Exceedance Probabilities for Large Earthquakes From DIY Local Earthquake Ensemble Nowcasting and Forecasting
John B Rundle, Ian Baughman, Andrea Donnellan, Lisa Grant Ludwig, Geoffrey Fox, Kazuyoshi Nanjo

TL;DR
This paper introduces a data-driven method for local earthquake nowcasting and forecasting large earthquakes using small earthquake counts, validated against existing models and applied to the Los Angeles area.
Contribution
It presents a novel ensemble-based approach leveraging the Gutenberg-Richter relation for improved local earthquake probability forecasts.
Findings
Method shows significant skill in ROC tests.
Forecast accuracy improves with time since last major quake.
Validated against UCERF3 forecasts for Los Angeles and San Francisco.
Abstract
This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
