Mine data fusion technology based on multi-feature joint decision making

Authors

• HAN Shanshan, Institute of Gravity, Magnetic and Electrical Method Technology, Chang’an University, Xi’an 710054, ChinaFollow
• ZHANG Jifeng, Institute of Gravity, Magnetic and Electrical Method Technology, Chang’an University, Xi’an 710054, China; Multi-parameter comprehensive Simulation Laboratory of Geophysical Field, Chang’an University, Xi’an 710054, ChinaFollow
• FAN Tao, CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, China; CCTEG Xi’an Transparent Geology Technology Co., Ltd., Xi’an 712000, China; State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, China
• LIU Qiang, CCTEG Xi’an Research Institute (Group) Co., Ltd., Xi’an 710077, China; CCTEG Xi’an Transparent Geology Technology Co., Ltd., Xi’an 712000, China; State Key Laboratory of Intelligent Coal Mining and Strata Control, Beijing 100013, China
• SONG Xiaojiao, Shaanxi Geology and Mining Second Comprehensive Geophysical Exploration Brigade Co., Ltd., Xi’an 710016, China
• QI Zhipeng, Institute of Gravity, Magnetic and Electrical Method Technology, Chang’an University, Xi’an 710054, China; Multi-parameter comprehensive Simulation Laboratory of Geophysical Field, Chang’an University, Xi’an 710054, China
• ZHENG Hangzhou, Institute of Gravity, Magnetic and Electrical Method Technology, Chang’an University, Xi’an 710054, China

Abstract

Background In the field of geological exploration of coal mines, methods such as in-seam seismic exploration, the transient electromagnetic method, and audio-frequency electrical penetration offer unique technical advantages. However, each of these methods can only reflect a single physical property of a medium, suffering from limitations including the one-sidedness of information and the multiple solutions of interpretations, thus failing to adapt to the new situations of current coal mine exploration. Multi-source data fusion promotes a technological development by leaps from one-sided detection to comprehensive analysis through mechanisms of information complementation, feature enhancement, and field-source integration. Therefore, this technology can enhance the accuracy of both the positioning and morphological identification of anomalous geobodies. Methods The data fusion technology based on wavelet decomposition, which employs principal component analysis (PCA) for low-frequency components and multi-feature joint decision-making for high-frequency components, was used to integrate geophysical data obtained using the three methods (i.e., in-seam seismic exploration, transient electromagnetic method, and audio-frequency electrical penetration). The purpose is to enhance the comprehensive analytical capacity for geological information and to achieve accurate detection of geological structures and anomalies. First, raw data obtained using the three methods were preprocessed to eliminate noise interference and normalize data formats. Subsequently, using the wavelet transform, the resulting data from various sources were then decomposed into the coefficients of subbands corresponding to different frequencies. Based on their importance in geological characterization, the coefficients of low- and high-frequency subbands were integrated using targeted fusion rules. Finally, the fused geophysical data were determined through inverse wavelet transform. Results and Conclusions Compared to data from a single source, the fused data incorporated the characteristics of structural boundaries delineated using the in-seam seismic exploration, supplemented by information on underground electrical contrast obtained using the electrical method. Therefore, these data allow for the presentation of more abundant details of geological structure characteristics. The proposed data fusion technology holds broad application prospects in fields such as geologic hazard prediction and resource exploration, providing a novel, effective approach for the development of geophysical exploration technology.

Keywords

data fusion, wavelet transform, feature identification, principal component analysis (PCA), in-seam wave, transient electromagnetics, audio-frequency electrical penetration

DOI

10.12363/issn.1001-1986.25.07.0487

Recommended Citation

HAN Shanshan, ZHANG Jifeng, FAN Tao, et al. (2026) "Mine data fusion technology based on multi-feature joint decision making," Coal Geology & Exploration: Vol. 54: Iss. 4, Article 18.
DOI: 10.12363/issn.1001-1986.25.07.0487
Available at: https://cge.researchcommons.org/journal/vol54/iss4/18

