A spatial random field model to characterize complexity in earthquake slip

By P. M. Mai, Gregory C. Beroza. 2002

622 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/bb0c79e796f6c0cae5109b5e9464d541c22c37dc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Geophysics

Citations in the corpus

Macroscopic Asymmetry of Dynamic Rupture on a Bimaterial Interface With Velocity- Weakening Friction (2006, background): “…(H = 0) that are C© 2008 The Authors, GJI, 173, 674–692 Journal compilation C© 2008 RAS theoretically expected for self-similar seismicity with constant average stress drop (Andrews 1980), and of stresses (H ≈ 0 or H < 0) derived from kinematic source slip inversions (Tsai 1997; Mai & Beroza 2002).”
Uncertainty modeling and visualization for tsunami hazard and risk mapping: a case study for the 2011 Tohoku earthquake (2016, methodology): “New methods for developing stochastic earthquake source models, which are based on spectral analysis of slip heterogeneity of an inverted source model and spectral synthesis of slip random fields (Mai and Beroza 2002; Lavallée et al. 2006), facilitate the generation of possible scenarios having diff”
Prediction of Broadband Ground-Motion Time Histories: Hybrid Low/High- Frequency Method with Correlated Random Source Parameters (2006, methodology): “…elements of N1, , and , 1 1N N2 3 respectively, as the required independent Gaussian random variables, and the power spectral density suggested by Mai and Beroza (2002): 22 2P(k , k ) [1 (k C ) (k C ) ] , (2)x y x L y W where CL and CW are coherence lengths along the strike and dip, respectively.”
Quantifying near‐field and off‐fault deformation patterns of the 1992 Mw 7.3 Landers earthquake (2015, methodology): “This is in agreement with modeled fault slip distributions determined from inversion of seismic and geodetic data, which also exhibit a fractal structure [Mai and Beroza, 2002].”
Correlation of earthquake source parameters inferred from dynamic rupture simulations (2010, background): “following). Several models have been proposed in the literature to describe the spatial correlation, or the power spectrum, associated with the slip or the stress spatial variability [e.g., Lavalle´e et al., 2006; Mai and Beroza, 2002 ].”
Strong Ground-Motion Prediction from Stochastic-Dynamic Source Models (2003, methodology): “field using the method developed by Mai and Beroza (2002) .”
Earthquake source characteristics from dynamic rupture with constrained stochastic fault stress (2007, methodology): “This parameter also appears in the spectral description of von Karman fields adopted byMai and Beroza [2002] and Figure 1.”