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Operational Aftershock Forecasting for 2017-2018 Seismic Sequence in Western Iran

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Operational Aftershock Forecasting for 2017-2018 Seismic Sequence in Western Iran vEGU21: Gather Online | 19 – 30 April 2021 EGU21-15824, Session SM7.1 Operational Aftershock Forecasting for 2017-2018 Seismic Sequence in Western Iran Hossein Ebrahimian & Fatemeh Jalayer Department of Structures for Engineering and Architecture, University of Naples Federico II (UNINA), Italy Starting Point Methodology Application Conclusion Conceptual framework for quasi real-time hazard and impact forecasting within an ongoing seismic sequence in terms of occurrence, ground-shaking, damage, and losses in a prescribed forecasting interval (in the order of hours to days) Regional data, building inventory, population density, seismic micro-zonation, other required Quasi real-time earthquake catalog thematic maps related to the monitored area ETAS: Epidemic Type Aftershock Aftershock Sequence-tuned updating of model Sequence model; spatio-temporal occurrence model(s) parameters occurrence; every earthquake within the sequence is a potential triggering event for subsequent earthquakes by generating its Operational forecasting of own Modified Omori aftershock decay. aftershock occurrence Ground motion Forecasting of aftershock ground- prediction model(s) shaking This study Empirical/Analytical Forecasting of aftershock damage Fragility model(s) Retrospective early Loss model(s) Impact Forecasting forecasting of seismicity associated with the 2017- 2018 seismic sequence Expected Expected activities in western Iran Financial Losses Fatalities EGU21-15824 Starting Point Methodology Application Conclusion Fully simulation‐based framework for robust estimation of seismicity distribution in a prescribed forecasting time within an ongoing seismic sequence A Bayesian updating approach A stochastic procedure is used in The procedure leads to the basedonanadaptiveMCMC order to generate plausible stochastic spatial distribution of simulation technique is used to sequences of events that are the forecasted events and learn the ETAS model parameters going to occur during the consequently to the uncertainty conditioned on the events that forecasting interval (the real in the estimated number of have already taken place in the sequence is unknown at the time events, corresponding to a ongoing seismic sequence before of forecasting). given forecasting interval the forecasting interval. (Robust seismicity forecasting) STAGE 01 STAGE 02 STAGE 03 Learning ETAS model parameters Generating plausible sequences Estimating spatial distribution of events Ebrahimian H, Jalayer F (2017) Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock clustering models. Sci Rep 7, 9803. https://doi.org/10.1038/s41598-017-09962-z. Ebrahimian H, Jalayer F, Maleki Asayesh B, Zafarani H (2021) Operational aftershock forecasting for 2017-2018 Kermanshah seismic sequence in Western Iran. Bull. Seismol Soc Am (in Preparation). EGU21-15824 Starting Point Methodology Application Conclusion The conditional rate of occurrence of events (the seismicity rate) based on ETAS model mM mMl jl Kt Kr ETAS txym,,,θ ,seqtl , M e Ke pq 22 ttj tt c rd j j ETAStxy,,θ ,seqtl , M The rate ETAS is at time t (with respect to a reference time), in the cell unit centered at the Cartesian coordinate (x, y)A (where A is the aftershock zone), with the magnitude M≥m, conditioned on: the vector of ETAS model parameters [, K, , c, p, d, q]. the observation history up to the time t denoted as seqt={(tj, xj , yj, mj), tj <t, mj ≥Ml}; lower magnitude Ml (≥ cut‐off magnitude Mc of the catalog of events up to the time t). Forecasting interval IAT1 IAT2 IAT3 IATi Mainshock T t t t ti o 1 2 3 Tstart Tend Time (t) Sequence of events (seq) To ≤ ti <Tstart, mi ≥ Ml, i=1:No IAT: Inter‐arrival time EGU21-15824 Starting Point Methodology Application Conclusion Robust seismicity forecasting E,,,Nxymseq M N xymM ,, lb l Tend txym,,,θ ,seq , M d t pθ | seq,d M θ ETAS ll θ Tstart A robust estimate of the average number of events (E[·]) in the spatial cell unit centered at (x, y) with M≥m in the The conditional probability forecasting interval [Tstart, Tend], which is calculated over density function (PDF) for the domain of the model parameters . given the seq and Ml. Nb(x, y, m|Ml): average number of events occurred due to the background seismicity with magnitude M ≥m in the forecasting interval [Tstart, Tend]. Forecasting interval IAT1 IAT2 IAT3 IATi Mainshock T t t t ti o 1 2 3 Tstart Tend Time (t) Sequence of events (seq) To ≤ ti <Tstart, mi ≥ Ml, i=1:No IAT: Inter‐arrival time EGU21-15824 Starting Point Methodology Application Conclusion Robust seismicity forecasting E,,,Nxymseq M N xymM ,, lb l Tend ETAS txym,,,seqg ,,θ seq , Mlll d t p seqg|θ,,seq M d seqg pθ|seq,d M θ θ seqg Tstart The robust estimate for the average number of aftershock events should also consider all the plausible sequences of events seqg (i.e., the domain seqg)that can happen during the forecasting time interval. A plausible seqg is defined as the events within the forecasting interval defined as seqg={(IATi =ti-ti-1, xi, yi, mi), Tstart≤ ti ≤Tend, mi ≥Ml}. Theuseoftheterm“robust” here implies that a set of possible model parameters is used to estimate the conditional number of events N(x, y, m|seq, Ml) rather than a single nominal model parameter. EGU21-15824 Starting Point Methodology Application Conclusion Robust seismicity forecasting E,,,Nxymseq M N xymM ,, lb l Tend ETAS txym,,,seqg ,,θ seq , Mlll d t p seqg|θ,,seq M d seqg pθ|seq,d M θ θ seqg Tstart This Equation can be solved via a fully simulation‐based framework: • Vector of model parameters are sampled from p(|seq, Ml) using an adaptive Markov Chain Monte Carlo (MCMC) simulation technique. (1) The samples are used to generate plausible sequences seqg taking place within the forecasting interval [Tstart, Tend] according to p(seqg| seq, Ml). Note: The sequence of events that precede Tend is {seq, seqg}, where seq remains unchanged (observed data) among plausible samples; Thus, a robust estimate for the average number of events can be obtained based on the plausible model parameters. EGU21-15824 Starting Point Methodology Application Conclusion About the model parameter K mM mMl jl Kt Kr ETAS txym,,,θ ,seqtl , M e Ke pq 22 ttj tt c rd j j [, K, , c, p, d, q] ETAStxy,,θ ,seqtl , M Method (a) Calculate K: Considering that K is directly affected by No (i.e., the number of events taken place before the forecasting interval [Tstart, Tend]), it has an analytical closed‐form expression, and its distribution can be derived based on other ETAS parameters. Thus, the vector of model parameters has six parameters =[, , c, p, d, q]. Tstart Forecasting interval IAT1 IAT2 IAT3 IATi Mainshock txy,,θ ,seq , Ml