High-Frequency Time-Lapse Seismic Spatial Autocorrelation Imaging Shallow Velocity Variations

Shallow velocity variations can be caused by different reasons, which could be related to infrastructure security. Among seismic-based temporal velocity analysis methods, ambient-noise-based spatial autocorrelation (SPAC) provides the finest shallow imaging resolution. We present a continuous time-lapse SPAC (tSPAC) approach to retrieve the shallow velocity variations. In our field application, due to soil moisture changes caused by water leakage, the seismic velocity changes over the time. Based on the SPAC method, we use the extracted surface wave (Rayleigh wave) to estimate the shear wave velocity as a function of depth. Using a time-lapse manner, we demonstrate that velocity variations due to water leakage can be detected from passive ambient seismic noise. The field deployment results agree well with the ground truth of experiment setups. The success of our study demonstrates that the in situ near-surface seismic velocity can be accurately imaged by tSPAC. This technique can be used to monitor seismic velocity change and further investigate not only the fluid saturation, but also other associated changing conditions, such as stress and temperature.