Using mixing model to interpret the water sources and ratios in an under-sea mine

Hongyu Gu (), Huayong Ni, Fengshan Ma, Gang Liu, Xin Hui and Jiayuan Cao
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Hongyu Gu: Chengdu Center, China Geological Survey
Huayong Ni: Nanjing Center, China Geological Survey
Fengshan Ma: Institute of Geology and Geophysics, Chinese Academy of Sciences
Gang Liu: Xian Center, China Geological Survey
Xin Hui: Beijing Jingtou Urban Utility Tunnel Investment Co., Ltd
Jiayuan Cao: China Merchants Chongqing Communications Research and Design Institute Co., Ltd

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 104, issue 2, No 27, 1705-1722

Abstract: Abstract Identification of water sources is a key issue of water inrush. This study applied a mixing model based on hydrochemical data to identify water sources and proportions. This study highlighted (1) the importance of model scale and reaction evaluation before using the mixing model, (2) a newly proposed criterion based on eigenvalue analysis to identify the number of end-members, and (3) linear mixing model based on PCA (principal component analysis). 2.5 km2 area was an appropriate scale to mixing model because tectonics and lithology were simple. Ion activity, ion exchange, and cycle time of water were evaluated, indicating that groundwater components were dominated by the mixing process. Tracers, such as K, Na, Ca, Mg, Cl, SO4, δ18O, δD, EC, TH, and TDS, were used as tracers in the mixing model. Five end-members (representing seawater, Quaternary water, freshwater, Ca-rich water, and Mg-rich water) were identified based on eigenvalue analysis and hydrochemical evolution analysis. A linear mixing algorithm was programmed using Matlab to compute the ratio of each end-member. The results showed that seawater was the dominated water sources (70% at most) threatening the mining operations, especially at the deep levels. Quaternary water mainly recharged the middle level and made up 50% at − 420 m level. Freshwater recharged the shallow level and made up to 40% at − 150 m level. Ca-rich water and Mg-rich water decreased with time. Finally, cross test and extension test of this method showed a high precision in reconstructing ion concentrations, low sensitivity to noise data, and highly extendible to future data.

Keywords: Water inrush; Water sources; Proportion; Mixing model; Hydrochemistry; PCA (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11069-020-04242-y

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