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

What is it about?

Human activities, ocean tides, and earth plate movements generate seismic ambient noise of the solid Earth. The surface wave energy in seismic ambient noise is strong, which can cover up the relatively weak body wave signals. In this study, effective classification criteria are established based on images of the time-domain waveforms and the F-K spectra of the seismic ambient noise data and the noise data mainly composed of surface wave or body wave are classified respectively. Finally, the obtained high signal-to-noise ratio subsurface image including reflected wave image and S-wave velocity structure can be used for reliable geological interpretation. In addition, taking advantage of the high signal-to-noise ratio wavefields, this study manifests the possibility of imaging the deeper Earth precisely when only seismic ambient noise is available. Moreover, the classification criteria enable to shorten the data acquisition time and reduce the acquisition cost.

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Photo by Dhruv on Unsplash

Photo by Dhruv on Unsplash

Why is it important?

By combining waveform analysis with frequency-wavenumber (F-K) spectra to distinguish between body waves and surface waves, we introduce a technique that significantly enhances the interpretation accuracy and signal-to-noise ratio of passive seismic data. It not only improves the quality of subsurface imaging but also serves as a validation tool, aiding in the identification and elimination of misleading geological interpretations.

Perspectives