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

• Published in: Natural hazards (Dordrecht) Vol. 104; no. 2; pp. 1705 - 1722
• Main Authors: Gu, Hongyu, Ni, Huayong, Ma, Fengshan, Liu, Gang, Hui, Xin, Cao, Jiayuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2020
• Subjects: Civil Engineering; Earth and Environmental Science; Earth Sciences; Environmental Management; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Natural Hazards; Original Paper; Water sources; Mixing model; Hydrochemistry; Proportion; Water inrush; PCA
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-020-04242-y

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 km 2 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, SO 4 , δ 18 O, 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.