Reference

[1] 张继锋,孙乃泉,刘最亮,等. 电磁法在煤矿水害隐患探测方面的综述[J]. 煤田地质与勘探,2023,51(2):301−316

ZHANG Jifeng,SUN Naiquan,LIU Zuiliang,et al. Electromagnetic methods in the detection of water hazards in coal mines:A review[J]. Coal Geology & Exploration,2023,51(2):301−316

[2] 薛国强,潘冬明,于景邨. 煤矿采空区地球物理探测应用综述[J]. 地球物理学进展,2018,33(5):2187−2192

XUE Guoqiang,PAN Dongming,YU Jingcun. Review the applications of geophysical methods for mapping coal–mine voids[J]. Progress in Geophysics,2018,33(5):2187−2192

[3] 陈勐韬,杨文采,颜萍,等. 基于SVD分解的二维空间数据融合方法[J]. 地球物理学进展,2020,35(3):940−949

CHEN Mengtao,YANG Wencai,YAN Ping,et al. Two–dimensional spatial data fusion method based on SVD decomposition[J]. Progress in Geophysics,2020,35(3):940−949

[4] 李红蕾,陈石,庄建仓,等. 多源布格重力异常数据贝叶斯融合算法及其在川滇地区的应用[J]. 地球物理学报,2021,64(9):3232−3245

LI Honglei,CHEN Shi,ZHUANG Jiancang,et al. A Bayesian data fusion algorithm for multi–source Bouguer gravity anomalies and its application in the Sichuan–Yunnan region[J]. Chinese Journal of Geophysics,2021,64(9):3232−3245

[5] 徐凯军,刘鑫,于会臻,等. 基于重磁数据融合的青格里底区块火成岩岩性识别[J]. 石油地球物理勘探,2025,60(1):225−233

XU Kaijun,LIU Xin,YU Huizhen,et al. Lithology identification of igneous rocks in Qinggelidi Block based on gravity and magnetic data fusion[J]. Oil Geophysical Prospecting,2025,60(1):225−233

[6] 吕鹏飞,薛国强,武欣. 利用多源地球物理图像融合方法识别物性异常:以航空电磁和航磁数据为例[J]. 地球物理学报,2023,66(6):2658−2669

LYU Pengfei,XUE Guoqiang,WU Xin. Multi–source geophysical image fusion method to identify physical anomaly:A case study of airborne electromagnetic and magnetic data[J]. Chinese Journal of Geophysics,2023,66(6):2658−2669

[7] 赵维俊,秦涛,李建平,等. 基于地球物理数据融合的大兴安岭中段东缘地壳结构研究[J]. 地质学报,2024,98(7):2064−2083

ZHAO Weijun,QIN Tao,LI Jianping,et al. Crustal structure of the eastern margin of the middle Great Kinghan Range based on geophysical data fusion[J]. Acta Geologica Sinica,2024,98(7):2064−2083

[8] 林同奎,黄旭日,熊威,等. 智能预测和常规地震属性融合的产能“甜点”预测方法[J]. 石油物探,2023,62(6):1142−1153

LIN Tongkui,HUANG Xuri,XIONG Wei,et al. Productivity “sweet spot” prediction method combining intelligent estimation and conventional seismic attributes[J]. Geophysical Prospecting for Petroleum,2023,62(6):1142−1153

[9] 赵军才,马江涛,刘洋,等. 基于多数据融合和自适应加权混合损失函数约束的地震波初至智能拾取方法[J]. 石油物探,2025,64(4):691−700

ZHAO Juncai,MA Jiangtao,LIU Yang,et al. An intelligent first–arrival picking method based on multi–data fusion and adaptive weighted hybrid loss function[J]. Geophysical Prospecting for Petroleum,2025,64(4):691−700

[10] 陈雨茂,刘浩杰,盖姗姗,等. 基于导航金字塔多尺度分解多属性融合方法的致密砂岩储层裂缝预测[J]. 大庆石油地质与开发,2025,44(3):128−138

CHEN Yumao,LIU Haojie,GAI Shanshan,et al. Fracture prediction of tight sandstone reservoir by multi–attributes fusion method based on steerable pyramid multi–scales decomposition[J]. Petroleum Geology & Oilfield Development in Daqing,2025,44(3):128−138

