ETAS-Approach Accounting for Short-Term Incompleteness of Earthquake Catalogs

2021 ◽  
Author(s):  
Sebastian Hainzl
Keyword(s):  
Aftershock Sequence ◽  
Additional Parameter ◽  
Spatiotemporal Evolution ◽  
Short Term ◽  
Seismicity Rate ◽  
Detection Algorithms ◽  
Estimated Parameters ◽  
Epidemic Type Aftershock Sequence ◽  
Aftershock Sequences ◽  
Completeness Magnitude

ABSTRACT The epidemic-type aftershock sequence (ETAS) model is a powerful statistical model to explain and forecast the spatiotemporal evolution of seismicity. However, its parameter estimation can be strongly biased by catalog deficiencies, particularly short-term incompleteness related to missing events in phases of high-seismic activity. Recent studies have shown that these short-term fluctuations of the completeness magnitude can be explained by the blindness of detection algorithms after earthquakes, preventing the detection of events with a smaller magnitude. Based on this assumption, I derive a direct relation between the true and detectable seismicity rate and magnitude distributions, respectively. These relations only include one additional parameter, the so-called blind time Tb, and lead to a closed-form maximum-likelihood formulation to estimate the ETAS parameters directly accounting for varying completeness. Tests using synthetic simulations show that the true parameters can be resolved from incomplete catalogs. Finally, I apply the new model to California’s most prominent mainshock–aftershock sequences in the last decades. The results show that the model leads to superior fits with Tb decreasing with time, indicating improved detection algorithms. The estimated parameters significantly differ from the estimation with the standard approach, indicating higher b-values and larger trigger potentials than previously thought.

scholarly journals Long-term earthquake forecasts based on the epidemic-type aftershock sequence (ETAS) model for short-term clustering

2012 ◽  
Vol 2 (1) ◽  
pp. 8 ◽  
Author(s):  
Jiancang Zhuang
Keyword(s):  
Time Window ◽  
Space Time ◽  
Aftershock Sequence ◽  
Maximum Magnitude ◽  
Short Term ◽  
Magnitude Distribution ◽  
Seismicity Rate ◽  
Distribution Of The Maximum ◽  
Epidemic Type Aftershock Sequence ◽  
Term Clustering

Based on the ETAS (epidemic-type aftershock sequence) model, which is used for describing the features of short-term clustering of earthquake occurrence, this paper presents some theories and techniques related to evaluating the probability distribution of the maximum magnitude in a given space-time window, where the Gutenberg-Richter law for earthquake magnitude distribution cannot be directly applied. It is seen that the distribution of the maximum magnitude in a given space-time volume is determined in the longterm by the background seismicity rate and the magnitude distribution of the largest events in each earthquake cluster. The techniques introduced were applied to the seismicity in the Japan region in the period from 1926 to 2009. It was found that the regions most likely to have big earthquakes are along the Tohoku (northeastern Japan) Arc and the Kuril Arc, both with much higher probabilities than the offshore Nankai and Tokai regions.

scholarly journals Combining a Locally Restricted Anisotropic Spatial Kernel with an ETAS Incomplete Model for Better Forecasts of the 2019 Ridgecrest Sequence

2021 ◽  
Author(s):  
Christian Grimm ◽  
Sebastian Hainzl ◽  
Martin Käser ◽  
Helmut Küchenhoff
Keyword(s):  
Aftershock Sequence ◽  
Large Area ◽  
Short Term ◽  
Aftershock Activity ◽  
Time Model ◽  
Strong Earthquakes ◽  
Etas Model ◽  
Incomplete Model ◽  
Epidemic Type Aftershock Sequence ◽  
Aftershock Sequences

