Efficient Tunnel Detection with Waveform Inversion of Back-scattered Surface Waves

Abstract

An efficient subsurface imaging method employing back-scattered surface waves is developed to detect near-surface underground elastic-wave velocity anomalies, such as tunnels, sinkholes, fractures, faults, and abandoned manmade infrastructures. The back-scattered surface waves are generated by seismic waves impinging on the velocity anomalies and diffracting back toward the source. These wave events contain plentiful information of the subsurface velocity anomalies including spatial location, shape, size, and velocity of the interior medium. Studies have demonstrated that the back-scattered surface waves can be easily distinguished in the frequency-wavenumber (F-k) domain and have less interference by other wave modes. Based on these features, a near-surface velocity anomaly detection method by using waveform inversion of the back-scattered surface waves (BSWI) is proposed. The main objective of this thesis is to review the theoretical background and study the feasibility of the proposed BSWI method. The proposed BSWI method is tested with numerical and real-world examples. First, the numerical example uses the conventional full-waveform inversion (FWI) method as a benchmark to demonstrate the efficiency of BSWI method in detecting shallow velocity anomalies. Then, the BSWI method is tested with field data. In this study, 2D seismic data were acquired over a manmade concrete tunnel located on the main campus of the University of Kansas (KU). Different workflows including FWI method and BSWI method are applied to the acquired data and tested for imaging the known tunnel. The field example demonstrates that BSWI can accurately image the tunnel. Compared with FWI, BSWI is less demanding in data processing. Finally, this thesis concludes that the proposed BSWI method is capable of efficiently detecting a near-surface tunnel with the minimum amount of data processing which lends it as a method suitable for application in the field.