Testing physics and statics based hybrid ETAS models

2021 ◽  
Author(s):  
Shubham Sharma ◽  
Shyam Nandan ◽  
Sebastian Hainzl
Keyword(s):  
Synthetic Data ◽  
Large Earthquake ◽  
Aftershock Sequence ◽  
Additional Stress ◽  
Etas Model ◽  
Epidemic Type Aftershock Sequence ◽  
Stress Changes ◽  
Alternative Approaches ◽  
Large Earthquakes ◽  
Model Approach

<p>Currently, the Epidemic Type Aftershock Sequence (ETAS) model is state-of-the-art for forecasting aftershocks. However, the under-performance of ETAS in forecasting the spatial distribution of aftershocks following a large earthquake make us adopt alternative approaches for the modelling of the spatial ETAS-kernel. Here we develop a hybrid physics and statics based forecasting model. The model uses stress changes, calculated from inverted slip models of large earthquakes, as the basis of the spatial kernel in the ETAS model in order to get more reliable estimates of spatiotemporal distribution of aftershocks. We evaluate six alternative approaches of stress-based ETAS-kernels and rank their performance against the base ETAS model. In all cases, an expectation maximization (EM) algorithm is used to estimate the ETAS parameters. The model approach has been tested on synthetic data to check if the known parameters can be inverted successfully. We apply the proposed method to forecast aftershocks of mainshocks available in SRCMOD database, which includes 192 mainshocks with magnitudes in the range between 4.1 and 9.2 occurred from 1906 to 2020. The probabilistic earthquake forecasts generated by the hybrid model have been tested using established CSEP test metrics and procedures. We show that the additional stress information, provided to estimate the spatial probability distribution, leads to more reliable spatiotemporal ETAS-forecasts of aftershocks as compared to the base ETAS model.</p>

scholarly journals The Relevance of Foreshocks in Earthquake Triggering: A Statistical Study

2018 ◽  
Author(s):  
Eugenio Lippiello ◽  
Cataldo Godano ◽  
Lucilla De Arcangelis
Keyword(s):  
Statistical Study ◽  
Aftershock Sequence ◽  
Statistical Features ◽  
Earthquake Triggering ◽  
Etas Model ◽  
Epidemic Type Aftershock Sequence ◽  
The One ◽  
Triggering Process ◽  
Large Earthquakes ◽  
Good Agreement

An increase of seismic activity is often observed before large earthquakes. Events responsible for this increase are usually named foreshock and their occurrence probably represents the most reliable precursory pattern. Many foreshocks statistical features can be interpreted in terms of the standard mainshock-to-aftershock triggering process and are recovered in the Epidemic Type Aftershock Sequence ETAS model. Here we present a statistical study of instrumental seismic catalogs from four different geographic regions. We focus on some common features of foreshocks in the four catalogs which cannot be reproduced by the ETAS model. In particular we find in instrumental catalogs a significantly larger number of foreshocks than the one predicted by the ETAS model. We show that this foreshock excess cannot be attributed to catalog incompleteness. We therefore propose a generalized formulation of the ETAS model, the ETAFS model, which explicitly includes foreshock occurrence. Statistical features of aftershocks and foreshocks in the ETAFS model are in very good agreement with instrumental results.

The prognostic value of foreshocks - a statistical reevaluation

2021 ◽  
Author(s):  
Ester Manganiello ◽  
Marcus Herrmann ◽  
Warner Marzocchi
Keyword(s):  
Physical Process ◽  
Large Earthquake ◽  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Earthquake Triggering ◽  
Epidemic Type Aftershock Sequence ◽  
Sequence Selection ◽  
Short Time ◽  
Scientific Questions ◽  
Large Earthquakes