Abstract Strong earthquakes cause aftershock sequences that are clustered in time according to a power decay law, and in space along their extended rupture, shaping a typically elongate pattern of aftershock locations. A widely used approach to model seismic clustering is the Epidemic Type Aftershock Sequence (ETAS) model, that shows three major biases: First, the conventional ETAS approach assumes isotropic spatial triggering, which stands in conflict with observations and geophysical arguments for strong earthquakes. Second, the spatial kernel has unlimited extent, allowing smaller events to exert disproportionate trigger potential over an unrealistically large area. Third, the ETAS model assumes complete event records and neglects inevitable short-term aftershock incompleteness as a consequence of overlapping coda waves. These three effects can substantially bias the parameter estimation and particularly lead to underestimated cluster sizes. In this article, we combine the approach of Grimm (2021), which introduced a generalized anisotropic and locally restricted spatial kernel, with the ETAS-Incomplete (ETASI) time model of Hainzl (2021), to define an ETASI space-time model with flexible spatial kernel that solves the abovementioned shortcomings. We apply different model versions to a triad of forecasting experiments of the 2019 Ridgecrest sequence, and evaluate the prediction quality with respect to cluster size, largest aftershock magnitude and spatial distribution. The new model provides the potential of more realistic simulations of on-going aftershock activity, e.g.~allowing better predictions of the probability and location of a strong, damaging aftershock, which might be beneficial for short term risk assessment and desaster response.

scholarly journals Short-term earthquake forecasting experiment before and during the L’Aquila (central Italy) seismic sequence of April 2009

2015 ◽  
Vol 57 (6) ◽  
Author(s):  
Maura Murru ◽  
Jiancang Zhuang ◽  
Rodolfo Console ◽  
Giuseppe Falcone
Keyword(s):  
Central Italy ◽  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Productivity Function ◽  
Epidemic Type Aftershock Sequence ◽  
Forecasting Performance ◽  
The One ◽  
Lower Magnitude

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In this paper, we compare the forecasting performance of several statistical models, which are used to describe the occurrence process of earthquakes in forecasting the short-term earthquake probabilities during the L’Aquila earthquake sequence in central Italy in 2009. These models include the Proximity to Past Earthquakes (PPE) model and two versions of the Epidemic Type Aftershock Sequence (ETAS) model. We used the information gains corresponding to the Poisson and binomial scores to evaluate the performance of these models. It is shown that both ETAS models work better than the PPE model. However, in comparing the two types of ETAS models, the one with the same fixed exponent coefficient (<span>alpha)</span> = 2.3 for both the productivity function and the scaling factor in the spatial response function (ETAS I), performs better in forecasting the active aftershock sequence than the model with different exponent coefficients (ETAS II), when the Poisson score is adopted. ETAS II performs better when a lower magnitude threshold of 2.0 and the binomial score are used. The reason is found to be that the catalog does not have an event of similar magnitude to the L’Aquila mainshock (M<sub>w</sub> 6.3) in the training period (April 16, 2005 to March 15, 2009), and the (<span>alpha)</span>-value is underestimated, thus the forecast seismicity is underestimated when the productivity function is extrapolated to high magnitudes. We also investigate the effect of the inclusion of small events in forecasting larger events. These results suggest that the training catalog used for estimating the model parameters should include earthquakes of magnitudes similar to the mainshock when forecasting seismicity during an aftershock sequence.</p></div></div></div>

Operational Earthquake Forecasting during the 2019 Ridgecrest, California, Earthquake Sequence with the UCERF3-ETAS Model

2020 ◽  
Vol 91 (3) ◽  
pp. 1567-1578 ◽  
Author(s):  
Kevin R. Milner ◽  
Edward H. Field ◽  
William H. Savran ◽  
Morgan T. Page ◽  
Thomas H. Jordan
Keyword(s):  
High Performance ◽  
Aftershock Sequence ◽  
Lessons Learned ◽  
Ease Of Use ◽  
Earthquake Forecasting ◽  
Earthquake Rupture ◽  
Etas Model ◽  
Epidemic Type Aftershock Sequence ◽  
Aftershock Sequences ◽  
High Performance Computing Cluster

