Changes in the properties of the statistics of physical and biophysical fields as earthquake precursor

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

“…Gapeev et al proposed an empirical model of geoacoustic emission (GAE) signals constructed using statistical methods to help identify abnormal changes in GAE signals, which plays an important role in earthquake prediction [16]. In 2022, Volvach et al [17] discussed the precursor phenomena of statistical changes in physical and biophysical fields related to earthquakes, providing important guidance for reducing losses caused by earthquakes and taking corresponding preventive measures. However, statistical methods also have limitations.…”

Statistical precursors of a strong earthquake on April 6, 2009 on the Apennine Peninsula

“…As well as in ( Kogan, 2015 ; Kogan et al., 2021 ; Volvach et al., 2022 ) let us assume that any physical or geophysical fields which is measured in seismically active zones can be submitted as sum of two variables: …”

“…Further, we assume that for the discrete interval in (3) the condition is true, where is the standard of fluctuations of the values of the random process . In this case, small-scale fluctuations of the empirical probability density , corresponding to the considered interval of implementation number and corresponding to a finite volume of measurements, will significantly decrease as a result of averaging when performing the specified convolution (both in its continuous and discrete versions) ( Kogan, 2015 ; Kogan et al., 2021 ; Volvach et al., 2022 ). Thus, the appearance in (1) of the term , which is associated with the process of preparing an approaching earthquake and does not depend (or weakly depends) on the background noise , plays the role of a natural regularizing factor.…”

“…As follows from Figure 3 (see also Figure 2 ), at point , which corresponds to 00:00 on October 26, 2007, there is the deepest minimum of the dependence over the entire considered time period. Taking into account the statistical meaning of functional (2) ( Kogan, 2015 ; Kogan et al., 2021 ; Volvach et al., 2022 ), such a minimum with a high probability corresponds to the largest width of the probability density of a “weak” random process , which is independent (or weakly dependent) of the background noise and is caused by seismic phenomena in the local sector of the geophysical system, including the Apennine Peninsula. In other words, this minimum most likely corresponds to the largest range of variations in tectonic compression of lithospheric plates (The authors consider it legitimate to state that the amplitude of this variation is proportional to the effective magnitude of the specified compression; in this article, this assumption is not used.)…”

“…In this paper, using the example of the April 6, 2009 earthquake in L’Aquila (Italy) ( Vittori et al., 2011 ; Blumetti et al., 2010 ; Amoruso and Crescentini, 2009 ), the problem of detecting short-term precursors of approaching high-magnitude earthquakes is considered. The applied technique is reduced to the assessment of the relative changes in the level of chaos (entropy value) for the statistics of measured data in a mode close to real time ( Kogan, 2015 ; Kogan et al., 2021 ; Volvach et al., 2022 ). The corresponding measurements can refer to any physical (or biophysical) fields associated with processes in seismically active regions.…”