天然气水合物（地球物理属性）的神经网络识别方法及软件开发
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摘要
目前天然气水合物已被公认为未来能源的替代品,存在巨大的经济效益和潜在的战略意义,很多国家政府和科研单位为此投入了巨大资金进行水合物研究并已取得了一定的成果。在天然气水合物研究中,天然气水合物的识别占有极其重要的地位,可以为天然气水合物储量估算、钻探、开采提供科学的参考依据。为此,探索一条天然气水合物识别的新方法无疑具有重要的理论意义和实践意义。
本文通过借助于神经网络方法,对测井、地震数据进行处理,实现天然气水合物的识别。本研究得到了国家863高新技术研究与发展计划项目“天然气水合物勘探开发关键技术”课题(编号2006AA09202)的子课题“天然气水合物矿体的高精度地震定量评价软件开发”的资助,重点研究了天然气水合物神经网络识别与软件开发。论文以神经网络算法为天然气水合物识别和预测的主要算法,通过建立神经网络识别体系结构,搭建于分析、处理、解释和预测功能为一体的平台,设计神经网络识别体系模型,进而开发出具有实际应用价值的天然水合物神经网络识别系统。研究思路是在对测井数据与地震数据预处理、地震属性提取基础上,选用自组织神经网络进行分类,达到岩性识别和矿体边界识别的目的,选用BP神经网络方法,达到对水合物储量参数估算和预测的结果,从测井曲线、平面、地质体三种模式识别结果进行比对,为将来的勘探研究提供科学的数据资料。
天然气水合物只有在特定环境才能存在。本文阐述了天然气水合物存在三个必要特征：充足的烃类物质、温压条件及水合物聚集和移动的稳定空间(地质构造环境)。目前水合物的研究识别主要方法是地震特征、地球化学特征及地貌标志。根据这些识别方法,本文对国内外已展开的水合物研究进行简单的叙述,对目前水合物研究的关键问题进行分析。
神经网络能有效地解决地质数据关系复杂、非线性求解问题。本文阐述了神经网络的概念、特征、结构及基本原理,重点介绍了BP神经网络和自组织神经网络。两种神经网络有不同的网络结构、算法描述。BP神经网络是有导师学习方式,需要有网络输出与期望输出对比,而自组织神经网络属于无导师学习型,根据输入自动调整,主要完成聚类操作。在对它们的优点和不足进行反复研究、分析后,我们认为BP神经网络更适用于储量参数预测和估算,自组织神经网络更适用于岩性分类、矿体识别。
测井数据包含丰富的地质纵向信息,能较好的反映地层、岩性和储量参数间的差异性。利用测井数据进行储量参数估计和预测,所采用的方法是不同的。储量参数估算的方法是通过取测井的数据实测数据建模,并对该井测井曲线上的储量参数进行估算。储量参数预测则是通过取模型井的实测数据为网络训练的样本数据进行建模,将其它井的测井曲线输入到此模型中,得到该井的储量参数预测结果实现的。岩性分类选用自组织神经网络方法,测井曲线上的样本数据输入到模型中,得到岩性分类结果。分类的个数,由使用者提前定好。
地震数据进行预处理,提取地震属性数据。地震属性数据能更好的反映出地质信息,更利于水合物BSR及振幅空白带识别。地震属性数据进行主成分成析,选取部分属性,利用组织神经网络对地震属性数据进行分类,结果与测井结果反复比对,得出水合物矿体边界图及雕刻图。
地震—测井联合反演能将测井数据频带宽与地震数据横向信息相结合。计算并选取合适的子波,求得的合成记录与地震数据反复比对,最终确定层位。利用BP神经网络,井旁地震数据为输入,测井输出,建立波阻抗模型。将整个地震体数据输入,得到波阻抗数据体。用同样的方法,将神狐海域地震属性数据作为输入,储量参数作为输出,预测储量参数结果。
天然气水合物神经网络识别系统(SNET)采用QT4.3为开发平台,结合INT公司的图形处理插件开发而成。QT是一个跨平台的C++图形用户界面应用程序框架,使程序开发后可移植性强。它是完全面向对象,易扩展并允许真正的组件编程。INT公司的图形插件软件CarnacGeo,很好地解决了地震数据和测井数据的显示问题。
本软件采用中国南海神狐海域的地震、测井资料进行测试,包括3个地震剖面数据、1个地震体3D体数和8个站位测井曲线数据。测试结果得到了测井分类图8张、测井预测图40张。地震属性图30张,地震分类图5张,雕刻图1张,地震平面储量参数图2张,波阻抗图1张。研究成果在“863”项目成果验收发挥了重要作用,表明其方法具有实际应用价值,可用于天然气水合物识别。
天然气水合物地球物理勘探和神经网络算法仍然处于研究阶段,还有很多地方仍需改进。水合物识别系统整合了三种方法来进行识别：测井识别、地震识别、地震—测井联合识别。结果的显示方式可以以剖面形式显示,也可以以空间平面方式显示。
Currently, natural gas hydrate has been recognized as an alternative energy in the future. There is a huge potential economic and strategic significance, so governments and research institutions in many countries invested huge funds for this hydrate research and have achieved certain results. In gas hydrate research, the identification occupies an extremely important position, and it can provide scientific references for estimating gas hydrate reserves, drilling and mining. To this end, exploring a new method for identification of gas hydrate is undoubtedly of great theoretical and practical significance.
The thesis discusses the identification of the natural gas hydrate through the application of neural networks and processing of logging and seismic data. This research was funded by the sub-project "high-precision seismic quantitative evaluation software development of gas hydrate ore " under national 863 high-tech research and development project "key technologies of natural gas hydrate exploration and development" (No.2006AA09202), focusing on neural network necognition of gas hydrates and software development.
In this thesis, a platform integrating analysis, processing, interpretation and forecast was built and a neural network system model was designed, through establishment of neural network identification architecture and with neural network algorithm as the main algorithm for the identification and prediction of gas hydrate. Based on above work, a practical natural gas hydrate neural network recognition system was developed. The research logic is selecting self-organizing neural network for classification based on the pre-processing of logging & seismic data and extraction of seismic attributes, to identify the lithologic and ore body boundary identification; selecting BP neural network to estimate and predict parameters of hydrate reserves; then comparing the results of three identifications from log presentation, surface, geological bodies as to provide scientific data for future exploration research.
Gas hydrates can exist only in specific environment. This thesis presents three prerequisites for the existence of gas hydrate:sufficient hydrocarbons, temperature and pressure conditions and the stability of hydrate accumulation and moving space (geological environment). The main methods for hydrate identification research currently focus on seismic features, geochemistry and landscape signs. Based on these identification methods, the thesis provides a brief description on undergoing natural gas hydrate researches both at home and abroad, and analyzes the primary challenges on hydrate research.
Neural network can effectively deal with the complex relationship and non-linear equation of geological data. The thesis discusses the concept, characteristics, structure and basic principles of neural networks with focus on the BP and self-organizing neural networks which have different network structures and algorithm descriptions. BP neural network is instructed learning, which includes the comparison of network output with expected output, while self-organizing neural network is unsupervised learning, which mainly completes clustering operation according to automatic input adjustment. After repeated study on their advantages and disadvantages, analysis, we believe that BP neural network is more suitable for forecasting and estimating reserves parameters, while self-organizing neural network is more suitable for rock classification, ore body identification.
Logging data contains a wealth of longitudinal geological information, which can better reflect the discrepancy among stratum, lithology and reserve parameters. Different methods are applied when logging data is used to estimate and predict reserve parameters. To estimate reserve parameters needs build a model based on the measured logging data, then make estimation of reserve parameters located on log presentation; while to predict reserve parameters needs build a model based on network training sample data which is obtained from the measurement of model well, then input the log presentation of other wells into the newly-established model and make prediction of reserve parameters. Lithological classification uses self-organizing neural network method:input the sample data on the log presentation into the model to get classification results. The number of classification shall be preset by the user.
Seismic data is pre-processed to extract seismic attribute data which can better reflect the geological information as to facilitate the identification of hydrate BSR and amplitude blanking belts. Seismic attribute data is analyzed for its principal ingredients and partial attributes are selected, then the self-organizing neural network is used to classify the selected data. Finally gas hydrate ore body boundary maps and carving figures are obtained by repeated comparison of classification results with logging results.
Seismic—logging joint inversion can combine the frequency bandwidth of log data and horizontal information of seismic data. Then appropriate wavelet is calculated and selected, and synthetic seismogram is compared with the seismic data repeatedly to finally determine the layer. BP neural network is used to set up the wave impedance model with well seismic data as input and logging as output, and the entire seismic volume data is input into the model to get wave impedance data volume. The same method is used to predict reserve parameter results in Shenhu sea area by taking seismic attribute data as input and reserve parameter as output.
Neural network recognition system of natural gas hydrate (SNET) takes QT 4.3 as its development platform and integrates INT company's graphics processing plug-in module. QT is a cross-platform C++graphical user interface application framework that enables easy portability after program development. It is fully object-oriented, easily extensible and allows true component programming. INT's graphics plug-in software CarnacGeo is a good solution to the display problems for seismic data and well log data.
