Moho depth model of South America from a machine learning approach

Abstract

Crustal structure models play an important role in the characterization of seismogenic zones, in the regional delimitation of geological provinces and particularly, in understanding the genesis and evolution of sedimentary basins. Despite the increasing number of new seismic surveys and seismographic networks in South America, crustal thickness measurements are still scarce and irregularly sampled, reducing the resolution of crustal model maps. To overcome these challenges, a novel approach based on machine learning techniques is proposed, in order to explore higher resolution gravity datasets in the interpolation of crustal thickness measurement points obtained from previous seismic/seismological compilations. The algorithm used in this study is based on Gaussian processes prediction methods, which allowed inferring the depth of Moho to South America. The prediction error of the model obtained from the training and testing database was 3.48 km, which is compatible with the uncertainties derived from the H-k stacking analysis. The depth range varied from 69.8 km beneath the Andes to 4.3 km in oceanic regions. The average Moho depth for the South American Platform is 39.1 km, allowing a spatial correlation of deeper and shallower Moho regions with different types of continental basins. Compared to other models, the model resulting from this study presents fine-scale features highlighting the limits of the main tectonic domains and a good agreement with the suture zones. Overall, this study demonstrates the potential of applying machine learning tools in crustal-scale imaging using sparse datasets, providing new advances in Moho modeling of the South America, as well as new perspectives on its the history and tectonic evolution.