<p>The ability to forecast large earthquakes on short time scales is strongly limited by our understanding of the earthquake nucleation process. Foreshocks represent promising seismic signals that may improve earthquake forecasting as they precede many large earthquakes. However, foreshocks can currently only be identified as such after a large earthquake occurred. This inability is because it remains unclear whether foreshocks represent a different physical process than general seismicity (i.e., mainshocks and aftershocks). Several studies compared foreshock occurrence in real and synthetic catalogs, as simulated with a well-established earthquake triggering/forecasting model called Epidemic-Type Aftershock Sequence (ETAS) that does not discriminate between foreshocks, mainshocks, and aftershocks. Some of these studies show that the spatial distribution of foreshocks encodes information about the subsequent mainshock magnitude and that foreshock activity is significantly higher than predicted by the ETAS model. These findings attribute a unique underlying physical process to foreshocks, making them potentially useful for forecasting large earthquakes. We reinvestigate these scientific questions using high-quality earthquake catalogs and study carefully the influence of subjective parameter choices and catalog artifacts on the results. For instance, we use data from different regions, account for the short-term catalog incompleteness and its spatial variability, and explore different criteria for sequence selection and foreshock definition.</p>

scholarly journals The Relevance of Foreshocks in Earthquake Triggering: A Statistical Study

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 173 ◽  
Author(s):  
Eugenio Lippiello ◽  
Cataldo Godano ◽  
Lucilla de Arcangelis
Keyword(s):  
Statistical Study ◽  
Aftershock Sequence ◽  
Statistical Features ◽  
Earthquake Triggering ◽  
Etas Model ◽  
Epidemic Type Aftershock Sequence ◽  
The One ◽  
Triggering Process ◽  
Large Earthquakes ◽  
Good Agreement

An increase of seismic activity is often observed before large earthquakes. Events responsible for this increase are usually named foreshock and their occurrence probably represents the most reliable precursory pattern. Many foreshocks statistical features can be interpreted in terms of the standard mainshock-to-aftershock triggering process and are recovered in the Epidemic Type Aftershock Sequence ETAS model. Here we present a statistical study of instrumental seismic catalogs from four different geographic regions. We focus on some common features of foreshocks in the four catalogs which cannot be reproduced by the ETAS model. In particular we find in instrumental catalogs a significantly larger number of foreshocks than the one predicted by the ETAS model. We show that this foreshock excess cannot be attributed to catalog incompleteness. We therefore propose a generalized formulation of the ETAS model, the ETAFS model, which explicitly includes foreshock occurrence. Statistical features of aftershocks and foreshocks in the ETAFS model are in very good agreement with instrumental results.

Detection and characterization of fluid-driven earthquake clusters

2020 ◽  
Author(s):  
Sebastian Hainzl ◽  
Tomas Fischer
Keyword(s):  
Fluid Flow ◽  
Synthetic Data ◽  
Aftershock Sequence ◽  
Migration Patterns ◽  
Long Valley Caldera ◽  
Epidemic Type Aftershock Sequence ◽  
Aftershock Sequences ◽  
Stress Changes ◽  
Earthquake Sequences ◽  
Earthquake Clusters

<p>Natural earthquake clusters are often related to a mainshock, which triggers the sequence by its induced stress changes. These clusters are called mainshock-aftershock sequences and statistically well explained by earthquake-earthquake interactions according to the Epidemic Type Aftershock Sequence (ETAS) model. Additionally, aseismic processes such as slow slip, dike propagation or fluid flow might also play a role in the initiation and driving of the earthquake sequence. Earthquake swarms, which lacks a dominant earthquake, are often believed to indicate such transient aseismic forcing signals. However, swarm-type clusters can also occur by chance in ETAS-simulations and thus not necessarily related to aseismic drivers. Thus, more sophisticated quantification of the space-time-magnitude characteristics of earthquake sequences are required for discrimination. Migration patterns are one of those properties which can be indicative for aseismic triggering. We suggest simple measures to identify and quantify migration patterns and test those for synthetic data, data from fluid injection experiments, and natural swarm activity related to fluid flow in NW Bohemia and Long Valley caldera. We analyze their potential to discriminate from ETAS-type clusters and compare it with those of time-magnitude characteristics of the activity such as seismic moment ratios and skewness. Our results are finally used to discriminate earthquake clusters in California and elsewhere.</p>

Earthquake clusters expected from bare statistics: How bursts and swarms emerge from exogenous and epidemic aftershock processes.

