基于地震正演模拟和SVM的煤与瓦斯突出危险区预测研究
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摘要
煤与瓦斯突出是指煤矿井下采掘过程中发生的一种瓦斯突然从煤层中大量涌出的复杂动力现象,它直接影响到煤矿生产的各个环节,严重威胁矿井安全生产和职工的人身安全。定量查明并预测煤与瓦斯突出危险区,是当前煤矿生产中亟待解决的重要课题,也是建设数字矿山需要关注的重要领域与目标。
论文针对煤矿应用需求,在国家自然科学基金项目等项目支持下,围绕煤与瓦斯突出危险区定量预测目标,从时、空多角度分析了瓦斯突出危险区的波(主要是地震波)、场(瓦斯含量数据、煤厚、埋深、瓦斯压力)等信息的演变特征,揭示了地震属性、地质数据变化与瓦斯突出危险区之间的耦合关系。采用地震数值模拟的方法,通过建立煤与瓦斯地质模型正演地震剖面,从中提取地震属性并对其进行优化约简,运用支持向量机(SVM)方法定量研究了地震属性约简集和瓦斯含量两者之间的非线性关系,进而预测煤与瓦斯突出危险区,探讨了利用叠后地震数据预报煤与瓦斯突出危险区的可行性和有效性,从而形成了瓦斯突出危险区信息特征提取与优选技术。论文取得的主要研究成果如下:
构建了多个含瓦斯煤层的地质和地球物理典型模型。依据地质和钻井数据,利用Backus等效介质理论和Hudson等效介质理论,针对不同的煤层结构分别计算出了其所对应的物性参数,并以此为基础构建了瓦斯富集条件下的地质和地球物理典型模型。
提出了基于地震正演模拟、地震属性技术和支持向量机(SVM)预测煤与瓦斯突出危险区的研究方法,并将其成功应用于实际预测中。首先,运用有限差分算法构建了多个典型正演地震剖面,通过对地震剖面煤层反射波的属性分析,获得了相应的地震属性;其次,使用粗糙集算法对地震属性进行了约简,确定影响瓦斯突出的主要地震属性集;使用支持向量机(SVM)算法定量研究了地震属性约简集和瓦斯含量两者之间的非线性关系,并使用晋城矿区某煤矿的实测数据对提出的方法进行了验证,结果表明,论文提出的方法具有较高的可靠性和实用性,为利用叠后地震数据预测瓦斯突出危险区提供了一条新途径。
以淮南矿区某矿深部采区为例,进行了煤与瓦斯突出危险区预测实证研究。首先,构建了研究区的地质与地球物理模型,运用有限差分算法生成了正演地震剖面,通过对地震属性和地震谱分解的分析,地震属性与有关地质属性的组合,构建了三个瓦斯危险区预测模型,并对预测模型的有效性进行了检验和比较。结果表明,将煤层厚度和埋深两个地质参数与地震正演模拟属性数据进行组合构建的煤与瓦斯突出危险区预测模型,较具实用性、实效性和可操作性。最后,利用GIS平台,进行了煤与瓦斯突出危险区预测,进行了成果空间展示。
Coal and gas outburst refers to a complex kinetic phenomenon that gas is given off in great amount from coalbeds in the mining process in underground workplace of coal mine. The outburst has a direct impact on each and every aspect of the production of coal mining, and poses a huge threat to safe production and life of the miners. Quantitative ascertaining and predicting of coal and gas outburst area is a critical problem that needs to be addressed at present time in the process of coal mining, and it is also an important concern and goal for building digitalized mines.
Supported by such programs as the National Natural Science Funds and taking into consideration the application requirement of coal mines, this dissertation is centered on the discussion of quantitative prediction goal of coal and gas outburst risk area, analyzes the evolving properties of such information as waves (mainly seismic waves) and fields (data of gas content, coal thickness, depth of burial, and gas pressure) of coal and gas outburst risk areas from the temporal and spatial perspectives, and finally reveals the coupling relationship between seismic properties, geological data variation and gas outburst risk areas. Adopting the methodology of seismic modeling data and building the forward seismic section of coal-and-gas geological model, the current dissertation extracts, optimizes and reduces seismic attributes, on the strength of which the non-linear relationship between reductions set of seismic attributes and gas bearing capacity is studied quantitatively by using Support Vector Machine (SVM) algorithm. Hence, the prediction is made of the coal and gas outburst risk area, and feasibility and validity of predicting coal and gas outburst risk area is approached by referring to post-stack seismic data. The research findings achieved in this dissertation are mainly as follows:
Several typical geological and geographical models on gaseous coalbeds are constructed. On the basis of geological and drilling data, this research employs Effective Medium Theory respectively by Backus and Hudson to compute the physical property parameters of different coalbeds respectively with regards to their structures. Hence, the typical geological and geophysical model is constructed under the condition of gas concentration.
