Source time functions of earthquakes based on a stochastic differential equation

Source time functions are essential observable quantities in seismology; they have been investigated via kinematic inversion analyses and compiled into databases. Given the numerous available results, some empirical laws on source time functions have been established, even though they are complicated and fluctuated time series. Theoretically, stochastic differential equations, including a random variable and white noise, are suitable for modeling complicated phenomena. In this study, we model source time functions as the convolution of two stochastic processes (known as Bessel processes). We mathematically and numerically demonstrate that this convolution satisfies some of the empirical laws of source time functions, including non-negativity, finite duration, unimodality, a growth rate proportional to t(3), omega(-2)-type spectra, and frequency distribution (i.e., the GutenbergRichter law). We interpret this convolution and speculate that the stress drop rate and fault impedance follow the same Bessel process.

Automated summary

In this study, we model source time functions using stochastic differential equations and demonstrate that the convolution of two stochastic processes (known as Bessel processes) satisfies several empirical laws of source time functions. This includes non-negativity, finite duration, unimodality, a growth rate proportional to t(3), omega(-2)-type spectra, and the frequency distribution known as the Gutenberg-Richter law. We also speculate that the stress drop rate and fault impedance follow the same Bessel process.