Cave Recognition Under Tensor Flow Platform

Abstract

Based on the analysis of Tensor flow’s advantage of depth learning, this paper constructs CNN's network layer and its key parameters to realize the classification and recognition of underground caverns on Tensor flow frame platform by CNN technology. Firstly, the classification of the cave in the Tarim Basin of Xinjiang is carried out, and the classification result is taken as the input of the training set. Secondly, by analyzing the tensor board characteristics, and constantly optimize the learning rate, network layer and other training parameters, and finally get a higher precision model. Finally, the model is used to predict the unknown data. The experimental results show that the method and the process can obtain high model accuracy, and the weight parameters and bias parameters can converge well, have a higher prediction rate for the cave, innovate a new method of cave identification, and widen the tensor flow’s application areas, will greatly advance the depth of learning in the exploration industry application and development.