Based on seismic forward modeling, seismic property technology and Support Vector Machine, research methodologies of predicting coal and gas outburst risk areas are proposed and successfully put to practical prediction. First, the finite difference algorithm is used to construct several typical forward seismic sections; the property of reflective waves on the seismic section coalbed is then analyzed, thus the corresponding seismic properties are obtained. Second, the seismic properties are reduced by using rough set algorithm, and the main seismic property set is determined of influencing gas outburst. Finally, the non-linear relationship between reductions set of seismic properties and gas bearing capacity is studied quantitatively by using Support Vector Machine (SVM) algorithm; by using field data from One Coal Mine of Jincheng Mining Area, the proposed methodologies are verified. The findings indicate that the methodologies proposed in this current dissertation are highly reliable and practicable, and provide a new approach to predict gas outburst risk area by having recourse to post-stack seismic data.
The positivistic research is undertaken on the predictoin of coal and gas outburst risk areas by taking the deep mining area in Huainan Mining Area as an example. To start with, the geological and geophysical model of the research area is built, and the seismic forward section is then generated by utilizing finite difference algorithm. Through the analysis of seismic properties and seismic spectral decomposition, and the grouping of seismic properties and geological properties associated with seismic properties, three predicting models are established on gas risk areas to test and compare their efficiency. The result shows that the model for predicting coal-and-gas outburst risk area, which is constructed by combining the two geological parameters of coalbed thickness and burial depth with seismic forward simulated property data, is the more practical, time-effective and operable. Finally, the GIS platform is used to present the achievements for predicting coal and gas outburst risk area.
This dissertation has 71 diagrams and graphs, 28 tables, and 102 reference entries.
论文针对煤矿应用需求,在国家自然科学基金项目等项目支持下,围绕煤与瓦斯突出危险区定量预测目标,从时、空多角度分析了瓦斯突出危险区的波(主要是地震波)、场(瓦斯含量数据、煤厚、埋深、瓦斯压力)等信息的演变特征,揭示了地震属性、地质数据变化与瓦斯突出危险区之间的耦合关系。采用地震数值模拟的方法,通过建立煤与瓦斯地质模型正演地震剖面,从中提取地震属性并对其进行优化约简,运用支持向量机(SVM)方法定量研究了地震属性约简集和瓦斯含量两者之间的非线性关系,进而预测煤与瓦斯突出危险区,探讨了利用叠后地震数据预报煤与瓦斯突出危险区的可行性和有效性,从而形成了瓦斯突出危险区信息特征提取与优选技术。论文取得的主要研究成果如下:
构建了多个含瓦斯煤层的地质和地球物理典型模型。依据地质和钻井数据,利用Backus等效介质理论和Hudson等效介质理论,针对不同的煤层结构分别计算出了其所对应的物性参数,并以此为基础构建了瓦斯富集条件下的地质和地球物理典型模型。
提出了基于地震正演模拟、地震属性技术和支持向量机(SVM)预测煤与瓦斯突出危险区的研究方法,并将其成功应用于实际预测中。首先,运用有限差分算法构建了多个典型正演地震剖面,通过对地震剖面煤层反射波的属性分析,获得了相应的地震属性;其次,使用粗糙集算法对地震属性进行了约简,确定影响瓦斯突出的主要地震属性集;使用支持向量机(SVM)算法定量研究了地震属性约简集和瓦斯含量两者之间的非线性关系,并使用晋城矿区某煤矿的实测数据对提出的方法进行了验证,结果表明,论文提出的方法具有较高的可靠性和实用性,为利用叠后地震数据预测瓦斯突出危险区提供了一条新途径。
以淮南矿区某矿深部采区为例,进行了煤与瓦斯突出危险区预测实证研究。首先,构建了研究区的地质与地球物理模型,运用有限差分算法生成了正演地震剖面,通过对地震属性和地震谱分解的分析,地震属性与有关地质属性的组合,构建了三个瓦斯危险区预测模型,并对预测模型的有效性进行了检验和比较。结果表明,将煤层厚度和埋深两个地质参数与地震正演模拟属性数据进行组合构建的煤与瓦斯突出危险区预测模型,较具实用性、实效性和可操作性。最后,利用GIS平台,进行了煤与瓦斯突出危险区预测,进行了成果空间展示。
Coal and gas outburst refers to a complex kinetic phenomenon that gas is given off in great amount from coalbeds in the mining process in underground workplace of coal mine. The outburst has a direct impact on each and every aspect of the production of coal mining, and poses a huge threat to safe production and life of the miners. Quantitative ascertaining and predicting of coal and gas outburst area is a critical problem that needs to be addressed at present time in the process of coal mining, and it is also an important concern and goal for building digitalized mines.
Supported by such programs as the National Natural Science Funds and taking into consideration the application requirement of coal mines, this dissertation is centered on the discussion of quantitative prediction goal of coal and gas outburst risk area, analyzes the evolving properties of such information as waves (mainly seismic waves) and fields (data of gas content, coal thickness, depth of burial, and gas pressure) of coal and gas outburst risk areas from the temporal and spatial perspectives, and finally reveals the coupling relationship between seismic properties, geological data variation and gas outburst risk areas. Adopting the methodology of seismic modeling data and building the forward seismic section of coal-and-gas geological model, the current dissertation extracts, optimizes and reduces seismic attributes, on the strength of which the non-linear relationship between reductions set of seismic attributes and gas bearing capacity is studied quantitatively by using Support Vector Machine (SVM) algorithm. Hence, the prediction is made of the coal and gas outburst risk area, and feasibility and validity of predicting coal and gas outburst risk area is approached by referring to post-stack seismic data. The research findings achieved in this dissertation are mainly as follows:
Several typical geological and geographical models on gaseous coalbeds are constructed. On the basis of geological and drilling data, this research employs Effective Medium Theory respectively by Backus and Hudson to compute the physical property parameters of different coalbeds respectively with regards to their structures. Hence, the typical geological and geophysical model is constructed under the condition of gas concentration.
