A New Method of Cementing Quality Evaluation Based on Improved DenseNet
In order to solve the problems of low efficiency and low accuracy of cementing quality evaluation,an evaluation method based on improved DenseNet convolutional neural network is proposed.In this method,the large-scale and small-scale features of the cementing quality feature map can be obtained simultaneously by adding a multi-scale convolutional layer,thereby improving the coverage of the receptive field and enhancing the adaptability of the model to different scales;by embedding the CBAM mechanism,the model fully extracts useful information for evaluation tasks in two dimensions of space and channel,enhances the model's ability to focus on features and perception capabilities,and improves the accuracy of evaluation results and its robustness;at the same time,by reducing the number of network layers,the number of model parameters is reduced,and the computational efficiency and generalization ability of the model are improved.The experimental results show that the accuracy rate of the three types of evaluation samples in the test set is 95.86%,which is about 4.9 percentage points higher than that of DenseNet-121,and the number of parameters is significantly reduced;compared with BP neural network and support vector machine,it is 9 points higher percent or so.Therefore,it is revealed that the research program of implementing the cementing quality evaluation using the improved DenseNet model is not only feasible,but also superior to other similar machine learning methods.
国家科技期刊平台
NSTL
万方数据
维普
