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

YIN Haiyang; CHEN Tongjun; SONG Xiong; XU Haicheng; LI Wan

doi:10.12363/issn.1001-1986.22.10.0801

Volume 51 Issue 5

May 2023

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COAL GEOLOGY & EXPLORATION > 2023 > 51(5): 164-170. > DOI: 10.12363/issn.1001-1986.22.10.0801

YIN Haiyang，CHEN Tongjun，SONG Xiong，et al. Methods for predicting the thickness of coal seams based on seismic attribute optimization and machine learning[J]. Coal Geology & Exploration，2023，51(5)：164−170. DOI: 10.12363/issn.1001-1986.22.10.0801

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

YIN Haiyang^{1, 2,},
CHEN Tongjun^{1, 2,*, ,},
SONG Xiong^{1, 2},
XU Haicheng^{1, 2},
LI Wan^{1, 2}

1.
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
2.
Key Laboratory of Coalbed Methane Resource and Reservoir Formation Process, Ministry of Education, Xuzhou 221008, China

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Received Date: October 23, 2022
Revised Date: February 17, 2023
Available Online: May 17, 2023

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Abstract

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)<RFR (0.11)<MLR (0.20); (5) The SNR of the input data had significant impacts on the prediction of the coal seams’ thickness, and a higher SNR corresponded to better prediction performance. Coal thickness prediction method based on seismic attribute optimization and SVR is an effective way to realize high-precision interpretations of the coal seams’ thickness.

