The study of the hypocenter distribution of seismic events related to fault structures is a crucial topic since it is linked to the geological features and to the dynamics of an investigated area. The hypocenter spatial distribution of earthquakes is used in a novel algorithmic method to clusterize earthquakes to accurately identify the strike and the dip parameters of seismogenic faults. Our algorithm works as a fast and efficient explorer in a five-dimensional space (x, y, z, τ, ϕ). It randomly selects several seismic events (pivots) and counts in all angular directions, for each pivot, how many hypocenters can be included in a prefixed volume (two dimensions larger than the third). The result is a volume that contains the maximum number of earthquakes occurring within a minimum distance from a flat area corresponding to the searched fault. With this volume is associated a hypocenter occurrence density angular diagram and a likelihood function. The likelihood function is useful to individuate the best value of the fault thickness and to test the hypothesis of fault flatness. Our algorithm was tested on simulated data and then successfully applied to the real case of the 2009 Mw6.1 L’Aquila (Central Italy) seismic sequence.
A New Robust Algorithm for Fault-Plane Parameters Identification: The 2009 L’Aquila (Central Italy) Seismic Sequence Case
Tripaldi, Simona
2025-01-01
Abstract
The study of the hypocenter distribution of seismic events related to fault structures is a crucial topic since it is linked to the geological features and to the dynamics of an investigated area. The hypocenter spatial distribution of earthquakes is used in a novel algorithmic method to clusterize earthquakes to accurately identify the strike and the dip parameters of seismogenic faults. Our algorithm works as a fast and efficient explorer in a five-dimensional space (x, y, z, τ, ϕ). It randomly selects several seismic events (pivots) and counts in all angular directions, for each pivot, how many hypocenters can be included in a prefixed volume (two dimensions larger than the third). The result is a volume that contains the maximum number of earthquakes occurring within a minimum distance from a flat area corresponding to the searched fault. With this volume is associated a hypocenter occurrence density angular diagram and a likelihood function. The likelihood function is useful to individuate the best value of the fault thickness and to test the hypothesis of fault flatness. Our algorithm was tested on simulated data and then successfully applied to the real case of the 2009 Mw6.1 L’Aquila (Central Italy) seismic sequence.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
