Improved Seismicity Forecast with Spatially Varying Magnitude Distribution

Hiemer, Stefan; Kamer, Yavor

doi:10.1785/0220150182

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…These scores can then be compared to identify the best model. Although, (pseudo) prospective experiments have started to catch up in the field of earthquake research [Kagan and Jackson, 2010;Zechar and Jordan, 2010;Eberhard et al, 2012;Kagan and Jackson, 2011;Zechar et al, 2013;Ogata et al, 2013;Hiemer et al, 2014;Hiemer and Kamer, 2016;Schorlemmer et al, 2018;Nandan et al, 2019b,c], they still have not become the norm. In this regard, the work done by the collaboratory for the study of earthquake predicatbility (CSEP) and others [Schorlemmer et al, 2018;Kossobokov, 2013] has been very commendable, as they have tried to bring the prospective model validation on the center stage of earthquake forecasting research.…”

Section: Pseudo Prospective Forecasting Experimentsmentioning

confidence: 99%

Global models for short-term earthquake forecasting and predictive skill assessment

Nandan¹,

Kamer²,

Ouillon³

et al. 2022

Preprint

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We present rigorous tests of global short-term earthquake forecasts using Epidemic Type Aftershock Sequence models with two different time kernels (one with exponentially tapered Omori kernel (ETOK) and another with linear magnitude dependent Omori kernel (MDOK)). The tests are conducted with three different magnitude cutoffs for the auxiliary catalog (M3, M4 or M5) and two different magnitude cutoffs for the primary catalog (M5 or M6), in 30 day long pseudo prospective experiments designed to forecast worldwide M ≥ 5 and M ≥ 6 earthquakes during the period from January 1981 to October 2019. MDOK ETAS models perform significantly better relative to ETOK ETAS models. The superiority of MDOK ETAS models adds further support to the multifractal stress activation model proposed by Ouillon and Sornette (2005). We find a significant improvement of forecasting skills by lowering the auxiliary catalog magnitude cutoff from 5 to 4. We unearth evidence for a self-similarity of the triggering process as models trained on lower magnitude events have the same forecasting skills as models trained on higher magnitude earthquakes. Expressing our forecasts in terms of the full distribution of earthquake rates at different spatial resolutions, we present tests for the consistency of our model, which is often found satisfactory but also points to a number of potential improvements, such as incorporating anisotropic spatial kernels, and accounting for spatial and depth dependant variations of the ETAS parameters. The model has been implemented as a reference model on the global earthquake prediction platform RichterX, facilitating predictive skill assessment and allowing anyone to review its prospective performance.

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Section: Pseudo Prospective Forecasting Experimentsmentioning

confidence: 99%

Global models for short-term earthquake forecasting and predictive skill assessment

Nandan¹,

Kamer²,

Ouillon³

et al. 2022

Preprint

Self Cite

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“…The example of Central Italy sequence shows that the ND test may provide results which are significantly different from both catalog and test‐based methods, proving that the significance of b ‐value variations depends on the suitability of adopted method. This result opens a new perspective on the debate about the significance of b ‐value variations and, most of all, on the potential of b ‐value variations as an earthquake precursor (Brodsky, 2019; Dascher‐Cousineau et al., 2019; Gulia & Wiemer, 2019; Helmstetter et al, 2003; Hiemer & Kamer, 2015; Mignan, 2014; Wiemer & Wyss, 1997).…”

Section: Discussionmentioning

confidence: 86%

A Normalized Distance Test for Co‐Determining the Completeness Magnitude andb‐Value of Earthquake Catalogs

Lombardi

2021

JGR Solid Earth

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The quantitative study of the earthquake magnitude distribution started with the publication of what is traditionally referred as "Gutenberg-Richter Law" (GRL; Gutenberg & Richter, 1942; Ishimoto & Iida, 1939), stating the log-linearity of the empirical complementary cumulative distribution of magnitudes. In mathematical terms, the GRL may be described by the equation          10 log N M a b M (1) where a (productivity) and b (slope) are constants and N(M) is the number of events with magnitude equal to or above M. The parameter b represents the ratio of small to large earthquakes: a low b-value describes a data set with a large proportion of larger magnitudes, and vice versa. Following the publication of the GRL, a great amount of studies was carried out, showing a great attention of scientists, which continues to this day. Many of these studies concern some technical problems about the estimation of the b-value, given by the rounding and the uncertainties of magnitudes, the size and the incompleteness of catalogs and the methodologies applied (Bender, 1983; Kamer & Hiemer, 2015; Marzocchi

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“…In our tests of the D-HKJ model, we have assumed a fixed set of parameters for the Reasenberg's declustering algorithm based on the work of Helmstetter et al [2007]. However, other studies such as Schorlemmer and Gerstenberger [2007] have suggested that the parameters r , x , P, τ , and τ can vary within the range [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], [0-1], [0.9,0.99], [0.5, 2.5] and [3,15]. It is thus necessary to investigate the sensitivity of the forecasting potential of the D-HKJ model to the choice of the declustering parameters.…”

Section: Supporting Information Formentioning

confidence: 99%

“…Following the success of the D-HKJ model in the first RELM experiments, other researchers have proposed systematic improvements to this method, including incorporation of spatially variable estimates of b values (Hiemer & Kamer, 2016), model hybridization (Rhoades et al, 2014;Rhoades et al, 2017;Steacy et al, 2013), and so on.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Rates of Future Aftershocks of All Generations Is Essential to Develop Better Earthquake Forecast Models

Nandan

Ouillon²,

Sornette

et al. 2019

JGR Solid Earth

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Currently, one of the best performing earthquake forecasting models relies on the working hypothesis that the “locations of past background earthquakes reveal the probable location of future seismicity.” As an alternative, we present a class of smoothed seismicity models (SSMs) based on the principles of the epidemic‐type aftershock sequence (ETAS) model, which forecast the location, time, and magnitude of all future earthquakes using the estimates of the background seismicity rate and the rates of future aftershocks of all generations. Using the Californian earthquake catalog, we formulate six controlled pseudo‐prospective experiments with different combinations of three target magnitude thresholds: 2.95, 3.95, or 4.95 and two forecasting time horizons: 1 or 5 years. In these experiments, we compare the performance of (1) the ETAS model with spatially homogenous parameters, or GETAS; (2) the ETAS model with spatially variable parameters, or SVETAS; (3) three declustering‐based SSMs; (4) a simple SSM based on undeclustered data, and (5) a model based on strain rate data, in forecasting the location and magnitude of all (undeclustered) target earthquakes during many testing periods. In all conducted experiments, the SVETAS model comes out with consistent superiority compared to all the competing models. Consistently better performance of the SVETAS model with respect to declustering‐based SSMs highlights the importance of forecasting the future aftershocks of all generations for developing better earthquake forecasting models. Among the two ETAS models themselves, accounting for the optimal spatial variation of the parameters leads to stronger improvements in forecasting performance.

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Improved Seismicity Forecast with Spatially Varying Magnitude Distribution

Cited by 12 publications

References 49 publications

Global models for short-term earthquake forecasting and predictive skill assessment

Global models for short-term earthquake forecasting and predictive skill assessment

A Normalized Distance Test for Co‐Determining the Completeness Magnitude andb‐Value of Earthquake Catalogs

Forecasting the Rates of Future Aftershocks of All Generations Is Essential to Develop Better Earthquake Forecast Models

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