A prediction model of the industrial components and calorific values of coal seams based on multi-source log data
The industrial components and calorific values of coal seams serve as an important basis for the evaluation of coal quality,and the prediction of them based on log data allows for overcoming the deficiency in the experimental analysis of coal core samples.This study collected data from digital logs and coal quality analysis at different stages(e.g.,detailed survey and exploration)of a coal field in Ningxia.Based on the investigation of the coal quality and log responses,as well as statistical analysis,this study developed the methods for extracting log response characteristics,establishing sample sets,and processing data and established a deep neural net-work-based prediction model.Then,it confirmed the validity of the prediction model by comparing the predicted results of testing data with the results from the experimental analysis.
multi-source datageophysical loggingcoalindustrial componentcalorific valueprediction model
万方数据
维普
