In this study, a brief explanation is first given on solute Na/K geothermometers developed until now, and a new Na/K geothermometer model is presented after using world geothermal database (n=212) to the ANN as a training set and another database (n=112) as a test set. In this model Na and K values are treated as input values and geothermometer temperatures as output values. A multilayer feed-forward neural network is trained using a genetic algorithm for optimizing hidden layer neuron weights and linear regression for optimizing output neuron weights. The model is successfully evaluated and compared with actual deep temperature measurements to avoid training bias.

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