Predicting Stratigraphic Horizons using Machine Learning for Earth Model Building: a Case Study Offshore Sarawak, Malaysia

The paper presents the application of machine learning (ML) techniques to a complex deep-water Sarawak dataset to accelerate the prediction of the Mid Miocene Unconformity (MMU) for Earth model building (EMB). Leveraging locally trained convolutional neural networks (CNNs) and transfer learning with sparse local labels, the workflow improves horizon tracking and significantly reduces manual interpretation effort in high-confidence areas. The study demonstrates the integration of regional ML models into EMB workflows in geologically complex basins, while highlighting the essential role of human expertise in low-confidence zones—redirecting effort toward higher-value activities such as data evaluation and advancing subsurface understanding critical to petroleum system analysis.