Based on seismic forward modeling, seismic property technology and Support Vector Machine, research methodologies of predicting coal and gas outburst risk areas are proposed and successfully put to practical prediction. First, the finite difference algorithm is used to construct several typical forward seismic sections; the property of reflective waves on the seismic section coalbed is then analyzed, thus the corresponding seismic properties are obtained. Second, the seismic properties are reduced by using rough set algorithm, and the main seismic property set is determined of influencing gas outburst. Finally, the non-linear relationship between reductions set of seismic properties and gas bearing capacity is studied quantitatively by using Support Vector Machine (SVM) algorithm; by using field data from One Coal Mine of Jincheng Mining Area, the proposed methodologies are verified. The findings indicate that the methodologies proposed in this current dissertation are highly reliable and practicable, and provide a new approach to predict gas outburst risk area by having recourse to post-stack seismic data.
The positivistic research is undertaken on the predictoin of coal and gas outburst risk areas by taking the deep mining area in Huainan Mining Area as an example. To start with, the geological and geophysical model of the research area is built, and the seismic forward section is then generated by utilizing finite difference algorithm. Through the analysis of seismic properties and seismic spectral decomposition, and the grouping of seismic properties and geological properties associated with seismic properties, three predicting models are established on gas risk areas to test and compare their efficiency. The result shows that the model for predicting coal-and-gas outburst risk area, which is constructed by combining the two geological parameters of coalbed thickness and burial depth with seismic forward simulated property data, is the more practical, time-effective and operable. Finally, the GIS platform is used to present the achievements for predicting coal and gas outburst risk area.
This dissertation has 71 diagrams and graphs, 28 tables, and 102 reference entries.
引文
[1]何继善.瓦斯突出地球物理研究[M].长沙:中南工业大学出版社, 1999
[2]程五一,张序明等.煤与瓦斯突出区域预测理论及技术[M].北京:煤炭工业出版社,2005.
[3]周世宁.煤层瓦斯赋存与流动理论[M].北京:煤炭工业出版社,1997.
[4]俞启香.矿井瓦斯防治[M].徐州:中国矿业大学出版社,1992:66-67.
[5]辛海会,徐超,杜欣.煤与瓦斯突出工作面预测技术发展现状分析[J].山东煤炭科技,2010,(1):200-202.
[6]杨威,安明燕,王秋菊.人工智能神经网络在煤矿瓦斯重大危险源评价中的定量分析[J].露天采矿技术,2007,(5):57-59.
[7]聂百胜,何学秋,王恩元等.煤与瓦斯突出预测技术研究现状及发展趋势[J].中国安全科学学报,2003,13(6):40-43.
[8]南存全,冯夏庭.基于SVM的煤与瓦斯突出区域预测研究[J].岩石力学与工程学报,2005,24(2):263–267.
[9]杨凌霄,沈鹰,侯国栋.基于支持向量机的煤与瓦斯突出预测研究[J].河南理工大学学报,2006,25(5):348-352.
[10]孙林,杨世元.基于最小二乘支持向量机的煤层瓦斯含量预测[J].煤矿安全,2009,2:10-13.
[11]汤友谊,陈江峰,彭立世等.无线电波坑道透视构造煤的研究[J].煤炭学报,2002,27(3):254-258.
[12]郭然,董秀桃,张旭刚.综采面地质小构造无线电波坑道透视技术[J].煤炭科学技术.2009,37,11.
[13]岳洪波,李东会.矿井小构造与瓦斯的坑道无线电波透视法应用分析[J].矿业快报,2007,(4):65-67.
[14]汤友谊,陈江峰等.瓦斯突出煤体探测的物性前提及应用[J].焦作工学院学报,2000,19(6):407-410.
[15]石显鑫,蔡栓荣,冯宏等.利用声发射技术预测预报煤与瓦斯突出[J].煤田地质与勘探,1998,26(3):60-65.
[16]单德晗,卢国斌.声发射技术预测煤与瓦斯突出的研究[J].中国科技博览,2009,(28):130-131.
[17]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):1-7.
[18]聂韧,赵旭生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨[J].矿业安全与环保, 2004,(4):36-38.
[19]何学秋,刘明举.含瓦斯煤岩破坏电磁动力学[M].徐州:中国矿业大学出版社,1995.
[20]王恩元,何学秋,聂百胜等.电磁辐射法预测煤与瓦斯突出原理[J].中国矿业大学学报,2000,29(3):225-229.
[21]聂百胜,何学秋,王恩元等.用电磁辐射法非接触预测煤与瓦斯突出[J].煤矿安全,2000(2):41-43.
[22]白慧敏,李忠辉,沈荣喜等.电磁辐射技术在煤与瓦斯突出预测中的应用[J].煤矿安全, 2010,(6):26-28.
[23]漆旺生,凌标灿,蔡嗣经.煤与瓦斯突出预测研究动态及展望[J].中国安全科学学报,2003,13(12):1-4.
