Ken XS Hao (NIED) MaB Gerstenberger (GNS) J-RAPID

Transcription

1 Sophis'ca'on of seismic hazard evalua'on based on inves'ga'on of ground mo'on and damage on immediate vicinity of co-seismic faults during the 2016 Kumamoto earthquake Ø The Fudagawa Fault zone assessed by HERP from 2004 for long-term evalua'on and strong mo'on simula'ons. Ø Verify earthquake hazard assessment by near-fault inves'ga'ons. Ø Model avershock sequence behavior using cumula've and radiated energy. Ø he data accumula'on. Ken XS Hao (NIED) MaB Gerstenberger (GNS) J-RAPID Supported by Urgent Collabora've research program J-RAPID regarding the April 2016 Kumamoto earthquake

2 The Kumamoto M6.3, M7.3 EQ, Japan The Fudagawa & Hinagu ac've faults generated M6.3, M7.3 earthquakes and triggered many avershocks. The near-fault strong mo'on data and the studies are very few. The Japanese team inves'gates near-fault strong mo'on, surface traces of coseismic rupture, and damage, liquefac'on, and landslide distribu'ons. Using Kumamoto data to improve existed method for evalua'ng strong ground mo'on. The Christchurch M6.3, Kaikoura M7.8 EQ, New Zealand The Greendale and surrounding faults generated the Canterbury M7.1 earthquake and triggered the M6.3 Christchurch earthquake and other large avershocks. The New Zland team models avershock sequence behavior using cumula've and radiated energy methods. It have the poten'al to significantly improve avershock forecast models such as used for revision of the building design standard in Canterbury, NZ. The Kumamoto sequence is ideal for this due to the exis'ng high-quality data and the data currently being collected.

3 9 O NROE E O BKN 5 I=IK K 1=N DM =O =NEK=N DM = OD= E C OEI H= EK. HKS JMA Intensity J M A 2016/04/16 01: BKN IEHKBD2=C=S= KB 3E =CB= H K 2 (,=O -I7-(( :=IL 0 C 2 FES=N==H) Seismic Intensity es'mated from observed ( ) of NIED, K-NET, KiK-net, JMA, and local governments collected by (J-Risq, 2016) 2016/04/14 21:26

4 Field Inves'ga'on along with fault in May, June, July and Sept Minami-Aso Mashiki

5 Coseismic faults, Landslide, Damages in Kawayo, Aso Aso Bridge Background from Geospa'al Informa'on Authority of Japan

6 Damage Indices for wooden house Reference: 1999 D 0 D 0 D 1 D 1 D 2 D 2 D 3 D 3 D 4 D 4 D 5 D 5

7 Faults through buildings (to ) Buildings on faults(to ) Two death inside building( by TV) Faults through roads (to ) Fraction trace on faults (to W) Faults through field (to Faults with30-50cm through buildings (to Aso-Ohashi bridge (to W) ) Coseismic faults, Damage Indices, Microtremors in Kawayo, Aso-Ohashi bridge (SW ~ W) )

8 Microtremors and distribution of Average Velocity AVS in Kawayo AVS Soft harder Soft Single peak around 2 Hz showed at sites 43, 45, Hz Frequency H/V spectral examples Mul'ple peaks were shown at sites 41, 44 and LINE2 1Hz Proc. of the fall mee'ng of the Seismological Society of Japan LINE2 Hao et. al., 2016 LINE1 S-wave velocity structures LINE1

9 Coseismic fault rupturing, Damage Indices in Eastern Mashiki town June, About 50cm Dextral offset occurred on roads Strike N60E Background from Geospa'al Informa'on Authority of Japan

10 June, Coseismic fault rupturing, Damage Indices in Center Mashiki town May 20 16, oogle street view Before EQ, oogle street view Background from Geospa'al Informa'on Authority of Japan Dextral offset 40 cm on road, and Other cracks in East-West direc'on

11 June, Coseismic fault rupturing, Damage Indices in Center Mashiki town May, oogle street view Before EQ, oogle street view Background from Geospa'al Informa'on Authority of Japan

12 June, Coseismic fault rupturing, Damage Indices in Western Mashiki town 15cm Background from Geospa'al Informa'on Authority of Japan

13 Coseismic faults, Damage Indices, Microtremors and AVS in Machiki town 1Hz AVS >250 AVS Damage Decentralized Area 1Hz Damage Centralized Area AVS Hz Damage Decentralized Area Background from Geospa'al Informa'on Authority of Japan

14 Geological senng and borehole in Machiki town Damage level Fluvial terrace deposit Background from Geological Survey of Japan Proc. of the fall mee'ng of the Seismological Society of Japan 1Hz Strong mo'ons recorded (Hata et.al., 2016, SRL)

15 Microtremors and S-wave velocity structures in Machiki town LINE1 Damaged Area LINE2 LINE1 LINE2 Damaged Area LINE4 LINE3 Damaged Area Hao et. al., 2016

16 Summary and Conclusion We inves'gated near-fault strong mo'on, surface traces of coseismic rupture, and damage along the Fudagawa & Hinagu ac've faults. Ø Right-lateral strike-slip ruptured on the Fudagawa ac've faults. Ø The rupturing play first-order role for the damages of nearby the faults in Kawayo, Minami-Aso village and Mashiki town. Ø The sov sediments with AVS m/s amplified the strong mo'ons locally, especially in 1 Hz period. Ø New constructed buildings (aver 1981) protected from damage in some degree. Ø The treasures data is useful to improve existed method for evalua'ng strong ground mo'on.

