Fault connectivity improves earthquake prediction models

A recent study showed that seismic risk analyzes could be improved by connecting more errors in the earthquake prediction model.

Written by Jing Han Chung, Cal Northridge State University

Citation: Chung, JH, 2021, Fault Linking Improves Earthquake Prediction Models, Tumblr, http://doi.org/10.32858/temblor.157

During hurricane season, meteorologists predict the path of storms days before landfall. Likewise, seismologists use earthquake prediction models to estimate the likelihood of earthquakes of a given strength hitting specific areas in a given time frame. Just like meteorologists who use different tools to study and track hurricanes, seismologists use information gleaned from known errors, such as fault length and 3D error geometry, to develop their prediction models. As scientists discover and study new faults, they begin to consider whether separate faults could relate to each other during an earthquake because different assumptions of rupture length can alter earthquake risk calculations.

A study published in 2018 suggested that the most used earthquake prediction model in California – the Uniform Third California Earthquake Rupture Prediction (UCERF3) – used by the US Geological Survey and insurance companies, includes far too many earthquake ruptures that are longer than a known historic earthquake. Rupture (Schwartz, 2018). This means that the model does not accurately predict risk in California. However, a new study illustrates the opposite: for older models with short and segmented errors, the UCERF3 model actually better matches the recorded frequency of known rupture lengths by allowing more fault connections.

The third standardized California earthquake rupture prediction is the predominant earthquake prediction model used in California. The black rectangles on the map represent imperfections and the colors indicate the probability of an earthquake greater than 6.7 in the next 30 years. The white rectangles show the San Francisco and Los Angeles regions. Credit: California Earthquake Prospects Working Group

Address misconceptions

In the previous study, scientists compared historical earthquake rupture lengths to all possible rupture lengths in the UCERF3 model, says Morgan Page, a geophysicist at the US Geological Survey in Pasadena, California, and author of the new study. She says this is not the appropriate comparison.

In the model, hundreds of thousands of possible scenarios with different earthquake rupture lengths are produced, including very long ruptures, Page says. Among these scenarios, some are more likely to occur than others. Therefore, an appropriate comparison would not focus on each possible rupture length in the model, but rather on the rupture lengths in the most likely scenarios. Page says this range of model rupture lengths is comparable to known historical rupture lengths. In contrast to the Schwartz comparison, Page found that increasing faulty communication in the model – which leads to a longer error rupture in the model – actually improves the comparison between the model and reality.

When the model has fewer communication errors, says Ray Weldon, a University of Oregon geologist who was not part of this study, it generates far more earthquakes of medium size than has been recorded by historical earthquakes. The best way to solve this problem, Weldon says, is to allow the model to cause larger earthquakes by allowing longer faults to rupture.

Reality multi-aperture rupture

Another argument for connection error, Page says, is that we know that multi-fault fractures occur. “This is an argument for [testing fault] Connection [in these models]Weldon says.

Examples of multi-hurricane earthquakes include the 1992 magnitude 7.3 Landers earthquake in Southern California and the 2016 Kaikoura earthquake of magnitude 7.8 in New Zealand. In particular, the 2016 Kaikoura earthquake has created more than 20 faults, some of which have not been previously identified. Prediction models for such multi-error events may not produce the exact scenario describing the errors to be communicated, but these models can better estimate the realistic maximum magnitude. This is more important to earthquake risk and more meaningful than forecasting the exact details of every potential earthquake, says Page.

The San Andreas Fault is only one of many errors that scientists take into account when calculating risk in California. Credit: John Wiley (CC BY3.0)

Implications for earthquake prediction models

Because the prediction model allows for long-rupture earthquakes, the model essentially produces very large earthquakes that are less likely to occur, Weldon says. He adds that this reduces the possibility that the model produces moderate earthquakes as well.

Weldon says earthquakes of moderate size drive average household insurance rates in earthquake-prone areas, and those rates will decrease with fewer medium-sized earthquakes in the prediction model. On the other hand, he says, adding the potential for larger, smaller-frequency earthquakes into prediction models affects how and where new long-term infrastructure, such as power plants, is built.

The current model, which Paige helped build, is “a giant step in the right direction and one can argue whether the move is [having longer ruptures lengths] It was too far away or not far enough, “says Weldon. To improve future prediction models, he says, scientists must find a way to address how to compute unknown and dangerous errors that geologists cannot easily identify.

Further reading

Litchfield, NJ, Villamor, P., Dissen, RJV, Nicol, A., Barnes, PM, A. Barrell, DJ,… & Zinke, R. (2018). Surface rupture of multiple faults in the Earth’s crust in the 2016 M w 7.8 Kaikōura earthquake, New Zealand. Bulletin of the American Seismological Society, 108 (3b), 1496-1520.

Page, MT (2021). More fault communication is needed in seismic hazard analysis. Bulletin of the American Seismological Society, 111 (1), 391-397.

Schwartz, DP (2018). Rupture lengths of past and future faults in source seismic characterization – long and short. Bulletin of the American Seismological Society, 108 (5A), 2493-2520.

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