Abstract The first Uniform California Earthquake Rupture Forecast, Version 3–epidemic-type aftershock sequence (UCERF3-ETAS) aftershock simulations were running on a high-performance computing cluster within 33 min of the 4 July 2019 M 6.4 Searles Valley earthquake. UCERF3-ETAS, an extension of the third Uniform California Earthquake Rupture Forecast (UCERF3), is the first comprehensive, fault-based, epidemic-type aftershock sequence (ETAS) model. It produces ensembles of synthetic aftershock sequences both on and off explicitly modeled UCERF3 faults to answer a key question repeatedly asked during the Ridgecrest sequence: What are the chances that the earthquake that just occurred will turn out to be the foreshock of an even bigger event? As the sequence unfolded—including one such larger event, the 5 July 2019 M 7.1 Ridgecrest earthquake almost 34 hr later—we updated the model with observed aftershocks, finite-rupture estimates, sequence-specific parameters, and alternative UCERF3-ETAS variants. Although configuring and running UCERF3-ETAS at the time of the earthquake was not fully automated, considerable effort had been focused in 2018 on improving model documentation and ease of use with a public GitHub repository, command line tools, and flexible configuration files. These efforts allowed us to quickly respond and efficiently configure new simulations as the sequence evolved. Here, we discuss lessons learned during the Ridgecrest sequence, including sensitivities of fault triggering probabilities to poorly constrained finite-rupture estimates and model assumptions, as well as implications for UCERF3-ETAS operationalization.

Maximum Earthquake Size and Seismicity Rate from an ETAS Model with Slip Budget

2020 ◽  
Vol 110 (2) ◽  
pp. 874-885
Author(s):  
David Marsan ◽  
Yen Joe Tan
Keyword(s):  
Seismic Moment ◽  
Aftershock Sequence ◽  
Physical Parameters ◽  
Model Parameters ◽  
Seismicity Rate ◽  
Omori Law ◽  
Epidemic Type Aftershock Sequence ◽  
Earthquake Size ◽  
Maximum Earthquake ◽  
Seismicity Model

ABSTRACT We define a seismicity model based on (1) the epidemic-type aftershock sequence model that accounts for earthquake clustering, and (2) a closed slip budget at long timescale. This is achieved by not permitting an earthquake to have a seismic moment greater than the current seismic moment deficit. This causes the Gutenberg–Richter law to be modulated by a smooth upper cutoff, the location of which can be predicted from the model parameters. We investigate the various regimes of this model that more particularly include a regime in which the activity does not die off even with a vanishingly small spontaneous (i.e., background) earthquake rate and one that bears strong statistical similarities with repeating earthquake time series. Finally, this model relates the earthquake rate and the geodetic moment rate and, therefore, allows to make sense of this relationship in terms of fundamental empirical law (the Gutenberg–Richter law, the productivity law, and the Omori law) and physical parameters (seismic coupling, tectonic loading rate).

Testing of the foreshock hypothesis within an epidemic like description of seismicity

2020 ◽  
Author(s):  
G Petrillo ◽  
E Lippiello
Keyword(s):  
Seismic Activity ◽  
Good Description ◽  
Aftershock Sequence ◽  
Short Term ◽  
Etas Model ◽  
Temporal Clustering ◽  
Epidemic Type Aftershock Sequence ◽  
Preparatory Phase ◽  
Spatio Temporal ◽  
The One

Summary The Epidemic Type Aftershock Sequence (ETAS) model provides a good description of the post-seismic spatio-temporal clustering of seismicity and is also able to capture some features of the increase of seismic activity caused by foreshocks. Recent results, however, have shown that the number of foreshocks observed in instrumental catalogs is significantly much larger than the one predicted by the ETAS model. Here we show that it is possible to keep an epidemic description of post-seismic activity and, at the same time, to incorporate pre-seismic temporal clustering, related to foreshocks. Taking also into-account the short-term incompleteness of instrumental catalogs, we present a model which achieves very good description of the southern California seismicity both on the aftershock and on the foreshock side. Our results indicate that the existence of a preparatory phase anticipating mainshocks represents the most plausible explanation for the occurrence of foreshocks.

