Evidence for Truncated Exponential Probability Distribution of Earthquake Slip

Kiran Kumar Thingbaijam, Paul Martin Mai

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Earthquake ruptures comprise spatially varying slip on the fault surface, where slip represents the displacement discontinuity between the two sides of the rupture plane. In this study, we analyze the probability distribution of coseismic slip, which provides important information to better understand earthquake source physics. Although the probability distribution of slip is crucial for generating realistic rupture scenarios for simulation-based seismic and tsunami-hazard analysis, the statistical properties of earthquake slip have received limited attention so far. Here, we use the online database of earthquake source models (SRCMOD) to show that the probability distribution of slip follows the truncated exponential law. This law agrees with rupture-specific physical constraints limiting the maximum possible slip on the fault, similar to physical constraints on maximum earthquake magnitudes.We show the parameters of the best-fitting truncated exponential distribution scale with average coseismic slip. This scaling property reflects the control of the underlying stress distribution and fault strength on the rupture dimensions, which determines the average slip. Thus, the scale-dependent behavior of slip heterogeneity is captured by the probability distribution of slip. We conclude that the truncated exponential law accurately quantifies coseismic slip distribution and therefore allows for more realistic modeling of rupture scenarios. © 2016, Seismological Society of America. All rights reserverd.
Original languageEnglish (US)
Pages (from-to)1802-1816
Number of pages15
JournalBulletin of the Seismological Society of America
Issue number4
StatePublished - Jul 12 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1339-01-01, URF/1/2160-01-01
Acknowledgements: We thank all contributors to the SRCMOD database and all the participants of the Source Inversion Validation (SIV) project. M. Galis and J. Vyas are acknowledged for helpful discussions. We also thank S. Wesnousky and O. Zielke for reviewing an early version of the article. Careful and constructive comments by Associate Editor T. Pratt and two anonymous reviewers helped to improve the article. Research reported in this publication is supported by the King Abdullah University of Science and Technology (KAUST), baseline funding BAS/1339-01-01 and Competitive Research Grant URF/1/2160-01-01.


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