## 4 Conclusions  
In this study, an accurate FAWS algorithm is proposed based on compressive sensing theory. Fewer measurements of a random-spaced geophone array are needed to provide the desired dispersion curve of surface wave velocity for the non-destructive evaluation of the soil layers. In-site experimental testing is deployed in Shenyang metro line 9 to validate the capability of the proposed technique. A bandpass filter bank is introduced to reconstruct the multi-modal dispersion characters of wavenumber and surface wave velocities at various frequencies. According to the sparse features of the Rayleigh-type seismic surface wave of the Earth, an \(l_{1}\)-norm optimization algorithm is employed to reconstruct the dispersion curve of the velocity and wavenumber-frequency spectrum. As comparisons, both a conventional even-spaced geophone array and in-site downhole testing are carried out to evaluate the soils in Shenyang metro line 9 for the future construction. The dispersion curve identified by the FAWS method was picked up with a good agreement with the trend of the dispersion image of surface wave processed by the comparison technique. In addition, the experimental results indicate the proposed technique can identify both the wavenumber-frequency distribution and velocity-frequency image of the surface wave of subsoil with very good accuracy. Considering the measurements required are fewer than the traditional method, the proposed approach is a game-changing technique with the advantage of being low cost, rapid, and safe for the identification of the soil properties for both geotechnical research and engineering construction. It should be emphasized that the FAWS technique has its own specific requirements and limitations, which might need comparisons and supplements from other in-situ testing for a complete, accurate evaluation of deeper soil properties. \begin{table}
\begin{tabular}{c c c c c} \hline
**Bore Hole Number** & **Soil Type** & **Depth (m)** & **Shear Wave Velocity (m/s)** & **Compression Wave Velocity (m/s)** \\ \hline \multirow{3}{*}{ZDBC-01} & Topsoil & 2.9 & 170 & 445 \\  & Slit and clay & 5.3 & 200 & 529 \\  & Sand & 7.8 & 269 & 709 \\ \hline \multirow{3}{*}{ZDBC-02} & Topsoil & 2.2 & 154 & 424 \\  & Slit and clay & 4.3 & 195 & 532 \\  & Sand & 8.5 & 244 & 634 \\ \hline \multirow{3}{*}{ZDBC-03} & Topsoil & 3 & 161 & 431 \\  & Slit and clay & 5.2 & 199 & 523 \\ \cline{1-1}  & Sand & 6.8 & 261 & 681 \\ \hline \multirow{3}{*}{ZDBC-04} & Topsoil & 3.4 & 172 & 439 \\  & Slit and clay & 6 & 187 & 488 \\ \cline{1-1}  & Sand & 5.6 & 257 & 678 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental in-site downhole results at Olympic station of Shenyang metro line 9. Figure 8: Schematic diagram of conventional in-site downhole testing. In future work, the proposed technique requires additional study for the optimization of the arrangement of the geophones and the filter bank. In addition, the proposed in-situ FAWS technique will be applied to evaluate the soil properties on a larger scale for both engineering applications and geophysical research. More efficient and powerful algorithms, e.g., orthogonal matching pursuit (OMP), CoSaMP, and subspace-pursuit (SP), will be investigated in future work. Also, the characteristics of deep soil layer properties will also be studied to pursue the tomography of the subsoil. [PERSON], [PERSON] and [PERSON] conceived and designed the method; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. This study is financially supported by the project supported by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration with grant No. 2018A01, key special project of national key R&D plan, international scientific and technological innovation cooperation with grant No. 2016 YFE0105500, and natural science foundation of Heilongjiang province with grant No. QC2017037.