Study On Multisource Data Analysis And Prediction Of Gas Content

Relationship between methane content and geologic structure, coal properties, lithology and structure of wall rock, coal seam burial depth was studied based on gas geology. Main factors which affect methane content can be filtered. As an example of Gaocheng Mine, burial depth of bedrock is the leading controlling factor of methane content; followed by the depth correlation coefficient of 50m rock of roof, sand factor and coal seam thickness, while the Cenozoic stratum burial depth, moisture, ash of coal are less interrelated. Methane content prediction model was established using multiple linear regression, BP neural network and support vector machine theory. Methane content measured during exploration and coal production, inversed methane content using out-flow of methane, and methane content calculated using gas parameters were analyzed and regulated. Among multiple linear regression, BP neural network and support vector machine prediction model, support vector machine is proved to be the most accurate one and is useful to predict gas content.