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19 Interna'onal Promo'on for Seismic Hazard Assessment ² In line with the NIED s long-term goal, we promote interna'onal standardiza'on and coopera'on for Seismic Hazard and Risk Assessment. ² The 6th Interna'onal workshop held in Beppu, Japan in October ² Coopera'on between NIED and TEM for 7 years, between NIED and TEM and New Zealand for 3 years. ² We compare the basic NSHA models between the Japan, Taiwan and NZ. Japan Taiwan PJ (NIED-TEM) (2012~ ) Japanew ealand PJ (NIED-GNS) (2014 ~ ) Neighboring Regional Promotion Comparisons of the PSHA models in unified method v Examina'on and Valida'on of NSHM model by Recently Earthquakes v PSHA related topics: Source characteriza'on, Ground mo'on simula'on and predic'on, Subduc'on zone modelling and risk assessment The PSHA assessment is country-dependent, it has been difficult to compare so far. We used the hazard assessment plarorm developed by "Global Earthquake Model (GEM)" to establish interna'onal unified standards.

20 Urgent Collabora've research program J-RAPID regarding the April 2016 Kumamoto earthquake Representa've Thank you for your attention

21 Energy based modelling of earthquake occurrence Matt Gerstenberger Bill Fry GNS Science, New Zealand GNS Science

22 The Motivation Current aftershock models have statistical skill in forecasting aftershocks, however further improvements are fundamentally limited for various reasons including: Incomplete recordings of aftershock data (e.g., Omi et al, 2013) Other energy not captured in magnitude-based earthquake catalogues The hypothesis: Aftershock models based on released or recorded energy will provided significantly better aftershock forecasts than catalogue based models We are testing this on the Christchurch, Kumamoto and Kaikoura aftershock sequences GNS Science

23 New Zealand aftershock forecast models Statistical models based on catalogues of Earthquake magnitudes and locations Increasing time scale Short-term clustering STEP & ETAS (aftershocks) Medium-term clustering EEPAS 1&2 (years-decades ) Long-term smoothed seismicity PPE, NSHM (Gaussian), All models were tested in CSEP testing centres prior to their use in the ensemble Subsequent testing has shown the combined Model outperforms any individual model

24 Aftershock forecasting in New Zealand: The time-dependent forecast model for Canterbury Requested by Government For revision of building design standards & rebuild planning Aimed to capture uncertainty in our understanding of short-term and long-term rates 1yr to 50-yr forecast: hybrid combination of multiple forecast models on three different time scales Kaikoura: multiple uses including shortening retrofit times for unreinforced masonry

25 Missing energy: How many earthquakes do we miss in an aftershock sequence (East Cape M7.1 9/2017): minimum bound Number of detected earthquakes GNS Science

26 What is a magnitude? Missing energy: magnitude scales and discrete events Darfield: 6+ Fault Ruptures Kumamoto: 1? Fault Rupture Kaikoura: ~13 Fault Ruptures GNS Science

27 The Back Projection Method GNS Science

28 Energy released so far during the Kaikoura Sequence (T<4s) 25 Days Zoom GNS Science

29 Cumulative energy release in the Kaikoura Sequence: first 25 days GNS Science

30 Mapping the energy release (in hours) GNS Science

31 GNS Science

32 Temporal evolution of energy release GNS Science

33 Deep Learning & Energy Recorded: Modelling earthquake occurrence with Spiking Neural Networks and waveform data Spikes are created from filtered waveform data Using Neucube to model the Spiking Neural Network 3D cube trained in unsupervised model Currently only investigated 12hr time period, but is showing forecast skill for Christchurch aftershocks GNS Science Figs from N. Kasabov AUT University, NZ

34 Summary and conclusions We have done initial time-space mapping of energy released during the Canterbury and Kaikoura Earthquake Sequences Initial results show Omori-like behavior with significant spatial variation Energy recorded: Models based on Spiking Neural Networks using waveform data have shown short-term predictive skill for Canterbury What s next Progress on understanding the uncertainty and spatial variability in the energy release (including attenuation, understanding non-uniqueness, etc) Calibration of existing models and development of new aftershock and earthquake occurrence models Spiking Neural Networks: investigation of alternative waveform filters, use of geodetic data and longer time-frames Testing of all models using Collaboratory for the Study of Predictability (CSEP) forecast testing tools (NZ and Japan?)

35 Thank you.