# Research on Coal Structure Prediction Method Based on Genetic Algorithm-BP Neural Network
## 1 Introduction  
The coal structure is among the important research topics in the prevention and control of coal and gas outbursts and coalbed methane development in coal mines. China is a big country of coal production, and its coal resource comprises about 70% of the primary energy. Coal is the main energy used in China, and that scenario will not greatly change for a long time. Most of the coal-bearing basins in China have a complex tectonic evolution background and have experienced the superimposed transformation of multi-stage tectonic action, not only leading to the complex and variable structure of coal-bearing basins but also causing strong extrusion and crumpling deformation of the coal structure, forming widely distributed tectonic coal [1]. Tectonic coal is the main carrier of coal and gas outbursts, and structural coal reduces the strength, permeability, and transformability of the coal reservoir, which seriously affects the surface development effect of coalbed methane [2]. Therefore, identifying tectonic coal and predicting its distribution area are crucial to coal mine safety and production and coalbed methane exploration and development. The identification methods of the coal structure can be divided into downhole sampling and logging curve identification [3]. The identification results of underground sampling are accurate, but it requires sampling technology and has a sampling cost, the explained coal structure depth is discontinuous, and overall characterization of the coal seam cannot be achieved. At the same time, due to the low mechanical strength of coal samples, they are easily damaged in the process of drilling, transportation, and preservation, so coal cannot be identified on a large scale. Thanks to its continuous depth, rich data, and relatively low cost, logging curve recognition can achieve an overall description of the coal seam and coal body structure at the well point, and it can better reflect the longitudinal change law of the coal seam and coal body structure. The identification of the coal structure using the early logging curve is mainly based on the logging response characteristics of different coal structures. Studying the shape characteristics of different coal structures on different logging curves allows corresponding qualitative and fixed-thickness criteria templates to be set to judge the coal structure. This method is simple and intuitive, but it is highly subjective as it requires human judgment, and the judgment results of different people under the same template will have great differences [4; 5; 6]. Some scholars have also analyzed the range of logging curve response values corresponding to different coal structures and then divided the coal structure of the study area according to the logging response and logging curves. This method has limited accuracy, and the response range differences in different coal structures often overlap [7; 8]. In order to further improve the recognition accuracy, some scholars adopt the empirical formula method and select multiple logging curves to be combined for simple function operation to strengthen the logging response characteristics of different coal structures [9; 10]. Quantitative indexes of the coal structure, such as the coal structure index n and geological intensity factor GSI, are introduced, and a multiple regression formula between those and the logging curve is established to indicate the coal structure. This kind of method has high identification efficiency, but the assignment of coal samples requires a detailed observation of them, and accurate assignment cannot be achieved from only report data without physical data [11; 12; 13; 14]. In multi-solution logging interpretation, the curve response and coal structure are not in simple linear correspondence. In order to identify the coal structure in a more complex geological environment, machine learning methods that are better at solving nonlinear problems must be applied to identify the coal structure.