Earthquake prediction as a decision-making problem

Molchan, G.

doi:10.1007/bf00945169

Cited by 186 publications

(163 citation statements)

References 14 publications

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“…For the first example, the gain is 3.0; for the second forecast it is 0.9/0.7 ≈ 1.3. Measures to reduce earthquake damage could likely be applied more effectively if the prediction gain or predictive skill of a method is higher (Molchan and Kagan, 1992;Molchan, 1997;Molchan, 2003).…”

Section: K2005 Short-term Forecast Testmentioning

confidence: 99%

“…Previously Kossobokov (2003a) K2005 comments (p. 6) that in Figure 1a by Kossobokov (2004) the difference between the observational curves and the random prediction is spread over a smaller range than in the K2005 plot. These figures represent the "error diagrams" (Molchan and Kagan, 1992;Molchan, 1997;Molchan, 2003) with two variables that characterize prediction efficiency: the fraction of space-time alarm area µ, and the fraction of failures to predict, ν. However, in any short-term forecast test the most important part of the error diagram is for small values of µ.…”

Section: Introductionmentioning

confidence: 99%

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Comment on ‘Testing earthquake prediction methods: “The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥5.8”’ by V.G. Kossobokov

Kagan

Jackson

2006

Tectonophysics

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Section: K2005 Short-term Forecast Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comment on ‘Testing earthquake prediction methods: “The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥5.8”’ by V.G. Kossobokov

Kagan

Jackson

2006

Tectonophysics

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“…Our prediction scheme depends on three parameters: time window s, threshold N 0 , and duration D of alarms. The quality of this kind of prediction is evaluated with help of ''error diagrams'' which are a key element in evaluating a prediction algorithm [Kagan and Knopoff, 1987;Molchan, 1997Molchan, , 2003.…”

Section: Prediction Scheme and Error Diagrammentioning

confidence: 99%

“…To quantify the effectiveness and reliability of such predictions we use error diagrams [Kagan and Knopoff, 1987;Molchan, 1997].…”

Section: Introductionmentioning

confidence: 99%

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Predictability of volcano eruption: Lessons from a basaltic effusive volcano

Grasso

Zaliapin

2004

Geophysical Research Letters

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[1] Volcano eruption forecast remains a challenging and controversial problem despite the fact that data from volcano monitoring significantly increased in quantity and quality during the last decades. This study uses pattern recognition techniques to quantify the predictability of the 15 Piton de la Fournaise (PdlF) eruptions in the 1988 -2001 period using increase of the daily seismicity rate as a precursor. Lead time of this prediction is a few days to weeks. We formulate a simple prediction rule, use it for retrospective prediction of the 15 eruptions, and test the prediction quality with error diagrams. The best prediction performance corresponds to averaging the daily seismicity rate over 5 days and issuing a prediction alarm for 5 days. 65% of the eruptions are predicted for an alarm duration less than 20% of the time considered. Even though this result is concomitant of a large number of false alarms, it is obtained with a crude counting of daily events that are available from most volcano observatories.

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Influences on the location of repeating earthquakes determined from a and b value imaging

Kawamura

Chen

2017

Geophysical Research Letters

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To explore where earthquakes tend to recur, we statistically investigated repeating earthquake catalogs and background seismicity from different regions (Parkfield, Hayward, Calaveras, and Chihshang Faults). We show that the location of repeating earthquakes can be mapped using the spatial distribution of the seismic a and b values obtained from the background seismicity. Molchan's error diagram statistically confirmed that repeating earthquakes occur within areas with high a values (2.8–3.8) and high b values (0.9–1.1) on both strike‐slip and thrust fault segments. However, no significant association held true for fault segments with more complicated geometry or for wider areas with a complex fault network. The productivity of small earthquakes responsible for high a and b values may thus be the most important factor controlling the location of repeating earthquakes. We inferred that the location of high creep rate in planar/listric fault structures might be indicated by a values of ~3 and b values of ~1.

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Earthquake prediction as a decision-making problem

Cited by 186 publications

References 14 publications

Comment on ‘Testing earthquake prediction methods: “The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥5.8”’ by V.G. Kossobokov

Comment on ‘Testing earthquake prediction methods: “The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥5.8”’ by V.G. Kossobokov

Predictability of volcano eruption: Lessons from a basaltic effusive volcano

Influences on the location of repeating earthquakes determined from a and b value imaging

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