钻孔岩性识别条件下的数字爆破技术研究
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摘要
为了实现露天矿山爆破作业过程的数字化精细爆破,钻孔的岩性识别、爆破装药专家系统是亟待解决的问题,而钻孔的实时岩性识别是诸问题中的关键问题。基于LWD200型全液压矿用钻机,采用其现场实地的自学习功能,提取钻机钻进不同岩层对应的诸多指标;通过现场获取的大量岩性识别数据,运用现代数学方法对其进行处理,构建出在一定置信度条件下的诸指标与岩性的关系。之后取消自学习功能进入自动岩性识别工作状态,实现钻机进行钻进的过程中实时的岩性及位置的采集。通过远程传输至设计平台进行数字爆破设计,实现数字化精细爆破。
In order to realize the digital fine blasting in the explosive operations of open pit,the establishment of an expert system of blast charge and lithologic identification of borehole is the problem urgently to be solved. Based on LWD200 full hydraulic drilling rig,the present study used its self-learning function in site to collect various indexes during drilling in different strata. Using the modern mathematical method to analyze the large amounts of lithologic identification data obtained in site,the relation between the indexes and lithology with certain confidence level was established. Then the state of self-learning function is switched to the state of automatic lithologic identification to gather the data of lithology and location in real time during the drilling process. Finally,the above data was transmitted remotely to the digital blasting design platform to realize the digital fine blasting
In order to realize the digital fine blasting in the explosive operations of open pit,the establishment of an expert system of blast charge and lithologic identification of borehole is the problem urgently to be solved. Based on LWD200 full hydraulic drilling rig,the present study used its self-learning function in site to collect various indexes during drilling in different strata. Using the modern mathematical method to analyze the large amounts of lithologic identification data obtained in site,the relation between the indexes and lithology with certain confidence level was established. Then the state of self-learning function is switched to the state of automatic lithologic identification to gather the data of lithology and location in real time during the drilling process. Finally,the above data was transmitted remotely to the digital blasting design platform to realize the digital fine blasting
引文
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[18]孙文彬,刘希亮,谭正龙,等.基于抛掷爆破预测的BP神经网络参数优化方法[J].煤炭学报,2012,37(S1):59-64.SUN Wenbin,LIU Xiliang,TAN Zhenglong,et al. Parameter optimization of BP-neural network based on the forecast of cast blasting[J].Journal of China Coal Society,2012,37(S1):59-64.
[19]董师师,黄哲学.随机森林理论浅析[J].集成技术,2013,2(1):1-7.DONG Shishi,HUANG Zhexue. A brief theoretical overview of random forests[J]. Journal of Integration Technology,2013,2(1):1-7.
[20]张华伟,王明文,甘丽新.基于随机森林的文本分类模型研究[J].山东大学学报:理学版,2006,41(3):139-143.ZHANG Huawei,WANG Mingwen,GAN Lixin. Automatic text classification model based on random forest[J]. Journal of Shan Dong University:Science Edition,2006,41(3):139-143.
[21]赵吉锋,现场混装炸药远程监控系统设计[D].大连:大连海事大学,2012.
[2]吴立新,殷作如,邓智毅.论21世纪的矿山-数字矿山[J].煤炭学报,2000,25(4):337-342.WU Lixin,YIN Zuoru,DENG Zhiyi. Research to the mine in the21stcentury:digital mine[J]. Jurnal of China Coal Society,2000,25(4):337-342.
[3]费鸿禄,郭连军.爆破施工的数字化[J].爆破,2015,32(3):31-39.FEI Honglu,GUO Lianjun.Digitization of blasting construction[J].Blasting,2015,32(3):31-39.
[4]汪旭光.中国爆破技术现状与发展[R].广州:中国工程爆破协会,2012.
[5]段云,熊代余,徐国全.钻孔数字化与钻孔岩性自动识别技术[J].金属矿山,2015(10):125-129.DUAN Yun,XIONG Daiyu,XU Guoquan. A new technology for digital drilling and automatic lithology identification[J]. Metal Mine,2015(10):125-129.
