On the training sample size and classification performance: An experimental evaluation in seismic facies classification

• Main Authors: Babikir, I., Elsaadany, M., Sajid, M., Laudon, C.
• Format: Article
• Published: Elsevier B.V. 2023
• Online Access: http://scholars.utp.edu.my/id/eprint/37516/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159765394&doi=10.1016%2fj.geoen.2023.211809&partnerID=40&md5=bb201dd3f5ec9479db4d5d1224cf93c6

Machine learning algorithms (MLAs) perform better when enough high-quality training data is provided. However, a lack of training data is frequent in seismic facies classification and many other supervised learning applications. Data labeling for seismic facies classification is time-consuming and r...