Edge detection of tomographic images using traditional and deep learning tools

Abstract

Edge detection is regularly used as a fundamental operation for correctly identifying and measuring some required features. In a grey-level image, an area where the grey-level value moves from a high value to a low value or vice versa is considered an edge. Edges are indicative of a boundary between an object and a background or between two objects. Consequently, edge detection in earth sciences is an important tool for locating geological features and determining their shapes and sizes. Edge detection usually forms a part of the geophysical interpretation or inversion procedure. Seismic tomography is a straightforward field of applying edge detection because the tomogram can be directly considered as an image. In the tomographic reconstruction of seismic travel time data, care must be taken to keep the propagation of data errors to the model space under control. The noise - especially the outliers in the data sets - can cause appreciable distortions in the tomographic imaging. To reduce the noise sensitivity well-developed tomography algorithms can be used. On the other hand, the quality of the tomogram can further be improved by using image processing tools. This is especially important in edge detection, as it is extremely sensitive to noise. In the paper, we present two ways to find robust edge detection. At first, remaining in the framework of traditional image processing a robust Cauchy–Steiner filter is used to improve the quality of edge detection in tomographic images. In the second part of the paper Deep Learning algorithm developed for edge detection is shown and investigating its noise sensitivity the robustness of the method is demonstrated.