The software is tested with seismic and logging data from Shenhu area of China South Sea, which contains three SEGY files, one seismic 3D body data and eight stations logging data. The results are eight logging classification maps, forty logging forecast figure, thirty seismic attributes figures, five seismic classification figure, one engraving figure, two seismic surface storage parameter figures and one wave impedance figure. The research results play an important role in the acceptance inspection of "863" project, which indicates that the method is a practical way to identify gas hydrate.
Geophysical exploration and neural network algorithm for gas hydrate are under development, and many areas still need improvement. Hydrate recognition system incorporates three methods:well logging, seismic identification and seismic—logging joint identification. The results can be displayed both in profile and space plane.
本文通过借助于神经网络方法,对测井、地震数据进行处理,实现天然气水合物的识别。本研究得到了国家863高新技术研究与发展计划项目“天然气水合物勘探开发关键技术”课题(编号2006AA09202)的子课题“天然气水合物矿体的高精度地震定量评价软件开发”的资助,重点研究了天然气水合物神经网络识别与软件开发。论文以神经网络算法为天然气水合物识别和预测的主要算法,通过建立神经网络识别体系结构,搭建于分析、处理、解释和预测功能为一体的平台,设计神经网络识别体系模型,进而开发出具有实际应用价值的天然水合物神经网络识别系统。研究思路是在对测井数据与地震数据预处理、地震属性提取基础上,选用自组织神经网络进行分类,达到岩性识别和矿体边界识别的目的,选用BP神经网络方法,达到对水合物储量参数估算和预测的结果,从测井曲线、平面、地质体三种模式识别结果进行比对,为将来的勘探研究提供科学的数据资料。
天然气水合物只有在特定环境才能存在。本文阐述了天然气水合物存在三个必要特征：充足的烃类物质、温压条件及水合物聚集和移动的稳定空间(地质构造环境)。目前水合物的研究识别主要方法是地震特征、地球化学特征及地貌标志。根据这些识别方法,本文对国内外已展开的水合物研究进行简单的叙述,对目前水合物研究的关键问题进行分析。
神经网络能有效地解决地质数据关系复杂、非线性求解问题。本文阐述了神经网络的概念、特征、结构及基本原理,重点介绍了BP神经网络和自组织神经网络。两种神经网络有不同的网络结构、算法描述。BP神经网络是有导师学习方式,需要有网络输出与期望输出对比,而自组织神经网络属于无导师学习型,根据输入自动调整,主要完成聚类操作。在对它们的优点和不足进行反复研究、分析后,我们认为BP神经网络更适用于储量参数预测和估算,自组织神经网络更适用于岩性分类、矿体识别。
测井数据包含丰富的地质纵向信息,能较好的反映地层、岩性和储量参数间的差异性。利用测井数据进行储量参数估计和预测,所采用的方法是不同的。储量参数估算的方法是通过取测井的数据实测数据建模,并对该井测井曲线上的储量参数进行估算。储量参数预测则是通过取模型井的实测数据为网络训练的样本数据进行建模,将其它井的测井曲线输入到此模型中,得到该井的储量参数预测结果实现的。岩性分类选用自组织神经网络方法,测井曲线上的样本数据输入到模型中,得到岩性分类结果。分类的个数,由使用者提前定好。
地震数据进行预处理,提取地震属性数据。地震属性数据能更好的反映出地质信息,更利于水合物BSR及振幅空白带识别。地震属性数据进行主成分成析,选取部分属性,利用组织神经网络对地震属性数据进行分类,结果与测井结果反复比对,得出水合物矿体边界图及雕刻图。
地震—测井联合反演能将测井数据频带宽与地震数据横向信息相结合。计算并选取合适的子波,求得的合成记录与地震数据反复比对,最终确定层位。利用BP神经网络,井旁地震数据为输入,测井输出,建立波阻抗模型。将整个地震体数据输入,得到波阻抗数据体。用同样的方法,将神狐海域地震属性数据作为输入,储量参数作为输出,预测储量参数结果。
天然气水合物神经网络识别系统(SNET)采用QT4.3为开发平台,结合INT公司的图形处理插件开发而成。QT是一个跨平台的C++图形用户界面应用程序框架,使程序开发后可移植性强。它是完全面向对象,易扩展并允许真正的组件编程。INT公司的图形插件软件CarnacGeo,很好地解决了地震数据和测井数据的显示问题。
本软件采用中国南海神狐海域的地震、测井资料进行测试,包括3个地震剖面数据、1个地震体3D体数和8个站位测井曲线数据。测试结果得到了测井分类图8张、测井预测图40张。地震属性图30张,地震分类图5张,雕刻图1张,地震平面储量参数图2张,波阻抗图1张。研究成果在“863”项目成果验收发挥了重要作用,表明其方法具有实际应用价值,可用于天然气水合物识别。
天然气水合物地球物理勘探和神经网络算法仍然处于研究阶段,还有很多地方仍需改进。水合物识别系统整合了三种方法来进行识别：测井识别、地震识别、地震—测井联合识别。结果的显示方式可以以剖面形式显示,也可以以空间平面方式显示。
Currently, natural gas hydrate has been recognized as an alternative energy in the future. There is a huge potential economic and strategic significance, so governments and research institutions in many countries invested huge funds for this hydrate research and have achieved certain results. In gas hydrate research, the identification occupies an extremely important position, and it can provide scientific references for estimating gas hydrate reserves, drilling and mining. To this end, exploring a new method for identification of gas hydrate is undoubtedly of great theoretical and practical significance.
The thesis discusses the identification of the natural gas hydrate through the application of neural networks and processing of logging and seismic data. This research was funded by the sub-project "high-precision seismic quantitative evaluation software development of gas hydrate ore " under national 863 high-tech research and development project "key technologies of natural gas hydrate exploration and development" (No.2006AA09202), focusing on neural network necognition of gas hydrates and software development.
In this thesis, a platform integrating analysis, processing, interpretation and forecast was built and a neural network system model was designed, through establishment of neural network identification architecture and with neural network algorithm as the main algorithm for the identification and prediction of gas hydrate. Based on above work, a practical natural gas hydrate neural network recognition system was developed. The research logic is selecting self-organizing neural network for classification based on the pre-processing of logging & seismic data and extraction of seismic attributes, to identify the lithologic and ore body boundary identification; selecting BP neural network to estimate and predict parameters of hydrate reserves; then comparing the results of three identifications from log presentation, surface, geological bodies as to provide scientific data for future exploration research.
Gas hydrates can exist only in specific environment. This thesis presents three prerequisites for the existence of gas hydrate:sufficient hydrocarbons, temperature and pressure conditions and the stability of hydrate accumulation and moving space (geological environment). The main methods for hydrate identification research currently focus on seismic features, geochemistry and landscape signs. Based on these identification methods, the thesis provides a brief description on undergoing natural gas hydrate researches both at home and abroad, and analyzes the primary challenges on hydrate research.
Neural network can effectively deal with the complex relationship and non-linear equation of geological data. The thesis discusses the concept, characteristics, structure and basic principles of neural networks with focus on the BP and self-organizing neural networks which have different network structures and algorithm descriptions. BP neural network is instructed learning, which includes the comparison of network output with expected output, while self-organizing neural network is unsupervised learning, which mainly completes clustering operation according to automatic input adjustment. After repeated study on their advantages and disadvantages, analysis, we believe that BP neural network is more suitable for forecasting and estimating reserves parameters, while self-organizing neural network is more suitable for rock classification, ore body identification.
Logging data contains a wealth of longitudinal geological information, which can better reflect the discrepancy among stratum, lithology and reserve parameters. Different methods are applied when logging data is used to estimate and predict reserve parameters. To estimate reserve parameters needs build a model based on the measured logging data, then make estimation of reserve parameters located on log presentation; while to predict reserve parameters needs build a model based on network training sample data which is obtained from the measurement of model well, then input the log presentation of other wells into the newly-established model and make prediction of reserve parameters. Lithological classification uses self-organizing neural network method:input the sample data on the log presentation into the model to get classification results. The number of classification shall be preset by the user.
Seismic data is pre-processed to extract seismic attribute data which can better reflect the geological information as to facilitate the identification of hydrate BSR and amplitude blanking belts. Seismic attribute data is analyzed for its principal ingredients and partial attributes are selected, then the self-organizing neural network is used to classify the selected data. Finally gas hydrate ore body boundary maps and carving figures are obtained by repeated comparison of classification results with logging results.
Seismic—logging joint inversion can combine the frequency bandwidth of log data and horizontal information of seismic data. Then appropriate wavelet is calculated and selected, and synthetic seismogram is compared with the seismic data repeatedly to finally determine the layer. BP neural network is used to set up the wave impedance model with well seismic data as input and logging as output, and the entire seismic volume data is input into the model to get wave impedance data volume. The same method is used to predict reserve parameter results in Shenhu sea area by taking seismic attribute data as input and reserve parameter as output.
