On the Statistical Significance of the Variability Minima of the Order Parameter of Seismicity by Means of Event Coincidence Analysis

Natural time analysis has led to the introduction of an order parameter for seismicity when considering earthquakes as critical phenomena. The study of the fluctuations of this order parameter has shown that its variability exhibits minima before strong earthquakes. In this paper, we evaluate the statistical significance of such minima by using the recent method of event coincidence analysis. Our study includes the variability minima identified before major earthquakes in Japan and Eastern Mediterranean as well as in global seismicity.

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Statistical significance of the precursory minima of the order parameter fluctuations of seismicity by modern methods of Statistical Physics

10.5194/egusphere-egu2020-7482 ◽

2020 ◽

Author(s):

Panayiotis A. Varotsos ◽

Nicholas V. Sarlis ◽

Efthimios S. Skordas ◽

Stavros-Richard G. Christopoulos

Keyword(s):

Time Series ◽

Order Parameter ◽

Statistical Physics ◽

Statistical Significance ◽

Time Analysis ◽

Seismic Electric Signals ◽

Natural Time ◽

Natural Time Analysis ◽

Global Seismicity ◽

Electric Signals

<p>An order parameter for seismicity was introduced in the frame of natural time analysis [1]. &#160;Recent studies of the fluctuations of this order parameter revealed the existence of minima preceding major earthquakes [2-7]. Here, we review the statistical significance of these minima by using recent methods of Statistical Physics, such as receiver operating characteristics [8] and event coincidence analysis [9,10]. These methods are also applied to the investigation [11] of the statistical significance of Seismic Electric Signals [12].</p><p>References</p><ol><li>Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Natural Time Analysis: The new view of time. Precursory Seismic Electric Signals, Earthquakes and other Complex Time-Series; Springer-Verlag: Berlin Heidelberg, 2011.</li> <li>Sarlis, N.V.; Skordas, E.S.; Varotsos, P.A.; Nagao, T.; Kamogawa, M.; Tanaka, H.; Uyeda, S. Minimum of the order parameter fluctuations of seismicity before major earthquakes in Japan, Proc. Natl. Acad. Sci. USA 110 (2013) 13734&#8211;13738, dx.doi.org/10.1073/pnas.1312740110.</li> <li>Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Study of the temporal correlations in the magnitude time series before major earthquakes in Japan. J. Geophys. Res.: Space Physics 119 (2014) 9192&#8211;9206, dx.doi.org/10.1002/2014JA020580.</li> <li>Sarlis, N.V.; Christopoulos, S.R.G.; Skordas, E.S. Minima of the fluctuations of the order parameter of global seismicity. Chaos 25 (2015) 063110, dx.doi.org/10.1063/1.4922300.</li> <li>Sarlis, N.V.; Skordas, E.S.; Christopoulos, S.-R.G.; Varotsos, P.A. Statistical significance of minimum of the order parameter fluctuations of seismicity before major earthquakes in Japan, Pure Appl. Geophys. 173 (2016) 165&#8211;172, dx.doi.org/10.1007/s00024-014-0930-8.</li> <li>Sarlis, N.V.; Skordas, E.S.; Mintzelas, A.; Papadopoulou, K.A. Micro-scale, mid-scale, and macro-scale in global seismicity identified by empirical mode decomposition and their multifractal characteristics. Scientific Reports 8 (2018) 9206, dx.doi.org/10.1038/s41598-018-27567-y.</li> <li>Mintzelas, A.; Sarlis, N. Minima of the fluctuations of the order parameter of seismicity and earthquake networks based on similar activity patterns. Physica A 527 (2019) 121293, dx.doi.org/10.1016/j.physa.2019.121293.</li> <li>Fawcett, T., An introduction to ROC analysis, Pattern Recognit. Lett. 27 (2006) 861&#8211;874, dx.doi.org/10.1016/j.patrec.2005.10.010.</li> <li>Donges, J.; Schleussner, C.F.; Siegmund, J.; Donner, R. Event coincidence analysis for quantifying statistical interrelationships between event time series. The European Physical Journal Special Topics 225 (2016) 471&#8211;487, dx.doi.org/10.1140/epjst/e2015-50233-y.</li> <li>Siegmund, J.F.; Siegmund, N.; Donner, R.V. CoinCalc - A new R package for quantifying simultaneities of event series. Computers & Geosciences 98 (2017) 64-72, dx.doi.org/10.1016/j.cageo.2016.10.004.</li> <li>Sarlis, N.V. Statistical Significance of Earth&#8217;s Electric and Magnetic Field Variations Preceding Earthquakes in Greece and Japan Revisited. Entropy 20 (2018) 561, dx.doi.org/10.3390/e20080561.</li> <li>Varotsos, P.; Lazaridou, M. Latest aspects of earthquake prediction in Greece based on seismic electric signals, Tectonophysics 188 (1991) 321&#8211;347, dx.doi.org/10.1016/0040-1951(91)90462-2.</li> </ol>

