Massive data set analysis in seismic explorations for oiland gas in crystalline basement

On the basis of the optimization-based approach to the analysis of massive ordered data sets, a new method is proposed for computer-aided interpretation of seismic exploratory data from the so-called crystalline basement of the Earth mantle, which underlies the relatively thin sedimentary cover having been, up to now, the almost exceptional object of seismic explorations. The seismic exploratory data sets, seismic sections and cubes, are a class of, respectively, twoand three-dimensional data arrays, which are analyzed in the course of gas and oil reserves prospecting with the purpose of studying the structure of the underground rock mass. The seismic data sets consist of synchronous records of re ected seismic signals registered by a large number of geophones (seismic sensors) placed along a straight line or in the nodes of a rectangular lattice on the earth surface. As the source of the initial seismic pulse, usually serves a series of explosions, responses to which are averaged in a special manner. The vertical time axis forming the resulting twoor three-dimensional picture is identi ed with depth, so that the peculiarities of the re ected signal under the respective sensor carry an information on the local properties of the rock mass at the respective point of the underground medium. In contrast to the above-lying sedimentary cover, the absence of pronounced re ecting surfaces in a crystalline body results in a great di culty of inferring the geological information from the basement interval of the seismic picture. The new method of seismic data analysis proposed in this work is aimed at nding fractured zones of the basement rock mass capable to accumulate oil or gas. The essence of the method consists in numerical evaluating distinctions in the local spatial texture of the seismic picture that are caused by di erences in physical properties of fractured and monolith rock. The problem of estimating the local texture over the whole data array at once is set as that of minimizing an objective function in that the texture model parameters at all the elements of the array occur as its arguments. A special separable structure of the objective function provides a high speed of the optimization procedure.