loading page

Fuzzy Kinematic Finite-Fault Inversion: 2. Application to the Mw6.2, 24/August/2016, Amatrice Earthquake
  • Navid Kheirdast,
  • Anooshiravan Ansari,
  • Susana Custódio
Navid Kheirdast
International Institute of Earthquake Engineering and Seismology (IIEES)
Author Profile
Anooshiravan Ansari
International Institute of Earthquake Engineering and Seismology (IIEES)

Corresponding Author:[email protected]

Author Profile
Susana Custódio
University of Lisbon
Author Profile


We validate the fuzzy kinematic finite-fault inversion method by studying the rupture process of the $M_w 6.2$, 24/Aug/2016, Amatrice, central Italy, earthquake. We jointly invert three different datasets to infer the spatio-temporal slip distribution, including static and high-rate GNSS data ($<=0.06$ Hz) and strong-motion data ($>0.06-0.5$ Hz). Each dataset is used to constrain a different frequency range of the source model, depending on the sensitivity of each dataset. Our slip solution confirms the main rupture features revealed by previous studies, including a slow nucleation phase at shallow depths, between 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. To select an adequate number of fuzzy basis functions, we propose two alternative procedures, that yield the same general slip features concerning the amplitude, distribution, and velocity of slip. The first approach consists of ensuring the inverse problem is formally over-determined and uses a constant number of basis functions at all frequencies. The second 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 obtained. 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 testifies the robustness of the inversion