ddd x y t No T t t t ti Txy, A o 1 2 3 Tstart Tend Time (t) o Total conditional intensity including also background seismicity rate Sequence of events (seq) To ≤ ti <Tstart, mi ≥ Ml, i=1:No Method (b) Learn K through the Bayesian Updating framework: The parameter K is drawn from the conditional distribution p(|seq, Ml),where=[, K, , c, p, d, q] has seven parameters. EGU21-15824 Starting Point Methodology Application Conclusion Kermanshah 2017‐2018 Seismic Sequence Seismic Sequence from 11/01/2017 up to 01/12/2019 36 Kurdistan 35.5 M7.3 Sanandaj 35 12/11/2017 Ezgele M5.9 25/8/2018 34.5 Kermanshah Sarpol-e Zahab Kermanshah M6.3 34 25/11/2018 Latitude 33.5 Ilam Map of active faults of Iran (prepared by: B. Maleki Asayesh, IIEES, Iran) 2.5 M < 3 Ilam 33 3 M < 4 4 M < 5 32.5 5 M < 6 6 M < 7 M 7 32 44.5 45 45.5 46 46.5 47 47.5 Longitude EGU21-15824 Starting Point Methodology Application Conclusion Kermanshah 2017‐2018 Seismic Sequence Ezgeleh MS Tazehabad event Mw7.3 Mw5.9 12/11/2017 25/08/2018 To=01/11/2017 06:00 UTC 630 casualties, immense Sarpol‐e Zahab event Mw6.3 buildings’ damages and 25/11/2018 economic losses From 12/11/2017 up to 18/04/2020 (i.e., in the time interval of around 2.5 years after the Ezgeleh MS), about 9000 seismic events were recorded by Iranian Seismological Center, IRSC, in the area shown in Figure. From this pool of seismicity, 2318 events have Mw≥2.5. In addition to the Ezgeleh MS, 19 events with Mw≥5.0, and more than 125 events with magnitude larger than 4 and less than 5 (4≤Mw<5) were recorded within this sequence. EGU21-15824 Starting Point Methodology Application Conclusion Kermanshah 2017‐2018 Seismic Sequence Ezgeleh MS Tazehabad event Sarpol‐e Zahab event Mw7.3 Mw5.9 Mw6.3 12/11/2017 25/08/2018 25/11/2018 To=01/11/2017 06:00 UTC Daily observed number of events (starting from 6:00 UTC each day) 100 M7.3 at 12/11/2017 - 18:18:16UTC M5.0 at 20/11/2017 - 15:23:39UTC 80 M5.5 at 11/12/2017 - 14:09:57UTC 60 M 2.5 40 M 3.0 Number of events 20 0 7 7 -17 17 v-17 c-17 ec-17 c- c-1 ec-17 De -No -Nov -Nov-1 2-Nov-17 02-Nov-17 07 12 17 2 27-Nov-17 02- 07-D 12-Dec-17 17-De 22-De 27-D EGU21-15824 Starting Point Methodology Application Conclusion Distribution of the ETAS model parameters (marginal PDF’s of posterior and prior) with their statistics (mean and COV) after Mw 7.3 at 12‐November 2017 [Tstart, Tend] (dd/mm‐hour) c [day] pd [km] qK sample mean=0.39 mean=0.04 mean=1.12 mean=1.36 mean=1.04 mean=208.40 [12/11‐21:00, prior COV=0.43 COV=0.29 COV=0.09 COV=0.19 COV=0.03 COV=0.34 13/11‐06:00]; ML=1.12 mean=0.99 Ml =2.5 COV=0.16 1 2 3 4 1 2 3 4 0.05 0.1 0.15 1 2 3 1 2 3 4 1 2 3 500 1000 1500 sample mean=0.64 mean=0.04 mean=1.11 mean=1.40 mean=1.05 mean=73.31 [13/11‐00:00, prior COV=0.24 COV=0.27 COV=0.08 COV=0.17 COV=0.03 COV=0.41 13/11‐06:00]; ML=1.21 M =2.5 mean=1.12 l COV=0.12 1 2 3 4 1 2 3 4 0.05 0.1 0.15 1 2 3 1 2 3 4 1 2 3 200 400 600 sample mean=0.72 mean=0.04 mean=1.20 mean=1.46 mean=1.05 mean=31.41 [13/11‐06:00, prior COV=0.20 COV=0.30 COV=0.12 COV=0.18 COV=0.03 COV=0.33 =1.54 14/11‐06:00]; ML mean=1.50 Ml =3.0 COV=0.12 1 2 3 4 1 2 3 4 0.05 0.1 0.15 1 2 3 1 2 3 4 1 2 3 50 100 150 mean= mean=2.00 mean=0.03 mean=1.10 mean=1.00 mean=1.50 Prior MLE – COV=0.30 COV=0.30 COV=0.50 COV=0.30 COV=0.30 COV=0.30 Note: To provide the forecast for each time window, the observation history, seq,comprisesallthe events form To up to Tstart with M≥Ml.
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