Susan L Beck George Zandt Kevin M Ward
Susan L. Beck George Zandt Kevin M. Ward Jonathan R. Delph
Work Flow for Ambient Noise Tomography Analysis After Bensen et al. 2007
A. Raw Data What we need to obtain Rayleigh-wave phase velocities from Ambient Noise Tomography: 1. Continuously recorded seismic waveform data 2. Contemporaneously operating stations 3. Correct (or common) instrument responses Wavelet Green’s Function (Earth’s response) Instrument Response Source Function
B. Pre-Processing Data Temporal normalization After trend, mean, and instrument responses removed, we normalize amplitudes in the waveforms. • Down-weights large amplitude signals that can dominate waveform • Leads to poor cross-correlations • Good for removing earthquake signals Raw Data After temporal normalization (running absolute mean method)
B. Pre-Processing Data Spectral Whitening/Normalization Removes spectral power biases from microseismic energy and other consistent noise sources Before spectral whitening After spectral whitening
C. Cross-Correlations and Stacking Time Matters Cross-correlation signal-to-noise ratio improves with increasing data • (small improvements after ~1 year)
C. Cross-Correlations and Stacking Moveout Clear moveout between station is observed • These waveforms can be analyzed via Frequency-Time Analysis to solve for fundamental-mode Rayleigh wave phase velocities between stations
C. Cross-Correlations and Stacking Symmetric Component In theory, positive and negative time lags should be the same. Reasons they aren’t: • Local multipathing/scattering • Direction and magnitude of energy input Stack positive and negative time lags to get “average” waveform (Green’s Function) and increase signal-to-noise ratio
D. Frequency-Time Analysis Uses a Gaussian filter on a seismogram and calculates the “power” around a frequency (period) of interest to obtain velocity Interstation Dispersion Curves!
E. Tomographic Inversion Will be discussed later.
- Slides: 10