[S-SS11] Earthquake prediction and forecast

[SSS11-P02] A retrospective analysis of earthquakes in North American plate through fractal characteristics and static stress distribution.

*Haritha Chandriyan¹, Ramakrushna Reddy², P.N.S Roy¹ (1.Department of Geology and Geophysics, IIT Kharagpur, West bengal, India., 2.National Taiwan University, Taiwan)

Keywords:Earthquake, Numerical precursor, static stress change

Searching for reliable precursor preceding strong earthquakes had been challenging; and it’s still remains an enigma. Epicentral and hypocentral distribution of earthquakes exhibits fractal statistics (e.g. Kagan and Knopoff, 1978; Kagan, 1981; Ogata and Katsura, 1991; Bak et al., 2002). Based on this concept, the characteristic variation in fractal correlation dimension (Dc) before the occurrence of strong earthquakes in southern and Baja California has been studied in detail by Chandriyan et al., (2022). Where we have identified the relative drop in Dc before four Mw> 7 event that ruptured since 1990. This drop in Dc can be considered as a numerical precursors and is accordance with many previous studies (e.g. Hirata et al., 1987; Lu et al., 2005; Mangalagiri et al., 2022; Murase, 2004; Roy and Padhi, 2007). Though the onset period of precursors are different, a general trend is the relative decrease in Dc preceded by 1992 Landers, 1999 Hectormine, 2010 El-Mayor Cucapah and 2019 Ridgecrest earthquakes. In the current study, we attempt to quantify the static stress variation from the day we observed the drop in Dc to prior the rupture of strong earthquakes in southern and Baja California. The recently available focal mechanism dataset derived based on deep learning algorithm (Chen et al., 2021) has been used for the static stress estimation. This newly generated catalog considered additional phases and polarities identified using convolutional neural network algorithm (Ross et al., 2018a, b). For each of the strong earthquake, we have identified the low Dc window and the static stress has been computed for the localized cluster detected through fractal analysis. Multiple static stress analysis have been carried out for each strong event by considering minimum magnitude of earthquakes as 3.5. We observed the presence of positive stressed region prior to the occurrence of strong earthquakes since the day Dc drops. We could successfully correlate the low Dc values with high stress distribution seen before strong earthquake in the study region. This result enunciate the idea of direct relationship exists between Dc and static stress changes. As a result, this concept can be applied in seismic hazard studies by analysing the Dc variation and computing the static stress for the clusters in order to identify reliable precursors.