Fourier amplitude spectrum prediction and generation of synthetic ground motion to New Zealand

Vemula, Sreenath; Raghukanth, S. T. G.; Ponnalagu, Alagappan

doi:10.1007/s11600-021-00707-1

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Tytuł artykułu

Fourier amplitude spectrum prediction and generation of synthetic ground motion to New Zealand

Autorzy

Vemula Sreenath , Raghukanth S. T. G. , Ponnalagu Alagappan

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00707-1

Warianty tytułu

Języki publikacji

Abstrakty

Developing a ground motion model (GMM) for Fourier amplitude spectrum (FAS) is essential in seismology and engineering for generating response spectrum and synthetic time histories. Despite data-driven techniques being efficient in modeling complex relations, very few GMMs are developed for FAS using them. An efficient hybrid data-driven algorithm combining genetic algorithm and artificial neural network is implemented using the GeoNet database with 905 records from 77 events in the current work. The input parameters of the model are moment magnitude, Joyner–Boore distance, shear wave velocity, depth to the top of the rupture plane, fault, and tectonic fags. The developed FAS model is statistically tested to be robust and has good agreement with the recorded data and other available GMMs. The developed GMM to FAS has an overall correlation coefficient in the range of 0.8108–0.9298 and sigma in the range of 0.26–0.4 (in log10 units). Further, synthetic time histories are generated from the predicted FAS values and are consistent with various ground motion parameters and the response spectra.

Słowa kluczowe

ground-motion model GMMs artificial neural network ANN Fourier amplitude spectrum FAS synthetic ground motion

modele ruchu naziemnego GMM sztuczna sieć neuronowa ANN widmo amplitudy Fouriera FAS syntetyczny ruch naziemny

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 1

Strony

39--70

Opis fizyczny

Bibliogr. 44 poz.

Twórcy

autor

Vemula Sreenath

vsreenath2@gmail.com

Indian Institute of Technology Madras, Chennai, India

https://orcid.org/0000-0003-0819-6266

autor

Raghukanth S. T. G.