Neural network recognition system of natural gas hydrate (SNET) takes QT 4.3 as its development platform and integrates INT company's graphics processing plug-in module. QT is a cross-platform C++graphical user interface application framework that enables easy portability after program development. It is fully object-oriented, easily extensible and allows true component programming. INT's graphics plug-in software CarnacGeo is a good solution to the display problems for seismic data and well log data.
The software is tested with seismic and logging data from Shenhu area of China South Sea, which contains three SEGY files, one seismic 3D body data and eight stations logging data. The results are eight logging classification maps, forty logging forecast figure, thirty seismic attributes figures, five seismic classification figure, one engraving figure, two seismic surface storage parameter figures and one wave impedance figure. The research results play an important role in the acceptance inspection of "863" project, which indicates that the method is a practical way to identify gas hydrate.
Geophysical exploration and neural network algorithm for gas hydrate are under development, and many areas still need improvement. Hydrate recognition system incorporates three methods:well logging, seismic identification and seismic—logging joint identification. The results can be displayed both in profile and space plane.
引文
[1]宋海斌,耿建华,张文生.南海海域天然气水合物的初步研究[J].地球物理学进展,2001,44(5)：689-693.
[2]kvenvold K A. Gas hydrate-geological perspective and global change. Rev. Geophys., 1993,3(2):173-187.
[3]龚建明,陈建文,戴春山,等.中国海域天然气水合物资源远景[J].海洋地质动态,2003,19(8)：53-56.
[4]雷怀彦,郑艳红,吴保祥.天然气水合物勘探方法-BSR适用性探析[J].海洋石油,2002,114：1-7.
[5]Miller, J.J., Lee, M.W. and Von Huene. R..An analysis of seismic reflection from the base of gas hydrate zone off Peru[J]. Am. Assoc. Pet.Geol Bull,1991,75:910-924.
[6]栾锡武,秦蕴珊,张川华,等.东海陆坡及相邻槽底天然气水合物的稳定域分析[J].地球物理学报,2003,46(3)：467-475.
[7]Lippmannn R P. An introduction to computing with neural nets. IEEE. ASSP, April, 1987:4-22.
[8]杨木壮,王明君,吕万军.南海西北坡天然气水合物成矿条件研究[M].北京：气象出版社,2008：1-23.
[9]沙志彬,王宏斌,杨木壮,等.天然气水合物成矿带的识别技术研究[J].现代地质,2008,22(3)：438-446.
[10]陆敬安,杨胜雄,吴能友,等.南海神狐海域天然气水合物地球物理测井评价[J].现代地质,2008,22(3)：447-451.
[11]刘峰,吴时国,孙运宝.南海北部陆坡水合物分解引起海底不稳定性分析[J].地球物理学报,2010,53(4)：936-953.
[12]牛滨华,孙春岩,苏新,等.勘查地球化学方法适用用勘查天然气水合物依据[J].现代地质,2005,19(1)：61-65.
[13]蒋少涌,杨涛,薛紫晨,等.南海北部海区海底沉积物中孔隙水的cl-和sO4-浓度那异常特征对其天然气水合物的指示意义[J].现代地质,2005,19(1)：45-54.
[14]祝有海,饶竹,刘坚,等.南海西沙海槽S14站位的地球化学异常特征及其意义[J]. 现代地质,2005,19：39-44.
[15]龚跃华,杨胜雄,王宏斌,等.南海北部神狐海域天然气水合物成矿特征[J].现代地质,2009,23(2)：210-216.
[16]邱燕,王英民,王存斌,等.珠江口盆地白云凹陷中央底辟带的发现及识别[J].地球科学,2006,31(2)：209-213.
[17]翟光明.中国石油地质志(卷16)：沿海陆架及毗邻海域油气区[M].北京：石油工业出版社,1990.
[18]黎明碧,金翔龙,初凤友,等.神狐—一流暗沙隆起中部背后生代地层层序划分及沉积演化[J].沉积学报,2002,13(1)：31-33.
[19]刘铁树,何仕斌.半山海北部陆缘盆地深水区油气勘探前景[J].中国海上油气(地质),2001,15(3)：164-170.
[20]Berner U, Faber E. Hydrocarbon gases in surface sediments of the South China Sea [M]//Jin X, Kudrass H R, Pautot G. Marine Geology and Geophysics of the South China Sea. Beijing:China Ocean Press,1992:199-211.
[21]杨涛,杨竞红,蒋少涌.南海北部地区海洋沉积物中孔隙水的氢、氧同位素组成特征[J].地球学报,2003,24(6)：511-514.
[22]蒲晓强,钟少军,于雯泉,等.南海北部陆坡NH 21孔沉积物中自生硫化物及其硫同位素对深部甲烷和水合物存在的指示[J].科学通报,2006,51(24)：2874-2880.
[23]赵省民,聂凤军,曾凡刚,等.内生热液矿床与天然气水合物矿床形成机制的比较研究[J].地球学报,2001,22(3)：237-242.
[24]孙美琴,徐茂泉,许文彬,等.海洋天然气水合物的成矿机理与资源评价概述[J].台湾海峡,2004,23(4)：521-529.
[25]Ginsburg G, Guseynova R A, Dadashev A A, et al.. Gas hydrates of the southern Caspian[J]. International Geology Review,1992,34 (8):765-782.
[26]Sassen, R., MacDonald IR. Evidence of structure Hydrate, Gulf of Mexico continental slope[J]. Organic Geo chemistry,1994,22 (6):1029-1032.
[27]Sassen, R., S. Joye, S T Sweet, et al.. Thermogenic gas hydrate and hydrocarbon gases in complex chemosynthetic communities, Gulf of Mexico continental slope[J].Organic Geochemistry,1999,30:485-497.
[28]方银霞,黎明碧,金翔龙,等.东海冲绳海槽天然气水合物形成条件[J].科技通 报,2003,19(1)：1-5.
[29]Dillon W P, Max M D. Oceanic gas hydrate[M]//Max M D. Natural Gas Hydrate in Oceanic and Permafrost Environments. Dordrecht:Kluwer Academic Publishers,2003: 61-63.
[30]许威,邱楠生,孙长宇,等.不同因素对天然气水合物稳定带厚度的影响[J].天然气地球科学,2010,21(3)：528-534.
[31]王淑红,宋海斌,颜文.天然气水合物稳定带的计算方法与参数选择探讨[J].现代地质,2005,19(1)：101-107.
[32]Handa Y P. Effect of hydrostatic pressure and salinity on the stability of gas hydrates [J]. J Phys Chem,1990,94:2652-2657.
[33]Zatsep ina O Y, Buffett B A. Phase equilibrium of gas hydrate:Implications for the formation of hydrate in the deep sea floor[J]. Geophysical Research Letters,1997, 24(13):1567-1570.
[34]Xu W, Ruppel C. Predicting the occurrence, distribution and evolution of methane gas hydrate in the porous marine sediments[J]. Journal of Geophysical Research,1999,104: 5081-5096.
[35]Diaconescu C C, Kieckhefer R M, Knapp J H. Geophysical evidence for gas hydrates in the deep water of the South Caspian Basin, Azerbaija[J]. Marine and Petroleum Geology,2001,18:209-221.
[36]蒋少涌,凌洪飞,杨竞红.海洋浅表层沉积物和孔隙水的天然气水合物地球化学异常识别标志[J].海洋地质与第四纪,2003,23(1)：87-93.
[37]Brooks J M, Field M E,Kennicutt M C Ⅲ. Observations of gas hydrates in marine sediments, offshore northern California [J].Marine Geology,1991,96:103-109.
[38]龚建时,王红霞,陈建文等.天然气水合物在沉积地层中的分布模式[J].海洋地质动态,2004,20(6)：6-8.
[39]陈多福,王茂春,夏斌.中低纬度高原冻土带天然气水合物的产出特征：青藏高原冻土带天然气水合物的形成与分布预测[J].地球物理学报,2005,48(1)：164-171.
[40]金庆焕,张光学,杨木壮.天然气水合物资源概论[M].北京：科学出版社,2006：1-20.
[41]Kayen R E, Lee H J.Pleistocene slope instability of gas hydrate laden sediment on the Beaufort Sea Margin. Marine Geo technology,1991,10:125-141.
[42]Paull C K, Buelow W J, Ussler III W et al.Increased continental-margin slumping frequency during sea-level low stands above gas hydrate-bearing sediments. Geology, 1996,24(2):143-146.
[43]Kayen R E, Lee H J. Pleistocene slope instability of gas hydrate laden sediment on the Beaufort Sea margin [J]. Mar. Geo technol.,1991,10:125-141
[44]Totland A,God O R. Beam:an interactive GIS application for acoustic abundance estimation[C]//Proceedings oft he lstinternational symposium on GIS in fishery science. 2001:195-201.
[45]许红,黄君权,夏斌,等.最新国际天然气水合物研究现状及资源潜力评估(下)天然气工业,2005,6：18-23.
[46]Hyndman R D, Dallimore S R. Natural gas hydrate studies in Canada[J]. The recorder,2001,26(2):11-20.