Seismotectonics of the northern Longitudinal Valley, Taiwan, inferred from aftershock sequences of 2018 Mw6.4 and 2019 Mw6.2 Hualien earthquakes

2020 ◽  
Author(s):  
Wei-Fang Sun ◽  
Hao Kuo-Chen ◽  
Zhuo-Kang Guan ◽  
Wen-Yen Chang
Keyword(s):  
Main Shock ◽  
Surface Deformation ◽  
Plate Boundary ◽  
Aftershock Sequence ◽  
Tectonic Structures ◽  
Seismicity Rate ◽  
Transitional Area ◽  
Main Shocks ◽  
Aftershock Sequences ◽  
Seismic Networks

&lt;p&gt;In the Hualien area, two Mw6.4 and Mw6.2 earthquakes, 20 km apart, occurred in February 2018 and April 2019 respectively. The former to the northeast, located offshore to &amp;#8203;&amp;#8203;the Liwu river, triggered several earthquake clusters along the Milun fault and southward to the Longitudinal Valley, the suture of the Eurasian and the Philippine Sea plates; the latter to the southwest, located in the Central Range, also triggered several seismic swarms in the Central Range, &amp;#160;along the Liwu river to the northeast and at Ji'an to the southeast. Except for the Milun fault, neither GPS nor InSAR observations detects significant surface deformation after the occurrence of these two main shocks, indicating that the earthquake ruptures mainly developed within the crust. Therefore, seismic observation becomes an efficient tool for revealing the seismotectonics of the two earthquake sequences. For monitoring the aftershock sequences, two days after the main shocks, we deployed two geophone arrays, 70 Z-component RefTek 125A TEXANs for two weeks in 2018 and 47 three-component Fairfield Nodal Z-Lands for one month in 2019, with 1-5 km station spacing around the Hualien City. These earthquake swarms were well recorded and analyzed through the dense seismic networks. The numbers of aftershock sequences manually identified are two-fold more than that issued by the Central Weather Bureau, Taiwan. The seismicity of the 2018 aftershock sequence, to depths of between 5-15 km, was significantly reduced within 10 days after the main shock. however, the seismicity of the 2019 aftershock sequence, to depths of between 2-50 km, was still above background seismicity rate 30 days after the main shock. The spatial distribution of the 2018 aftershock sequence could be related to a fault zone of the plate boundary, but that of the 2019 and the relocated 1986 aftershock sequences show a conjugate thrust fault pair beneath the eastern Central Range. Our results clearly depict several local tectonic structures that have not been observed at the northern tip of the Longitudinal Valley, not only a suture but also a transitional area from collision to subduction.&lt;/p&gt;

The overlap of aftershock coda waves and forecasting the first hour aftershocks

2020 ◽  
Author(s):  
Eugenio Lippiello ◽  
Giuseppe Petrillo ◽  
Cataldo Godano ◽  
Lucilla de Arcangelis ◽  
Anna Tramelli ◽  
...  
Keyword(s):  
Aftershock Sequence ◽  
Coda Waves ◽  
Statistical Features ◽  
Short Term ◽  
Geophysical Research ◽  
Ground Shaking ◽  
Epidemic Type Aftershock Sequence ◽  
Novel Approach ◽  
Single Station ◽  
Strong Ground

