Seismic survey data is the basis for geologic modeling and reservoir characterization since they are most evenly and relatively tightly distributed in the zone of interest. The use of modern computational methods in the interpretation process generates a huge amount of secondary data referred to as seismic attributes. The total volume of this data may be hundreds of times greater than the amount of post-processing data. Attributes hide large potentially useful components. Attributes help to more accurately outline faults, selvages, fractured zones, lithological facies and etc. Some of the most useful attributes are the elastic parameters models of the rocks obtained as a result of inverse computational methods. As a result of further mathematical transformations of these models, together with petrophysical models, experts can obtain models of useful engineering and exploration parameters. A huge number of attributes as well as their hidden linear and non-linear dependencies create the Big Data problem. In this article, we propose methods for searching for associations and the corresponding optimal filtering ranges (bandwidth or statistical) of attributes that significantly reduce future computational costs. Despite the fact that the algorithms are quite demanding on computing resources, their efficiency over time can be significantly increased through the use of parallelization methods.
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