Self-Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES Water Resources Research Pub Date : 2024-10-27 DOI:10.1029/2024wr037549
Scott J. Ikard, Kenneth C. Carroll, Scott C. Brooks, Dale F. Rucker, Gladisol Smith-Vega, Aubrey Elwes
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Abstract

A self-potential (SP) data-inversion algorithm was developed and tested on an analytical model of electrical-potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP-monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock-lined riverbed. The algorithm combined stochastic source-localization by particle-swarm-optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least-squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical-model data to evaluate the algorithm performance. Results indicated that true parameters of single-source models were inverted and approximated with small residual error, whereas inversion of analytical-model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non-uniqueness of the inverse-model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point-scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP-monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.
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以粒子群优化为先决条件的自电位层析成像技术--应用于监测基岩河流的透水交换
开发了一种自电势(SP)数据转换算法,并在单极化和多极化电源电势剖面数据的分析模型上进行了测试。然后,将所开发的算法应用于田纳西州橡树岭白杨溪东岔流冲积平原上测量的 SP 监测现场数据,对有利于从基岩衬砌的河床优先流入冲积平原的区域的电源进行成像,从而验证了所开发的算法。该算法通过粒子群优化(PSO)将以简化几何图形为特征的电信号源随机定位与通过正则化加权最小二乘最小化二次目标函数进行的电信号源层析相结合。通过 PSO 结果对断层扫描算法进行预处理,将先验信息纳入其中。在分析模型数据中加入不同比例的随机噪声,以评估算法性能。结果表明,单源模型的真实参数被反演和近似,残余误差很小,而代表多电场源的分析模型数据的反演准确地近似了电场源的位置,但由于反演模型解的非唯一性,某些参数计算错误。在测试过程中,对分析模型数据进行的源断层扫描产生了一个空间连续的参数场,该参数场确定了点尺度合成偶极子电流源的位置,其精确度因断层扫描中包含的先验信息而异。将该算法应用于 SP 监测现场数据时,可在与东岔口白杨溪的基岩河床和冲积平原相交的已知断层内绘制出电流源图像,并描绘出可归因于流体交换的动态电流状况。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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