Configurational entropy of critical earthquake populations

Abstract

[1] We present an approach to describe the evolution of distributed seismicity by configurational entropy. We demonstrate the detection of phase transitions in the sense of a critical point phenomenon in a 2D sitepercolation model and in temporal and spatial vicinity to the 1992, M7.3 Landers earthquake in Southern California. Our findings support the assumption of intermittent criticality in the Earth's crust. We also address the potential usefulness of the method for earthquake catalogue declustering.

Transition on the relationship between fractal dimension and Hurst exponent in the long-range connective sandpile models

“…2) [11][12][13]. Such process is similar to the dynamical process of the earthquake fault system which repeats by reloading energy and rebuilding correlation lengths towards criticality and the next great event [18][19][20]33,34]. For more details about the LRCS model, we refer the readers to our previous papers [11][12][13].…”

“…The LRCS model after a large avalanche can thus evoke a high value of connective probability P c , motivated by that a more active earthquake fault system will have higher probability to establish long-range connection due to the fault activity, the change in pore fluid pressure or the dynamic triggering of seismic waves. By using such self-adapted probability threshold P c of remote connection, the self-adapted LRCS model demonstrates a state of intermittent criticality [15][16][17] The large fluctuation in Z (t) is an important feature mimicking the intermittent criticality [15][16][17][18][19][20]. Large avalanches are then followed by a period of quiescence and a new approach back toward the critical state ( Fig.…”

“…We selected 500 events for every time window to calculate Bs and H s, and then shifted 50 events to calculate the successive B-and H -values of the next window. For the calculation of B, we applied the data binning technique proposed by Christensen and Moloney [21] to reduce the noise effect of large avalanches, i.e. the effect of finite statistics, and then adopted the weighted least-square regression to fit the frequency-size distribution.…”

The relationship between seismic frequency and magnitude as based on the Maximum Entropy Principle

“…Thereafter, during a period of over 100 years, the concept of entropy was widely applied in various scientific fields (Guo and Garland 2006;Emanuel 1995;Goltz and Bose 2002). As a state function, entropy has profound meaning: in thermodynamics, it is the measurement of unavailable energy; in statistical physics, it is the measurement of the number of microscopic states of a system; in informationism, it is the measurement of the uncertainty of random events (Wang 1988).…”

Integer and fractional-order entropy analysis of earthquake data series

“…They propose a method for calculating the entropy of earthquake foci that uses Voronoi cells to measure point density in three dimensions. Goltz and Böse [15] present an approach to describe the evolution of distributed seismicity by means of configurational entropy. Their findings support the assumption of intermittent criticality in the Earth's crust.…”