Abstract
The purpose of this work is to reveal the efficiency of some statistical non-linear methods so as to characterize a seismic zone linked to subduction in Mexico. The Pacific plate subducting into the North American plate produces an important number of earthquakes (EQs), whose magnitudes exceed Mn = 5. This region comprises the following States: Jalisco, to the northwest, Michoacan, Guerrero, and Oaxaca, to the southeast; it extends along roughly 1350 km (Figure 1). Therefore, the characterization of this region in all scopes is very important. Here, we focus on the application of non-linear methods in the Guerrero State, because it displays an important number of EQs (their magnitudes rise up to 6) and it has a different slip inclination to the rest of the subduction zone, and some authors (Singh et al., 1983; Pardo and Suarez, 1995) have considered that there are some lags of seismicity. The assumptions of the non-linear methods analyzed in this work are: that EQs are stochastic point processes; that the Fano Factor (FF) reveals the fractality of EQs; and that the NHGPPP adjusts to extreme events. The application of these methods to the Guerrero seismic sequence allows us to explain the phenomenological behaviour in the subduction zone. Traditionally, studies to characterize earthquakes’ processes focus on the tectonics mechanism, basically following deterministic approaches. Recently, some studies have investigated the time scale properties of seismic sequences with non-linear statistical approaches so as to understand the dynamics of the process. The deep comprehension of the correlation time structures governing observational time-series can provide information on the dynamical characterization of seismic processes and the underlying geodynamical mechanisms (Telesca et al., 2001). Scale-invariant processes provide relevant statistical features for characterizing seismic sequences. Since 1944, Gutenberg and Richter have found that earthquake magnitude size follows a power-law distribution. Other scale-invariant features were determined in Kagan (1992, 1994) and Kagan and Jackson (1991). A theory to explain the presence of scaleinvariance was proposed by Bak et al. (1988); they introduced the idea of self-organized criticality (SOC), beginning from a simple cellular automaton model, namely a sand pile (Turcotte, 1990; Telesca et al., 2001).
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