About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
SEG 2020
Conference paper
A benchmark dataset for semi-automatic seismic interpretation based on a New Zealand's seismic survey
Abstract
In the past decades, machine learning algorithms have been developed for accelerating the seismic interpretation process. For this purpose, benchmark availability for algorithm improvement in computational geophysics is imperative. However, in geosciences, the supply of open benchmark datasets is very limited. The purpose of our study was to evaluate a CNN algorithm response in comparison to an OpendTect tool for seismic horizon interpolation, and besides, offer a benchmark dataset for further algorithms evaluations.