Indian Institute of Technology Madras, Chennai, India

autor

Ponnalagu Alagappan

Indian Institute of Technology Madras, Chennai, India

Bibliografia

1. Abrahamson NA, Youngs RR (1992) A stable algorithm for regression analyses using the random effects model. Bull Seismol Soc Am 82(1):505–510
2. Ahmad I, El Naggar MH, Khan AN (2008) Neural network based attenuation of strong motion peaks in Europe. J Earthquake Eng 12(5):663–680. https://doi.org/10.1080/13632460701758570
3. Ahumada A, Altunkaynak A, Ayoub A (2015) Fuzzy logic-based attenuation relationships of strong motion earthquake records. Expert Syst Appl 42(3):1287–1297. https://doi.org/10.1016/j.eswa.2014.09.035
4. Akkar S, Sandıkkaya MA, Şenyurt M, Azari Sisi A, Ay BÖ, Traversa P, Douglas J, Cotton F, Luzi L, Hernandez B, Godey S (2014) Reference database for seismic ground-motion in Europe (RESORCE). Bull Earthq Eng 12(1):311–339. https://doi.org/10.1007/s10518-013-9506-8
5. Al Atik L, Abrahamson N (2010) An improved method for non-stationary spectral matching. Earthq Spectra 26(3):601–617. https://doi.org/10.1193/1.3459159
6. Ancheta TD, Darragh RB, Stewart JP, Seyhan E, Silva WJ, Chiou BSJ, Wooddell KE, Graves RW, Kottke AR, Boore AR, Boore DM, Kishida T, Donahue JL (2014) NGA-West2 database. Earthq Spectra 30(3):989–1005. https://doi.org/10.1193/070913EQS197M
7. Bayless J, Abrahamson N A (2018) An empirical model for Fourier amplitude spectra using the NGA‐West2 database. Jeff Bayless. Norman A. Abrahamson.
8. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(2):281–305
9. Berry MJ, Linoff GS (2004) Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons
10. Boore DM (1983) Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull Seismol Soc Am 73(6A):1865–1894. https://doi.org/10.1785/BSSA07306A1865
11. Boore DM (2003) Simulation of ground motion using the stochastic method. Pure Appl Geophys 160(3):635–676. https://doi.org/10.1007/PL00012553
12. Boore DM (2006) Orientation-independent measures of ground motion. Bull Seismol Soc Am 96(4A):1502–1511. https://doi.org/10.1785/0120050209
13. Bora SS, Scherbaum F, Kuehn N, Stafford P (2014) Fourier spectral-and duration models for the generation of response spectra adjustable to different source-, propagation-, and site conditions. Bull Earthq Eng 12(1):467–493. https://doi.org/10.1007/s10518-013-9482-z
14. Bora SS, Scherbaum F, Kuehn N, Stafford P, Edwards B (2015) Development of a response spectral ground-motion prediction equation (GMPE) for seismic-hazard analysis from empirical Fourier spectral and duration models. Bull Seismol Soc Am 105(4):2192–2218. https://doi.org/10.1785/0120140297
15. Bora SS, Scherbaum F, Kuehn N, Stafford P (2016) On the relationship between Fourier and response spectra: Implications for the adjustment of empirical ground-motion prediction equations (GMPEs). Bull Seismol Soc Am 106(3):1235–1253. https://doi.org/10.1785/0120150129
16. Bora SS, Cotton F, Scherbaum F (2019) NGA-West2 empirical Fourier and duration models to generate adjustable response spectra. Earthq Spectra 35(1):61–93. https://doi.org/10.1193/110317EQS228M
17. Bradley BA (2013) A New Zealand-specific pseudospectral acceleration ground-motion prediction equation for active shallow crustal earthquakes based on foreign models. Bull Seismol Soc Am 103(3):1801–1822. https://doi.org/10.1785/0120120021
18. Cacciola P (2010) A stochastic approach for generating spectrum compatible fully non-stationary earthquakes. Comput Struct 88(15–16):889–901. https://doi.org/10.1016/j.compstruc.2010.04.009
19. Campbell KW, Bozorgnia Y (2014) NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra. Earthq Spectra 30(3):1087–1115. https://doi.org/10.1193/062913EQS175M
20. Chiou BSJ, Youngs RR (2014) Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthq Spectra 30(3):1117–1153. https://doi.org/10.1193/072813EQS219M
21. Derras B, Bard PY, Cotton F (2014) Towards fully data driven ground-motion prediction models for Europe. Bull Earthq Eng 12(1):495–516. https://doi.org/10.1007/s10518-013-9481-0
22. Derras B, Bard PY, Cotton F (2016) Site-condition proxies, ground motion variability, and data-driven GMPEs: Insights from the NGA-West2 and RESORCE data sets. Earthq Spectra 32(4):2027–2056. https://doi.org/10.1193/060215EQS082M
23. Dhanya J, Raghukanth STG (2018) Ground motion prediction model using artificial neural network. Pure Appl Geophys 175(3):1035–1064. https://doi.org/10.1007/s00024-017-1751-3
24. Douglas, J. (2021), Ground motion prediction equations 1964–2021, http://www.gmpe.org.uk.
25. Gandomi M, Kashani AR, Farhadi A, Akhani M, Gandomi AH (2021) Spectral acceleration prediction using genetic programming based approaches. Appl Soft Comput 106:107326. https://doi.org/10.1016/j.asoc.2021.107326
26. Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6:47–51
27. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276. https://doi.org/10.1016/s1093-3263(01)00123-1
28. Gupta ID, Joshi RG (1993) On synthesizing response spectrum compatible accelerograms. Eur Earthq Eng 7(2):25–33
29. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366. https://doi.org/10.1016/0893-6080(89)90020-8
30. Kaiser A, Van Houtte C, Perrin N, Wotherspoon L, McVerry G (2017) Site characterisation of GeoNet stations for the New Zealand strong motion database. Bull N Z Soc Earthq Eng 50(1):39–49. https://doi.org/10.5459/bnzsee.50.1.39-49
31. Konno K, Ohmachi T (1998) Ground-motion characteristics estimated from spectral ratio between horizontal and vertical components of microtremor. Bull Seismol Soc Am 88(1):228–241
32. Lee SC, Han SW (2002) Neural-network-based models for generating artificial earthquakes and response spectra. Comput Struct 80(20–21):1627–1638. https://doi.org/10.1016/S0045-7949(02)00112-8
33. Lekshmy PR, Raghukanth STG (2021) A hybrid genetic algorithm-neural network model for power spectral density compatible ground motion prediction. Soil Dyn Earthq Eng 142:106528. https://doi.org/10.1016/j.soildyn.2020.106528
34. Lin CCJ, Ghaboussi J (2001) Generating multiple spectrum compatible accelerograms using stochastic neural networks. Earthquake Eng Struct Dynam 30(7):1021–1042. https://doi.org/10.1002/eqe.50
35. Olsen KB, Day SM, Dalguer LA, Mayhew J, Cui Y, Zhu J, Chourasia A (2009) ShakeOut-D: Ground motion estimates using an ensemble of large earthquakes on the southern San Andreas fault with spontaneous rupture propagation. Geophys Res Lett 36(4):L04303. https://doi.org/10.1029/2008GL036832
36. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49(12):1373–1379
37. Razafindrakoto HNT, Bradley BA, Graves RW (2016) Broadband ground motion simulation of the 2010–2011 Canterbury earthquake sequence. Bull Seismol Soc Am 108(4):2130–47. https://doi.org/10.1785/0120170388
38. Sabetta F, Pugliese A (1996) Estimation of response spectra and simulation of non-stationary earthquake ground motions. Bull Seismol Soc Am 86(2):337–352
39. Stafford P (2006) Engineering seismological studies and seismic design criteria for the Buller region, South Island. University of Canterbury, Christchurch, New Zealand, New Zealand, p 342
40. The Math Works, Inc. MATLAB, version 2020a
41. Thomas S, Pillai GN, Pal K, Jagtap P (2016) Prediction of ground motion parameters using randomized ANFIS (RANFIS). Appl Soft Comput 40:624–634. https://doi.org/10.1016/j.asoc.2015.12.013
42. Van Houtte C, Bannister S, Holden C, Bourguignon S, McVerry G (2017) The New Zealand strong motion database. Bull N Z Soc Earthq Eng 50(1):1–20. https://doi.org/10.5459/bnzsee.50.1.1-20
43. Vanmarcke EH, Gasparini DA. (1977) Simulated earthquake ground motions
44. Vemula S, Yellapragada M, Podili B, Raghukanth STG, Ponnalagu A (2021) Ground motion intensity measures for New Zealand. Soil Dynamics Earthquake Eng 150:106928

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-6c17d8e2-f036-4c8d-9e71-bb173fff28db