Machine Learning Augmented Time-Lapse Bathymetric Surveys: A Case Study From the Mississippi River Delta Front

Abstract

The subaqueous Mississippi River Delta Front is prone to seabed instabilities >1 m of vertical bathymetric change per year, but the ability to predict the location and magnitude of instability-driven depth change is limited. Here we demonstrate that data-driven geospatial models can predict MRDF depth change from a small amount (1% of full coverage) of training data. We predict depth change at 100 m2 resolution between 2005 and 2017 over a ~100 km2 area. Models trained on ~1% of full-coverage depth change data produce comparable and relatively low average predicted depth change errors (1–2 cm). K-nearest neighbors best reproduce the spatial variability of depth change and can interpolate and extrapolate from training data. This approach has immediate applications for geohazard monitoring on the MRDF and other geologically similar settings and can be applied in other settings if the drivers of depth change variance are well known.