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Intelligent inversion method for pre-stack seismic big data based on MapReduce
روش معکوس هوشمند برای داده های بزرگ لرزه ای قبل از پشته بر اساس MapReduce-2018 Seismic exploration is a method of oil exploration that uses seismic information; that is, according to the inversion
of seismic information, the useful information of the reservoir parameters can be obtained to carry out exploration
effectively. Pre-stack data are characterised by a large amount of data, abundant information, and so on, and
according to its inversion, the abundant information of the reservoir parameters can be obtained. Owing to the
large amount of pre-stack seismic data, existing single-machine environments have not been able to meet the
computational needs of the huge amount of data; thus, the development of a method with a high efficiency and
the speed to solve the inversion problem of pre-stack seismic data is urgently needed. The optimisation of the
elastic parameters by using a genetic algorithm easily falls into a local optimum, which results in a non-obvious
inversion effect, especially for the optimisation effect of the density. Therefore, an intelligent optimisation al
gorithm is proposed in this paper and used for the elastic parameter inversion of pre-stack seismic data. This
algorithm improves the population initialisation strategy by using the Gardner formula and the genetic operation
of the algorithm, and the improved algorithm obtains better inversion results when carrying out a model test with
logging data. All of the elastic parameters obtained by inversion and the logging curve of theoretical model are
fitted well, which effectively improves the inversion precision of the density. This algorithm was implemented
with a MapReduce model to solve the seismic big data inversion problem. The experimental results show that the
parallel model can effectively reduce the running time of the algorithm.
Keywords: Intelligent optimisation algorithm ، Pre-stack seismic data ، Elastic parameter inversion ، MapReduce |
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