Skip to main content
SpringerLink
Log in

Tsunamis: Bayesian Probabilistic Analysis

  • Living reference work entry
  • Latest version View entry history
  • First Online:
Encyclopedia of Complexity and Systems Science

  • 54 Accesses

Glossary

Aleatory variability:

In the present context, it is the assumed random variability of the parameters characterizing the future hazardous events or, in other words, the random variability in the model describing the physical system under investigation.

Bayesian statistics:

An approach to statistics which represents unknown quantities with probability distributions that in one interpretation represent the degree of belief that the unknown quantity takes any particular value. Data are considered fixed and the parameters of distributions representing the state of the world or hypotheses are updated as evidences are collected.

Bias:

The tendency of a measurement process or statistical estimate to over- or underestimate the value of a population parameter on average.

Conditional probability:

The probability that an event will occur under the condition or given knowledge that another event occurs.

Conjugacy:

In Bayesian statistics, the property of parametric families of distributions...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Bibliography

  • Aida I (1969) Numerical experiments for the tsunami propagation – the 1964 Niigata tsunami and the 1968 Tokachi-Oki tsunami. Bull Earthquake Res Inst 47:673–700

    Google Scholar 

  • Annaka T, Satake K, Sakakiyama T, Yanagisawa K, Shuto N (2007) Logic-tree approach for probabilistic tsunami hazard analysis and its applications to the Japanese coasts. Pure Appl Geophys 164:577–592

    Article  ADS  Google Scholar 

  • Blaser L, Ohrnberger M, Riggelsen C, Babeyko A, Scherbaum F (2011) Bayesian network for tsunami early warning. Geophys J Int 185(3):1431–1443. https://doi.org/10.1111/j.1365-246X.2011.05020.x

    Article  ADS  Google Scholar 

  • Box GEP, Tiao GC (1992) Bayesian inference in statistical analysis. Wiley Classics Library, New York, p 588

    Book  Google Scholar 

  • Burbidge D, Cummins PR, Mleczko R, Thio HK (2008) A probabilistic tsunami hazard assessment for Western Australia. Pure Appl Geophys. https://doi.org/10.1007/S00024-008-0421-X

    Article  ADS  Google Scholar 

  • Castello B, Olivieri M, Selvaggi G (2007) Local and duration magnitude determination for the Italian earthquake catalogue (1981–2002). Bull Seismol Soc Am 97:128–139

    Article  Google Scholar 

  • Congdon P (2006) Bayesian statistical modelling, Wiley series in probability and statistics. Wiley, Chichester, p 529

    Book  Google Scholar 

  • Davies G, Griffin J, Løvholt F, Glymsdal S, Harbitz C, Thio HK, Lorito S, Basili R, Selva J, Geist E, Baptista MA (2016) A global probabilistic tsunami hazard assessment from earthquake sources, Accepted Manuscript, “Tsunamis: geology, hazards and risks”. GSL Special Publications, London

    Google Scholar 

  • Draper D (2009) Bayesian statistics. Enc Complexity Earth Syst Sci 1:445–476

    Google Scholar 

  • Favalli M, Boschi E, Mazzarini F, Pareschi MT (2009) Seismic and landslide source of the 1908 Straits of Messina tsunami (Sicily, Italy). Geophys Res Lett 36:L16304. https://doi.org/10.1029/2009GL039135

    Article  ADS  Google Scholar 

  • Geist EL (2002) Complex earthquake rupture and local tsunamis. J Geophys Res 107:ESE2–1–ESE 2–16

    Article  Google Scholar 

  • Geist EL, Lynett PJ (2014) Source processes for the probabilistic assessment of tsunami hazards. Oceanography 27(2):86–93. https://doi.org/10.5670/oceanog.2014.43

    Article  Google Scholar 

  • Geist EL, Oglesby DD (2014) Tsunamis: stochastic models of occurrence and generation mechanisms. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, New York. https://doi.org/10.1007/978-3-642-27737-5_595-1

    Chapter  Google Scholar 

  • Geist EL, Parsons T (2006) Probabilistic analysis of tsunami hazards. Nat Hazards 37:277–314. https://doi.org/10.1007/s11069-005-4646-z

    Article  Google Scholar 

  • Geist EL, Parsons T (2010) Estimating the empirical probability of submarine landslide occurrence. In: Mosher DC et al (eds) Submarine mass movements and their consequences. Advances in natural and technological hazards research, vol 28. Springer, Dordrecht, p 377

