# Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine
## 1 Introduction  
Laminae are the most minor or thinnest virgin layers of a sedimentary rock or sediments that can be distinguished [1]. The field of petroleum geology involves the identification and quantification of organic matter in laminae [2; 3; 4], widely adopting the methods used in organic petrology to identify different macerals [5; 6]. The characteristics and maturity of insoluble organic matter are utilized in related research on organic matter sources, types, sedimentary environment, and resource evaluation [7; 8; 9; 10]. However, the quantitative analysis of macerals is usually performed manually [11; 12], which relies on the standards established by the International Committee for Coal and Organic Petrology [13] and American Society for Testing and Materials standard D2799-13 (2013) [14]. Furthermore, the experimenter's experience limits the accuracy of the quantitative analysis. Therefore, the analysis results may vary from specialist to specialist and do not accurately describe information on the distribution of maceral groups and minute differences in the laminae, limiting its large-scale analysis and application. In recent years, some advancedtechniques, such as the Advanced Mineral Identification and Characterization System and X-ray diffraction, can identify organic matter in source rocks. However, the focus of these techniques is on identifying the mineral composition of rocks and they cannot identify specific macroals [3; 15]. Consequently, the application of new methods to quantify macroals in source rocks is urgently needed. Previous studies have made considerable attempts to overcome the challenges related to the quantitative study of coal macroals using computer recognition techniques. In recent years, some computer scientists have utilized support vector machine (SVM), neural networks, and random forest plots to identify as well as quantify some macroal groups and mineral components [16; 17; 18; 19; 20; 21]. These scholars started from the perspective of improving the accuracy of the algorithm and proposed to achieve satisfactory results. Whereas the fluorescence mode commonly used in the manual calibration of macroals was rarely mentioned in previous studies, less attention has been paid to the macroals of conventional sedimentary rocks. In the studies of computer scientists, methods have been developed for identifying coal macroals; however, some issues need to be overcome. First and foremost, the content of organic matter in these rocks is usually below 5% [22; 23; 24], and it requires a combination of reflected-light and fluorescent-light photomicrographs to identify macroal groups [25; 26; 27]. Therefore, it may be hard to notice certain organic matters in a sample, resulting in an insufficient sample for selecting the region of interest (ROI). Second, the morphology of macroals in shale is more complex and laborious to distinguish than in coal; thus, it is difficult to calibrate certain macroals accurately. These issues undoubtedly complicate the quantification of the microscopic components in the laminae. The different combinations and content of macroals, as well as types of laminae, however, have significant effects on oil generation [3]. Due to the variation in the tiny laminae, it is complicated to describe them using conventional methods. To tackle these issues, this study first stitched and preprocessed hundreds of microscopic images to collect photomicrographs with a large field of view and performed image enhancement to improve the accuracy of computer recognition. Second, we classified the micrographs using SVM. Furthermore, we performed a statistical analysis of the area of the classification results. Notably, this is the first attempt at using computer recognition to identify the organic matter and its small variations in the tiny laminae of shale. Nevertheless, an interesting point has not been addressed: how to characterize the spatial distribution of macroals in the laminae with less organic matter content.