Neuro-Fuzzy Kinematic Finite-Fault Inversion: 2. Application To The Mw6.2, August/24/2016, Amatrice Earthquake

In this article, we validate the neuro-fuzzy kinematic finite-fault inversion method by studying the rupture process of the Mw6.2, Aug/24/2016, Amatrice, central Italy, earthquake. We jointly invert three different datasets to infer the spatio-temporal slip distribution, namely static and high-rate GNSS data (0.06 Hz) and strong-motion data (0.06-0.5 Hz). Each data set is used to constrain a different frequency range of the source model, depending on the sensitivity of the data set. The inferred slip shows a slow nucleation phase at shallow depths of 3-4 km, followed by a bilateral rupture that forms two asperities, one to the NW (Norcia) and another to the SE (Amatrice) of the hypocenter. Our inferred slip is compared with those previously obtained using well-established methods. In order to select an adequate number of fuzzy basis functions, we propose two alternative procedures, which yield the same general slip features. The first approach consists of ensuring that the inverse problem is formally over-determined and uses the same number of basis functions at all frequencies. The second approach is based on a maximum-likelihood analysis of the model misfit and selects a different number of basis functions for each frequency. The maximum-likelihood approach allows for more basis functions at high frequencies, where more detail in the spatial slip distribution is needed. The solution obtained with the maximum-likelihood approach provides a more physically plausible source time function, which shows less back slip artifacts. The accurate prediction of high-rate GNSS traces not used in the inversion attests the robustness of the inferred slip model.