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Title: GEOPHYSICS AND MACHINE LEARNING BASED SURVEYING OF GROUNDWATER POLLUTION: A CASE STUDY OF SOUTHERN ACID TAR LAGOON, INCUKALNS, LATVIA
GEOPHYSICS AND MACHINE LEARNING BASED SURVEYING OF GROUNDWATER POLLUTION: A CASE STUDY OF SOUTHERN ACID TAR LAGOON, INCUKALNS, LATVIA

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V. Zandersons; J. Jeskins; J. Karuss; K. Lamsters; D. Porsnovs
10.5593/sgem2021V/3.2/s12.14
1314-2704
978-619-7603-32-3
English
21
3.2
• Prof. DSc. Oleksandr Trofymchuk, UKRAINE
• Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Hydrology and Water Resources
Acid tar lagoons (ATLs) are a common source of groundwater pollution in industrialized countries worldwide. Even after recultivation, the spatial extent of the residual environmental pollution from such lagoons is often unknown, as the monitoring of groundwater is mostly done in discrete locations ? monitoring wells. Shallow surface geophysical methods, such as ground penetrating radar (GPR) and electrical resistivity tomography (ERT) are often used in monitoring groundwater and soil contamination. The greatest value from shallow surface exploration can be received by coupling multiple geophysical methods, which can be a challenge, considering the different geophysical properties measured. We propose the use of machine learning techniques to identify the different subsurface contamination zones. In this study we combine two-dimensional electrical resistivity measurements with GPR amplitude measurements using simple k-means clustering to map out residual soil contamination zones near the acid tar lagoon of Incukalns, Latvia. We then compare the clustering results with more conventional quasi-Newton two-dimensional ERT inversion results of the same site. Information obtained from clustering shows promise in future geophysical exploration, as comparable results can be obtained with fewer assumptions of subsurface geology. Our study demonstrates the potential of clustering methods to integrate different shallow-surface geophysical exploration methods and points out to possible future improvements in machine-learning based soil contamination mapping.
conference
https://www.sgem.org/index.php/jresearch-article?citekey=Zandersons202112115122
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21st International Multidisciplinary Scientific GeoConference SGEM 2021
21st International Multidisciplinary Scientific GeoConference SGEM 2021, 7 - 10 December, 2021
Proceedings Paper
STEF92 Technology
SGEM International Multidisciplinary Scientific GeoConference
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci
89-96
7 - 10 December, 2021
website
cdrom
8311
groundwater and soil pollution; ground penetrating radar; electrical resistivity tomography; unsupervised machine learning; clusterin