[47]Aurora-JO GMEC-NR-Can. Mallik 2006-2008 Gas Hydrate Research Project Progress, Fire in t he Ice, Met hane Hydrate Newslet ter 2008.
[48]Collett T S. Potential of gas hydrates outlined[J]. Oil Gas,1992,78 (1),124-130.
[49]Kvenvolden K A. Gas hydrates geological perspective and global change. Rev Geophys, 1993,31,(2):173-187.
[50]Trehu A M,Bohrmann G,等.ODP 204航次科学报告：天然气水合物的分布和动力学[J].海洋地质动态,2004,20(5)：15-16.
[51]刘争平,何永富.人工神经网络在测井解释中的应用[J].地球物理学报,1995,38(增刊)：323-330.
[52]周辉,何椎登,徐世浙.人工神经网络非线性地震波形反演[J].石油物探,1997,36(1)：61-70.
[53]Cakleron-Macias C, Sen M, Stoffa P. Artificial neural networks for parameter estimation in geophysics[J]. Geophysical Prospecting,2000,48(1):21-47.
[54]丁柱,杨长春,陶宏根,等.神经网络反演双侧向电阻率测井曲线的物理约束[J].地球物理学进展,2002,17(2)：331-336.
[55]高隽.人工神经网络原理及仿真实例[M].北京：机械工业出版社,2003,8：44-63.
[56]侯媛彬,杜京义,枉梅.神经网络[M].陕西：西安电子科技大学出版社,2007：55-70.
[57]靳蕃,范俊波,谭永东.神经网络与神经计算机原理应用[M].成都：西南交通大学出版 社,1997.
[58]杨立强,宋海斌,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1)：34-37.
[59]Robert J,Schalkoff.Artificial Neural NetWorkrs [M].MIT Press and The Mc-Graw-Hill Companies,Inc,1997.
[60]TKohonen.Self-organized formation of to pologically correct feather maps[J].Biol., 1982,43:59-69.
[61]程涛,晏克勤.地质勘测中力学参数反演问题的回归神经网络方法[J].黄石理工学院学报,2007,23(2)：36-39.
[62]陆敬安,梁金强,罗文造.径向基函数神经网络在计算天然气水合物饱和度中的应用研究[J].海洋通报,2009,28(4)：102-106.
[63]张绍红.概率神经网络技术在地震岩性反演中应用[J].辽宁工程技术大学学报,2007,11：16-18.
[64]杨文采.神经网络算法在地球物理反演中的应用[J].石油物探,1995,34(2)：116-120.
[65]丁柱,杨长春,陶宏根.神经网络反演双侧向电阻率测井曲线的物理约束[J].2002,17(2)：331-336.
[66]薛林福,潘保芝.用自组织神经经网络自动识别岩性[J].长春科技大学学报,1999,29(2)：144-147.
[67]侯俊生,尉中良.自组织映射神经网络方法实现测井相定量识别[J].测井技术,1996,20(3)：197-200.
[68]尹寿鹏,王贵文.测井沉积学研究综述[J].地球科学进展,1999,14(5)：440-445.
[69]陈立英.测井技术在天然气水合物储层识别中应用前景[J].海洋地质动态,2006,22：14-16.
[70]梁劲,王明君,陆敬安,等.南海神狐含水合物地层测井响应特征[J].现代地质,2010,6：506-514.
[71]高兴军,于兴河,李胜利,等.地球物理测井在天然气水合物勘探中的应用[J].地球物理学进展,2003,4：305-310.
[72]Hyndman R D, Yuan T, Moran K. The concentration of deep sea hydrate from downhill electrical resistivity logs and laboratory data [J]. Earth and Planetary science Letters, 1999,172:167-177.
[73]李舟波.地球物理测井数据处理与综合解释[M].长春：吉林大学出版社,2003.
[74]手冢各彦.天然气水合物的测井解释[J],海洋地质动态,2003,19(6)：21-23.
[75]Yoshihiro Tsuji, Hishshi Ishida, Masani Nakamizu, et al.. Overview of MITI Nankai through wells a milestone in the evaluation of Methand hydrate resource [J].Resource Geology,2004,54(1):3-10.
[76]史斗,孙威权,朱岳年.国外天然气水合物研究进展[M].兰州大学出版,1992.
[77]Mathews M. Logging characteristics of methane hydrate [J]. The log analyst.1986, 27(2):26-63.
[78]Archeries G E. The electrical resistivity logs as an aid in determining some reservoir characteristics [J]. Geophysics,1956,21(1):41-70.
[79]欧阳健等.石油测井解释与储层描述[M].北京：石油出版社,1994：12-27.
[80]梁劲,王明君,王宏斌,等.南海神狐海域天然气水合物声波测井速度与饱和度关系分析.现代地质,2009,23(2)：217-223.
[81]Willoughby E C, Schwalenberg K, Edwards R N, et al. Assessment of Marine Gas Hydrate Deposits:A Comparative Study of Seismic, Electromagnet and Seafloor Compliance Methods [C]//Anon. Proceedings of the Fifth International Conference on Gas Hydrates. Trondheim, Norway:[s.n],2005:802-811.
[82]Timothy S Collett. Energy Resource Potential of Natural Gas Hydrates [J]. American Association of Petroleum Geologists,2002,86(11):1971-1992.
[83]陈光进,孙长宇,马庆兰.气体水合物科学技术.北京：化学工业出版社,2008.
[84]赵省民,吴必豪,王亚平,等.海底天然气水合物赋存的间接标志[J].地科学-中国地质大学报,2000,11：624-628.
[85]赖泽武,张一伟,熊琦华,等.具有趋势的克里格方法在测井资料数据标准化中的应用[J].石油勘探与开发,1999,26(6)：91-94.
[86]周叶,王家华,李换鹏,等.测井解释数据预处理的计算机实现[J].测井技术,1994,18(6)：431-435.
[87]刘爱群,盖永浩.测井约束反演过程中测井资料统计分析研究[J].地球物理学进展,2007,22(5)：1487-1492.
[88]张学芳,董月昌,慎国强,等.曲线重构技术在测井约束反演中的应用[J].石油勘探与开发,2005,32(3)：70-72.
[89]张静,陈启林,李延丽.测井曲线重构技术在储层预测中的应用[J].西北油气勘探,2005,17(4)：47-52.
[90]George A,Mcmechan. Curves for a point source lying on a boundary[J]. Bulletin of the Seismological Society of America,1977,67:1521-1527.
[91]陆敬安,闫桂京.天然气水合物测井与评价技术进展.海洋地质动态,2007,23(6)：31-36.
[92]Collet T S,Ladd J, Detection of gas hydrate with dowmhole logs and assessment of gas hydrate concentrations(saturations)and gas volumes on the Blake Ridge with electrical resistivity log data,1999, Proceedings of the Ocean Drilling Program[J].Scientific Results,1999,164:179-192.
[93]葛瑞·马沃可,塔潘·木克基,杰克·德沃金.岩石物理手册[M].中国科技大学出版社,2008.
[94]梁劲,王宏斌,,梁金强.Jason反演技术在天然气水合物速度分析中的应用[J].南海地质研究,2006,18(1)：114-120.
[95]王祝文,李舟波,刘菁华.天然气水合物的测井识别和评价[J].海洋地质和第四纪地质,2003,23(2)：97-102.
[96]雷利安,徐美茹,曹学良等.带趋势序贯指示模拟方法在东濮凹陷薄砂岩储层预测中的应用.石油地球物理勘探,2005,40(1)：92-96
[97]陈钢花,王永刚.Faust公式在声波曲线重构中的应用.勘探地球物理进展,2005,28(2)：125-128
[98]Paull C k,Matsumoto R,Wallace P J. Proceedings of the Ocean Drilling Program Leg 164,Seientific Results[M].College Station Texas:Ocean Drilling Program,2000.
[99]Yoshihiro Tsuji,Hisashi Ishida,Masaru Nakamizu,et al. Overview of the MITI Nankai Trough wells:A milestone in the evaluation of methane hydrate resources[J].Resource Geology,2004,54:3-10.
[100]Expedition 311 Scientists Integrated Ocean Drilling Program Expedition 311 Preliminary Report,Cascadia Margin Gas Hydrates [M].Washington :Integrated Ocean Drilling Program Management International Inc,2005.
[101]JoelD. Walls等著.利用岩石物理性质、地震属性、神经网络描述北海储层的特征：一项实例研究[J].海洋石油,2001,108：66-69.
[102]侯伯刚,杨银池,武站国.地震属性及其在储层预测中影响因素[J].石油地球物理勘 探,2004,39(5)：553-574.
[103]Chen Q, Steve S. Seismic Attribute Technology for Reservoir Forecasting and Monitoring [J].The Leading Edge,1997,16(5):445-456.
[104]李坦,殷小舟.地震属性的地质意义分析[J].复杂油气藏,2009,9：25-36.
[105]张德林.地震储层横向预测技术与实例[M].北京：石油工业出版社,1992.
[106]鲍祥生,尹成,赵伟,等.储层顶测的地震属性优选技术研究[J].石油物探,2006,45(1)：28-33.