    Chapter  Google Scholar 

  • Geist EL, Ten Brink US, Gove M (2014) A framework for the probabilistic analysis of meteotsunamis. Nat Hazards 74:123–142. https://doi.org/10.1007/S11069-014-1294-1

    Article  Google Scholar 

  • Gelman A, Carlin JB, Stern HS, Rubin DB (2013) Bayesian data analysis. Chapman & Hall/CRC Press, Boca Raton, p 667

    Google Scholar 

  • Gonzalez FI, Geist EL, Jaffe B, Kaˆnoglu U, Mofjeld H, Synolakis CE, Titov VV, Arcas D, Bellomo D, Carlton D, Horning T, Johnson J, Newman J, Parsons T, Peters R, Peterson C, Priest G, Venturato A, Weber J, Wong F, Yalciner A (2009) Probabilistic tsunami hazard assessment at Seaside, Oregon, for near- and far-field seismic sources. J Geophys Res 114:C11023. https://doi.org/10.1029/2008JC005132

    Article  ADS  Google Scholar 

  • Gregory P (2005) Bayesian logical data analysis for the physical sciences. Cambridge University Press, Cambridge, p 468

    Book  Google Scholar 

  • Grezio A, Marzocchi W, Sandri L, Gasparini P (2010) A Bayesian procedure for Probabilistic Tsunami Hazard Assessment. Nat Hazards 53:159–174. https://doi.org/10.1007/s11069-009-9418-8

    Article  Google Scholar 

  • Grezio A, Marzocchi W, Sandri L, Argnani A, Gasparini P (2012) Probabilistic tsunami hazard assessment for messina strait area (Sicily – Italy). Nat Hazards. https://doi.org/10.1007/s11069-012-0246-x

    Article  Google Scholar 

  • Grezio A, Tonini R, Sandri L, Pierdominici S, Selva J (2015) A methodology for a comprehensive probabilistic tsunami hazard assessment: multiple sources and short-term interactions. J Mar Sci Eng 3:23–51. https://doi.org/10.3390/jmse3010023

    Article  Google Scholar 

  • Grezio A, Babeyko A, Baptista MA, Behrens J, Costa A, Davies G, Geist EL, Glimsdal S, González FI, Griffin J, Harbitz CB, LeVeque RJ, Lorito S, Løvholt F, Omira R, Mueller C, Paris R, Parsons T, Polet J, Power W, Selva J, Sørensen MB, Thio HK (2017) Probabilistic tsunami hazard analysis: multiple sources and global applications. Rev Geophys 55. https://doi.org/10.1002/2017RG000579

    Article  ADS  Google Scholar 

  • Grilli ST, Watts P (2005) Tsunami generation by submarine mass failure, I: modeling, experimental validation, and sensitivity analyses. J Waterway Port Coast Ocean Eng 131:283–297

    Article  Google Scholar 

  • Gutenberg B, Richter C (1944) Frequency of earthquakes in California. Bull Seism Soc Am 34:185–188

    Google Scholar 

  • Heidarzadeh M, Kijko A (2011) A probabilistic tsunami hazard assessment for the makran subduction zone at the Northwestern Indian Ocean. Nat Haz 56:577–593

    Article  Google Scholar 

  • Hoechner A, Babeyko AY, Zamora N (2016) Probabilistic tsunami hazard assessment for the Makran region with focus on maximum magnitude assumption. Nat Hazards Earth Syst Sci 16:1339–1350. https://doi.org/10.5194/nhess-16-1339-2016

    Article  ADS  Google Scholar 

  • Horspool N, Pranantyo I, Griffin J, Latief H, Natawidjaja DH, Kongko W, Cipta A, Bustaman B, Anugrah SD, Thio HK (2014) A probabilistic tsunami hazard assessment for Indonesia. Nat Hazards Earth Syst Sci 14:3105–3122. https://doi.org/10.5194/nhess-14-3105-2014

    Article  ADS  Google Scholar 

  • Jaynes ET (2003) In: Bretthorst GL (ed) Probability theory the logic of science. Cambridge University Press, Cambridge, p 727

    Chapter  Google Scholar 

  • Kagan YY (2002a) Seismic moment distribution revisited: I, statistical results. Geophys J Int 148:520–541

    Article  ADS  Google Scholar 

  • Kagan YY (2002b) Seismic moment distribution revisited: II, moment conservation principle. Geophys J Int 149:731–754