2021 ◽  
Author(s):  
Jordi Baro
Keyword(s):  
Heat Flow ◽  
Solid Earth ◽  
Point Process ◽  
Branching Process ◽  
Aftershock Sequence ◽  
Statistical Properties ◽  
Etas Model ◽  
Stress Changes ◽  
Earthquake Clusters ◽  
Statistical Laws

<p>Earthquake catalogs exhibit strong spatio-temporal correlations. As such, earthquakes are often classified into clusters of correlated activity. Clusters themselves are traditionally classified in two different kinds: (i) bursts, with a clear hierarchical structure between a single strong mainshock, preceded by a few foreshocks and followed by a power-law decaying aftershock sequence, and (ii) swarms, exhibiting a non-trivial activity rate that cannot be reduced to such a simple hierarchy between events. </p><p>The Epidemic Aftershock Sequence (ETAS) model is a linear Hawkes point process able to reproduce earthquake clusters from empirical statistical laws [Ogata, 1998]. Although not always explicit, the ETAS model is often interpreted as the outcome of a background activity driven by external forces and a Galton-Watson branching process with one-to-one causal links between events [Saichev et al., 2005]. Declustering techniques based on field observations [Baiesi & Paczuski, 2004] can be used to infer the most likely causal links between events in a cluster. Following this method, Zaliapin and Ben‐Zion (2013) determined the statistical properties of earthquake clusters characterizing bursts and swarms, finding a relationship between the predominant cluster-class and the heat flow in seismic regions.</p><p>Here, I show how the statistical properties of clusters are related to the fundamental statistics of the underlying seismogenic process, modeled in two point-process paradigms [Baró, 2020].</p><p>The classification of clusters into bursts and swarms appears naturally in the standard ETAS model with homogeneous rates and are determined by the average branching ratio (nb) and the ratio between exponents α and b characterizing the production of aftershocks and the distribution of magnitudes, respectively. The scale-free ETAS model, equivalent to the BASS model [Turcotte, et al., 2007], and usual in cold active tectonic regions, is imposed by α=b and reproduces bursts. In contrast, by imposing α<0.5b, we recover the properties of swarms, characteristic of regions with high heat flow. </p><p>Alternatively, the same declustering methodology applied to a non-homogeneous Poisson process with a non-factorizable intensity, i.e. in absence of causal links, recovers swarms with α=0, i.e. a Poisson Galton-Watson process, with similar statistical properties to the ETAS model in the regime α<0.5b.</p><p>Therefore, while bursts are likely to represent actual causal links between events, swarms can either denote causal links with low α/b ratio or variations of the background rate caused by exogenous processes introducing local and transient stress changes. Furthermore, the redundancy in the statistical laws can be used to test the hypotheses posed by the ETAS model as a memory‐less branching process. </p><p>References:</p><ul><li> <p>Baiesi, M., & Paczuski, M. (2004). <em>Physical Review E</em>, 69, 66,106. doi:10.1103/PhysRevE.69.066106.</p> </li> <li> <p>Baró, J. (2020).  <em>Journal of Geophysical Research: Solid Earth,</em> 125, e2019JB018530. doi:10.1029/2019JB018530.</p> </li> <li> <p>Ogata, Y. (1998) <em>Annals of the Institute of Statistical Mathematics,</em> 50(2), 379–402. doi:10.1023/A:1003403601725.</p> </li> <li> <p>Saichev, A., Helmstetter, A. & Sornette, D. (2005) <em>Pure appl. geophys.</em> 162, 1113–1134. doi:10.1007/s00024-004-2663-6.</p> </li> <li> <p>Turcotte, D. L., Holliday, J. R., and Rundle, J. B. (2007), <em>Geophys. Res. Lett.</em>, 34, L12303, doi:10.1029/2007GL029696.</p> </li> <li> <p>Zaliapin, I., and Ben‐Zion, Y. (2013), <em>J. Geophys. Res. Solid Earth</em>, 118, 2865– 2877, doi:10.1002/jgrb.50178.</p> </li> </ul>

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.