[107]潘元林.油气地质地球物理综合勘探技术[M].北京：地震出版社,1998.
[108]李常茂,耿瑞伦.关于天然气水合物钻探思考[J].探矿工程,2000,3：5-8.
[109]Kaul N, Rosenberger A, Villinger H. Comparison of measured and BSR derived heat flow values, Makran accretionary prism, Pakistan. M arine Geology,2000,164(1-2): 37-51.
[110]Chen D F, Laerence M. Akinetic model for the pattern and amounts of hydrate precipitated from a gas steam:Application to the Bush Hill Vent Site, Green Canyon Block 185, Gulf of Mexico. Journal of Geophysics Research,2003,108:2058-2072.
[111]Kalachand Sain.Seismic Methods for recognition and evaluation of gas-Hydrates[J]. The 12th International Conference of IACMAG,2008,10.
[112]阳飞舟,崔永福,李青.地震属性分类及其应用[J].内蒙古化工,2009,2：94-97.
[113]宁松华.地震属性分析在托浦台储层预测中的应用[J].石油天然气学报,2006,28(5)：70-73.
[114]张德林.地震储层横向预测技术与实例[M].北京：石油工业出版社,1992.
[115]张延玲,杨长春,贾曙光.地震属性技术的研究和应用[J].地球物理学进展,2005,20(4)：1129-1133.
[116]Snons Cheong, Wonsik kim, Namhyung Koo.韩国东海Ulleung盆地天然气水合物沉积层的地震属性分析[J].海洋地质动态,2008,25(2)：21-22.
[117]S.肖普拉,K.J.马弗特著.地震属性在构造和地层学中的应用[M].中国石油集团东方地球物理勘探有限责任公司,2008：1-40.
[118]郭华军,刘庆成.地震属性技术的历史、现状及发展趋势[J].物探与化探,2008,32(1)：19-23.
[119]吴坚,张塞,闫桂花.波阻抗反演技术区域层序地层研究中的应用[J].石油物理勘探, 2002,9(37)：58-60.
[120]马劲风,王学军,贾春环.波阻抗约束反演中的约束方法研究[J].石油物探,2000,39(2)：52-63.
[121]沙志彬,龚跃华,梁金强.地震属性剖面在天然气水合物识别中的应用[J].105-113.
[122]Harding T P. Seismic characteristics and identification of negative flower structures and positive structural inversion[J]. AAPG Bu letin,1985,69(4):582-590.
[123]刘企英.利用地震信息进行油气预测[M].北京：石油工业出版社,1994.
[124]王红军,薛鹏,谷磊,张庆华.三维地震属性分析技术在胡状集油田构造精细解释中的应用[J].内蒙古石油化工,2009,13：137-139.
[125]孙家振,李兰斌.地震地质综合解释教程[M].北京：中国地质大学出版,2002：148-165.
[126]吴满生,沈云发,王志章.井震结合地震属性反演方法及应用[J].内蒙古化工,2009,19：25-27.
[127]Holbrook W S,Hoskins H, Wood W T, el al. Methane hydrate and free gas on Blake Ridge from Vertical seismic profiling [J]. Science,1996,273:1840-1843.
[128]Katzman R, Holbrook W S, Paullc K. Combined vertical incidence and wide angle seismic study old gas hydrate zone, Blake Outer Ridge [J]. Journal of Geophysical Research,1994,99:17975-17995.
[129]宁维琪,韩宏伟.地震属性与储层参数联合优化新算法[J].石油勘探,2004,43(2)：118-122.
[130]钟俊义等.地震属性参数在安棚深层系储层预测中的应用[J].石油物探,2001,40(1)：82-85.
[131]王永刚等.地震属性的GA-BP优化方法[J].石油地球物理勘探,2002,37(3)：606-611.
[132]谢宋等.地震属性分析技术在子寅油田开发中的应用[J].石油物探,2003,45(1)：25-29.
[133]陈德道.储层地震属性优选问题探讨[J].石油地球物理勘探,1999,34(6)：614-626.
[134]马中高.利用模型正演优选地震属性进行储层预测[J].石油学报,2003,24(6)：35-39.
[135]印兴耀,周静毅.地震属性优化方法综述[J].石油地球物理勘探,2005,40(8)：35-39.
[136]姜岩,周在林,秦月霜。地震属性分析的神经网络岩性识别技术及应用[J].大庆石油地质与开发,2000,10：39-42.
[137]倪逸等.储层油气预测中地震属性优化选问题探讨[J].石油地球物理勘探,1998,34 (6)：614-625.
[138]刘文岭等.多信息储层预测地震属性提取与有效性分析方法[J],石油勘探,2002,41(1)：100-106.
[139]曹辉.一种减少地震属性不确定性的方法.石油物理勘探,2002,41(4)：422-424.
[140]程建远.三维地震资料微机解释性处理技术[M].北京：石油工业出版社,2002.
[141]Gao T H, Cao J Y, Zhang M L, Qi J B. Lithology Recognition During Oil Well Drilling Based on Fuzzy adaptive Hamming Network [A]. Proceedings o f the Sixth Inter national Conference on Intelligent Systems Design and Applications[C].2006:574-578.
[142]董震,潘和平.声波测井资料与地震属性关系研究综述[J],工程地球物理学报,2007,4(5)：488-494.
[143]卢宝坤,等.测井资料与地震属性关系研究综述[J].北京大学学报,2005,41(1)：154-160.
[144]周竹生,姚胜利,陈灵君,等.用改进的BP神经网络由地震属性预测测井特征[J].西部探矿工程,2007,11：110-113.
[145]宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3)：216-219.
[146]齐龙喻,胡学鹏,乔玉雷.基于多地震属性的高分辨率拟测井参数的预测[J].南开大学报,2008,41(1)：5-9.
[147]Mahapatra M,Maphapatra S.Seismic diffraction to mography technique using very fast simulated ammealing method or delineating small subsurface features [J].Jounal of Applied Geophysics,2009,67 (2):125-129.
[148]谢雄举,季玉新.优选地震属性预测储层参数的方法及其应用[J].石油物探,2004,43：127-131.
[149]王秀东,施龙青,陈清静.地震测井联合物性参数反演在煤炭岩性勘探中的应用[J].华北地震科学,2008,26(3)：1-4.
[150]孟宪民,蒋维平.波阻抗反演技术及其应用效果[J].中国煤炭地质,2009,1：52-54.
[151]Baelly M E. Practical Seismic Interpretation. International Human Resources Development Corporation, USA,1985.
[152]Sand IA.叠后反演技术：潜力与局限性.美国勘探地球物理学家学会第60届年会论文集.石油工业出版社,1992,273-275.
[153]何超群.测井约束地震反演的现状和发展[J].科技资讯,2008,1：5-6.
[154]延建忠,周胜利,周杰.测井约束地震特征反演及效果分析[J].断块油气田,2004,11(3)：14-15.
[155]杨文采.地球物理反演的理论与方法[M].北京：地质出版社,1997.
[156]Metropolis, Nicholas, Rosenbluth, et al.. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physics,1953,21:1087-1092.
[157]安鸿伟,纪大庆.混沌优化神经网络方法及其在地震多属性研究中的应用[J].中国海上油气,2008,20(4)：232-235.
[158]刘百红,李建华.测井和地震资料宽带约束反演的应用[J].石油物探,2004,43(1)：76-79.
[160]沈财余,江洁,赵华,等.测井约束地震反演解决地质问题能力的探讨[J].石油地球物理勘探,2002,37(4)：372-376.
[161]高少武,蔡加铭,赵波.地震和测井联合反演储层波阻抗技术[J].石油物探,2002,41(3)：279-284.
[162]Schultz P S, Ronen S, Hattori M, et al.. Seismic guided estimation of log propties Parts [J]. The Leading Edge,1994,13(5):305-310.
[163]R James Brown, Robert R. Stewart and Lawton D C.A proposed polarity standard for multicomponent seismic data. Geophysics,2002,67(4):1028-1037.
[164]李子顺,李斌,王平在.井旁二维人工合成记录制作方法及应用[J].石油地球物理勘探,1995,30：111-119.
[165]Watson I A,Line L R and Brittle K F. Heavy oil reservoir characterization using elastic wave properties. The Leading Edge,2002,21(8):736-739.
[166]靳玲.合成地震记录制作的影响因素及对策[J].石油物探,2004,3.
[167]孟宪军,杜世通.地震资料的测井约束多项式拟合外推反演[J].石油地球物理勘探,1995,30(3)：363-372.
[168]范祯祥,郑仙种,范书蕊,等.利用地震测井资料联合反演储层物性参数[J].石油地球物理勘探,1998,33(1)：38-53.
[169]Lu S., and G. A. McMechan. Estimation of gas hydrate and free gas saturation, concentration, and distribution from seismic data:Geophysics v.2002,67(2):582-593.
[170]乐友喜,王永刚,张军华.由地震属性向储层参数转换的综合效果分析[J].2002,41(2)：202-206.