    Article  ADS  Google Scholar 

  • Kagan YY, Jackson DD (2000) Probabilistic forecasting of earthquakes. Geophys J Int 143:438–453

    Article  ADS  Google Scholar 

  • Knighton J, Bastidas LA (2015) A proposed probabilistic seismic tsunami hazard analysis methodology. Nat Hazards. https://doi.org/10.1007/s11069-015-1741-7

    Article  Google Scholar 

  • Lane EM, Gillibrand PA, Wang X, Power W (2013) A probabilistic tsunami hazard study of the auckland region, part II: inundation modelling and hazard assessment. Pure Appl Geophys 170:1635–1646. https://doi.org/10.1007/s00024-012-0538-9

    Article  ADS  Google Scholar 

  • Lay T (2015) The surge of great earthquakes from 2004 to 2014. Earth Planet Sci Lett Invited Front Pap 409:133–146. https://doi.org/10.1016/j.epsl.2014.10.047

    Article  ADS  Google Scholar 

  • Lindley DV (2000) The philosophy of statistics. Statistician 49:293–337

    Google Scholar 

  • Lorito S, Selva J, Basili R, Romano F, Tiberti MM, Piatanesi A (2015) Probabilistic hazard for seismically induced tsunamis: accuracy and feasibility of inundation maps. Geophys J Int 200(1):574–588. https://doi.org/10.1093/gji/ggu408

    Article  ADS  Google Scholar 

  • Lorito S, Romano F, Lay T (2016) Tsunamigenic earthquakes (2004–2013): source processes from data inversion. In: Meyers RA (ed) Encyclopedia of complexity and systems science, vol 2015. Springer Science+Business Media, New York. https://doi.org/10.1007/978-3-642-27737-5_641-1

    Chapter  Google Scholar 

  • Maramai A, Graziani L, Alessio G, Burrato P, Colini L, Cucci L, Nappi R, Nardi A, Vilardo G (2005a) Near- and far field survey report of the 30 December 2002 Stromboli (Southern Italy) tsunami. Mar Geol 215(93):106

    Google Scholar 

  • Maramai A, Graziani L, Tinti S (2005b) Tsunami in the Aeolian Islands (southern Italy): a review. Mar Geol 215(11):21

    ADS  Google Scholar 

  • Marzocchi W, Jordan TH (2014) Testing for ontological errors in probabilistic forecasting models of natural systems. PNAS 111(33):11973–11978. https://doi.org/10.1073/pnas.1410183111/-/DCSupplemental

    Article  ADS  Google Scholar 

  • Marzocchi W, Lombardi AM (2008) A double branching model for earthquake occurrence. J Geophys Res 113:B08317. https://doi.org/10.1029/2007JB005472

    Article  ADS  Google Scholar 

  • Marzocchi W, Sandri L, Gasparini P, Newhall C, Boschi E (2004) Quantifying probabilities of volcanic events: the example of volcanic hazard at Mount Vesuvius. J Geophys Res 109:B11201. https://doi.org/10.1029/2004JB003155

    Article  ADS  Google Scholar 

  • Marzocchi W, Sandri L, Selva J (2008) BET_EF: a probabilistic tool for long- and short-term eruption forecasting. Bull Volcanol 70:623–632. https://doi.org/10.1007/s00445-007-0157-y

    Article  ADS  Google Scholar 

  • Marzocchi W, Taroni M, Selva J (2015) Accounting for epistemic uncertainty in PSHA: logic tree and ensemble modeling. Bull Seismol Soc Am 105:2151–2159. https://doi.org/10.1785/0120140131

    Article  Google Scholar 

  • Mueller C, Power W, Fraser S, Wang X (2015) Effects of rupture complexity on local tsunami inundation: implications for probabilistic tsunami hazard assessment by example. J Geophys Res Solid Earth 120:488–502. https://doi.org/10.1002/2014JB011301

    Article  ADS  Google Scholar 

  • O’Loughlin KF, Lander JF (2003) Caribbean tsunamis: a 500-year history from 1498–1998. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Okada Y (1985) Surface deformation due to shear and tensile faults in a half-space. Bull Seismol Soc Am 75:1135–1154

    Google Scholar 

  • Okada Y (1992) Internal deformation due to shear and tensile faults in a half-space. Bull Seismol Soc Am 82:1018–1040