&lt;p&gt;We show that short term post-seismic incompleteness can be interpreted in terms of the overlap of aftershock coda waves. We use this information to develop a novel procedure which gives accurate occurrence probabilities of post-seismic strong ground shaking within 30 minutes after the mainshock. This novel approach uses, as only information, the ground velocity recorded at a single station without requiring that signals are transferred and elaborated by operational units. We will also discuss how this information can be implemented in the Epidemic-Type Aftershock Sequence model in order to reproduce statistical features in time and magnitude of recorded aftershocks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main references &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;de Arcangelis L., Godano C. &amp; Lippiello E. (2018) &lt;em&gt;The Overlap of Aftershock Coda Waves and Short-Term Postseismic Forecasting. &lt;/em&gt;&lt;strong&gt;Journal of Geophysical Research: Solid Earth, &lt;/strong&gt;123: 5661-5674,doi:10.1029/2018JB015518&lt;/p&gt;&lt;p&gt;Lippiello E., Petrillo G. , Godano G. , Tramelli A., Papadimitriou E. &amp;, Karakostas V. (2019)&lt;em&gt; Forecasting of the first hour aftershocks by means of the perceived magnitude. &lt;/em&gt;&lt;strong&gt;Nature Communications&lt;/strong&gt; , 10, 2953, doi:10.1038/s41467-019-10763-3&lt;/p&gt;

How Do Statistical Parameters of Induced Seismicity Correlate with Fluid Injection? Case of Oklahoma

2021 ◽  
Author(s):  
Hideo Aochi ◽  
Julie Maury ◽  
Thomas Le Guenan
Keyword(s):  
Induced Seismicity ◽  
Aftershock Sequence ◽  
Statistical Parameters ◽  
Exponential Equation ◽  
Seismicity Rate ◽  
Epidemic Type Aftershock Sequence ◽  
Background Seismicity ◽  
Mechanical Interpretation ◽  
Independent Variable ◽  
Grid Points

Abstract The seismicity evolution in Oklahoma between 2010 and 2018 is analyzed systematically using an epidemic-type aftershock sequence model. To retrieve the nonstationary seismicity component, we systematically use a moving window of 200 events, each within a radius of 20 km at grid points spaced every 0.2°. Fifty-three areas in total are selected for our analysis. The evolution of the background seismicity rate μ is successfully retrieved toward its peak at the end of 2014 and during 2015, whereas the triggering parameter K is stable, slightly decreasing when the seismicity is activated. Consequently, the ratio of μ to the observed seismicity rate is not stationary. The acceleration of μ can be fit with an exponential equation relating μ to the normalized injected volume. After the peak, the attenuation phase can be fit with an exponential equation with time since peak as the independent variable. As a result, the evolution of induced seismicity can be followed statistically after it begins. The turning points, such as activation of the seismicity and timing of the peak, are difficult to identify solely from this statistical analysis and require a subsequent mechanical interpretation.

Background Seismicity before and after the 1976 Ms 7.8 Tangshan Earthquake: Is Its Aftershock Sequence Still Continuing?

2020 ◽  
Author(s):  
Yue Liu ◽  
Jiancang Zhuang ◽  
Changsheng Jiang
Keyword(s):  
Aftershock Sequence ◽  
Tangshan Earthquake ◽  
Aftershock Activity ◽  
Background Rate ◽  
Epidemic Type Aftershock Sequence ◽  
Aftershock Sequences ◽  
Background Seismicity ◽  
Before And After ◽  
Large Earthquakes ◽  
Current Stage

Abstract The aftershock zone of the 1976 Ms 7.8 Tangshan, China, earthquake remains seismically active, experiencing moderate events such as the 5 December 2019 Ms 4.5 Fengnan event. It is still debated whether aftershock sequences following large earthquakes in low-seismicity continental regions can persist for several centuries. To understand the current stage of the Tangshan aftershock sequence, we analyze the sequence record and separate background seismicity from the triggering effect using a finite-source epidemic-type aftershock sequence model. Our results show that the background rate notably decreases after the mainshock. The estimated probability that the most recent 5 December 2019 Ms 4.5 Fengnan District, Tangshan, earthquake is a background event is 50.6%. This indicates that the contemporary seismicity in the Tangshan aftershock zone can be characterized as a transition from aftershock activity to background seismicity. Although the aftershock sequence is still active in the Tangshan region, it is overridden by background seismicity.

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