[6]冯盼学,杨志强.爆破过程精准数字化建模与效果评估的应用[J].有色金属,2014(2):4-6.FENG Panxue,YANG Ziqiang. Application of precise digital blasting process modeling and effect evaluation[J]. Nonferrous Metals,2014(2):4-6.
[7]丁小华.露天矿安全高效爆破智能化动态设计系统的研究与应用[D].徐州:中国矿业大学,2014.
[8]沈立晋,刘颖,汪旭光.国内外露天矿山台阶爆破技术[J].工程爆破,2004,10(2):54-58.SHEN Lijin,LIU Ying,WANG Xuguang. Blasting technology in open pit mines at home and abroad[J]. Engineering Blasting,2004,10(2):54-58.
[9]谢全敏,夏元友,李新平.龙滩水电站蠕变体边坡的爆破振动控制研究[J].岩石力学与工程学报,2003,22(11):1929-1932.XIE Quanmin,XIA Yuanyou,LI Xinping. Study of blasting vibration control of creep mass slope of longtan hydropower station[J].Chinese Journal of Rock Mechanics and Engineering,2003,22(11):1929-1932.
[10]夏祥,李海波,李俊如.岩体爆生裂纹的数值模拟[J].岩土力学,2006,27(11):1987-1991.XIA Xiang,LI Haibo,LI Junru.Numerical simulation of blast-induced cracks in rock[J]. Rock and Soil Mechanics,2006,27(11):1987-1991.
[11]郭可伟,陈继府,史维升.空气间隔器在深孔台阶爆破中的应用[J].爆破器材,2013,42(2):44-47.GUO Kewei,CHEN Jifu,SHI Weisheng.The application of air interval charging in deep-hole blasting[J]. Explosive Materials,2013,42(2):44-47.
[12]傅洪贤,李克民.露天煤矿高台阶抛掷爆破参数分析[J].煤炭学报,2006,31(4):442-445.FU Hongxian,LI Kemin.Analysis of high bench cast blasting parameters in surface coal mines[J].Journal of China Coal Society,2006,31(4):442-445.
[13]李启月.深孔爆破破岩能量分析及其应用[D].长沙:中南大学,2008.
[14] ANDREWS R,NYGAARD G,ENGLER R,et al.Methods of using logs to quantify drill ability[C].Rocky Mountain Oil&Gas Technology Symposium,2007.
[15] GOETZ J N,BRENNING A,PETSCHKO H,et al.Evaluating machine learning and statistical prediction techniques for landslide susceptibility modelling[J]. Computers and Geosciences,2015(3):1025-1030.
[16] MANUEL Fernández-Delgado,EVA Cernadas,SEN’EN Barro.Do we need hundreds of classifiers to+solve real world classification problems[J].Journal of Machine Learning Research,2014,15:3133-3181.
[17]张洪,邹乐君,沈晓华.BP神经网络在测井岩性识别中的应用[J].地质与勘探,2002,38(6):63-65.ZHANG Hong,ZOU Lejun,SHEN Xiaohua.The application of BP neural network in well lithology identification[J]. Geology and Prospecting,2002,38(6):63-65.
[18]孙文彬,刘希亮,谭正龙,等.基于抛掷爆破预测的BP神经网络参数优化方法[J].煤炭学报,2012,37(S1):59-64.SUN Wenbin,LIU Xiliang,TAN Zhenglong,et al. Parameter optimization of BP-neural network based on the forecast of cast blasting[J].Journal of China Coal Society,2012,37(S1):59-64.
[19]董师师,黄哲学.随机森林理论浅析[J].集成技术,2013,2(1):1-7.DONG Shishi,HUANG Zhexue. A brief theoretical overview of random forests[J]. Journal of Integration Technology,2013,2(1):1-7.
[20]张华伟,王明文,甘丽新.基于随机森林的文本分类模型研究[J].山东大学学报:理学版,2006,41(3):139-143.ZHANG Huawei,WANG Mingwen,GAN Lixin. Automatic text classification model based on random forest[J]. Journal of Shan Dong University:Science Edition,2006,41(3):139-143.
[21]赵吉锋,现场混装炸药远程监控系统设计[D].大连:大连海事大学,2012.