AnnRG - An artificial neural network solute geothermometer

Abstract

Solute artificial neural network geothermometers offer the possibility to overcome the complexity given by the solute-mineral composition. Herein, we present a new concept, trained from high-quality hydrochemical data and verified by in-situ temperature measurements with a total of 208 data pairs of geochemical input parameters (Na+, K+, Ca2+, Mg2+, Cl−, SiO2, and pH) and reservoir temperature measurements being compiled. The data comprises nine geothermal sites with a broad variety of geochemical characteristics and enthalpies. Five sites with 163 samples (Upper Rhine Graben, Pannonian Basin, German Molasse Basin, Paris Basin, and Iceland) are used to develop the ANN geothermometer, while further four sites with 45 samples (Azores, El Tatio, Miavalles, and Rotorua) are used to encounter the established artificial neural network in practice to unknown data. The setup of the application, as well as the optimisation of the network architecture and its hyperparameters, are stepwise introduced. As a result, the solute ANN geothermometer, AnnRG (Artificial neural network Regression Geothermometer), provides precise reservoir temperature predictions (RMSE of 10.442 K) with a high prediction accuracy of R2 = 0.978. In conclusion, the implementation and verification of the first adequate ANN geothermometer is an advancement in solute geothermometry. Our approach is also a basis for further broadening and refining applications in geochemistry.

Keywords

• AnnRG
• Machine learning
• Artificial neural network
• Solute geothermometry
• Geochemical exploration
• Reservoir temperature prediction