[11] 徐乐意,易浩,张振波,等. 斜、平缆数据融合处理在珠江口盆地的应用[J]. 石油地球物理勘探,2024,59(3):473−483

XU Leyi,YI Hao,ZHANG Zhenbo,et al. Application of seismic fusion processing about flat cable and inclined cable in Pearl River Mouth Basin[J]. Oil Geophysical Prospecting,2024,59(3):473−483

[12] 王子烨,左仁广. 基于多源数据融合的喜马拉雅淡色花岗岩识别[J]. 地学前缘,2023,30(5):216−226

WANG Ziye,ZUO Renguang. Mapping Himalayan leucogranites by machine learning using multi–source data[J]. Earth Science Frontiers,2023,30(5):216−226

[13] 戴嵩,孙喜明,张精明,等. 基于多尺度卷积神经网络的多源数据融合岩性分类方法[J]. 激光与光电子学进展,2024,61(14):1437005

DAI Song,SUN Ximing,ZHANG Jingming,et al. Multiscale convolutional neural network–based lithology classification method for multisource data fusion[J]. Laser & Optoelectronics Progress,2024,61(14):1437005

[14] 黄茂林. 矿井直流电法与地震联合反演方法研究[D]. 徐州：中国矿业大学，2025.

HUANG Maolin. Research on the joint inversion method of mine direct current method and earthquake[D]. Xuzhou：China University of Mining and Technology，2025.

[15] 许乐红. 大地电磁法和天然地震全波形联合反演研究[D]. 北京：中国地质大学(北京)，2024.

XU Lehong. Joint inversion of magnetotelluric and full wave inversion of natural earthquake[D]. Beijing：China University of Geosciences (Beijing)，2024.

[16] 汪勇辰. 巷道掘进震电数据综合处理与异常判识研究[D]. 淮南：安徽理工大学，2025.

WANG Yongchen. Research on comprehensive processing and anomaly identification of seismic and electrical data of roadway excavation[D]. Huainan：Anhui University of Science & Technology，2025.

[17] 张晓峰. 瞬变电磁法探测煤田采空区的应用研究[D]. 西安：长安大学，2007.

ZHANG Xiaofeng. The applied research of TEM in detecting coalfield goaf[D]. Xi’an：Chang’an University，2007.

[18] 王超凡,赵永贵,靳洪晓,等. 地震CT及其在采空区探测中的应用[J]. 地球物理学报,1998,41(增刊1):367−375

WANG Chaofan,ZHAO Yonggui,JIN Hongxiao,et al. Seismic tomography and its application to the investigation of buried worked–out area[J]. Chinese Journal of Geophysics,1998,41(Sup.1):367−375

[19] 孟召平,师修昌,刘珊珊,等. 废弃煤矿采空区煤层气资源评价模型及应用[J]. 煤炭学报,2016,41(3):537−544

MENG Zhaoping,SHI Xiuchang,LIU Shanshan,et al. Evaluation model of CBM resources in abandoned coal mine and its application[J]. Journal of China Coal Society,2016,41(3):537−544

[20] 原文涛. 瞬变电磁法在采空区及陷落柱探测中的应用[J]. 物探与化探,2012,36(增刊1):164−167

YUAN Wentao. The application of transient electromagnetic method to the detection of goaf and collapse columns[J]. Geophysical and Geochemical Exploration,2012,36(Sup.1):164−167

[21] 吴杨云. 资源整合矿区水害探测及防治技术可行性研究[J]. 中国安全生产科学技术,2012,8(增刊1):19−23

WU Yangyun. A study on the detection of water hazards in newly integrated areas & feasibility research on prevention and control technologies[J]. Journal of Safety Science and Technology,2012,8(Sup.1):19−23

[22] 郭金栋,王恩元. 煤炭能源安全测度指标体系与综合评价[J]. 中国安全科学学报,2010,20(11):112−118

GUO Jindong,WANG Enyuan. Measurement indicator system & comprehensive appraisal for coal energy security in China[J]. China Safety Science Journal,2010,20(11):112−118

