EventSet API

The weaknesses of current stochastic event sets

Current models are almost fully dependent on faults, with off-fault hazard very low. But large, damaging shocks have recently struck California (2019 M 7.1 Ridgecrest), New Zealand (2010 M 7.1 Canterbury) and Japan (2008 M 6.9 Iwate-Miyagi) on unknown faults. And these are three best mapped places on Earth, so elsewhere, the problem is much worse. Current models assign maximum magnitudes to faults, but four recent shocks were much larger than expected on those faults (2016 M 7.8 Kaikoura, New Zealand; 2011 M 9.0 Tohoku, Japan; and 2023 M 7.8 Kahramanmaraş, Turkey). 

These are just a few examples of the limitations of fault-based ‘characteristic earthquake’ models, in which operators use their judgment to assign maximum magnitudes from fault lengths or seismic histories, and estimate earthquake repeat times from fault slip rates. However convenient and widely used, earthquakes don’t obey these rules. Just as important, fault mapping is a highly skilled and intensive endeavor, and so most of the world’s faults are inadequately mapped and have highly uncertain slip rates. Finally, each country relies on its own local datasets to make these judgments. These regional biases render the model in one country incompatible with that of its neighbors, as is evident from border discontinuities.

Temblor’s modern, globally uniform alternative

Temblor’s Global Earthquake Activity Rate (T-GEAR) model furnishes consistent earthquake frequencies and severities on a fine 0.1° x 0.1° grid for M≥5 quakes worldwide. Its predecessor, GEAR1(Bird et al., Bull. Seismol. Soc. Amer., 2015) has been under independent test by the Collaboratory for the Study of Earthquake Predictability (CSEP) for 9 years, where it has outperformed its competitors in every year. Temblor built T-GEAR with 50 times the data of GEAR1, and 5 times the resolution of its strain-rate component. The result is that T-GEAR retrospectively tests 19% better than GEAR1. 

Here is how T-GEAR is constructed: 

GPS velocities were not available when the traditional, fault-based, approach to probabilistic hazard and event set generation was developed in the 1980’s. But this modern uniform dataset provides an alternative way to capture earthquake generation, as faults are revealed by their signature in strain. EventSet is built by random draws of T-GEAR, providing in 50,000 realizations of next year, which comes to 80 million M≥5 earthquakes, with M≥7 ruptures as extended sources. Concentrated seismicity occurs along plate boundaries (San Andreas, Japan Trench), and distributed seismicity in plate interiors (e.g., central U.S., northern Europe). The model is seamless and uniform in approach, and so the event set at all localities is intercomparable (e.g., Los Angeles vs. Tokyo vs. Istanbul).

EventSet API, Notebook, and Technical Support

To make quake filtering simple, fast, and intuitive, the EventSet API comes with a Jupyter Notebook (left below). One can use polygons, disks, or shapefiles. The API outputs CSV files with clear headersand an ID # and year for each event. The Notebook also includes plotting functions (right). Tembloralso provides expert technical support. The California run below, with 50,000 events, took just 40 sec. Uses of EventSet Building or evaluating parametric deals, and comparing EventSet to other event sets. This is important in countries with low model confidence, or where the existing event sets are shorter than 50,000 years.

Please contact us for a 30-min demo: Ross Stein, Ph.D., CEO, ross@temblor.net, Volkan Sevilgen, M.Sc.,CTO, volkan@temblor.net, Gabriel Lotto, Ph.D., EventSet Tech Support, gabriel@temblor.net

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