Identification of seismic facies objects using time-frequency attributes and self-organizing neural networks
Abstract
The relevance of this work is driven by the need for more reliable and reproducible seismic facies interpretation in complex depositional settings, where conventional methods are primarily based on amplitude attributes and manual selection of frequency bands that are often insufficient for detecting subtle geological variations and largely depend on subjective interpreter decisions. Aim of the study is to develop and validate an integrated workflow for three-dimensional seismic facies clustering that enables automated delineation of volumetric geological bodies using multi-frequency attributes derived from seismic data. Research methods include application of continuous wavelet transform for spectral decomposition of the seismic signal that creates a multi-frequency feature data set, as well as the use of Kohonen maps for clustering high-dimensional data while preserving their topological relationships. The trained self-organizing map is then applied to the 3D seismic volume, enabling delineation and extraction of volumetric geological objects. Results show t that the proposed approach effectively identifies meandering channel bodies, levee deposits, and stratigraphic boundaries, when applied to data from the South Caspian Basin and provides three-dimensional facies modeling. Overall, the method reduces interpretational subjectivity, improves the reliability and reproducibility of results, and offers advantages for reservoir characterization, exploration activities, and resource estimation.
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