Automatic Extraction of Surface Wave Dispersion Curves Using Unsupervised Learning and High‐Resolution Tau‐p Transform

Abstract

Abstract Dispersion curves for surface wave recordings are required input for many surface wave inversion methods to image subsurface shear wave velocity distribution, while the conventional extraction of dispersion curves requires significant amount of human interaction. This step impedes efficiency enhancement of surface wave analysis and its automation. In this paper, we present an unsupervised learning scheme to achieve efficient automatic picking of dispersion curves of the fundamental mode for surface wave gathers. This scheme is composed of four major steps: computing a frequency velocity(f‐v) spectrum for the surface wave gather using a high‐resolution Tau‐p transform improved by the iteratively shrinkage Thresholding algorithm (ISTA) algorithm, generating clusters points along the dispersion energy in the f‐v spectrum via a weighted Kmeans algorithm, filtering these cluster points by principal component analysis (PCA) and Local Outlier Factor (LOF) algorithms to remove the erroneous clusters, and fitting the remaining clusters to form the dispersion curve. Tests with synthetic and field noisy surface wave recordings demonstrated the effectiveness of this approach and its potential in automatic processing of noisy surface wave data sets.

Keywords

• automatic picking
• surface wave
• dispersion curve
• unsupervised learning
• high resolution dispersion spectrum