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Self-Organizing Endmember Mixing Analysis (SEMMA): High-Resolution Apportionment of Mixed-Recharge Water Inrush Sources
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| Created: | Jul 06, 2026 at 7:59 a.m. (UTC) | |
| Last updated: | Jul 06, 2026 at 8:15 a.m. (UTC) | |
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| Sharing Status: | Public |
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Abstract
The accurate identification and tracking of water inrush sources have become essential prerequisites for developing water hazard mitigation strategies. However, deep hydrogeological systems exhibit cryptic fluid responses. Qualitative hydrogeological assessments are inherently limited in their capacity to resolve multi-aquifer fluid mixing processes. Widely adopted end-member mixing analysis (EMMA) approaches face constraints, include reliance on principal component analysis (PCA) for linear dimensionality reduction and subjective end member selection, incomplete tracer system representation, and oversimplified mixing models collectively amplify analytical uncertainty., leads to irreversible loss of critical topological information; To overcome these limitations, this study proposes a self-organizing map enhanced end member mixing analysis framework (SEMMA). The SEMMA model combines self-organizing maps (SOM) with hydrogeological analysis to classify and qualitatively interpret nonlinear hydrochemical data. Crucially, it overcomes EMMA’s topological limitations and enables high-resolution quantitative source apportionment of water inflows using common ions: K⁺, Ca²⁺, Na⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, SiO₂, NO₃⁻, F⁻. This framework provides a theoretically rigorous and practically applicable paradigm for revealing deep groundwater circulation paths in complex tectonic settings, and for supporting fine-scale water resource management and proactive hazard control in highly heterogeneous aquifer systems.
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