A machine learning approach using legacy geophysical datasets to model Quaternary marine paleotopography

Abstract

High-resolution subsurface marine mapping tools, including chirp and 3D seismic, enable the reconstruction of ancient landscapes that have been buried and subsequently submerged by marine transgression. However, the established methods for paleotopographic reconstruction require time consuming field and data interpretation efforts. Here we present a novel methodology using machine learning to estimate Marine Isotope Stage 2 (MIS2) paleotopography over a large (22 000 km²) area of the Northern Gulf of Mexico with meter-scale accuracy (2.7 m mean prediction error, 4.3 m 1-σ mean uncertainty). A relatively small area (3300 km²) of high-resolution (30 × 30 m) interpreted paleotopography is used as training and validation data, while modern bathymetry and MIS2 paleovalley location (binary deep/shallow paleotopography) are used as predictors. This approach merges the high-resolution of modern mapping techniques and the broad coverage of low-resolution legacy geophysical data. Machine learning-modeled paleotopography is not a substitute for precise high-resolution paleotopography reconstruction techniques, but it can be used to reasonably approximate paleotopography over large areas with greatly reduced expense and expertise.