[24]郝吉生,袁崇孚.模糊神经网络技术在煤与瓦斯突出预测中的应用[J].煤炭学报,1999,24(6):624-627.
[25]张宏伟,陈学华,王魁军.地层结构的应力分区与煤瓦斯突出预测分析[J].岩石力学与工程学报,2000,19(4):462-465.
[26]郭德永,韩德馨,张建国.平顶山矿区构造煤分布规律及成因研究[J].煤炭学报,2002,27(3):249-253.
[27]牛立东.基于瓦斯地质理论的煤与瓦斯突出机理分析[J].山西煤炭管理干部学院学报,2008,(1):100-101.
[28]彭苏萍,高云峰,杨瑞召等.AVO探测煤层瓦斯富集的理论探讨和初步实践-以淮南煤田为例[J].地球物理学报,2005,48(6): 1475-1486.
[29] S. Vlastos, E. liu, et al. Numerical simulation of wave propagation in media with discrete distributions of fractures: effects of fractrue sizes and spatial distributions [J]. Geophysics, J. Int., 2003(152): 649-668.
[30]董守华.煤弹性各向异性系数测试与P波方位各向异性裂缝评价技术[D].徐州:中国矿业大学,2004.
[31]崔若飞,钱进,陈同俊等.利用地震P波确定煤层瓦斯富集带的分布[J].煤田地质与勘探,2007,35(6):054-057.
[32]崔若飞,钱进,高级等.煤田地震勘探技术新进展(3)-地震属性技术[J].中国科技论文在线.
[33]彭苏萍,杜文凤,苑春方等.不同结构类型煤体地球物理特征差异分析和纵横波联合识别与预测方法研究[J].地质学报,2008,82(10):1311-1322.
[34]刘伍,崔若飞,钱进.利用方位角道集处理方法预测煤层裂隙[J].中国煤炭地质,2008,20(3):59-61.
[35] Per Avseth,Tapan Mekerji,Gary Mavko著.李来林等译.定量地震解释[M],石油工业出版社,2009年4月:171-173.
[36]中国矿业学院物探教研室编.中国煤田地球物理勘探[M].煤炭工业出版社, 1981年6月: 183-187.
[37]董守华著.地震资料煤层横向预测与评价方法[M].中国矿业大学出版社. 2004年9月:1-5
[38] Aimoghrabi H, Lang J. Layers and bright spots[J].Geophyics,1986,51(3):699-709 .
[39] Farr J B. High-resolution seismic methods improve stratigraphic exploration. Oil & Gas Journal[J], 1977, 75(48):182-188.
[40]唐文榜.地震反射法中薄煤层分辨能力的研究[J].地球物理学报,1987,30(6):641-652
[41]王大曾.瓦斯地质[M].北京:煤炭工业出版社,1992:46-68.
[42]姚宇平,周世宁.含瓦斯煤的力学性质[J].中国矿业大学学报, 1988, 17(1):1-7.
[43]冯增朝,赵阳升,杨栋,等.瓦斯排放与煤体变形规律试验研究[J].辽宁工程技术大学学报, 2006, 25(1):21~23.
[44] Vapnik V N. Support vector method for function approximation,regression and signal processing[J]. Neural information processing systems, Cambridge,MA: MIT press.1996,9:281-287.
[45] C.J.C Burges. A tutorial on support vector machines for pattern recognition. Knowledge discovery and data mining[J}. 1998,2(2):121-167.
[46]李国政,王猛,曾华军.支持向量机导论[M].北京:电子工业出版社,2000.5.
[47] C Comes,V Vapnik. Support Vector Networks[J].Machine Learning,1995,20(3): 273-297.
[48] Osuna E.,Freund R. and Girosi F. Training support vector machines: An application to face detection. In:Puerto Rico,edt. Proceedings of CVPR’97,1997.
[49] Platt J C .Fast Training of SVMs Using Sequential Minimal Optimization[J].Cambridge, MA: MIT Press,1998:185-208.
[50] Keerthis S S.A fast iterative nearest point algorithm for support vector machine classifier design[J]. IEEE Transactions on Neural Networks,2000,11(1):124-136.
[51] S. Abe and T. moue. Fuzzy support vector machines for multiclass problems[C]. Proceedings of the Tenth European Symposium on Artificial Neural Networks,2002: 113-118.
[52] C. W. Hsu and C. J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):41 5-425.
[53] C Chang,C J Lin. LIBSVM:a library for support vector machines [J].2005
[54] U .KreB e1.Pairwise classification and support vector machine[J].Cambridge,MA:MIT Press, 1999: 255-268.
[55] C Huang,L S Davis,J R G Townshend. An assessment of support vector machines for land cover classification[J]. Int J Remote Sens,2002,23:725-749.
[56] Guyon I, Elissee A. An Introduction to Variable and Feature Selection[J],Joural of machine learning research, 2003,3:1157- 1182.
[57] R Nilsson, JM Pela, J Bjirkegren, J Tegner, Consistent Feature Selection for Pattern Recognition[J], Journal of Machine laming Research 2007,8:5 89-612.
[58]俞胜益.基于支持向量机的瓦斯预警专家系统的研究[D].西安:西安科技大学图书馆,2009,5-6.
