A seismic ambient noise data classification method based on waveform and frequency-wavenumber analysis: Application to reliable geological interpretation adjacent to Well Songke-2, Northeast China

Abstract

Passive seismic interferometry techniques enable the retrieval of virtual shot gathers at receiver locations. However, it is difficult to distinguish body waves from surface waves in seismic ambient noise data, which leads to failed dispersion measurements, artificial deep reflections, and misleading geologic interpretations. We develop a new method that uses the waveforms and their corresponding f- k spectra to identify body waves and surface waves. Our technique functions as a data classification procedure before wavefield retrieval and subsequent imaging. A field data example is conducted to test the feasibility of this technique. The field data results demonstrate that our technique can reconstruct satisfactory high signal-to-noise-ratio wavefields for reliable imaging and interpretation purposes. The method can also serve as a validation tool for identifying surface wave-related artificial events in the obtained virtual source reflection image. The marker boundaries of the Songliao Basin in Northeast China are interpreted in the obtained profile and are in agreement with the well-logging data. In addition, two normal faults are detected, which correspond to the extensional rift environment during the early Cretaceous.