Recognition and Sensitivity Analysis of Karstic Limestone Dissolution Degree Using Convolutional Neural Network

To reasonably and efficiently recognize dissolution degrees of karstic limestones,a corresponding convolution neural network model (CNN) model was established based on experiments on a limestone quarried from Qixing District,Guilin.In the experiments,the specimens were treated with dry-wet cycles in acidic environments considering different pH values and different numbers of the dry-wet cycles.The effects of pH values and dry-wet cycle numbers on the recognition accuracy of the model were analyzed.How the data sample number and the network-related parameter settings affect the model was also discussed.The results show that the dissolution-induced patterns and pores on specimen surfaces become more obvious and the recognition accuracy becomes higher with a decrease in the pH value and an increase in the dry-wet cycle number.When the number of learning and prediction data samples is low,the recognition accuracy positively correlates with the sample number.When the ratio of the learning sample number to the prediction sample number is close to 4∶1,the model presents the highest recognition accuracy.For a relatively large number of data samples,the accuracy decreases with the increase in the sample number.The recognition accuracy shows different sensitivities to the network parameters.When the learning rate is 0.1 and the number of iterations and sample updates is 50,the accuracy reaches the peak.The proposed CNN model provides a new way to effectively recognize dissolution degrees of karstic limestones,and the recognition accuracy may reach 97.6%.