Paper
9 December 2015 Monitoring underground water quality based on high-density resistivity method
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Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98081T (2015) https://doi.org/10.1117/12.2209489
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China
Abstract
Underground water is different from surface water. Once contaminated, underground water is difficult to recover, so it is necessary to give priority to the prevention of the quality of underground water. High-density resistivity method is very important in the environmental engineering geophysical prospecting and it is widely used in mineral resources as well as monitoring the underground-water quality. In the experiment, multi-tools joint inversion is applied to build the model in order to increase the accuracy. In contrast with the pollution-free water model which is owned by the RES2DMOD, the inversion result of underground water quality with the high density resistivity method is useful to monitor the underground water quality, showing that different degree of water pollution depends on the position of abnormal and there is a more significant abnormal value in the vertical direction of the deep abnormal than that of the shallow abnormal, and high and low resistance pollution depends on the different value and forms of abnormal resistance. In conclusion, monitoring the underground water quality by the high density resistivity method is efficient. In the future research, it is necessary to accomplish more precise inversion models combining with field measurements to find out the optimal solution to monitor underwater quality.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yanyan Xu "Monitoring underground water quality based on high-density resistivity method", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98081T (9 December 2015); https://doi.org/10.1117/12.2209489
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KEYWORDS
Pollution

Resistance

Electrodes

Data processing

Data modeling

Metals

Water contamination

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