Methods for predicting the thickness of coal seams based on seismic attribute optimization and machine learning

Authors

YIN Haiyang, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, ChinaFollow
CHEN Tongjun, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, ChinaFollow
SONG Xiong, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, China
XU Haicheng, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, China
LI Wan, School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China; Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, China

Abstract

Accurate location of coal seams is the key technology of unattended mining and predicting the thickness of coal seams is an important content in the seismic interpretation of coalfields. This study constructed a forward model involving wedge-shaped coal seams by referencing the actual thickness and physical properties of strata. Moreover, this study compared and analyzed the effects of signal-to-noise ratio (SNR) and regression methods on the prediction of the coal seams’ thickness through the forward modeling of seismic profiles and the extraction and optimization of seismic attributes. The results of this study are as follows: (1) Some seismic attributes were strongly correlated with, and can be used to predict, the thickness of coal seams; (2) The information redundancy among seismic attributes cannot be ignored. However, there was no essential difference between the seismic attributes optimized using principal component analysis (PCA) and multi-dimensional scaling (MDS); (3) When the SNR was low (10 dB), the root-mean-square (RMS) error of the prediction results of different algorithms was in the order of random forest regression (RFR, 1.07)< support vector machine regression (SVR, 1.15)< multivariate linear regression (MLR, 1.84); (4) When the SNR was high (25 dB), the RMS error of the prediction results of these algorithms was in the order of SVR (0.05)

Keywords

forward modeling, prediction of coal seams’ thickness, seismic attribute optimization, multivariate regression, effective evaluation

DOI

10.12363/issn.1001-1986.22.10.0801

Recommended Citation

YIN Haiyang, CHEN Tongjun, SONG Xiong, et al. (2023) "Methods for predicting the thickness of coal seams based on seismic attribute optimization and machine learning," Coal Geology & Exploration: Vol. 51: Iss. 5, Article 17.
DOI: 10.12363/issn.1001-1986.22.10.0801
Available at: https://cge.researchcommons.org/journal/vol51/iss5/17

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