[59] Hochberg J,Jackson K,Stallings C et al. NADIR: An Automated System For Detecting Networking Intrusion And Misuse [J].Computers And Security, 1993,12(3):235-248.
[60]郭丽娟,孙世宇,段修生.支持向量机及核函数研究[J].科学技术与工程,2008,8(2):487-490.
[61]王亮中,侯杰.支持向量机及其核函数[J].辽阳石油化工高等专科学校学报,2001,17(4):31‐34.
[62]邓小文.支持向量机参数选择方法分析[J].福建电脑,2005,11:30-31.
[63]奉国和.SVM分类核函数及参数选择比较[J].计算机工程与设计,2011,43(7):123-124.
[64]张学工.关于统计学习理论与支持向量机[J].自动化学报.2000,26(1):32-42.
[65] Quincy Chen and Steve Sidney. Seismic attribute technology for reservoir forecasting and monitoring [J]. The Leading Edge, 1997, 16(5):445-456.
[66]邹才能等编著.油气勘探开发实用地震新技术[M].北京:石油工业出版社, 2002.
[67]印兴耀等编著.地震技术新进展[M]东营:中国石油大学出版社,2006.3
[68] Pawlak Z. Rough sets. International Journal of Computer and Information Sciences[J], 1982, 11:341-356
[69] Pawlak Z. Rough Sets-Theoretical Aspects of Reasoning about Data. Dordrecht[M],Kluwer Academic Publishers, 1991
[70] Kryszkiewicz M.Comparative study of alternative types of knowledge reduction in insistent systems[J].International Journal of Intelligent System,2001,16:105-120.
[71]张文修,梁怡,吴伟志.信息系统与知识发现[M].北京:科学出版社,2003.
[72] Beynon M. Redacts within the variable precision rough sets model: A further investigation[J]. European Journal of Operational Research, 2001, 134: 592-605.
[73] Zhang W X, Mi J S, Wu W Z. Approaches to knowledge reductions in inconsistent systems[J]. International Journal of Intelligent Systems, 2003, 18: 989-1000.
[74] Qiu G F, Li H Z, Xu L D, et al. A knowledge processing method for intelligent systems based on inclusion degree[J]. Expert Systems, 2003, 20(4): 187-195.
[75] Mi J S, Wu W Z, Zhang W X. Approaches to knowledge reduction based on variable precision rough set model[J]. Information Sciences, 2004, 159: 255-272.
[76] Zhang M, Wu W Z. Knowledge reduction in information systems with fuzzy decisions[J]. Journal of Engineering Mathematics, 2003, 20(2): 53-58.
[77] Leung Y, Wu W Z, Zhang W X. Knowledge acquisition in incomplete information systems: A rough set approach[J]. European Journal of Operational Research, in press.
[78] Wu W Z, Zhang M, Li H Z, et al. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence[J]. Information Sciences, 2005, 174: 143-164.
[79] Skowron A, Rauszwer C. The discernibility matrices and functions in information systems[J]. In: Slowinski R,ed. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory.Dordrecht: Kluwer Academic Publishers, 1992. 331-362.
[80]武波,马玉祥.专家系统[M].第二版.北京:北京理工大学出版社, 2001.
[81]何国建,陶宏才.一种基于粗集理论的属性约简改进算法[J].计算机应用,2004,11(24):75~80
[82]廖勇,李元香,张凌海.一种基于关系模型的模糊数据库系统模型[J].计算机工程与设计, 2002, 23(10).
[83]黄海滨.机器学习及其主要策略[ J] .河池师范高等专科学校学报, 2000, 20(4) .
[84] Pawlak Z. Rough set approach to multi-attribute decision analysis [J]. European Journal of Operational Research, 1994, 72: 443-459
[85]古发明,尹成,丁峰.应用粗集理论优选地震属性的方法研究[J].西南石油大学学报,2007,11(29):1-4
[86]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社, 2001.
[87]贺懿.地震储层参数非线性反演与预测方法研究[硕士学位论文].中国海洋大学.2008.
[88]葛瑞·马沃克,塔潘·.木克基等.岩石物理手册[M].合肥.中国科学技术大学出版社,2008.
[89]王德利,何樵登.裂隙型单斜介质中弹性参数系数的计算及波的传播特性[J].吉林大学学报,2002,32(2):181-186.
[90]马在田,曹景忠,王家林等.计算地球物理学概论[M].同济大学出版社. 1997.
[91]杨顶辉,滕吉文.各向异性介质中三分量地震记录FCT有限差分模拟[J].石油地球物理勘探,1997,32(2):181-190.
[92] Alford R M,Kelly K R,Boore D M.Accuracy of finite-difference modeling of the acoustic wave equation[J]. Geophysics,1974,39(6):838-842.
[93] Virieux J.SH-wave propagation in heterogeneous media:Velocity-stress finite-difference method[J]. Geophysics, 1984,49(11):1933-1957.
[94] Levander A R. Fourth-order finite-difference P-SV seismograms[J]. Geophysics, 1988, 53(11): 1425-1436.
[95] Crase E. High-order(space and time)finite-difference modeling of elastic wave equation[C]. Ann.Inernat. Mtg.Soc.Explo.Geophys. Expanded Abstracts,1990:987-991.