[171]张风军,黄隆基,范宜仁,等.测井资料在地震控制下的迭代反演[J].测井技术,21(3)：179-184.
[172]何樵登,梁光河.利用地震资料和测井约束反演地层参数—一种测井数据的地震外推方法[J].石油物探,1995,34(2)：1-7.
[173]Taner M T, Schuelke J S,O'Doherty R, et al.. Seismic Attributes Revisited.64th Ann Intenat Mtg. Soc Expl Geophys, Expanded Abstracts,1994,1:104-106.
[2]kvenvold K A. Gas hydrate-geological perspective and global change. Rev. Geophys., 1993,3(2):173-187.
[3]龚建明,陈建文,戴春山,等.中国海域天然气水合物资源远景[J].海洋地质动态,2003,19(8)：53-56.
[4]雷怀彦,郑艳红,吴保祥.天然气水合物勘探方法-BSR适用性探析[J].海洋石油,2002,114：1-7.
[5]Miller, J.J., Lee, M.W. and Von Huene. R..An analysis of seismic reflection from the base of gas hydrate zone off Peru[J]. Am. Assoc. Pet.Geol Bull,1991,75:910-924.
[6]栾锡武,秦蕴珊,张川华,等.东海陆坡及相邻槽底天然气水合物的稳定域分析[J].地球物理学报,2003,46(3)：467-475.
[7]Lippmannn R P. An introduction to computing with neural nets. IEEE. ASSP, April, 1987:4-22.
[8]杨木壮,王明君,吕万军.南海西北坡天然气水合物成矿条件研究[M].北京：气象出版社,2008：1-23.
[9]沙志彬,王宏斌,杨木壮,等.天然气水合物成矿带的识别技术研究[J].现代地质,2008,22(3)：438-446.
[10]陆敬安,杨胜雄,吴能友,等.南海神狐海域天然气水合物地球物理测井评价[J].现代地质,2008,22(3)：447-451.
[11]刘峰,吴时国,孙运宝.南海北部陆坡水合物分解引起海底不稳定性分析[J].地球物理学报,2010,53(4)：936-953.
[12]牛滨华,孙春岩,苏新,等.勘查地球化学方法适用用勘查天然气水合物依据[J].现代地质,2005,19(1)：61-65.
[13]蒋少涌,杨涛,薛紫晨,等.南海北部海区海底沉积物中孔隙水的cl-和sO4-浓度那异常特征对其天然气水合物的指示意义[J].现代地质,2005,19(1)：45-54.
[14]祝有海,饶竹,刘坚,等.南海西沙海槽S14站位的地球化学异常特征及其意义[J]. 现代地质,2005,19：39-44.
[15]龚跃华,杨胜雄,王宏斌,等.南海北部神狐海域天然气水合物成矿特征[J].现代地质,2009,23(2)：210-216.
[16]邱燕,王英民,王存斌,等.珠江口盆地白云凹陷中央底辟带的发现及识别[J].地球科学,2006,31(2)：209-213.
[17]翟光明.中国石油地质志(卷16)：沿海陆架及毗邻海域油气区[M].北京：石油工业出版社,1990.
[18]黎明碧,金翔龙,初凤友,等.神狐—一流暗沙隆起中部背后生代地层层序划分及沉积演化[J].沉积学报,2002,13(1)：31-33.
[19]刘铁树,何仕斌.半山海北部陆缘盆地深水区油气勘探前景[J].中国海上油气(地质),2001,15(3)：164-170.
[20]Berner U, Faber E. Hydrocarbon gases in surface sediments of the South China Sea [M]//Jin X, Kudrass H R, Pautot G. Marine Geology and Geophysics of the South China Sea. Beijing:China Ocean Press,1992:199-211.
[21]杨涛,杨竞红,蒋少涌.南海北部地区海洋沉积物中孔隙水的氢、氧同位素组成特征[J].地球学报,2003,24(6)：511-514.
[22]蒲晓强,钟少军,于雯泉,等.南海北部陆坡NH 21孔沉积物中自生硫化物及其硫同位素对深部甲烷和水合物存在的指示[J].科学通报,2006,51(24)：2874-2880.
[23]赵省民,聂凤军,曾凡刚,等.内生热液矿床与天然气水合物矿床形成机制的比较研究[J].地球学报,2001,22(3)：237-242.
[24]孙美琴,徐茂泉,许文彬,等.海洋天然气水合物的成矿机理与资源评价概述[J].台湾海峡,2004,23(4)：521-529.
[25]Ginsburg G, Guseynova R A, Dadashev A A, et al.. Gas hydrates of the southern Caspian[J]. International Geology Review,1992,34 (8):765-782.
[26]Sassen, R., MacDonald IR. Evidence of structure Hydrate, Gulf of Mexico continental slope[J]. Organic Geo chemistry,1994,22 (6):1029-1032.
[27]Sassen, R., S. Joye, S T Sweet, et al.. Thermogenic gas hydrate and hydrocarbon gases in complex chemosynthetic communities, Gulf of Mexico continental slope[J].Organic Geochemistry,1999,30:485-497.
[28]方银霞,黎明碧,金翔龙,等.东海冲绳海槽天然气水合物形成条件[J].科技通 报,2003,19(1)：1-5.
[29]Dillon W P, Max M D. Oceanic gas hydrate[M]//Max M D. Natural Gas Hydrate in Oceanic and Permafrost Environments. Dordrecht:Kluwer Academic Publishers,2003: 61-63.
[30]许威,邱楠生,孙长宇,等.不同因素对天然气水合物稳定带厚度的影响[J].天然气地球科学,2010,21(3)：528-534.
[31]王淑红,宋海斌,颜文.天然气水合物稳定带的计算方法与参数选择探讨[J].现代地质,2005,19(1)：101-107.
[32]Handa Y P. Effect of hydrostatic pressure and salinity on the stability of gas hydrates [J]. J Phys Chem,1990,94:2652-2657.
[33]Zatsep ina O Y, Buffett B A. Phase equilibrium of gas hydrate:Implications for the formation of hydrate in the deep sea floor[J]. Geophysical Research Letters,1997, 24(13):1567-1570.
[34]Xu W, Ruppel C. Predicting the occurrence, distribution and evolution of methane gas hydrate in the porous marine sediments[J]. Journal of Geophysical Research,1999,104: 5081-5096.
[35]Diaconescu C C, Kieckhefer R M, Knapp J H. Geophysical evidence for gas hydrates in the deep water of the South Caspian Basin, Azerbaija[J]. Marine and Petroleum Geology,2001,18:209-221.
[36]蒋少涌,凌洪飞,杨竞红.海洋浅表层沉积物和孔隙水的天然气水合物地球化学异常识别标志[J].海洋地质与第四纪,2003,23(1)：87-93.
[37]Brooks J M, Field M E,Kennicutt M C Ⅲ. Observations of gas hydrates in marine sediments, offshore northern California [J].Marine Geology,1991,96:103-109.
[38]龚建时,王红霞,陈建文等.天然气水合物在沉积地层中的分布模式[J].海洋地质动态,2004,20(6)：6-8.
[39]陈多福,王茂春,夏斌.中低纬度高原冻土带天然气水合物的产出特征：青藏高原冻土带天然气水合物的形成与分布预测[J].地球物理学报,2005,48(1)：164-171.
[40]金庆焕,张光学,杨木壮.天然气水合物资源概论[M].北京：科学出版社,2006：1-20.
[41]Kayen R E, Lee H J.Pleistocene slope instability of gas hydrate laden sediment on the Beaufort Sea Margin. Marine Geo technology,1991,10:125-141.
[42]Paull C K, Buelow W J, Ussler III W et al.Increased continental-margin slumping frequency during sea-level low stands above gas hydrate-bearing sediments. Geology, 1996,24(2):143-146.
[43]Kayen R E, Lee H J. Pleistocene slope instability of gas hydrate laden sediment on the Beaufort Sea margin [J]. Mar. Geo technol.,1991,10:125-141
[44]Totland A,God O R. Beam:an interactive GIS application for acoustic abundance estimation[C]//Proceedings oft he lstinternational symposium on GIS in fishery science. 2001:195-201.
[45]许红,黄君权,夏斌,等.最新国际天然气水合物研究现状及资源潜力评估(下)天然气工业,2005,6：18-23.
[46]Hyndman R D, Dallimore S R. Natural gas hydrate studies in Canada[J]. The recorder,2001,26(2):11-20.
[47]Aurora-JO GMEC-NR-Can. Mallik 2006-2008 Gas Hydrate Research Project Progress, Fire in t he Ice, Met hane Hydrate Newslet ter 2008.
[48]Collett T S. Potential of gas hydrates outlined[J]. Oil Gas,1992,78 (1),124-130.
[49]Kvenvolden K A. Gas hydrates geological perspective and global change. Rev Geophys, 1993,31,(2):173-187.
[50]Trehu A M,Bohrmann G,等.ODP 204航次科学报告：天然气水合物的分布和动力学[J].海洋地质动态,2004,20(5)：15-16.