    Google Scholar 

  • Omira R, Baptista MA, Matias L (2015) Probabilistic tsunami hazard in The Northeast Atlantic from near- and far-field tectonic sources. Pure Appl Geophys 172:901–920. 2014 Springer, Basel. https://doi.org/10.1007/S00024-014-0949-X

    Article  ADS  Google Scholar 

  • Orfanogiannaki K, Papadopoulos G (2007) Conditional probability approach of the assessment of tsunami potential: application in three tsunamigenic regions of the Pacific Ocean. Pure Appl Geophys 164:593–603

    Article  ADS  Google Scholar 

  • Parsons T (2008) Monte Carlo method for determining earthquake recurrence parameters from short paleoseismic catalogs: example calculations for California. J Geophys Res 113. https://doi.org/10.1029/2007JB004998

  • Parsons T, Geist EL (2009) Tsunami probability in the caribbean region. Pure Appl Geophys 165(2008):2089–2116. https://doi.org/10.1007/s00024-008-0416-7

    Article  ADS  Google Scholar 

  • Paté-Cornell M (1996) Uncertainties in risk analysis: six levels of treatment. Reliab Eng Syst Saf 54:95–111

    Article  Google Scholar 

  • Polet J, Kanamori H (2009) Tsunami earthquakes. In: Meyers A (ed) Encyclopedia of complexity and systems science. Springer, New York. https://doi.org/10.1007/978-0-387-30440-3_567

    Chapter  Google Scholar 

  • Popper KR (1983) Realism and the aim of science. Hutchinson, London

    Google Scholar 

  • Power W, Wang X, Lane EM, Gillibrand PA (2013) A probabilistic tsunami hazard study of the auckland region, part I: propagation modelling and tsunami hazard assessment at the shoreline. Pure Appl Geophys 170:1621. https://doi.org/10.1007/s00024-012-0543-z

    Article  ADS  Google Scholar 

  • Reid RO, Bodine BR (1968) Numerical model for storm surges in Galveston Bay. J Waterways and Harbors Div ACE 94:33–57

    Google Scholar 

  • Sakai T, Takeda T, Soraoka H, Yanagisawa K, Annaka T (2006) development of a probabilistic tsunami hazard analysis in Japan. In: Proceedings of ICONE14 international conference on nuclear engineering, Miami, 17–20 July, ICONE14–89183

    Google Scholar 

  • Satake K (1995) Linear and nonlinear computations of the 1992 Nicaragua earthquake tsunami. Pure App Geophys 144:455–470

    Article  ADS  Google Scholar 

  • Satake K (2002) Tsunamis. In: Lee WHK, Kanimori H, Jennings PC, Kisslinger C (eds) International handbook of earthquake and engineering seismology, vol 81A. Academic Press, Amsterdam, pp 437–451

    Chapter  Google Scholar 

  • Selva J, Sandri L (2013) Probabilistic seismic hazard assessment: combining cornell-like approaches and data at sites through Bayesian inference. Bull Seismol Soc Am 103(3):1709–1722. https://doi.org/10.1785/0120120091

    Article  Google Scholar 

  • Selva J, Tonini R, Molinari I, Tiberti MM, Romano F, Grezio A, Melini D, Piatanesi A, Basili R, Lorito S (2016) Quantification of source uncertainties in seismic probabilistic tsunami hazard analysis (SPTHA). Geophys J Int 2016. https://doi.org/10.1093/gji/ggw107

    Article  ADS  Google Scholar 

  • Shin JY, Chen S, Kim T-W (2015) Application of Bayesian Markov Chain Monte Carlo Method with mixed gumbel distribution to estimate extreme magnitude of tsunamigenic earthquake. KSCE J Civ Eng 19(2):366–375. https://doi.org/10.1007/s12205-015-0430-0

    Article  Google Scholar 

  • Shuto N (1991) Numerical simulation of tsunamis – its present and near future. Nat Hazards 4:171–191

    Article  Google Scholar 

  • Smith WHF, Sandwell DT (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957–1962

    Google Scholar 

  • Sørensen MB, Spada M, Babeyko A, Wiemer S, Grünthal G (2012) Probabilistic tsunami hazard in the Mediterranean Sea. J Geophys Res 117:B01305. https://doi.org/10.1029/2010JB008169