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Natural-time analysis of critical phenomena: The case of seismicity

EPL (Europhysics Letters) ◽

10.1209/0295-5075/92/29002 ◽

2010 ◽

Vol 92 (2) ◽

pp. 29002 ◽

Cited By ~ 25

Author(s):

P. A. Varotsos ◽

N. V. Sarlis ◽

E. S. Skordas ◽

S. Uyeda ◽

M. Kamogawa

Keyword(s):

Critical Phenomena ◽

Time Analysis ◽

Natural Time ◽

Natural Time Analysis

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Natural Time Analysis: The Area under the Receiver Operating Characteristic Curve of the Order Parameter Fluctuations Minima Preceding Major Earthquakes

Entropy ◽

10.3390/e22050583 ◽

2020 ◽

Vol 22 (5) ◽

pp. 583 ◽

Cited By ~ 5

Author(s):

Nicholas V. Sarlis ◽

Efthimios S. Skordas ◽

Stavros-Richard G. Christopoulos ◽

Panayiotis A. Varotsos

Keyword(s):

Order Parameter ◽

Characteristic Curve ◽

Activity Patterns ◽

Space Physics ◽

Operating Characteristics ◽

Time Analysis ◽

Natural Time ◽

Natural Time Analysis ◽

Electric Signals ◽

Receiver Operating

It has been reported that major earthquakes are preceded by Seismic Electric Signals (SES). Observations show that in the natural time analysis of an earthquake (EQ) catalog, an SES activity starts when the fluctuations of the order parameter of seismicity exhibit a minimum. Fifteen distinct minima—observed simultaneously at two different natural time scales and deeper than a certain threshold—are found on analyzing the seismicity of Japan from 1 January 1984 to 11 March 2011 (the time of the M9 Tohoku EQ occurrence) 1 to 3 months before large EQs. Six (out of 15) of these minima preceded all shallow EQs of magnitude 7.6 or larger, while nine are followed by smaller EQs. The latter false positives can be excluded by a proper procedure (J. Geophys. Res. Space Physics 2014, 119, 9192–9206) that considers aspects of EQ networks based on similar activity patterns. These results are studied here by means of the receiver operating characteristics (ROC) technique by focusing on the area under the ROC curve (AUC). If this area, which is currently considered an effective way to summarize the overall diagnostic accuracy of a test, has the value 1, it corresponds to a perfectly accurate test. Here, we find that the AUC is around 0.95 which is evaluated as outstanding.

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Natural time analysis of critical phenomena

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1108138108 ◽

2011 ◽

Vol 108 (28) ◽

pp. 11361-11364 ◽

Cited By ~ 86

Author(s):

P. Varotsos ◽

N. V. Sarlis ◽

E. S. Skordas ◽

S. Uyeda ◽

M. Kamogawa

Keyword(s):

Critical Phenomena ◽

Time Analysis ◽

Natural Time ◽

Natural Time Analysis

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Natural time analysis: Important changes of the order parameter of seismicity preceding the 2011 M9 Tohoku earthquake in Japan

EPL (Europhysics Letters) ◽

10.1209/0295-5075/125/69001 ◽

2019 ◽

Vol 125 (6) ◽

pp. 69001 ◽

Cited By ~ 9

Author(s):

