Seismicity anomalies prior to 8 June 2008, &amp;lt;i&amp;gt;M&amp;lt;sub&amp;gt;w&amp;lt;/sub&amp;gt;&amp;lt;/i&amp;gt;=6.4 earthquake in Western Greece

Chouliaras, G.

doi:10.5194/nhess-9-327-2009

“…These methods are receiver operating characteristic (ROC), positive predictive value (PPV), and Shannon information entropy. We note that quiescence has been identified as a precursor to major earthquakes in previous research (Kanamori, 1981;Wyss and Haberman, 1988;Haberman, 1988;Main and Meredith, 1991;Huang et al, 2001;Chouliaras, 2009;Weimer and Wyss, 1994;Torman et al, 2010;Rundle et al, 2011;Katsumata, 2011;Nanjo, 2020).…”

Section: Discussionsupporting

confidence: 48%

Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Rundle

¹

,

Donnellan

²

,

Fox

³

et al. 2022

Preprint

0

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Yes Key Points! Interval statistics have been used to conclude that major earthquakes are random events in time and cannot be anticipated or predicted ! Machine learning is a powerful new technique that enhances our ability to understand the information content of earthquake catalogs ! We show that catalogs contain significant information on current hazard and future predictability for large earthquakes Plain Language SummaryThe question of whether earthquake occurrence is random in time, or perhaps chaotic with order hidden in the chaos, is of major importance to the determination of risk from these events. It was shown many years ago that if aftershocks are removed from the earthquake catalogs, what remains are apparently events that occur at random time intervals, and therefore not predictable in time. In the present work, we enlist machine learning methods using Receiver Operating Characteristic (ROC) analysis. With these methods, probabilities of large events and their associated information value can be computed. Here information value is defined using Shannon Information Entropy, shown by Claude Shannon (Shannon, 1948) to define the surprise value of a communication such as a string of computer bits. Random messages can be shown to have high entropy, surprise value, or uncertainty, whereas low entropy is associated with reduced uncertainty and high reliability. An earthquake nowcast probability associated with reduced uncertainty and greater reliability is most desirable. Examples of the latter could be the statements that there is a 90% probability of a major earthquake within 3 years, or a 5% chance of a major earthquake within 1 year. Despite the random intervals between major earthquakes, we find that it is possible to make low uncertainty, high reliability statements on current hazard by the use of machine learning methods.

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“…These methods are receiver operating characteristic (ROC), positive predictive value (PPV), and Shannon information entropy. We note that quiescence has been identified as a precursor to major earthquakes in previous research (Kanamori, 1981;Wyss and Haberman, 1988;Haberman, 1988;Main and Meredith, 1991;Huang et al, 2001;Chouliaras, 2009;Weimer and Wyss, 1994;Torman et al, 2010;Rundle et al, 2011;Katsumata, 2011;Nanjo, 2020).…”

Section: Discussionsupporting

confidence: 48%

Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Rundle

¹

,

Donnellan

²

,

Fox

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Yes Key Points! Interval statistics have been used to conclude that major earthquakes are random events in time and cannot be anticipated or predicted ! Machine learning is a powerful new technique that enhances our ability to understand the information content of earthquake catalogs ! We show that catalogs contain significant information on current hazard and future predictability for large earthquakes Plain Language SummaryThe question of whether earthquake occurrence is random in time, or perhaps chaotic with order hidden in the chaos, is of major importance to the determination of risk from these events. It was shown many years ago that if aftershocks are removed from the earthquake catalogs, what remains are apparently events that occur at random time intervals, and therefore not predictable in time. In the present work, we enlist machine learning methods using Receiver Operating Characteristic (ROC) analysis. With these methods, probabilities of large events and their associated information value can be computed. Here information value is defined using Shannon Information Entropy, shown by Claude Shannon (Shannon, 1948) to define the surprise value of a communication such as a string of computer bits. Random messages can be shown to have high entropy, surprise value, or uncertainty, whereas low entropy is associated with reduced uncertainty and high reliability. An earthquake nowcast probability associated with reduced uncertainty and greater reliability is most desirable. Examples of the latter could be the statements that there is a 90% probability of a major earthquake within 3 years, or a 5% chance of a major earthquake within 1 year. Despite the random intervals between major earthquakes, we find that it is possible to make low uncertainty, high reliability statements on current hazard by the use of machine learning methods.

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“…It was the largest strike‐slip earthquake that occurred in western Greece during the past 25 years [ Ganas et al , 2009] and its importance for seismic hazard assessment in Greece is thus obvious [ Tselentis et al , 2010]. A few years of anomalous seismicity pattern preceding the event was reported by Chouliaras [2009]. Papadopoulos et al [2009] attributed the event to an unusual earthquake “storm” that struck Greece in 2008, in which four Mw > 6.2 earthquakes occurred during an unusually short time interval of 6 months.…”

Section: Real Data Examplementioning

confidence: 99%

Toward understanding slip inversion uncertainty and artifacts

Zahradnı́k¹,

Gallovič²

2010

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[1] The main concept of seismic source tomography, the projection lines along which the observed signals are back-projected to the fault, is extended to complete wavefields. The so-called "dynamic projection strips" (DPSs) are defined, and a method to construct the strips from individual waveforms is described. In this way, each individual station role in the inversion can be better understood. Synthetic models with two asperities (two unilateral and one bilateral rupture scenarios) are used as examples. They are analyzed using two independent slip inversion methods with similar results, bias of the rupture speed for all scenarios and a strong false asperity in the middle of the bilateral fault. Both artifacts can be explained by the DPS analysis as inherent nonuniqueness of the inverse problem due to the joint effect of the two true asperities. Removal of some slip artifacts by imposing various constraints is discussed, but most of the constraints are hardly applicable in practice. As such, it is recommended to look at least for possible indications of the most significant inversion artifacts. This seems to be feasible through combining DPSs derived from real data and synthetic models of various rupture scenarios for a given fault and stations. The ideas are applied to the Movri Mountain earthquake in Greece, Mw6.3, 8 June 2008. It appears that the earthquake was predominantly unilateral, however with a nonunique space-time slip pattern. Few equivalent nonsmooth models, all fitting the data equally well, are illustrated.

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Seismicity anomalies prior to 8 June 2008, <i>M<sub>w</sub></i>=6.4 earthquake in Western Greece

Cited by 30 publications

References 23 publications

Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?

Toward understanding slip inversion uncertainty and artifacts

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