## 1 Introduction  
China's resource endowment ensures coal will remain its primary energy source for the foreseeable future [1; 2; 3]. Coal quality indicators critically influence the economic viability of mining operations and coal chemical industries. Traditional methods for assessing these indicators depend on laboratory tests of physical samples, which are time-intensive, costly, and constrained by limited core availability [4; 5; 6]. This results in fragmented, discontinuous data that inadequately characterizes coal quality vertically and lacks horizontal quantitative resolution. These limitations hinder coal resource evaluation, development, and utilization, particularly in enabling precise stratification assessment or efficient, targeted mining. Geophysical logging methods offer a promising alternative to address these challenges. Prior studies demonstrate that bulk density logging effectively identifies coal quality parameters, while natural gamma, compensated neutron, and resistivity logging sensitively detect clay mineral content, moisture, and ash-related mineral types [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Conventional approaches to interpreting coal quality from logging data involve constructing predictive models tailored to specific coal seams using multiple logging parameters [8]. For example, coal rank is often determined via density-neutron cross plots [9; 10], while oil-rich coal is identified by integrating true density with natural gamma [11]. Industrial composition has been predicted using volumetric and probabilistic models based on geophysical logging parameters [12; 13; 14; 15; 16; 17]. Notably, ash yield correlates positively with density and natural gamma [9; 18; 19; 20], whereas volatile matter and fixed carbon exhibit negative correlations with density and acoustic transit time [12; 21; 22]. [PERSON] et al. applied intersection plots, regression analysis, and machine learning to predict industrial composition using natural gamma, resistivity, and density [13; 23]. [PERSON] et al. employed neural networks to evaluate moisture, ash, and volatile matter via density, acoustic, and natural gamma data [24]. [PERSON] and [PERSON] utilized multivariate linear fitting and volumetric models, respectively, to predict industrial composition [14; 16], while [PERSON] et al. classified sulfur content (>3% vs. <3%) using natural gamma, spontaneous potential, resistivity, density, and acoustic data, establishing multivariate linear models for sulfur prediction in North China's Shanxi and Taiyuan Formation coals [25]. Despite these advances, existing models predominantly target isolated parameters such as industrial composition, coal rank, or sulfur content [21; 24; 25; 10; 26]. A systematic approach to invert geophysical logging responses for comprehensive coal quality indicators such as moisture, ash, volatile matter, fixed carbon, total sulfur, and calorific value remains underdeveloped, This critical knowledge gap severely impedes the establishment of robust petrophysical inversion models capable of meeting the operational demands for efficient, high-resolution identification of deep coal seam quality and data-driven optimization of intelligent coal mining systems. The absence of such methodologies constrains our ability to leverage logging data for predictive quality assessment across heterogeneous coal reservoirs, ultimately limiting the implementation of closed-loop, precision mining strategies essential for resource efficiency and automation in complex subsurface environments. To address this gap, this study analyzes the No. 7 and No. 8 coal seams of the Shanxi Formation in the Xuzhuang and Kongzhuang mines of northern Jiangsu, China. Integrating scanning electron microscopy, X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, we investigate the coal quality and mineral composition of these seams.