Extending the Frequency Band of Surface-Wave Dispersion Curves by Combining Ambient Noise and Earthquake Data and Self-Adaptive Normalization

Dispersion curves of surface waves are widely used in subsurface structural inversions. However, the non-uniqueness problem in inversion is inevitable with limited data. Extending the dispersion curves in frequency is helpful for providing better constraints. The dispersion spectra extracted from ambient noise and earthquake data do not overlap and can be combined to provide complementary information. Besides, because multiple modes can be present at the same frequencies in the dispersion spectrum with highly non-uniform energies, dispersion curves with lower energies are hidden by those with higher energies, thus the extracted dispersion spectrum is often not fully exploited by simple normalization. We propose to use a self-adaptive normalization technique to uncover the hidden information and extend the dispersion curves in frequency. Both ambient noise and earthquake data recorded by the USArray Transportable Array are processed by simple and self-adaptive normalizations, and a Markov chain Monte Carlo Bayesian inversion is conducted to estimate the velocity structure. The inversion results reveal the importance of extending dispersion curves and a significant improvement by the combined ambient noise and earthquake datasets and the self-adaptive normalization technique.