Mineralisation predictive targeting using TensorFlow (Google) deep neural networks

Summary

Simple two-layer feedforward supervised neural network (NN) has been described and used for mineral predictive targeting. However, the simple NN has some limitations. For instance, it requires the geophysical responses of over the target be positively high relative to non-target areas.

The release of Google’s TensorFlow (TF) for Python (https://www.tensorflow.org/) in 2015 has made it possible to apply the more powerful and robust Deep Neural Network (DNN) to geoscience data for mineral predictive targeting.

We test the TF DNN using the magnetic data over a kimberlite in the Canadian Shield and compare the results with those from the simple two-layer NN. The DNN results are better.

DNNs are applied to the helicopter TDEM data from Nuqrah, western Arabian Shield and the TDEM from Kabinakagami Lake greenstone belt in Superior craton in Ontario to illustrate the utility of predictive targeting of DNN..

• Targeting