Drilling core identification based on natural image

The traditional on-site core identification and recording mainly rely on the experience of technicians, and there are many uncertain factors. Limited by the site conditions, using mobile phones or cameras to capture the natural images is the most convenient way to collect the core information. Therefore, it is necessary to study the feature information extraction technology of core image and apply it to the identification and prediction of core type and other information. Specifically, a large number of core samples were collected, the thin-section identification method was employed to determine the core types and names, and then the core images were taken under different lighting and scale conditions to form the training data sets of convolutional neural networks corresponding to the image and name markers. In order to solve the problem of the different identification accuracy generated by data augmentation and different training batches on different test datasets, a joint core identification method based on multiple training models was proposed, and multiple models were used simultaneously to identify the images so as to comprehensively determine the final identification results. Besides, 8 datasets were selected to test the model, and the accuracy of joint identification using multiple models (4 models were used herein) was improved by 20.34% at maximum (9.13% on average) compared to the single model without data augmentation, and 4.41% at maximum (2.75% on average) compared to the single model with data augmentation. In general, the identification accuracy for each test set is significantly improved, with a total identification accuracy of 91.56%. Hence, the proposed method effectively avoids the problem that a single model has the performance good for some datasets but poor for the others during the identification. In order to quickly use the core identification model in field drilling, a core identification mobile APP was developed using the TensorFlow Lite framework, so that the core images could be taken directly with the mobile phone at the scene for identification. The test results of geothermal exploration in Boye County, Baoding City, Hebei Province, show that APP has the field identification accuracy up to 85%, which is lower than that obtained in the laboratory test. This indicates that the shooting environment and core state in the field are more complex than that during laboratory testing, but it can still be used as an auxiliary tool to provide an important reference for field workers. The research shows that the identification accuracy of core images can be further improved by using the more complex convolutional neural networks, expanding the core image datasets, adopting the more effective data augmentation methods and strategies, and establishing a proprietary core identification model for a specific area, so as to provide more effective information for the decision-making of intelligent drilling.