An identification method for groundwater point pollution source identification based on sparse regularization
- 中山大学学报(自然科学版) 2020年59卷第5期页码：40-48
- 作者机构：
  
  中山大学航空航天学院，广东广州 510006
- 作者简介：
  
  杨方浩（1995年生），男；研究方向：污染源识别；E-mail: yangfh6@mail2.sysu.edu.cn
  汪利（1988年生），男；研究方向：力学反问题；E-mail: wangli75@mail.sysu.edu.cn
- 基金信息：
  
  国家自然科学基金(11972380)
- DOI：10.13471/j.cnki.acta.snus.2019.07.12.2019B082
  中图分类号： TV138
- 纸质出版日期：2020-09-25，
  
  收稿日期：2019-07-12，
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杨方浩,吕中荣,汪利.一种基于稀疏正则化的地下水点污染源识别法[J].中山大学学报(自然科学版),2020,59(05):40-48.

YANG Fanghao,LV Zhongrong,WANG Li.An identification method for groundwater point pollution source identification based on sparse regularization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):40-48.
杨方浩,吕中荣,汪利.一种基于稀疏正则化的地下水点污染源识别法[J].中山大学学报(自然科学版),2020,59(05):40-48. DOI： 10.13471/j.cnki.acta.snus.2019.07.12.2019B082.

YANG Fanghao,LV Zhongrong,WANG Li.An identification method for groundwater point pollution source identification based on sparse regularization[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):40-48. DOI： 10.13471/j.cnki.acta.snus.2019.07.12.2019B082.

摘要

提出了一种基于稀疏正则化的方法识别地下水点污染源。首先，对地下水一维对流‒弥散方程时域有限元格式进行拉普拉斯变换得到频域方程，然后建立以

范数项为约束的地下水点污染源识别问题的目标函数，从而克服空间分布稀疏的点污染源识别问题的不适定性；接着，利用交替优化法进行迭代求解。研究结果表明，所提方法能在噪声条件下有效识别地下水点污染源的位置和强度变化。

Abstract

This paper proposes a method based on sparse regularization to identify groundwater point pollution sources. Firstly

the time domain finite element discretized equation of groundwater one-dimensional convection-diffusion equation is used to obtain the frequency domain equation by Laplace transform

and then the objective function of the groundwater point pollution source identification problem constrained by the

norm term is established

thus overcoming the ill-posed problem of the point source identification due to sparse spatial distribution. The identification equation is then solved iteratively using the alternating optimization method. The research results show that the proposed method can effectively identify the position and intensity changes of groundwater pollution sources under noise conditions.

关键词

对流-弥散稀疏正则化污染源识别交替优化法正则化参数

Keywords

convection-dispersionsparse regularizationpollution source identificationalternating optimization methodregularization parameter

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