Convolutional Neural Networks (CNN)-based fault detection method is an emerging technology that shows great promise for the seismic interpreter. One of the more successful deep learning CNN methods uses synthetic data to train a CNN model. Although the synthetics are all normal faults, a common CNN practice is to augment the training data by rotating and flipping each image. Different types of noise are added to the synthetics to allow the algorithm to learn to see through the noise as a human interpreter does. A more traditional fault analysis workflows is based on seismic attributes and image processing. In contrast to CNN, the image processing “convolutions” have been predetermined based on concepts of signal analysis In this paper, we build a CNN architecture to predict faults from 3D seismic data, and then compare the results to those obtained using an image processing-based fault detection for datasets exhibiting different data quality.

Presentation Date: Tuesday, October 13, 2020

Session Start Time: 1:50 PM

Presentation Time: 3:55 PM

Location: 361A

Presentation Type: Oral

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