Geothermal fluid source discrimination method based on geochemical index and machine learning fusion

By integrating geochemical indicators with machine learning to identify the source of geothermal fluids, an end-member feature library is constructed. By utilizing attention mechanisms and physical constraints, high-precision quantitative identification and dynamic prediction of geothermal fluid sources are achieved. This solves the problems of insufficient multi-factor coupling analysis and insufficient dynamic prediction in traditional methods, and supports the refined management of geothermal resource exploration.

CN121743985APending Publication Date: 2026-03-27CHINA UNIV OF GEOSCIENCES (BEIJING)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing technologies for identifying the source of geothermal fluids suffer from insufficient ability to analyze multiple factors coupled together, ambiguity in the interpretation of isotope indices, and a lack of dynamic evolution prediction capabilities, making it difficult to meet the complex needs of geothermal resource exploration and development.

Method used

A geothermal fluid source identification method based on the fusion of geochemical indicators and machine learning is adopted. Through the acquisition and feature construction of multi-source geochemical data, an endmember feature library is constructed using unsupervised clustering. A machine learning model with an attention mechanism is trained, and quantitative inversion of mixed proportions is carried out in combination with physical constraints. Finally, a time series prediction model is constructed to achieve dynamic evolution trend prediction.

Benefits of technology

It has achieved high-precision quantitative identification and dynamic prediction of geothermal fluid sources, solved the problem of multi-factor nonlinear coupling, eliminated ambiguity in isotope interpretation, and provided a basis for refined management decisions throughout the entire life cycle of geothermal fields.

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Abstract

The invention discloses a geothermal fluid source discrimination method based on geochemical index and machine learning fusion, and relates to the technical field of geothermal resource intelligent exploration, and the method comprises the steps: firstly collecting and calculating multi-dimensional geochemical indexes, and constructing a standardized feature sequence; establishing an end member feature library by using unsupervised clustering; identifying the source type of the fluid through a discrimination model fused with an attention mechanism; aiming at the mixed source fluid, constructing an optimization model embedded with physical and chemical constraints, and quantitatively inverting the contribution proportion of each end member; and finally, on the basis of the time sequence proportion data obtained by inversion, predicting a future evolution trend by adopting a time convolutional network-long and short-term memory network model fused with an attention mechanism. According to the geothermal fluid source discrimination method based on geochemical index and machine learning fusion provided by the invention, the whole-process intelligent analysis of the geothermal fluid source from qualitative identification to quantitative prediction is realized.
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