Statistical tools for maximum possible earthquake magnitude estimation

Kijko, Andrzej; Singh, Mayshree

doi:10.2478/s11600-011-0012-6

Cited by 139 publications

(76 citation statements)

References 29 publications

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“…Consequently, the unbiased value of m max cannot be estimated by applying the standard maximum likelihood procedure. A more realistic estimation of m max can be provided by introducing additional information (Pisarenko, 1991;Pisarenko et al, 1996), such as the condition that the largest observed earthquake magnitude m obs max , within the span of the entire earthquake catalog, is equal to the largest expected earthquake magnitude Em obs max ; t. As shown in Kijko (2004) and Kijko and Singh (2011), the introduction of such a condition leads to E Q -T A R G E T ; t e m p : i n t r a l i n k -; d f 2 5 ; 3 1 3 ; 4 9 7 m max m…”

Section: Estimation Of M Maxmentioning

confidence: 99%

Estimation of Earthquake Hazard Parameters from Incomplete Data Files. Part III. Incorporation of Uncertainty of Earthquake‐Occurrence Model

Kijko¹,

Smit²,

Sellevoll³

2016

Bulletin of the Seismological Society of America

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104

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Most probabilistic seismic-hazard analysis procedures require that at least three seismic source parameters be known, namely the mean seismic activity rate λ, the Gutenberg-Richter b-value, and the area-characteristic (seismogenic source) maximum possible earthquake magnitude m max . In almost all currently used seismic-hazard assessment procedures that utilize these three parameters, it is explicitly assumed that all three remain constant over time and space. However, closer examination of most earthquake catalogs has indicated that significant spatial and temporal variations existed in the seismic activity rate λ, as well as in the Gutenberg-Richter b-value. In this study, the maximum likelihood estimation of these earthquake hazard parameters considers the incompleteness of the catalogs, the uncertainty in the earthquake magnitude determination, as well as the uncertainty associated with the applied earthquake-occurrence models. The uncertainty in the earthquake-occurrence models is introduced by assuming that both the mean seismic activity rate λ and the Gutenberg-Richter b-value are random variables, each described by the gamma distribution. This approach results in the extension of the classic frequency-magnitude Gutenberg-Richter relation and the

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Section: Estimation Of M Maxmentioning

confidence: 99%

Estimation of Earthquake Hazard Parameters from Incomplete Data Files. Part III. Incorporation of Uncertainty of Earthquake‐Occurrence Model

Kijko¹,

Smit²,

Sellevoll³

2016

Bulletin of the Seismological Society of America

Self Cite

100

104

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“…see Equation 42 in Kijko and Singh (2011). We denote this estimator by R-W. Another approximate 100(1 − α)% upper confidence bound for T M was derived in Robson and Whitlock (1964):…”

Section: Robson -Whitlockmentioning

confidence: 99%

Estimating the Maximum Possible Earthquake Magnitude Using Extreme Value Methodology: The Groningen Case

et al. 2017

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The area-characteristic, maximum possible earthquake magnitude T M is required by the earthquake engineering community, disaster management agencies and the insurance industry. The Gutenberg-Richter law predicts that earthquake magnitudes M follow a truncated exponential distribution. In the geophysical literature several estimation procedures were proposed, see for instance Kijko and Singh (Acta Geophys., 2011) and the references therein. Estimation of T M is of course an extreme value problem to which the classical methods for endpoint estimation could be applied. We argue that recent methods on truncated tails at high levels (Beirlant et al., Extremes, 2016; Electron. J. Stat., 2017) constitute a more appropriate setting for this estimation problem. We present upper confidence bounds to quantify uncertainty of the point estimates. We also compare methods from the extreme value and geophysical literature through simulations. Finally, the different methods are applied to the magnitude data for the earthquakes induced by gas extraction in the Groningen province of the Netherlands.

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“…Presently, there is no universally accepted technique for estimating the value of m max . However, Wells and Coppersmith (1994), Wheeler (2009), and Mueller (2010) described deterministic techniques for its assessment, while Kijko (2004) and Kijko and Singh (2011) presented probabilistic techniques. The importance of the correct assessment of the regional-characteristic, maximum possible earthquake magnitude, m max, is that it is pivotal in seismic risk assessments as this value can be the difference between life and death for large numbers of people.…”

Section: Intensity Attenuation Relationsmentioning

confidence: 99%

“…The importance of the correct assessment of the regional-characteristic, maximum possible earthquake magnitude, m max, is that it is pivotal in seismic risk assessments as this value can be the difference between life and death for large numbers of people. The methodology and assumptions for estimating m max used in this study is described thoroughly by Kijko and Singh (2011).…”

Section: Intensity Attenuation Relationsmentioning

confidence: 99%

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Comparison and Quantitative Study of Vulnerability/Damage Curves in South Africa

Pule

Fourie

Kijko

et al. 2015

South African Journal of Geology

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Southern Africa is considered a stable continental region in spite of several reported medium size earthquakes, some of which caused considerable damage and casualties. The 1969 Ceres 6.3 magnitude earthquake is considered the most destructive and caused serious damage estimated at US$24 million, with 12 mortalities and many more injured. Others include six mining related tremors which caused significant damage i.e. Welkom 1976, Klerksdorp 1977, Welkom 1989 and Carletonville 1992 Most buildings and structures in South Africa are not designed to resist even relatively low intensity earthquake. Most architects, engineers and builders in South Africa do not consider seismic resistance as a design requirement. In this work, potential damage caused by strong earthquake was estimated for three classes of buildings situated in Sandton, Cape Town, Durban and Port Elizabeth. The effect of earthquakes causing damage was studied by considering the "worst case-scenario", i.e. the occurrence of an earthquake with the maximum possible magnitude for an area. In four studied urban areas, expected damage was estimated for three classes of buildings: unreinforced masonry, bearing wall, low rise, reinforced concrete shear wall, without moment resisting frame, medium rise, and reinforced concrete shear wall, without moment resisting frame, high rise. The results of the analysis showed that in case of occurrence of a strong earthquake, the most damage is expected for the building classified as 'unreinforced masonry, bearing wall, low rise, and reinforced concrete shear wall'.

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Statistical tools for maximum possible earthquake magnitude estimation

Cited by 139 publications

References 29 publications

Estimation of Earthquake Hazard Parameters from Incomplete Data Files. Part III. Incorporation of Uncertainty of Earthquake‐Occurrence Model

Estimation of Earthquake Hazard Parameters from Incomplete Data Files. Part III. Incorporation of Uncertainty of Earthquake‐Occurrence Model

Estimating the Maximum Possible Earthquake Magnitude Using Extreme Value Methodology: The Groningen Case

Comparison and Quantitative Study of Vulnerability/Damage Curves in South Africa

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