Impact of Geometric Spreading in the Fourier Domain on Source Spectra: Observations of Steep Amplitude Decay and Frequency Dependence from the 2019 Ridgecrest, California, Earthquake, Sequence

ABSTRACT This study characterizes the impact of attenuation on source spectra for events of the 2019 Ridgecrest, California, sequence, for M ∼4–7 at distances from ∼5 to 400 km. Fourier amplitudes display a steep rate of apparent geometric spreading: R−1.6 within 60 km. Over a transition zone from ∼60 to 140 km, the apparent geometric spreading is strongly frequency dependent. This makes the robust retrieval of information on near-distance ground-motion amplitudes and source spectra intractable via traditional regressions of observations at >60 km and creates challenges for modeling the strong ground motions. Apparent source spectra and near-source observations for the Ridgecrest events are characterized by strong amplitudes despite relatively low corner frequencies. The spectral shapes are consistent on average with a Brune single-corner source model with stress ∼40 bars (4 MPa) and kappa (high-frequency site attenuation) = 0.025 for events of M 4–5.5. The largest two events are consistent in shape with the Boore, Di Alessandro, and Abrahamson (2014) double-corner model, with amplitudes being consistent with a stress of ∼40 bars for the M 6.4 event and ∼10 bars (1 MPa) for the M 7.1 event. The referenced values of stress as obtained from the corner frequency are model dependent. Comparison of the amplitude levels of the source model with observations suggests that either (1) near-distance (<10 km) finite-fault effects are strong or (2) the commonly assumed values of scaling constants in the source models are significantly biased. Fourier models of source, path, and site are difficult to connect to corresponding response spectral models due to nonuniqueness in the mapping of parameters sets between domains. Model calibration is essential in this context; it is not advisable to change single model parameters without verifying that the modified model matches direct observations, even in “plug and play” models that have separated model components.