[96] Sch?lkopf B, Burges C, Smola A. Advances in Kernel Methods: Support Vector Learning[M]. Cambridge, MA, USA: MIT Press, 1999: 373.
[97] Sch?lkopf B, Smola A. Learning with kernels: Support vector machines, regularization, optimization, and beyond[M]. Cambrigde: the MIT Press, 2002: 644.
[98]国家煤矿安全监察局.防治煤与瓦斯突出规定[M].北京:煤炭工业出版社, 2009:29.
[99]张宏.地震谱分解算法对比与局限性分析[J] .勘探地球物理进展, 2007, 30 ( 6) : 409- 414.
[100] Ramos A C B, Castagna J P. Useful approximations for converted-wave AVO [J] . Geophysics, [54] 2001,66( 6) : 1 721- 1 734.
[101]郑晓东. AVO理论和方法的一些新进展[J] .石油地球物理勘探, 1992, 27( 3) : 307-317.
[102]张克,汪云甲,陈同俊等.基于正演模拟和SVM的瓦斯突出危险区预测[J].中国矿业大学学报,2011,6(3):453-458.
[2]程五一,张序明等.煤与瓦斯突出区域预测理论及技术[M].北京:煤炭工业出版社,2005.
[3]周世宁.煤层瓦斯赋存与流动理论[M].北京:煤炭工业出版社,1997.
[4]俞启香.矿井瓦斯防治[M].徐州:中国矿业大学出版社,1992:66-67.
[5]辛海会,徐超,杜欣.煤与瓦斯突出工作面预测技术发展现状分析[J].山东煤炭科技,2010,(1):200-202.
[6]杨威,安明燕,王秋菊.人工智能神经网络在煤矿瓦斯重大危险源评价中的定量分析[J].露天采矿技术,2007,(5):57-59.
[7]聂百胜,何学秋,王恩元等.煤与瓦斯突出预测技术研究现状及发展趋势[J].中国安全科学学报,2003,13(6):40-43.
[8]南存全,冯夏庭.基于SVM的煤与瓦斯突出区域预测研究[J].岩石力学与工程学报,2005,24(2):263–267.
[9]杨凌霄,沈鹰,侯国栋.基于支持向量机的煤与瓦斯突出预测研究[J].河南理工大学学报,2006,25(5):348-352.
[10]孙林,杨世元.基于最小二乘支持向量机的煤层瓦斯含量预测[J].煤矿安全,2009,2:10-13.
[11]汤友谊,陈江峰,彭立世等.无线电波坑道透视构造煤的研究[J].煤炭学报,2002,27(3):254-258.
[12]郭然,董秀桃,张旭刚.综采面地质小构造无线电波坑道透视技术[J].煤炭科学技术.2009,37,11.
[13]岳洪波,李东会.矿井小构造与瓦斯的坑道无线电波透视法应用分析[J].矿业快报,2007,(4):65-67.
[14]汤友谊,陈江峰等.瓦斯突出煤体探测的物性前提及应用[J].焦作工学院学报,2000,19(6):407-410.
[15]石显鑫,蔡栓荣,冯宏等.利用声发射技术预测预报煤与瓦斯突出[J].煤田地质与勘探,1998,26(3):60-65.
[16]单德晗,卢国斌.声发射技术预测煤与瓦斯突出的研究[J].中国科技博览,2009,(28):130-131.
[17]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):1-7.
[18]聂韧,赵旭生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨[J].矿业安全与环保, 2004,(4):36-38.
[19]何学秋,刘明举.含瓦斯煤岩破坏电磁动力学[M].徐州:中国矿业大学出版社,1995.
[20]王恩元,何学秋,聂百胜等.电磁辐射法预测煤与瓦斯突出原理[J].中国矿业大学学报,2000,29(3):225-229.
[21]聂百胜,何学秋,王恩元等.用电磁辐射法非接触预测煤与瓦斯突出[J].煤矿安全,2000(2):41-43.
[22]白慧敏,李忠辉,沈荣喜等.电磁辐射技术在煤与瓦斯突出预测中的应用[J].煤矿安全, 2010,(6):26-28.
[23]漆旺生,凌标灿,蔡嗣经.煤与瓦斯突出预测研究动态及展望[J].中国安全科学学报,2003,13(12):1-4.
[24]郝吉生,袁崇孚.模糊神经网络技术在煤与瓦斯突出预测中的应用[J].煤炭学报,1999,24(6):624-627.
[25]张宏伟,陈学华,王魁军.地层结构的应力分区与煤瓦斯突出预测分析[J].岩石力学与工程学报,2000,19(4):462-465.
[26]郭德永,韩德馨,张建国.平顶山矿区构造煤分布规律及成因研究[J].煤炭学报,2002,27(3):249-253.
[27]牛立东.基于瓦斯地质理论的煤与瓦斯突出机理分析[J].山西煤炭管理干部学院学报,2008,(1):100-101.
[28]彭苏萍,高云峰,杨瑞召等.AVO探测煤层瓦斯富集的理论探讨和初步实践-以淮南煤田为例[J].地球物理学报,2005,48(6): 1475-1486.
[29] S. Vlastos, E. liu, et al. Numerical simulation of wave propagation in media with discrete distributions of fractures: effects of fractrue sizes and spatial distributions [J]. Geophysics, J. Int., 2003(152): 649-668.
