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Enhanced collapse feature extraction from high-resolution seismic data using convolutional neural network
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Second EAGE Digitalization Conference and Exhibition, Mar 2022, Volume 2022, p.1 - 6
Abstract
For the purpose of developing a challenging marine field in the Persian Gulf, a high-resolution seismic survey was acquired with the aim of evaluating different geohazards elements prior to the drilling. Carbonate rocks were dominant lithology in the area of study and as a result, a large number of collapse features have been developed which were evaluated as the major geohazards elements. We applied neural network classification for extraction of collapse geobodies for subsequent geohazards analysis. The acquired seismic survey consists of several densely spaced 2D lines in different azimuths which were finally merged into an integrated 3D seismic cube for further analysis. Strong time-distorted acquisition footprints were the main challenging issue regarding the classification procedure using high-resolution seismic data. They remained in the final processed data despite several remedial actions in the processing steps. Footprint artifacts share similar seismic character with the collapse features and were classified as geohazards using neuron-based neural network classification. We successfully examined and applied convolutional neural network to discriminate them from true collapse features.