# Analysis of Aftershocks from California and Synthetic Series by Using Visibility Graph Algorithm

## TL;DR

This paper uses a network analysis method to study real and synthetic earthquake data, revealing patterns in seismic activity before and after major quakes.

## Contribution

The study introduces the use of Visibility Graph Algorithm to detect seismic trends and aftershock dynamics in both real and synthetic data.

## Key findings

- The Visibility Graph Algorithm revealed a significant decrease in network parameters after major earthquakes due to aftershocks.
- The same behavior was observed in synthetic seismicity series, suggesting the spring-block model mimics real aftershock relaxation.
- The technique could help in understanding and potentially forecasting seismic behavior.

## Abstract

The use of the Visibility Graph Algorithm (VGA) has proven to be a valuable tool for analyzing both real and synthetic seismicity series. Specifically, VGA transforms time series into a network representation in which structural properties such as node connectivity, clustering, and community structure can be quantitatively measured, thereby revealing underlying correlations and dynamics that may remain hidden in traditional linear or spectral analyses. The time series transformation into complex networks with VGA provides a new approach to analyze seismic dynamics, allowing scientists to extract trends and behaviors that may not be possible by classical time-series analysis. On the other hand, many studies attempt to find viable trends in order to identify preparation mechanisms prior to a strong earthquake or to analyze the aftershocks. In this work, the seismic activity of Southern California Earthquake was analyzed focusing only on the significant earthquakes. For this purpose, seismic series preceding and following each earthquake were constructed using a windowing method with different overlaps and the slope of the connectivity (k) versus magnitude (M) graph (k-M slope) and the average degree were computed from the mapped complex networks. The results revealed a significant decrease in these parameters after the earthquake, due to the contribution of the aftershocks from the main event. Interestingly, the study was extended to synthetic seismicity series and the same behavior was observed for both k-M slope and average degree. This finding suggests that the spring-block model reproduces a relaxation mechanism following a large-magnitude event like those of real seismic aftershocks. However, this conclusion contrasts with conclusions drawn by other researchers. These results highlight the utility of VGA in studying events that precede and follow major earthquakes. This technique may be used to extract some useful trends in seismicity, which could eventually be employed for a deeper understanding and possible forecasting of seismic behavior.