[51]刘争平,何永富.人工神经网络在测井解释中的应用[J].地球物理学报,1995,38(增刊)：323-330.
[52]周辉,何椎登,徐世浙.人工神经网络非线性地震波形反演[J].石油物探,1997,36(1)：61-70.
[53]Cakleron-Macias C, Sen M, Stoffa P. Artificial neural networks for parameter estimation in geophysics[J]. Geophysical Prospecting,2000,48(1):21-47.
[54]丁柱,杨长春,陶宏根,等.神经网络反演双侧向电阻率测井曲线的物理约束[J].地球物理学进展,2002,17(2)：331-336.
[55]高隽.人工神经网络原理及仿真实例[M].北京：机械工业出版社,2003,8：44-63.
[56]侯媛彬,杜京义,枉梅.神经网络[M].陕西：西安电子科技大学出版社,2007：55-70.
[57]靳蕃,范俊波,谭永东.神经网络与神经计算机原理应用[M].成都：西南交通大学出版 社,1997.
[58]杨立强,宋海斌,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1)：34-37.
[59]Robert J,Schalkoff.Artificial Neural NetWorkrs [M].MIT Press and The Mc-Graw-Hill Companies,Inc,1997.
[60]TKohonen.Self-organized formation of to pologically correct feather maps[J].Biol., 1982,43:59-69.
[61]程涛,晏克勤.地质勘测中力学参数反演问题的回归神经网络方法[J].黄石理工学院学报,2007,23(2)：36-39.
[62]陆敬安,梁金强,罗文造.径向基函数神经网络在计算天然气水合物饱和度中的应用研究[J].海洋通报,2009,28(4)：102-106.
[63]张绍红.概率神经网络技术在地震岩性反演中应用[J].辽宁工程技术大学学报,2007,11：16-18.
[64]杨文采.神经网络算法在地球物理反演中的应用[J].石油物探,1995,34(2)：116-120.
[65]丁柱,杨长春,陶宏根.神经网络反演双侧向电阻率测井曲线的物理约束[J].2002,17(2)：331-336.
[66]薛林福,潘保芝.用自组织神经经网络自动识别岩性[J].长春科技大学学报,1999,29(2)：144-147.
[67]侯俊生,尉中良.自组织映射神经网络方法实现测井相定量识别[J].测井技术,1996,20(3)：197-200.
[68]尹寿鹏,王贵文.测井沉积学研究综述[J].地球科学进展,1999,14(5)：440-445.
[69]陈立英.测井技术在天然气水合物储层识别中应用前景[J].海洋地质动态,2006,22：14-16.
[70]梁劲,王明君,陆敬安,等.南海神狐含水合物地层测井响应特征[J].现代地质,2010,6：506-514.
[71]高兴军,于兴河,李胜利,等.地球物理测井在天然气水合物勘探中的应用[J].地球物理学进展,2003,4：305-310.
[72]Hyndman R D, Yuan T, Moran K. The concentration of deep sea hydrate from downhill electrical resistivity logs and laboratory data [J]. Earth and Planetary science Letters, 1999,172:167-177.
[73]李舟波.地球物理测井数据处理与综合解释[M].长春：吉林大学出版社,2003.
[74]手冢各彦.天然气水合物的测井解释[J],海洋地质动态,2003,19(6)：21-23.
[75]Yoshihiro Tsuji, Hishshi Ishida, Masani Nakamizu, et al.. Overview of MITI Nankai through wells a milestone in the evaluation of Methand hydrate resource [J].Resource Geology,2004,54(1):3-10.
[76]史斗,孙威权,朱岳年.国外天然气水合物研究进展[M].兰州大学出版,1992.
[77]Mathews M. Logging characteristics of methane hydrate [J]. The log analyst.1986, 27(2):26-63.
[78]Archeries G E. The electrical resistivity logs as an aid in determining some reservoir characteristics [J]. Geophysics,1956,21(1):41-70.
[79]欧阳健等.石油测井解释与储层描述[M].北京：石油出版社,1994：12-27.
[80]梁劲,王明君,王宏斌,等.南海神狐海域天然气水合物声波测井速度与饱和度关系分析.现代地质,2009,23(2)：217-223.
[81]Willoughby E C, Schwalenberg K, Edwards R N, et al. Assessment of Marine Gas Hydrate Deposits:A Comparative Study of Seismic, Electromagnet and Seafloor Compliance Methods [C]//Anon. Proceedings of the Fifth International Conference on Gas Hydrates. Trondheim, Norway:[s.n],2005:802-811.
[82]Timothy S Collett. Energy Resource Potential of Natural Gas Hydrates [J]. American Association of Petroleum Geologists,2002,86(11):1971-1992.
[83]陈光进,孙长宇,马庆兰.气体水合物科学技术.北京：化学工业出版社,2008.
[84]赵省民,吴必豪,王亚平,等.海底天然气水合物赋存的间接标志[J].地科学-中国地质大学报,2000,11：624-628.
[85]赖泽武,张一伟,熊琦华,等.具有趋势的克里格方法在测井资料数据标准化中的应用[J].石油勘探与开发,1999,26(6)：91-94.
[86]周叶,王家华,李换鹏,等.测井解释数据预处理的计算机实现[J].测井技术,1994,18(6)：431-435.
[87]刘爱群,盖永浩.测井约束反演过程中测井资料统计分析研究[J].地球物理学进展,2007,22(5)：1487-1492.
[88]张学芳,董月昌,慎国强,等.曲线重构技术在测井约束反演中的应用[J].石油勘探与开发,2005,32(3)：70-72.
[89]张静,陈启林,李延丽.测井曲线重构技术在储层预测中的应用[J].西北油气勘探,2005,17(4)：47-52.
[90]George A,Mcmechan. Curves for a point source lying on a boundary[J]. Bulletin of the Seismological Society of America,1977,67:1521-1527.
[91]陆敬安,闫桂京.天然气水合物测井与评价技术进展.海洋地质动态,2007,23(6)：31-36.
[92]Collet T S,Ladd J, Detection of gas hydrate with dowmhole logs and assessment of gas hydrate concentrations(saturations)and gas volumes on the Blake Ridge with electrical resistivity log data,1999, Proceedings of the Ocean Drilling Program[J].Scientific Results,1999,164:179-192.
[93]葛瑞·马沃可,塔潘·木克基,杰克·德沃金.岩石物理手册[M].中国科技大学出版社,2008.
[94]梁劲,王宏斌,,梁金强.Jason反演技术在天然气水合物速度分析中的应用[J].南海地质研究,2006,18(1)：114-120.
[95]王祝文,李舟波,刘菁华.天然气水合物的测井识别和评价[J].海洋地质和第四纪地质,2003,23(2)：97-102.
[96]雷利安,徐美茹,曹学良等.带趋势序贯指示模拟方法在东濮凹陷薄砂岩储层预测中的应用.石油地球物理勘探,2005,40(1)：92-96
[97]陈钢花,王永刚.Faust公式在声波曲线重构中的应用.勘探地球物理进展,2005,28(2)：125-128
[98]Paull C k,Matsumoto R,Wallace P J. Proceedings of the Ocean Drilling Program Leg 164,Seientific Results[M].College Station Texas:Ocean Drilling Program,2000.
[99]Yoshihiro Tsuji,Hisashi Ishida,Masaru Nakamizu,et al. Overview of the MITI Nankai Trough wells:A milestone in the evaluation of methane hydrate resources[J].Resource Geology,2004,54:3-10.
[100]Expedition 311 Scientists Integrated Ocean Drilling Program Expedition 311 Preliminary Report,Cascadia Margin Gas Hydrates [M].Washington :Integrated Ocean Drilling Program Management International Inc,2005.
[101]JoelD. Walls等著.利用岩石物理性质、地震属性、神经网络描述北海储层的特征：一项实例研究[J].海洋石油,2001,108：66-69.
[102]侯伯刚,杨银池,武站国.地震属性及其在储层预测中影响因素[J].石油地球物理勘 探,2004,39(5)：553-574.
[103]Chen Q, Steve S. Seismic Attribute Technology for Reservoir Forecasting and Monitoring [J].The Leading Edge,1997,16(5):445-456.
[104]李坦,殷小舟.地震属性的地质意义分析[J].复杂油气藏,2009,9：25-36.
[105]张德林.地震储层横向预测技术与实例[M].北京：石油工业出版社,1992.
[106]鲍祥生,尹成,赵伟,等.储层顶测的地震属性优选技术研究[J].石油物探,2006,45(1)：28-33.
[107]潘元林.油气地质地球物理综合勘探技术[M].北京：地震出版社,1998.
[108]李常茂,耿瑞伦.关于天然气水合物钻探思考[J].探矿工程,2000,3：5-8.
[109]Kaul N, Rosenberger A, Villinger H. Comparison of measured and BSR derived heat flow values, Makran accretionary prism, Pakistan. M arine Geology,2000,164(1-2): 37-51.
[110]Chen D F, Laerence M. Akinetic model for the pattern and amounts of hydrate precipitated from a gas steam:Application to the Bush Hill Vent Site, Green Canyon Block 185, Gulf of Mexico. Journal of Geophysics Research,2003,108:2058-2072.
