Using mixing model to interpret the water sources and ratios in an under-sea mine
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Using mixing model to interpret the water sources and ratios in an under‑sea mine Hongyu Gu1 · Huayong Ni4 · Fengshan Ma3 · Gang Liu5 · Xin Hui6,7 · Jiayuan Cao2,3 Received: 21 November 2018 / Accepted: 20 August 2020 © Springer Nature B.V. 2020
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, S O4, 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
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1106 9-020-04242-y) contains supplementary material, which is available to authorized users. * Hongyu Gu [email protected] Extended author information available on the last page of the article
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Natural Hazards
1 Introduction Water inrush is a major threat to production and safety of life in the mining area, especially in a coastal mine. Prediction of this kind of hazard is difficult, especially in terms of identification of water sources and computation of proportion, though specialists have carried out this complex work for so many years. Approaches, such as environmental isotopes, electrical prospecting, transient electromagnetic method, and pumping test, have made significant contributions to predict this kind of hidden hazards (Zhidan and Baotang 1994; Ogilvy and Bogoslovsky 2010; Qin et al. 2012; Mollidor et al. 2013; Xue et al. 2013; Yeh et al. 2014; Wu et al. 2019). However, the weakness of these approaches is obvious: high cost, local
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