SUMMARY
The dispersive information of the higher surface wave modes is beneficial for improving the resolution of the inverted S-wave velocity structure, increasing the penetration depth and enhancing the robustness of the inversion. The recently proposed frequency-Bessel (F-J) transformation can achieve the relatively stable measurement of the higher-mode surface waves. However, sometimes the phenomena of the mode losses and aliases would appear in the dispersion energy image of the seismic records, which may lead to the mode misidentification. And the wrong mode identification is likely to cause the negative impacts on the dispersion inversion and geological interpretation about the survey site. In view of this, we recommend a staging strategy for the inversion of multimodal surface wave dispersion curves. The pattern search (PS) is used to invert the reliable segment of the fundamental-mode surface wave phase velocities for the first stage. For the second stage, the inverted result of the first stage is set as the initial model, the PS with embedded Kuhn-Munkres (PSEKM) algorithm is adopted for inverting the observed phase velocities of all modes. And for each frequency, a weighted bipartite graph is established between the observed values with no-explicitly-specified-mode-order (NESMO) and predicted values of the model m during the inversion, then the maximum match is determined by the Kuhn-Munkres algorithm for calculating the minimum distance between the observed and predicted data sets. The mode-order information of the observed phase velocities with NESMO would be dynamically evaluated for each model m occurred in the inversion process. The synthetic reconstruction tests have confirmed the effectiveness of the novel workflow. Also, the performance of the preconditioned steepest-descent (PSD) algorithm of local optimization methods and influence of the mode misidentification on the inversion result are also clarified in the synthesis tests. The comparison results show that the proposed workflow can realize the nice data fitting and model reconstruction without the time-consuming manual mode-identification for the higher-mode surface waves. Then, the new workflow is applied to the analysis of the actual surface wave data sets collected on two roadbeds, which is still satisfactory. Finally, we discuss the role of the staging strategy of the workflow.
Summary
Mini-Sosie, which is convenient and environmentally friendly with high energy output, is gaining increasing attention as a seismic exploration source. Passive surface wave survey using ambient noise have become a powerful tool for exploration in urban areas due to the advantages of being efficient and nondestructive. A 2-dimensional (2D) or pseudo-1D array that can attenuate phase velocity overestimation due to directional noise are the optimal choice, but such arrays are limited by the complex environment of the experimental site on the one hand and the need to ensure that the medium beneath the array meets the assumption of lateral isotropy on the other. However, the fully 1D linear array as an alternative lacks the ability to suppress the directional effect of the noise source. In this study, we develop a novel approach for linear array analysis of passive surface waves combined with mini-Sosie technique, called LAPSS. We use the mini-Sosie technique with fixed-frequency impact in the in-line direction to provide the theoretical phase velocity values at several frequency points as a reference for the biased dispersion image obtained from the linear array analysis, so as to estimate the azimuth of the noise source and to perform the correction of the biased dispersion image. We present the detailed workflow of LAPSS and compare the performance of LAPSS with PLAS (which has proven to be superior to the frequency-wavenumber method, spatial autocorrelation method, refraction microtremor, and multichannel analysis of passive surface waves) in achieving unbiased dispersion image with different noise source distributions through synthetic tests. The results show that the accuracy of the two methods is comparable, but LAPSS greatly improves the computational efficiency, has stronger generalizability and depends on weaker assumptions. Finally, a field experiment is conducted to verify the feasibility and effectiveness of LAPSS in obtaining unbiased dispersion images using a fully linear array.
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