P. A. Varotsos ◽

N. V. Sarlis ◽

E. S. Skordas

Keyword(s):

Order Parameter ◽

Tohoku Earthquake ◽

Time Analysis ◽

Natural Time ◽

Natural Time Analysis

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Estimating the Epicenter of a Future Strong Earthquake in Southern California, Mexico, and Central America by Means of Natural Time Analysis and Earthquake Nowcasting

Entropy ◽

10.3390/e23121658 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1658

Author(s):

Jennifer Perez-Oregon ◽

Panayiotis K. Varotsos ◽

Efthimios S. Skordas ◽

Nicholas V. Sarlis

Keyword(s):

Central America ◽

Strong Earthquake ◽

Southern California ◽

Eastern Mediterranean ◽

Activity Patterns ◽

Time Analysis ◽

Time Period ◽

Natural Time ◽

Natural Time Analysis ◽

Earthquake Potential

It has recently been shown in the Eastern Mediterranean that by combining natural time analysis of seismicity with earthquake networks based on similar activity patterns and earthquake nowcasting, an estimate of the epicenter location of a future strong earthquake can be obtained. This is based on the construction of average earthquake potential score maps. Here, we propose a method of obtaining such estimates for a highly seismically active area that includes Southern California, Mexico and part of Central America, i.e., the area N1035W80120. The study includes 28 strong earthquakes of magnitude M ≥7.0 that occurred during the time period from 1989 to 2020. The results indicate that there is a strong correlation between the epicenter of a future strong earthquake and the average earthquake potential score maps. Moreover, the method is also applied to the very recent 7 September 2021 Guerrero, Mexico, M7 earthquake as well as to the 22 September 2021 Jiquilillo, Nicaragua, M6.5 earthquake with successful results. We also show that in 28 out of the 29 strong M ≥7.0 EQs studied, their epicenters lie close to an estimated zone covering only 8.5% of the total area.

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Natural time analysis of critical phenomena: The case of pre-fracture electromagnetic emissions

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.4807908 ◽

2013 ◽

Vol 23 (2) ◽

pp. 023117 ◽

Cited By ~ 24

Author(s):

S. M. Potirakis ◽

A. Karadimitrakis ◽

K. Eftaxias

Keyword(s):

Critical Phenomena ◽

Time Analysis ◽

Natural Time ◽

Natural Time Analysis ◽

Electromagnetic Emissions

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Identifying the Occurrence Time of the Deadly Mexico M8.2 Earthquake on 7 September 2017

Entropy ◽

10.3390/e21030301 ◽

2019 ◽

Vol 21 (3) ◽

pp. 301 ◽

Cited By ~ 3

Author(s):

Nicholas Sarlis ◽

Efthimios Skordas ◽

Panayiotis Varotsos ◽

Alejandro Ramírez-Rojas ◽

Elsa Flores-Márquez

Keyword(s):

Order Parameter ◽

Earthquake Occurrence ◽

Dynamic Features ◽

Time Analysis ◽

Occurrence Time ◽

Dynamic Phase ◽

Natural Time ◽

Natural Time Analysis ◽

Dynamic Phase Transition ◽

System Approaches

It has been shown that some dynamic features hidden in the time series of complex systems can be unveiled if we analyze them in a time domain termed natural time. In this analysis, we can identify when a system approaches a critical point (dynamic phase transition). Here, based on natural time analysis, which enables the introduction of an order parameter for seismicity, we discuss a procedure through which we could achieve the identification of the occurrence time of the M8.2 earthquake that occurred on 7 September 2017 in Mexico in Chiapas region, which is the largest magnitude event recorded in Mexico in more than a century. In particular, we first investigated the order parameter fluctuations of seismicity in the entire Mexico and found that, during an almost 30-year period, i.e., from 1 January 1988 until the M8.2 earthquake occurrence, they were minimized around 27 July 2017. From this date, we started computing the variance of seismicity in Chiapas region and found that it approached the critical value 0.070 on 6 September 2017, almost one day before this M8.2 earthquake occurrence.

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