Semi-quantitative identification of pore characteristics for Yungang grotto sandstone via acoustic emission signals and information fusion convolutional neural network

Diseases of grotto can be identified via artificial intelligence (AI) techniques, but most existing AI models cannot quantify the specific Indicators, such as pore parameters. In this work, an information fusion convolutional neural network (IFCNN) integrating acoustic emission (AE) time and frequency domain signals is constructed based on quantifiable labels of pore characteristics. And the model training process is introduced and the identification performance of IFCNN was compared with that of other machine learning methods. The specific pore indicators and its corresponding refined parameter range can inverted via six weathering levels in quantifiable labels. The IFCNN model is trained by using 6048 time-frequency domain AE series data and IFCNN demonstrated superior identification performance and generalization ability compared to other 1D-CNN and machine learning models. Furthermore, 3 extra sandstone samples with different degrees of weathering are used to further investigate the pore parameter recognition performance of the IFCNN pre-trained model. It's found that the effective T2 intervals of the 3 samples were all identified incorrectly, that is, micro fissures intervals greater than 200 ms were missed. However, the accuracy of identified total bulk porosity, average pore size, and pore development coefficient is better. Except for average pore size, all other indicators are within the given interval from semi-quantified label. When the pore size distribution is about 0.5 mu m-10 mu m, the weathering of Yungang sandstone can be described semi-quantitatively. It is envisioned that the proposed IFCNN serves as an accurate and ready-to-use assessment tool that aids researchers or cultural heritage conservators to quickly evaluate the pore parameters of sandstone grottoes just using AE device.