A Spatiotemporal Clustering Model for the Third Uniform California Earthquake Rupture Forecast (UCERF3‐ETAS): Toward an Operational Earthquake Forecast

Field, Edward H.; Milner, K. R.; Hardebeck, Jeanne L.; Page, Morgan T.; Elst, N. van der; Jordan, Thomas H.; Michael, Andrew J.; Shaw, Bruce E.; Werner, Maximilian J.

doi:10.1785/0120160173

Cited by 120 publications

(70 citation statements)

References 50 publications

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“…This empirical result may also provide a possible answer to the scientific question posed by Field et al (2017) in their Fig. 7: what is the likelihood that a large earthquake nucleates from within the fault area that has just ruptured?…”

Section: K-nn Spatial Distances From the Triggered Earthquakementioning

confidence: 80%

“…According to our results, the larger triggered earthquakes tend preferentially to nucleate in the outer region of the seismic sequences, that is, outside the blue area in the fig. 7 of Field et al (2017). Finally, the lack of such a correlation for the TABOO catalogue could be due to the much smaller events (and so smaller dimension of the ruptures that becomes comparable to the location uncertainty) with respect to the other two catalogues.…”

Section: K-nn Spatial Distances From the Triggered Earthquakementioning

confidence: 95%

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Empirical evaluation of the magnitude-independence assumption

Stallone

Marzocchi

2018

Geophysical Journal International

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The most common earthquake forecasting models assume that the magnitude of the next earthquake is independent from the past. This feature severely limits the capability to forecast large earthquakes with high probabilities. Here we investigate empirically on the magnitude-independence assumption, exploring if: (i) background and triggered earthquakes have the same frequency-magnitude distribution, (ii) variations of seismicity in the spacetime-magnitude domain encode some information on the future earthquakes size. For this purpose, and to verify the stability of the findings, we consider seismic catalogues covering different space-time-magnitude windows, such as the Alto Tiberina Near Fault Observatory (TABOO), the California and Japanese seismic catalogues. Our approach is inspired by the nearest-neighbour method proposed by Baiesi & Paczuski and elaborated by Zaliapin et al. to distinguish between triggered and background earthquakes. Here we implement the same metric-based correlation to identify the precursory seismicity of any triggered earthquake; this allows us to analyse, for each triggered earthquake, the space-time-magnitude distribution of the seismicity that likely contributed to its occurrence. Our results show that the magnitude-independence assumption holds reasonably well in all catalogues, with a remarkable exception that is consistent with a previous independent study; this departure from the magnitude-independence assumption shows that larger events tend to nucleate at a higher distance from the ongoing sequence. We also notice that the reliability of this assumption may depend on the spatial scale considered; it holds for seismic catalogues of large areas, but we identify possible departures in small areas, reflecting different ways to release locally seismic energy. Finally, we come across an important issue that may lead to misleading results in similar studies, that is, if a seismic catalogue appears overall complete above a fixed magnitude threshold, it may still yield spurious signals into the analysis. Specifically, we show that some significant departures from the magnitude-independence assumption do not survive when considering spatiotemporal variations of the magnitude of completeness.

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Section: K-nn Spatial Distances From the Triggered Earthquakementioning

confidence: 80%

Section: K-nn Spatial Distances From the Triggered Earthquakementioning

confidence: 95%

Empirical evaluation of the magnitude-independence assumption

Stallone

Marzocchi

2018

Geophysical Journal International

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“…The ETAS modeling framework introduced by Ogata (, ) is an efficient way to simulate earthquake flow (e.g., Field, ; Field et al, ; Harte, ; Helmstetter et al, ; Helmstetter & Sornette, ; Lippiello et al, ; Ogata, ). An ETAS model generates a marked point field by a self‐exciting clustering mechanism.…”