[30]董守华.煤弹性各向异性系数测试与P波方位各向异性裂缝评价技术[D].徐州:中国矿业大学,2004.
[31]崔若飞,钱进,陈同俊等.利用地震P波确定煤层瓦斯富集带的分布[J].煤田地质与勘探,2007,35(6):054-057.
[32]崔若飞,钱进,高级等.煤田地震勘探技术新进展(3)-地震属性技术[J].中国科技论文在线.
[33]彭苏萍,杜文凤,苑春方等.不同结构类型煤体地球物理特征差异分析和纵横波联合识别与预测方法研究[J].地质学报,2008,82(10):1311-1322.
[34]刘伍,崔若飞,钱进.利用方位角道集处理方法预测煤层裂隙[J].中国煤炭地质,2008,20(3):59-61.
[35] Per Avseth,Tapan Mekerji,Gary Mavko著.李来林等译.定量地震解释[M],石油工业出版社,2009年4月:171-173.
[36]中国矿业学院物探教研室编.中国煤田地球物理勘探[M].煤炭工业出版社, 1981年6月: 183-187.
[37]董守华著.地震资料煤层横向预测与评价方法[M].中国矿业大学出版社. 2004年9月:1-5
[38] Aimoghrabi H, Lang J. Layers and bright spots[J].Geophyics,1986,51(3):699-709 .
[39] Farr J B. High-resolution seismic methods improve stratigraphic exploration. Oil & Gas Journal[J], 1977, 75(48):182-188.
[40]唐文榜.地震反射法中薄煤层分辨能力的研究[J].地球物理学报,1987,30(6):641-652
[41]王大曾.瓦斯地质[M].北京:煤炭工业出版社,1992:46-68.
[42]姚宇平,周世宁.含瓦斯煤的力学性质[J].中国矿业大学学报, 1988, 17(1):1-7.
[43]冯增朝,赵阳升,杨栋,等.瓦斯排放与煤体变形规律试验研究[J].辽宁工程技术大学学报, 2006, 25(1):21~23.
[44] Vapnik V N. Support vector method for function approximation,regression and signal processing[J]. Neural information processing systems, Cambridge,MA: MIT press.1996,9:281-287.
[45] C.J.C Burges. A tutorial on support vector machines for pattern recognition. Knowledge discovery and data mining[J}. 1998,2(2):121-167.
[46]李国政,王猛,曾华军.支持向量机导论[M].北京:电子工业出版社,2000.5.
[47] C Comes,V Vapnik. Support Vector Networks[J].Machine Learning,1995,20(3): 273-297.
[48] Osuna E.,Freund R. and Girosi F. Training support vector machines: An application to face detection. In:Puerto Rico,edt. Proceedings of CVPR’97,1997.
[49] Platt J C .Fast Training of SVMs Using Sequential Minimal Optimization[J].Cambridge, MA: MIT Press,1998:185-208.
[50] Keerthis S S.A fast iterative nearest point algorithm for support vector machine classifier design[J]. IEEE Transactions on Neural Networks,2000,11(1):124-136.
[51] S. Abe and T. moue. Fuzzy support vector machines for multiclass problems[C]. Proceedings of the Tenth European Symposium on Artificial Neural Networks,2002: 113-118.
[52] C. W. Hsu and C. J. Lin. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):41 5-425.
[53] C Chang,C J Lin. LIBSVM:a library for support vector machines [J].2005
[54] U .KreB e1.Pairwise classification and support vector machine[J].Cambridge,MA:MIT Press, 1999: 255-268.
[55] C Huang,L S Davis,J R G Townshend. An assessment of support vector machines for land cover classification[J]. Int J Remote Sens,2002,23:725-749.
[56] Guyon I, Elissee A. An Introduction to Variable and Feature Selection[J],Joural of machine learning research, 2003,3:1157- 1182.
[57] R Nilsson, JM Pela, J Bjirkegren, J Tegner, Consistent Feature Selection for Pattern Recognition[J], Journal of Machine laming Research 2007,8:5 89-612.
[58]俞胜益.基于支持向量机的瓦斯预警专家系统的研究[D].西安:西安科技大学图书馆,2009,5-6.
[59] Hochberg J,Jackson K,Stallings C et al. NADIR: An Automated System For Detecting Networking Intrusion And Misuse [J].Computers And Security, 1993,12(3):235-248.
[60]郭丽娟,孙世宇,段修生.支持向量机及核函数研究[J].科学技术与工程,2008,8(2):487-490.
[61]王亮中,侯杰.支持向量机及其核函数[J].辽阳石油化工高等专科学校学报,2001,17(4):31‐34.
[62]邓小文.支持向量机参数选择方法分析[J].福建电脑,2005,11:30-31.
[63]奉国和.SVM分类核函数及参数选择比较[J].计算机工程与设计,2011,43(7):123-124.
[64]张学工.关于统计学习理论与支持向量机[J].自动化学报.2000,26(1):32-42.
[65] Quincy Chen and Steve Sidney. Seismic attribute technology for reservoir forecasting and monitoring [J]. The Leading Edge, 1997, 16(5):445-456.
[66]邹才能等编著.油气勘探开发实用地震新技术[M].北京:石油工业出版社, 2002.