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.

EnKF estimation of the viscoelastic deformation and the viscosity

2020 ◽  
Author(s):  
Makiko Ohtani
Keyword(s):  
Data Assimilation ◽  
Crustal Deformation ◽  
Surface Deformation ◽  
Synthetic Data ◽  
Ground Surface ◽  
Inelastic Strain ◽  
Large Earthquake ◽  
Forward Model ◽  
Viscoelastic Deformation ◽  
Large Earthquakes

<p>Following large earthquakes, postseismic crustal deformations are often observed for more than years. They include the afterslip and the viscoelastic deformation of the crust and the upper mantle, activated by the coseismic stress change. The viscoelastic deformation gives the stress change on the neighboring faults, hence affects the seismic activity of the surrounding area, for a long period after the large earthquake. So, estimating the viscoelastic deformation after the large earthquakes is important.</p><p>In order to estimate the time evolution of the viscoelastic deformation after a large earthquake, we also need to know the viscoelastic structure around the area. Recently, the Ensemble Kalman filter method (EnKF), a sequential data assimilation method, starts to be used for the crustal deformation data to estimate the physical variables (van Dinther et al., 2019, Hirahara and Nishikiori, 2019). With data assimilation, we get a more provable estimation by combining the data and the time-forward model than only using the data. Hirahara and Nishikiori (2019) used synthetic data and showed that EnKF could effectively estimate the frictional parameters on the SSE (slow slip event) fault, addition to the slip velocity. In the present study, I applied EnKF to estimate the viscosity and the inelastic strain after a large earthquake, both the physical property and the variables.</p><p>First, I constructed the forward model simulating the evolution of the viscoelastic deformation, following the equivalent body force method (Barbot and Fialko, 2010; Barbot et al., 2017). This method is appropriate for applying EnKF, because the ground surface deformation rate is represented by the inelastic strain at the moment, and the history of the strain is not required. Then, we applied EnKF based on the forward model and executed some numerical experiments using a synthetic postseismic crustal deformation data.</p><p>In this presentation, I show the result of a simple setting. I assumed the medium to be two layers with a homogeneous viscoelastic region underlying an elastic region. The synthetic data is made by giving a slip on a fault at time <em>t</em> = 0 and simulating the time evolution of the ground surface deformation. For assimilation, I assumed that the slip on the fault and the stress distribution just after the large earthquake is known. Then we executed the assimilation every 30 days after the large earthquake. I found that I can get a good estimation of the viscosity after <em>t</em> > 150 days.</p>

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.

Stress-forecasting: a viable alternative to earthquake prediction in a dynamic Earth

1998 ◽  
Vol 89 (2) ◽  
pp. 121-133 ◽  
Author(s):  
Stuart Crampin
Keyword(s):  
Earthquake Prediction ◽  
Crustal Deformation ◽  
Large Earthquake ◽  
Viable Alternative ◽  
Earthquake Hazards ◽  
Seismic Shear ◽  
Stress Changes ◽  
Wave Splitting ◽  
Self Organised Criticality ◽  
Large Earthquakes

AbstractSelf-organised criticality of the crust appears to make deterministic earthquake prediction of time, place and magnitude of individual large earthquakes inherently impossible. This closes one line of approach to mitigating earthquake hazards. This paper suggests that a viable alternative to earthquake prediction is monitoring the build-up of stress before a large earthquake can occur. A new understanding of rock deformation allows stress changes to be monitored with seismic shear-wave splitting (seismic birefringence). With a suitable monitoring installation, this would allow the stochastic proximity of impending earthquakes to be recognised so that earthquakes could be forecast in the sense of recognising that crustal deformation was preparing for a large earthquake. Such stress-forecasting is not prediction, but, in many circumstances, a possible forecast crescendo of increasing urgency is exactly what is needed to best mitigate hazard to life and property.

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