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GEOPHYSICAL DATA PROCESSING AND IDENTIFYING FOR GAS HYDRATE IN THE SLOPE OF SOUTH CHINA SEA

机译:南海坡地气体水合物的地球物理数据处理与识别

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In order to determine the nature and distribution of marine gas hydrate, a series of geophysical data processing techniques are used in this paper. By using the traditional seismic data processing, purpose seismic data processing, wave impedance inversion techniques and geophysical well logging data processing based on Self-organizing feature map neural network, a great deal of useful information are abstracted to determine the gas hydrate zone beneath the seabed. The results show (1) the conventional seismic data processing techniques contains much useful information and provide a visual and qualitative evidence of the presence of gas hydrate in sediments under normal condition; (2) special processing techniques, such as attribute extraction and wave impedance inversion, is necessary so as to mine more effective data, they could compensate the shortage of conventional seismic data processing techniques used for distinguishing gas-bearing reservoirs; (3) as a kind of intelligent information processing technology, SOFM neural network is feasible for lithologic identification by logging data and has a high rate of identification of gas hydrate. In the end, the author hopes it may provide some useful clues to the exploration of gas hydrate.
机译:为了确定性质和海洋天然气水合物的分布,一系列地球物理数据处理技术的在本文中被使用。通过使用传统的地震数据处理,目的地震数据处理,波阻抗反演技术和地球物理测井数据基于处理的自组织特征映射神经网络,有用的大量信息被抽象,以确定海底下的天然气水合物区。结果表明(1)在传统的地震数据处理技术包含了许多有用的信息,并提供在正常条件下沉积物气体水合物的存在的视觉和定性证据; (2)的特殊处理技术,如属性提取和波阻抗反演,是必要的,以便矿更有效的数据,它们可以补偿传统的地震数据处理的用于区分含气储层技术的不足; (3)作为一种智能信息处理技术,自组织网络是用于通过测井数据岩性识别可行的,具有气体水合物的识别率较高。最后,笔者希望它可以提供一些有用的线索,以天然气水合物的勘探。

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