Machine learning assisted crustal velocity proxy: A case study over the Tibetan Plateau and its surroundings

Abstract

So far only a few individuals have attempted to use Machine Learning approaches to anticipate GPS site velocity for crustal deformation research. Generally, a dense network of Continuously Operating Reference Station (CORS) is employed to monitor crustal deformation continuously. Campaign-mode GPS surveys are often used to densify the existing CORS network. Even sometimes, it is very challenging to establish a station in the location of our interest due to logistical problems and regional geographical considerations. However, this process is expensive, and crustal movement studies are eventually hampered by data missing issues owing to logistical restrictions. Thus, to obtain velocity vectors at the desired locations, we implemented machine learning (ML) techniques, such as support vector machines, decision trees and Gaussian processing regression, to accomplish the crustal movement precisely. We inspected data from 1271 permanent continuous and campaign-mode GPS stations, located on the Tibetan plateau and its environs. This study demonstrates the effectiveness of these ML techniques in forecasting velocity vectors (easting velocity (VE) and northing velocity (VN)) and enhancing plate movement characterisation. The correlation coefficient between (CR > 0.98 at training and CR > 0.96 at test phase) predicted and actual velocity vectors are satisfactory, making these ML predictive models considerably reliable for estimating geodetic velocity vectors. This ML algorithm demonstrates a remarkable achievement in the field of geodetic study in a cost-effective manner.