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References

[1]	葛世荣,郝尚清,张世洪,等. 我国智能化采煤技术现状及待突破关键技术[J]. 煤炭科学技术,2020,48(7):28−46. GE Shirong,HAO Shangqing,ZHANG Shihong,et al. Status of intelligent coal mining technology and potential key technologies in China[J]. Coal Science and Technology,2020,48(7):28−46.
[2]	张运龙,丁峰,尹成,等. 基于楔形模型的波形结构属性特征研究[J]. 物探化探计算技术,2018,40(5):587−593. DOI: 10.3969/j.issn.1001-1749.2018.05.05 ZHANG Yunlong,DING Feng,YIN Cheng,et al. Study on the characteristics of waveform structure attributes based on wedge model[J]. Computing Techniques for Geophysical and Geochemical Exploration,2018,40(5):587−593. DOI: 10.3969/j.issn.1001-1749.2018.05.05
[3]	路拓,侯恩科,牛超,等. 基于Love型槽波频散特性的工作面煤厚解释方法[J]. 煤炭学报,2022,47(8):2992−3000. DOI: 10.13225/j.cnki.jccs.WX22.0865 LU Tuo,HOU Enke,NIU Chao,et al. Interpretation method of coal seam thickness based on dispersion characteristics of Love channel waves[J]. Journal of China Coal Society,2022,47(8):2992−3000. DOI: 10.13225/j.cnki.jccs.WX22.0865
[4]	JI Guangzhong,LI Hui,WEI Jiuchuan,et al. Preliminary study on wave field and dispersion characteristics of channel waves in VTI coal seam media[J]. Acta Geophysica,2019,67(5):1379−1390. DOI: 10.1007/s11600-019-00326-x
[5]	SUO Chonghui, CHANG Suoliang, PENG Shimi, et al. Study and application of seismic attributes on coal seam thickness prediction[C]//Applied Mechanics and Materials. Switzerland: Trans Tech Publications Ltd., 2012.
[6]	WANG Man, WANG Yingwei, ZHANG Dongming. Research on evaluation technology of gas anomaly zone in Xiadian Mine based on 3D seismic monitoring technology[J]. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: 1–20.
[7]	WU Yanhui,WANG Wei,ZHU Guowei,et al. Application of seismic multiattribute machine learning to determine coal strata thickness[J]. Journal of Geophysics and Engineering,2021,18(6):834−844. DOI: 10.1093/jge/gxab054
[8]	WIDESS M B. How thin is a thin bed?[J]. Geophysics,2012,38(6):1176−1180.
[9]	ZOU Guangui,XU Zhiliang,PENG Suping,et al. Analysis of coal seam thickness and seismic wave amplitude:A wedge model[J]. Journal of Applied Geophysics,2018,148:245−255. DOI: 10.1016/j.jappgeo.2017.11.013
[10]	谷琼. 面向非均衡数据集的机器学习及在地学数据处理中的应用[D]. 武汉: 中国地质大学, 2009. GU Qiong. Research of machine learning on imbalanced data sets and its application in geosciences data processing[D]. Wuhan: China University of Geosciences, 2009.
[11]	TROCCOLI E B,CERQUEIRN A G,LEMOS J B,et al. K−means clustering using principal component analysis to automate label organization in multi−attribute seismic facies analysis[J]. Journal of Applied Geophysics,2022,198:104555. DOI: 10.1016/j.jappgeo.2022.104555
[12]	WANG Zhiguo,GAO Dengliang,LEI Xiaolan,et al. Machine learning–based seismic spectral attribute analysis to delineate a tight–sand reservoir in the Sulige gas field of central Ordos Basin,western China[J]. Marine and Petroleum Geology,2020,113:104136. DOI: 10.1016/j.marpetgeo.2019.104136
[13]	邹冠贵,任珂,吉寅,等. 基于主成分分析和最近邻算法的断层识别研究[J]. 煤田地质与勘探,2021,49(4):15−23. DOI: 10.3969/j.issn.1001-1986.2021.04.003 ZOU Guangui,REN Ke,JI Yin,et al. Fault recognition based on principal component analysis and K−nearest neighbor algorithm[J]. Coal Geology & Exploration,2021,49(4):15−23. DOI: 10.3969/j.issn.1001-1986.2021.04.003
[14]	HARMON N,RYCHERT C,COLLIER J,et al. Mapping geologic features onto subducted slabs[J]. Geophysical Journal International,2019,219(2):725−733. DOI: 10.1093/gji/ggz290
[15]	王遥平. 基于化学计量学的油气源对比与实例研究[D]. 广州: 中国科学院大学(广州地球化学研究所), 2019. WANG Yaoping. Oil– and gas–source rock correlations and case studies based on chemometrics[D]. Guangzhou: University of Chinese Academy of Sciences (Guangzhou Institute of Geochemistry), 2019.
[16]	LIN Xiaobo,ZHANG Pingsong,MENG Fanbin,et al. A coal seam thickness prediction model based on CPSAC and WOA–LS–SVM:A case study on the ZJ Mine in the Huainan Coalfield[J]. Energies,2022,15(19):7324. DOI: 10.3390/en15197324
[17]	刘晓明,王新,徐慧. 基于多目标随机森林的煤层厚度同步预测方法[J]. 计算机工程与设计,2021,42(4):1116−1121. LIU Xiaoming,WANG Xin,XU Hui. Synchronous prediction method for coal thickness based on multi−objective random forest[J]. Computer Engineering and Design,2021,42(4):1116−1121.
[18]	HUANG Yaping,YAN Lei,CHENG Yan,et al. Coal thickness prediction method based on VMD and LSTM[J]. Electronics,2022,11(2):232. DOI: 10.3390/electronics11020232
[19]	李启成,郭雷,孙颍川,等. 地震属性融合技术在煤层厚度预测中的研究[J]. 地球物理学进展,2017,32(5):2014−2020. DOI: 10.6038/pg20170521 LI Qicheng,GUO Lei,SUN Yingchuan,et al. Seismic attributes fusion and its research in predicting thickness of coal[J]. Progress in Geophysics,2017,32(5):2014−2020. DOI: 10.6038/pg20170521
[20]	梁耍,王世博,谢洋,等. 基于LSTM的煤层厚度动态预测方法研究[J]. 煤炭科学技术,2021,49(增刊1):150−157. LIANG Shua,WANG Shibo,XIE Yang,et al. Dynamic prediction method of coal seam thickness based on LSTM[J]. Coal Science and Technology,2021,49(Sup.1):150−157.
[21]	WANG Xin,LI Yan,CHEN Tongjun,et al. Quantitative thickness prediction of tectonically deformed coal using extreme learning machine and principal component analysis:A case study[J]. Computers & Geosciences,2017,101:38−47.
[22]	WANG Xin,CHEN Tongjun,XU Hui. Thickness distribution prediction for tectonically deformed coal with a deep belief network:A case study[J]. Energies,2020,13(5):1169. DOI: 10.3390/en13051169
[23]	RODEN R,SMITH T,SACREY D. Geologic pattern recognition from seismic attributes:Principal component analysis and self−organizing maps[J]. Interpretation,2015,3(4):SAE59−SAE83. DOI: 10.1190/INT-2015-0037.1
[24]	TORGERSON W S. Multidimensional scaling:I. Theory and method[J]. Psychometrika,1952,17(4):401−419. DOI: 10.1007/BF02288916
[25]	KARAKUS M,KUMRAL M,KILIC O. Predicting elastic properties of intact rocks from index tests using multiple regression modelling[J]. International Journal of Rock Mechanics and Mining Sciences,2005,42(2):323−330. DOI: 10.1016/j.ijrmms.2004.08.005
[26]	CHERKASSKY V. The nature of statistical learning theory~[J]. IEEE Transactions on Neural Networks,1997,8(6):1564. DOI: 10.1109/TNN.1997.641482
[27]	BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5–32.

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