An Automatic Screening Method for the Passive Surface-Wave Imaging Based on the F–K Domain Energy Characteristics

Due to low cost and nondestructive characteristics, passive surface-wave imaging has shown great potential in urban near-surface exploration. However, the imaging methods are facing many challenges in practical applications, such as uneven noise source distribution and complex site environments, which will seriously affect the dispersion imaging quality of surface waves, and result in the failure of retrieving accurate dispersion curve and inaccurate inversion. Therefore, data screening is required to improve the accuracy of passive surface-wave dispersion imaging. This process is usually completed manually, which is time-consuming for processing the large datasets. To solve this problem, we propose an automatic data screening method for the near-surface passive surface-wave imaging. Based on the distribution characteristics of the surface-wave energy of noise data in the F–K domain, this proposed method uses the least-squares technique to fit the quadratic distribution of energy and sorts the noise time segments according to the defined correlation coefficients and bandwidth coefficients. Thus, we can automatically detect the noise time segments with high signal-to-noise ratio (SNR) without manual intervention. In order to verify the effectiveness of this proposed method, both synthetic data and field data are used in this study. The results show that this automatic data screening method significantly improves the accuracy of the passive surface-wave dispersion imaging, effectively expands the surface-wave energy band, and realizes rapid and automatic data screening.