Section: Analysis Of Epidemic Type Aftershock Sequence (Etas) Modelmentioning

confidence: 99%

Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

Zaliapin

Ben‐Zion

2020

JGR Solid Earth

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We introduce an algorithm for declustering earthquake catalogs based on the nearest-neighbor analysis of seismicity. The algorithm discriminates between background and clustered events by random thinning that removes events according to a space-varying threshold. The threshold is estimated using randomized-reshuffled catalogs that are stationary, have independent space and time components, and preserve the space distribution of the original catalog. Analysis of catalog produced by the Epidemic Type Aftershock Sequence model demonstrates that the algorithm correctly classifies over 80% of background and clustered events, correctly reconstructs the stationary and space-dependent background intensity, and shows high stability with respect to random realizations (over 75% of events have the same estimated type in over 90% of random realizations). The declustering algorithm is applied to the global Northern California Earthquake Data Center catalog with magnitudes m ≥ 4 during 2000-2015; a Southern California catalog with m ≥ 2.5, 3.5 during 1981-2017; an area around the 1992 Landers rupture zone with m ≥ 0.0 during 1981-2015; and the Parkfield segment of San Andreas fault with m ≥ 1.0 during 1984-2014. The null hypotheses of stationarity and space-time independence are not rejected by several tests applied to the estimated background events of the global and Southern California catalogs with magnitude ranges Δm < 4. However, both hypotheses are rejected for catalogs with larger range of magnitudes Δm > 4. The deviations from the nulls are mainly due to local temporal fluctuations of seismicity and activity switching among subregions; they can be traced back to the original catalogs and represent genuine features of background seismicity.We analyze global seismicity, earthquakes in California, and a synthetic catalog produced by the ETAS model. Table 1 summarizes information about the examined data.

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“…Many of these forecasts are empirical statistic models and, notably, rely heavily on high-quality seismic data both before and during the mainshock (Gerstenberger et al, 2005;Omi et al, 2015). Other models embed physics-based rules that rely on local information about long-term seismicity, the state of stress, and the geometry of the faults (e.g., Segou & Parsons, 2016;Field et al, 2017). All of these approaches raise the critical issue of calibrating hazard in poorly instrumented areas.…”

Section: Introductionmentioning

confidence: 99%

What Controls Variations in Aftershock Productivity?

et al. 2020

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The number of aftershocks increases with mainshock size following a well‐defined scaling law. However, excursions from the average behavior are common. This variability is particularly concerning for large earthquakes where the number of aftershocks varies by factors of 100 for mainshocks of comparable magnitude. Do observable factors lead to differences in aftershock behavior? We examine aftershock productivity relative to the global average for all mainshocks ( MW>6.5) from 1990 to 2019. A global map of earthquake productivity highlights the influence of tectonic regimes. Earthquake depth, lithosphere age, and plate boundary type correspond well with earthquake productivity. We investigate the role of mainshock attributes by compiling source dimensions, radiated seismic energy, stress drop, and a measure of slip heterogeneity based on finite‐fault source inversions for the largest earthquakes from 1990 to 2017. On an individual basis, stress drop, normalized rupture width, and aspect ratio most strongly correlate with aftershock productivity. A multivariate analysis shows that a particular set of parameters (dip, lithospheric age, and normalized rupture area) combines well to improve predictions of aftershock productivity on a cross‐validated data set. Our overall analysis is consistent with a model in which the volumetric abundance of nearby stressed faults controls the aftershock productivity rather than variations in source stress. Thus, we suggest a complementary approach to aftershock forecasts based on geological and rupture properties rather than local calibration alone.

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A Spatiotemporal Clustering Model for the Third Uniform California Earthquake Rupture Forecast (UCERF3‐ETAS): Toward an Operational Earthquake Forecast

Cited by 120 publications

References 50 publications

Empirical evaluation of the magnitude-independence assumption

Empirical evaluation of the magnitude-independence assumption

Earthquake Declustering Using the Nearest‐Neighbor Approach in Space‐Time‐Magnitude Domain

What Controls Variations in Aftershock Productivity?

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