[23] 赵苏启,武强,郭启文,等. 导水陷落柱突水淹井的综合治理技术[J]. 中国煤炭,2004,30(7):25−27

ZHAO Suqi,WU Qiang,GUO Qiwen,et al. Comprehensive control technologies of water bursting from water conducted collapsing pole[J]. China Coal,2004,30(7):25−27

[24] 郑士田. 地面定向钻进技术在煤矿陷落柱突水防治中的应用[J]. 煤炭科学技术,2018,46(7):229−233

ZHENG Shitian. Application of ground directional borehole technology to control prevention karst collapsed column water inrush in coal mines[J]. Coal Science and Technology,2018,46(7):229−233

[25] WU Qiang,MU Wenping,XING Yuan,et al. Source discrimination of mine water inrush using multiple methods:A case study from the Beiyangzhuang Mine,Northern China[J]. Bulletin of Engineering Geology and the Environment,2019,78(1):469−482.

[26] 黄震,汪振鹏,古启雄,等. 弱胶结充填型断层破碎带岩体渗流特性[J]. 煤炭学报,2025,50(12):5357−5369

HUANG Zhen,WANG Zhenpeng,GU Qixiong,et al. Characterizing seepage behavior of weakly cemented filling fault fracture zone[J]. Journal of China Coal Society,2025,50(12):5357−5369

[27] VANCE T C,HUANG T,BUTLER K A. Big data in Earth science:Emerging practice and promise[J]. Science,2024,383(6688):eadh9607.

[28] 刘强,胡继武,王盼,等. Love型槽波物理模拟及对断层的响应研究[J]. 煤田地质与勘探,2023,51(12):116−122

LIU Qiang,HU Jiwu,WANG Pan,et al. Analogue modeling of Love–type channel waves and their response to faults[J]. Coal Geology & Exploration,2023,51(12):116−122

[29] 刘天放，潘冬明，李德春，等. 槽波地震勘探[M]. 徐州：中国矿业大学出版社，1994.

[30] 刘天放,程久龙,潘冬明,等. 槽波的吸收衰减[J]. 煤炭学报,1993,18(5):83−86

LIU Tianfang,CHENG Jiulong,PAN Dongming,et al. Channel wave attenuation by medium absorption[J]. Journal of China Coal Society,1993,18(5):83−86

[31] 杨思通,程久龙. 煤巷小构造Rayleigh型槽波超前探测数值模拟[J]. 地球物理学报,2012,55(2):655−662

YANG Sitong,CHENG Jiulong. The method of small structure prediction ahead with Rayleigh channel wave in coal roadway and seismic wave field numerical simulation[J]. Chinese Journal of Geophysics,2012,55(2):655−662

[32] 杨真，冯涛，WANG Shugang. 0. 9m薄煤层SH型槽波频散特征及波形模式[J]. 地球物理学报，2010，53(2)：442–449.

YANG Zhen，FENG Tao，WANG Shugang. Dispersion characteristics and wave shape mode of SH channel wave in a 0. 9m–thin coal seam[J]. Chinese Journal of Geophysics，2010，53(2)：442–449.

[33] 胡运兵,吴燕清,宋劲. 薄层状介质中反射地震超前探测的特性分析[J]. 河南理工大学学报(自然科学版),2006,25(6):469−473

HU Yunbing,WU Yanqing,SONG Jin. Analysis on characteristics of reflection seismic pilot detecting technology in thin layered media[J]. Journal of Henan Polytechnic University (Natural Science),2006,25(6):469−473

[34] 阴建康,闫述,陈明生. 瞬变电磁法小发射回线探测装置及其应用[J]. 煤田地质与勘探,2007,35(3):66−68

YIN Jiankang,YAN Shu,CHEN Mingsheng. Small transmitter loop device and its application in transient electromagnetic method[J]. Coal Geology & Exploration,2007,35(3):66−68

[35] 张继锋,陈昌涧,李宇腾,等. 基于改进粒子群算法的SOTEM电场分量E_x反演[J]. 煤田地质与勘探,2025,53(4):213−221

ZHANG Jifeng,CHEN Changjian,LI Yuteng,et al. Inversion of electric field component E_x in the SOTEM method based on the improved particle swarm optimization algorithm[J]. Coal Geology & Exploration,2025,53(4):213−221

[36] 牛之琏. 时间域电磁法原理[M]. 长沙：中南大学出版社，2007.

