Machine learning to compute, from optical images, the horizontal ground displacement field caused by an earthquake

Optical images acquired by satellite are widely used to measure the displacement field caused by earthquakes co-seismic ruptures both in the near and the far field. So far, this technique relies mostly on image correlation to locate pixels from an image acquired before the event on another acquired after. However, such approach suffers from several limitations inherent in the correlation method used, such as the need for sufficient texture to make objects « recognizable » from one image to the other, or limited changes in the landscape over time, for the same reason. These limitations can lead to noisy results and even prevent any measurement from being made.
To overcome such limitations, machine learning can be used instead of correlation to train a model to compute displacement maps from a pair of images. Steady and significant progress have been made in machine learning technics, and especially for image processing and computer vision, in recent years, and they need to be adapted to our case study. First, a training dataset is carefully designed, to enable the network to learn how to measure pixel displacements in satellite images, with sub-pixel accuracy, in the most realistic way possible. Since no ground truth is available, we build synthetic examples, where a realistic and known deformation is applied to one of the images in a pair of 10-m-resolution Sentinel-2 satellite images, which originally contains no displacement. This realistic synthetic dataset is then used to feed a model.
Our network is capable of estimating a displacement field from images whose resolution differs signifiantly from that of the training dataset (for example, from a 0.5-m-resolution Pléiades image pair) and achieves results comparable to those of state-of-the-art methods, with even finer details, at both pixel and sub-pixel resolution levels. However, the ability of machine learning to overcome limitations due to landscape changes caused by time remains to be proven.