[67]印兴耀等编著.地震技术新进展[M]东营:中国石油大学出版社,2006.3
[68] Pawlak Z. Rough sets. International Journal of Computer and Information Sciences[J], 1982, 11:341-356
[69] Pawlak Z. Rough Sets-Theoretical Aspects of Reasoning about Data. Dordrecht[M],Kluwer Academic Publishers, 1991
[70] Kryszkiewicz M.Comparative study of alternative types of knowledge reduction in insistent systems[J].International Journal of Intelligent System,2001,16:105-120.
[71]张文修,梁怡,吴伟志.信息系统与知识发现[M].北京:科学出版社,2003.
[72] Beynon M. Redacts within the variable precision rough sets model: A further investigation[J]. European Journal of Operational Research, 2001, 134: 592-605.
[73] Zhang W X, Mi J S, Wu W Z. Approaches to knowledge reductions in inconsistent systems[J]. International Journal of Intelligent Systems, 2003, 18: 989-1000.
[74] Qiu G F, Li H Z, Xu L D, et al. A knowledge processing method for intelligent systems based on inclusion degree[J]. Expert Systems, 2003, 20(4): 187-195.
[75] Mi J S, Wu W Z, Zhang W X. Approaches to knowledge reduction based on variable precision rough set model[J]. Information Sciences, 2004, 159: 255-272.
[76] Zhang M, Wu W Z. Knowledge reduction in information systems with fuzzy decisions[J]. Journal of Engineering Mathematics, 2003, 20(2): 53-58.
[77] Leung Y, Wu W Z, Zhang W X. Knowledge acquisition in incomplete information systems: A rough set approach[J]. European Journal of Operational Research, in press.
[78] Wu W Z, Zhang M, Li H Z, et al. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence[J]. Information Sciences, 2005, 174: 143-164.
[79] Skowron A, Rauszwer C. The discernibility matrices and functions in information systems[J]. In: Slowinski R,ed. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory.Dordrecht: Kluwer Academic Publishers, 1992. 331-362.
[80]武波,马玉祥.专家系统[M].第二版.北京:北京理工大学出版社, 2001.
[81]何国建,陶宏才.一种基于粗集理论的属性约简改进算法[J].计算机应用,2004,11(24):75~80
[82]廖勇,李元香,张凌海.一种基于关系模型的模糊数据库系统模型[J].计算机工程与设计, 2002, 23(10).
[83]黄海滨.机器学习及其主要策略[ J] .河池师范高等专科学校学报, 2000, 20(4) .
[84] Pawlak Z. Rough set approach to multi-attribute decision analysis [J]. European Journal of Operational Research, 1994, 72: 443-459
[85]古发明,尹成,丁峰.应用粗集理论优选地震属性的方法研究[J].西南石油大学学报,2007,11(29):1-4
[86]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社, 2001.
[87]贺懿.地震储层参数非线性反演与预测方法研究[硕士学位论文].中国海洋大学.2008.
[88]葛瑞·马沃克,塔潘·.木克基等.岩石物理手册[M].合肥.中国科学技术大学出版社,2008.
[89]王德利,何樵登.裂隙型单斜介质中弹性参数系数的计算及波的传播特性[J].吉林大学学报,2002,32(2):181-186.
[90]马在田,曹景忠,王家林等.计算地球物理学概论[M].同济大学出版社. 1997.
[91]杨顶辉,滕吉文.各向异性介质中三分量地震记录FCT有限差分模拟[J].石油地球物理勘探,1997,32(2):181-190.
[92] Alford R M,Kelly K R,Boore D M.Accuracy of finite-difference modeling of the acoustic wave equation[J]. Geophysics,1974,39(6):838-842.
[93] Virieux J.SH-wave propagation in heterogeneous media:Velocity-stress finite-difference method[J]. Geophysics, 1984,49(11):1933-1957.
[94] Levander A R. Fourth-order finite-difference P-SV seismograms[J]. Geophysics, 1988, 53(11): 1425-1436.
[95] Crase E. High-order(space and time)finite-difference modeling of elastic wave equation[C]. Ann.Inernat. Mtg.Soc.Explo.Geophys. Expanded Abstracts,1990:987-991.
[96] Sch?lkopf B, Burges C, Smola A. Advances in Kernel Methods: Support Vector Learning[M]. Cambridge, MA, USA: MIT Press, 1999: 373.
[97] Sch?lkopf B, Smola A. Learning with kernels: Support vector machines, regularization, optimization, and beyond[M]. Cambrigde: the MIT Press, 2002: 644.
[98]国家煤矿安全监察局.防治煤与瓦斯突出规定[M].北京:煤炭工业出版社, 2009:29.
[99]张宏.地震谱分解算法对比与局限性分析[J] .勘探地球物理进展, 2007, 30 ( 6) : 409- 414.
[100] Ramos A C B, Castagna J P. Useful approximations for converted-wave AVO [J] . Geophysics, [54] 2001,66( 6) : 1 721- 1 734.
[101]郑晓东. AVO理论和方法的一些新进展[J] .石油地球物理勘探, 1992, 27( 3) : 307-317.
[102]张克,汪云甲,陈同俊等.基于正演模拟和SVM的瓦斯突出危险区预测[J].中国矿业大学学报,2011,6(3):453-458.