[111]Kalachand Sain.Seismic Methods for recognition and evaluation of gas-Hydrates[J]. The 12th International Conference of IACMAG,2008,10.
[112]阳飞舟,崔永福,李青.地震属性分类及其应用[J].内蒙古化工,2009,2：94-97.
[113]宁松华.地震属性分析在托浦台储层预测中的应用[J].石油天然气学报,2006,28(5)：70-73.
[114]张德林.地震储层横向预测技术与实例[M].北京：石油工业出版社,1992.
[115]张延玲,杨长春,贾曙光.地震属性技术的研究和应用[J].地球物理学进展,2005,20(4)：1129-1133.
[116]Snons Cheong, Wonsik kim, Namhyung Koo.韩国东海Ulleung盆地天然气水合物沉积层的地震属性分析[J].海洋地质动态,2008,25(2)：21-22.
[117]S.肖普拉,K.J.马弗特著.地震属性在构造和地层学中的应用[M].中国石油集团东方地球物理勘探有限责任公司,2008：1-40.
[118]郭华军,刘庆成.地震属性技术的历史、现状及发展趋势[J].物探与化探,2008,32(1)：19-23.
[119]吴坚,张塞,闫桂花.波阻抗反演技术区域层序地层研究中的应用[J].石油物理勘探, 2002,9(37)：58-60.
[120]马劲风,王学军,贾春环.波阻抗约束反演中的约束方法研究[J].石油物探,2000,39(2)：52-63.
[121]沙志彬,龚跃华,梁金强.地震属性剖面在天然气水合物识别中的应用[J].105-113.
[122]Harding T P. Seismic characteristics and identification of negative flower structures and positive structural inversion[J]. AAPG Bu letin,1985,69(4):582-590.
[123]刘企英.利用地震信息进行油气预测[M].北京：石油工业出版社,1994.
[124]王红军,薛鹏,谷磊,张庆华.三维地震属性分析技术在胡状集油田构造精细解释中的应用[J].内蒙古石油化工,2009,13：137-139.
[125]孙家振,李兰斌.地震地质综合解释教程[M].北京：中国地质大学出版,2002：148-165.
[126]吴满生,沈云发,王志章.井震结合地震属性反演方法及应用[J].内蒙古化工,2009,19：25-27.
[127]Holbrook W S,Hoskins H, Wood W T, el al. Methane hydrate and free gas on Blake Ridge from Vertical seismic profiling [J]. Science,1996,273:1840-1843.
[128]Katzman R, Holbrook W S, Paullc K. Combined vertical incidence and wide angle seismic study old gas hydrate zone, Blake Outer Ridge [J]. Journal of Geophysical Research,1994,99:17975-17995.
[129]宁维琪,韩宏伟.地震属性与储层参数联合优化新算法[J].石油勘探,2004,43(2)：118-122.
[130]钟俊义等.地震属性参数在安棚深层系储层预测中的应用[J].石油物探,2001,40(1)：82-85.
[131]王永刚等.地震属性的GA-BP优化方法[J].石油地球物理勘探,2002,37(3)：606-611.
[132]谢宋等.地震属性分析技术在子寅油田开发中的应用[J].石油物探,2003,45(1)：25-29.
[133]陈德道.储层地震属性优选问题探讨[J].石油地球物理勘探,1999,34(6)：614-626.
[134]马中高.利用模型正演优选地震属性进行储层预测[J].石油学报,2003,24(6)：35-39.
[135]印兴耀,周静毅.地震属性优化方法综述[J].石油地球物理勘探,2005,40(8)：35-39.
[136]姜岩,周在林,秦月霜。地震属性分析的神经网络岩性识别技术及应用[J].大庆石油地质与开发,2000,10：39-42.
[137]倪逸等.储层油气预测中地震属性优化选问题探讨[J].石油地球物理勘探,1998,34 (6)：614-625.
[138]刘文岭等.多信息储层预测地震属性提取与有效性分析方法[J],石油勘探,2002,41(1)：100-106.
[139]曹辉.一种减少地震属性不确定性的方法.石油物理勘探,2002,41(4)：422-424.
[140]程建远.三维地震资料微机解释性处理技术[M].北京：石油工业出版社,2002.
[141]Gao T H, Cao J Y, Zhang M L, Qi J B. Lithology Recognition During Oil Well Drilling Based on Fuzzy adaptive Hamming Network [A]. Proceedings o f the Sixth Inter national Conference on Intelligent Systems Design and Applications[C].2006:574-578.
[142]董震,潘和平.声波测井资料与地震属性关系研究综述[J],工程地球物理学报,2007,4(5)：488-494.
[143]卢宝坤,等.测井资料与地震属性关系研究综述[J].北京大学学报,2005,41(1)：154-160.
[144]周竹生,姚胜利,陈灵君,等.用改进的BP神经网络由地震属性预测测井特征[J].西部探矿工程,2007,11：110-113.
[145]宋维琪.应用地震属性与测井数据反演储层参数[J].勘探地球物理进展,2003,26(3)：216-219.
[146]齐龙喻,胡学鹏,乔玉雷.基于多地震属性的高分辨率拟测井参数的预测[J].南开大学报,2008,41(1)：5-9.
[147]Mahapatra M,Maphapatra S.Seismic diffraction to mography technique using very fast simulated ammealing method or delineating small subsurface features [J].Jounal of Applied Geophysics,2009,67 (2):125-129.
[148]谢雄举,季玉新.优选地震属性预测储层参数的方法及其应用[J].石油物探,2004,43：127-131.
[149]王秀东,施龙青,陈清静.地震测井联合物性参数反演在煤炭岩性勘探中的应用[J].华北地震科学,2008,26(3)：1-4.
[150]孟宪民,蒋维平.波阻抗反演技术及其应用效果[J].中国煤炭地质,2009,1：52-54.
[151]Baelly M E. Practical Seismic Interpretation. International Human Resources Development Corporation, USA,1985.
[152]Sand IA.叠后反演技术：潜力与局限性.美国勘探地球物理学家学会第60届年会论文集.石油工业出版社,1992,273-275.
[153]何超群.测井约束地震反演的现状和发展[J].科技资讯,2008,1：5-6.
[154]延建忠,周胜利,周杰.测井约束地震特征反演及效果分析[J].断块油气田,2004,11(3)：14-15.
[155]杨文采.地球物理反演的理论与方法[M].北京：地质出版社,1997.
[156]Metropolis, Nicholas, Rosenbluth, et al.. Equation of state calculations by fast computing machines [J]. Journal of Chemical Physics,1953,21:1087-1092.
[157]安鸿伟,纪大庆.混沌优化神经网络方法及其在地震多属性研究中的应用[J].中国海上油气,2008,20(4)：232-235.
[158]刘百红,李建华.测井和地震资料宽带约束反演的应用[J].石油物探,2004,43(1)：76-79.
[160]沈财余,江洁,赵华,等.测井约束地震反演解决地质问题能力的探讨[J].石油地球物理勘探,2002,37(4)：372-376.
[161]高少武,蔡加铭,赵波.地震和测井联合反演储层波阻抗技术[J].石油物探,2002,41(3)：279-284.
[162]Schultz P S, Ronen S, Hattori M, et al.. Seismic guided estimation of log propties Parts [J]. The Leading Edge,1994,13(5):305-310.
[163]R James Brown, Robert R. Stewart and Lawton D C.A proposed polarity standard for multicomponent seismic data. Geophysics,2002,67(4):1028-1037.
[164]李子顺,李斌,王平在.井旁二维人工合成记录制作方法及应用[J].石油地球物理勘探,1995,30：111-119.
[165]Watson I A,Line L R and Brittle K F. Heavy oil reservoir characterization using elastic wave properties. The Leading Edge,2002,21(8):736-739.
[166]靳玲.合成地震记录制作的影响因素及对策[J].石油物探,2004,3.
[167]孟宪军,杜世通.地震资料的测井约束多项式拟合外推反演[J].石油地球物理勘探,1995,30(3)：363-372.
[168]范祯祥,郑仙种,范书蕊,等.利用地震测井资料联合反演储层物性参数[J].石油地球物理勘探,1998,33(1)：38-53.
[169]Lu S., and G. A. McMechan. Estimation of gas hydrate and free gas saturation, concentration, and distribution from seismic data:Geophysics v.2002,67(2):582-593.
[170]乐友喜,王永刚,张军华.由地震属性向储层参数转换的综合效果分析[J].2002,41(2)：202-206.
[171]张风军,黄隆基,范宜仁,等.测井资料在地震控制下的迭代反演[J].测井技术,21(3)：179-184.
[172]何樵登,梁光河.利用地震资料和测井约束反演地层参数—一种测井数据的地震外推方法[J].石油物探,1995,34(2)：1-7.
[173]Taner M T, Schuelke J S,O'Doherty R, et al.. Seismic Attributes Revisited.64th Ann Intenat Mtg. Soc Expl Geophys, Expanded Abstracts,1994,1:104-106.