Research On Implementation Technology Of Automatic Classification Of Interregional Sandstone Microscopic Images

Efficient identification of sandstones is significant for the selection of building ma-terials,resource exploration and reservoir evaluation of oil and gas.Traditional manual method is subjective and time-consuming,and lacks reusability.As the development of computer technology,the sandstone thin sections are taken into microscopic images and are quickly identified by the image processing and machine learning method.To ensure the accuracy and reliability of the prediction model,traditional machine learn-ing assumes that the training samples are enough and the distributions between training and testing samples are identical and independent.However,these assumptions do not hold when identifying the sandstones collected from an unfamiliar region.On one hand,the training data will be not enough as the manual labeling is time-consuming.On the other hand,due to the diverse formation environments and acquisition condi-tions,the sandstone microscopic images vary from the regions.For the above interregional sandstone microscopic image automatic classification problem,we propose two solutions:one is nearest neighbor based dataset reconstruc-tion,the other is transfer learning based method called Festra.The former reconstructs a new training set for target data by nearest neighbor algorithm and further elabora-tion,then trains an accuracy target classifier based on the new training set.The lat-ter trains a high-quality target classifier by combining feature selection and enhanced TrAdaBoost.The goal of both methods is reducing the distribution difference between source and target region,and improving the performance and efficiency on the sand-stone microscopic image classification.The experimental results show the validity of both methods.The main contributions can be summarized as follows:1.We propose a nearest neighbor based method for interregional sandstone microscopic image classification.We propose a nearest neighbor based dataset reconstruction method to reduce the distribution difference between source and target region.The method reconstructs a new training set by nearest neighbor algorithm based on the Euclidean distance,and elaborates it through feature se-lection and class balance.Finally the target classifier is trained based on the new training set.2.We propose a method for interregional sandstone microscopic image clas-sification based on transfer learning.We propose a transfer learning method Festra for the distribution difference to improve the accuracy of the sandstone microscopic images classification.Festra contains two parts:one is feature se-lection,which mainly screens out the irrelevant and redundant features,the other is enhanced TrAdaBoost,which reduces the difference between the source and target region.3.We conduct empirical studies to assert the validity of Festra for interre-gional sandstone microscopic image classification.Based on the sandstone microscopic images taken from different regions and geologic time in Tibet,we conduct experiments according to the four research questions embarked from different perspectives.We use different performance measures,classifiers and similarity measures to evaluate the validity of Festra as completely as possible.The experiment results show the effectiveness of Festra.Moreover,we also con-duct empirical studies and result analyses for the possible improvements and expansions of the method.