Effect of preprocessing on performances of machine learning-based mineral composition analysis on gas hydrate sediments, Ulleung Basin, East Sea

Abstract

Gas hydrate (GH) is an unconventional resource estimated at 1,000−120,000 trillion m3 worldwide. Research on GH is ongoing to determine its geological and flow characteristics for commercial production. After two large-scale drilling expeditions to study the GH-bearing zone in the Ulleung Basin, the mineral composition of 488 sediment samples was analyzed using X-ray diffraction (XRD). Because the analysis is costly and dependent on experts, a machine learning model was developed to predict the mineral composition using XRD intensity profiles as input data. However, the model's performance was limited because of improper preprocessing of the intensity profile. Because preprocessing was applied to each feature, the intensity trend was not preserved even though this factor is the most important when analyzing mineral composition. In this study, the profile was preprocessed for each sample using min-max scaling because relative intensity is critical for mineral analysis. For 49 test data among the 488 data, the convolutional neural network (CNN) model improved the average absolute error and coefficient of determination by 41% and 46%, respectively, than those of CNN model with feature-based preprocessing. This study confirms that combining preprocessing for each sample with CNN is the most efficient approach for analyzing XRD data. The developed model can be used for the compositional analysis of sediment samples from the Ulleung Basin and the Korea Plateau. In addition, the overall procedure can be applied to any XRD data of sediments worldwide.