Near mean-field behavior in the generalized Burridge-Knopoff earthquake model with variable range stress transfer

Simple models of earthquake faults are important for understanding the mechanisms for their observed behavior in nature, such as Gutenberg-Richter scaling. Because of the importance of long-range interactions in an elastic medium, we generalize the Burridge-Knopoff slider-block model to include variable range stress transfer. We find that the Burridge-Knopoff model with long-range stress transfer exhibits qualitatively different behavior than the corresponding long-range cellular automata models and the usual Burridge-Knopoff model with nearest-neighbor stress transfer, depending on how quickly the friction force weakens with increasing velocity. Extensive simulations of quasiperiodic characteristic events, mode-switching phenomena, ergodicity, and waiting-time distributions are also discussed. Our results are consistent with the existence of a mean-field critical point and have important implications for our understanding of earthquakes and other driven dissipative systems.Comment: 24 pages 12 figures, revised version for Phys. Rev.

Application of DInSAR-GPS optimization for derivation of fine-scale surface motion maps of Southern California

A method based on random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is used to derive 3-D surface motion maps from sparse global positioning system (GPS) measurements and differential interferometric synthetic aperture radar (DInSAR) interferogram in the southern California region. The minimization of the Gibbs energy function is performed analytically, which is possible in the case when neighboring pixels are considered independent. The problem is well posed and the solution is unique and stable and not biased by the continuity condition. The technique produces a 3-D field containing estimates of surface motion on the spatial scale of the DInSAR image, over a given time period, complete with error estimates. Significant improvement in the accuracy of the vertical component and moderate improvement in the accuracy of the horizontal components of velocity are achieved in comparison with the GPS data alone. The method can be expanded to account for other available data sets, such as additional interferograms, lidar, or leveling data, in order to achieve even higher accuracy

Gravity changes from a stress evolution earthquake simulation of California

The gravity signal contains information regarding changes in density at all depths and can be used as a proxy for the strain accumulation in fault networks. A stress evolution time-dependent model was used to create simulated slip histories over the San Andreas Fault network in California. Using a linear sum of the gravity signals from each fault segment in the model, via coseismic gravity Green's functions, a time-dependent gravity model was created. The steady state gravity from the long-term plate motion generates a signal over 5 years with magnitudes of ±~2 μGal; the current limit of portable instrument observations. Moderate to large events generate signal magnitudes in the range of ~10 to ~80 μGal, well within the range of ground-based observations. The complex fault network geometry of California significantly affects the spatial extent of the gravity signal from the three events studied.Peer reviewe

Gas phase reaction rates of some positive ions with water at 296 K

Measuring rate constants for reactions of various gas phases with water by flowing afterglow techniqu