[37] 高生. 瞬变电磁法小回线装置在煤矿水文地质勘探中的应用[J]. 中国煤炭地质,2022,34(增刊2):84−89

GAO Sheng. Application of hydrological exploration in coal mine with the TEM small loop device[J]. Coal Geology of China,2022,34(Sup.2):84−89

[38] 刘英. 浅析瞬变电磁法不同回线装置的优缺点[J]. 物探装备,2009,19(5):325−327

LIU Ying. The advantages and disadvantages comparison among TEM different loops[J]. Equipment for Geophysical Prospecting,2009,19(5):325−327

[39] 秦泰山. 音频电透视探测工作面顶底板富水异常区技术研究[J]. 煤炭与化工,2022,45(10):59−62

QIN Taishan. Study on audio electric perspective detection technology of roof and floor water abundance abnormal area[J]. Coal and Chemical Industry,2022,45(10):59−62

[40] 郭鹏飞,谢雄刚,白雯,等. 音频电透视法在防治矿井水患中的应用[J]. 工业安全与环保,2018,44(4):34−36

GUO Pengfei,XIE Xionggang,BAI Wen,et al. Application of audio frequency electric perspective method in detecting mine flooding zones[J]. Industrial Safety and Environmental Protection,2018,44(4):34−36

[41] 刘建国. 音频电透视法在煤矿工作面顶板水探测中的应用[J]. 煤炭与化工,2021,44(7):78−80

LIU Jianguo. Application of audio electric perspective method in mine working face roof water detection[J]. Coal and Chemical Industry,2021,44(7):78−80

[42] 吴正飞. 矿井音频电透视在煤矿顶板砂岩富水区透明化中的应用[J]. 价值工程,2024,43(23):131−135

WU Zhengfei. Application of audio electric perspective in transparentness of coal mine roof sandstone water–rich area[J]. Value Engineering,2024,43(23):131−135

[43] 刘国兴. 电法勘探原理与方法[M]. 北京：地质出版社，2005.

[44] 韩德品,蒙超,石显新,等. 层状全空间电测深曲线类型与新方法研究[J]. 煤炭学报,2017,42(11):2953−2958

HAN Depin,MENG Chao,SHI Xianxin,et al. Study on the curve–type and new method of layered full space electrical sounding[J]. Journal of China Coal Society,2017,42(11):2953−2958

[45] 岳建华,李志聃. 矿井直流电法及在煤层底板突水探测中的应用[J]. 中国矿业大学学报,1997,26(1):94−98

YUE Jianhua,LI Zhidan. Mine DC electrical methods and application to coal floor water invasion detecting[J]. Journal of China University of Mining & Technology,1997,26(1):94−98

[46] 刘树才,岳建华,李志聃. 矿井电测深理论曲线变化规律研究[J]. 中国矿业大学学报,1996,25(3):101−105

LIU Shucai,YUE Jianhua,LI Zhidan. Studies on changing law of theoretical electrical sounding curves in coal mine[J]. Journal of China University of Mining & Technology,1996,25(3):101−105

[47] 陈武凡. 小波分析及其在图像处理中的应用[M]. 北京：科学出版社，2002.

[48] 杨莎莎,田小平,吴成茂. 基于小波变换的多聚焦图像自适应融合[J]. 西安邮电学院学报,2012,17(3):24−29

YANG Shasha,TIAN Xiaoping,WU Chengmao. Multi–focus image fusion scheme based on wavelet transform[J]. Journal of Xi’an University of Posts and Telecommunications,2012,17(3):24−29

[49] 李娜,牒谨,刘颖. 基于相关滤波的目标跟踪方法[J]. 西安邮电大学学报,2018,23(4):58−67

LI Na,DIE Jin,LIU Ying. On correlation filter based object tracking algorithms[J]. Journal of Xi’an University of Posts and Telecommunications,2018,23(4):58−67