    Article  ADS  Google Scholar 

  • Stigler SM (1983) Who discovered Bayes Theorem? Am Stat 37:290–296

    MATH  Google Scholar 

  • Suppasri A, Imamura F, Koshimura S (2012) Probabilistic tsunami hazard analysis and risk to coastal populations in Thailand. J Earthquake and Tsunami 06:1250011. [27 Pages]. https://doi.org/10.1142/S179343111250011x

    Article  Google Scholar 

  • Synolakis CE (1987) The runup of solitary waves. J Fluid Mech 185:523–545

    Article  ADS  MathSciNet  Google Scholar 

  • Tanioka Y, Satake K (1996) Tsunami generation by horizontal displacement of ocean bottom. Geophys Res Lett 23:861–865

    Article  ADS  Google Scholar 

  • Tatsumi D, Calder CA, Tomita T (2014) Bayesian near-field tsunami forecasting with uncertainty estimates. J Geophys Res Oceans 119:2201–2211. https://doi.org/10.1002/2013JC009334

    Article  ADS  Google Scholar 

  • Thio HK, Li W (2015) Probabilistic tsunami hazard analysis of the cascadia subduction zone and the role of epistemic uncertainties and aleatory variability. In: 11th Canadian conference on earthquake engineering, Victoria, pp 21–24

    Google Scholar 

  • Thio HK, Somerville P, Polet J (2010) Probabilistic tsunami hazard in California, PEER Report 2010/108 Pacific Earthquake Engineering Research Center

    Google Scholar 

  • Tinti S, Maramai A, Graziani L (2004) The new catalogue of Italian Tsunamis. Nat Haz 33(439):465

    Google Scholar 

  • Tinti S, Armigliato A, Tonini R, Maramai A, Graziani L (2005) Assessing the hazard related to tsunamis of tectonic origin: a hybrid statistical-deterministic method applied to Southern Italy coasts. ISET J Earthquake Tech 42:189–201

    Google Scholar 

  • Tinti S, Argnani A, Zaniboni F, Pagnoni G, Armigliato A (2007) Tsunamigenic potential of recently mapped submarine mass movements offshore eastern Sicily (Italy): numerical simulations and implications for the 1693 tsunami. IASPEI—JSS002—abstract n. 8235 IUGG XXIV General Assembly, Perugia, 2–13 July 2007

    Google Scholar 

  • TPSWG Tsunami Pilot Study Working Group (2006) Seaside, Oregon tsunami pilot study— modernization of FEMA flood hazard maps. NOAA OAR Special Report, NOAA/OAR/PMEL, Seattle, p 94 + 7 appendices

    Google Scholar 

  • Vannucci G, Gasperini P (2004) The new release of the database of earthquake mechanisms of the mediterranean area (EMMA2). Ann Geophys Suppl 47:307–334

    Google Scholar 

  • Watts P, Grilli ST, Tappin D, Fryer GJ (2005) Tsunami generation by submarine mass failure, II: predictive equations and case studies. J Waterway Port Coast Ocean Eng 131:298–310

    Article  Google Scholar 

  • Wells DL, Coppersmith KJ (1994) New empirical relationships among magnitude, rupture length, rupture width, rupture area and surface displacement. Bull Seismol Soc Am 84:974–1002

    Google Scholar 

  • Yadav RBS, Tsapanos TM, Tripathi JN, Chopra S (2013) An evaluation of tsunami hazard using Bayesian approach in the Indian Ocean. Tectonophysics 593:172–182

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy

    Anita Grezio & Jacopo Selva

  2. Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy

    Stefano Lorito

  3. USGS, Menlo Park, CA, USA

    Tom Parsons

Authors
  1. Anita Grezio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefano Lorito
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tom Parsons
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jacopo Selva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anita Grezio .

Editor information

Editors and Affiliations

  1. Ramtech Ltd., Larkspur, CA, USA

    Robert A. Meyers

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media LLC

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Grezio, A., Lorito, S., Parsons, T., Selva, J. (2019). Tsunamis: Bayesian Probabilistic Analysis. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_645-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27737-5_645-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27737-5

  • Online ISBN: 978-3-642-27737-5

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Tsunamis: Bayesian Probabilistic Analysis
    Published:
    06 June 2019

    DOI: https://doi.org/10.1007/978-3-642-27737-5_645-2

  2. Original

    Tsunamis: Bayesian Probabilistic Analysis
    Published:
    07 February 2017

    DOI: https://doi.org/10.1007/978-3-642-27737-5_645-1

Search

Navigation