【作者】 张克；

【导师】 汪云甲；

【作者基本信息】 中国矿业大学 ， 地图制图学与地理信息工程， 2011， 博士

【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。更多还原

【Abstract】 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.更多还原