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A new method for calculating the earthquake seismic distribution

 


During an earthquake, the Earth’s crust moves or slides along cracks in the rock called faults. These movements can be detected and recorded by geophysical instruments located at various locations on the Earth’s surface. Recordings from geophysical instruments have a different orientation relative to the epicenter, and thus record a different side of the fault slip. An important problem in seismology is reconciling these different measurements to determine the true direction of slip of many earthquake faults, as well as the large-scale stresses they create.

The process of determining the distribution of slips that create a particular set of geophysical observations is called slip reflection. In the computer age, it has traditionally been accomplished by a variety of least squares fitting procedures that attempt to match potential coupon distributions with the observed data. However, this technique faces a number of challenges, including ensuring a physically plausible solution, properly dealing with complex observed uncertainty, and determining a slip distribution that varies spatially.

To address these problems, modern slip-slip techniques have begun to employ a probabilistic approach using Markov Monte Carlo chain (MCMC) methods. The traditional MCMC approach overcomes many of the problems encountered by optimization technique such as least squares but can have difficulty when faced with the strongly irregular distribution of seismic observations. To address this, Tomita et al. He developed MCMC’s transdimensional technology. In the trans-dimensional approach, the number of model parameters is not predetermined, but rather emerges naturally from the complexity of the input data.

The authors devised their approach from the reverse jump MCMC (rj-MCMC) technique, an existing framework for implementing multidimensional MCMC computations. To evaluate their approach, they simulated the effects of an earthquake located in an undersea trench several hundred kilometers from various geodetic observation sites. They considered three scenarios: two with a mixture of land and sea observation sites and one with only land sites.

In the mixed scenarios, the rj-MCMC technique and the least squares method reasonably reproduced the coupon distribution. However, only the rj-MCMC computation can handle the asymmetric scenario of only wild-type observations.

Finally, they applied the rj-MCMC method to the observational data of the 2011 Tohoku Aoki earthquake off Japan in the Pacific Ocean. Their score is very similar to previous work on this event but provides a better expression of the more important slips. Overall, the cross-dimensional probabilistic approach appears to be a promising tool for future seismic studies. (Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2020JB020991, 2021)

—Morgan Reinberg, science writer

Text © 2021. AGU. CC BY-NC-ND 3.0 Unless otherwise noted, images are subject to copyright. Any reuse without the express permission of the copyright owner is prohibited.

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