Automatic picking of multimodal Rayleigh-wave dispersion curves from multicomponent data with an energy-density-based clustering method

SUMMARY Rayleigh wave is widely used for characterizing shallow subsurface structures. The conventional Rayleigh-wave methods rely on the manual picking of dispersion curves, and the dispersion curves of multicomponent data are usually merged manually. The manual processing of multicomponent Rayleigh waves reduces the efficiency of the method, especially when the data size and the number of modes are large. To overcome these limitations, we develop an energy-density-based clustering method, namely the Energy-Density-Based Spatial Clustering of Applications with Noise (E-DBSCAN) algorithm. The E-DBSCAN algorithm extracts energy clusters and dispersion curves from a single dispersion image. It considers the dispersion-energy values of the surface wave and is able to pick the dispersion curve more reliably compared with the conventional DBSCAN algorithm. We propose a two-step clustering approach for the automatic picking of multimodal dispersion curves from multicomponent data: we first extract the energy clusters in the dispersion spectra of horizontal- and vertical-component data using E-DBSCAN, respectively, and combine them in the frequency–velocity domain. Then, we extract multimodal dispersion curves from the combined multicomponent energy clusters with E-DBSCAN or DBSCAN. Numerical results show that our proposed method has fairly high accuracy and estimates more abundant multimodal dispersion curves than the single-component method. Two field examples, including an active-source and an ambient-noise data set, prove the validity of our method and the outperformance of multicomponent results compared with the single-component results. Our proposed method has a relatively low dependence on parameter selection and is also applicable to multi-offset data, which is valuable for picking multimodal dispersion curves.