Nowcasting Earthquakes with "QuakeGPT": A Generative Earthquake Pretrained Transformer

Nowcasting Earthquakes with "QuakeGPT": A Generative Earthquake Pretrained Transformer | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on Nowcasting Earthquakes with "QuakeGPT": A Generative Earthquake Pretrained Transformer Authors : John B. Rundle 0000-0002-1966-4144 [email protected] , John B Rundle , Geoffrey C Fox , Andrea Donnellan , and Lisa Grant Ludwig Authors Info & Affiliations https://doi.org/10.22541/au.176463802.23514503/v1 81 views 79 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic (ROC) methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time (Rundle et al., 2023a, 2024c). Here we discuss a new method for earthquake nowcasting that uses an AI-enhanced deep learning model "QuakeGPT" that is based on an attentionbased science transformer adapted for time series forecasting. Such dot product attention-based transformers were introduced by Vaswani et al. (2017), and are the basis for the new large language models such as ChatGPT. To use these science transformers, they must Sirst be trained on a large corpus of data. A problem is that the existing history of reliable earthquake catalog data extends back in time only a few decades, which is almost certainly too short to train a model for reliable nowcasting/forecasting. As a result, we turn to earthquake simulations to train the transformer model. SpeciSically we discuss a simple stochastic earthquake simulation model "ERAS" (Earthquake Rescaled Aftershock Seismicity) that has recently been introduced (Rundle et al, 2024c). The ERAS model is similar to the more common "ETAS" models, the difference being that the ERAS model has only 2 novel, adjustable parameters, rather than the 6-8 adjustable parameters that characterize most ETAS models. Using this ERAS model, we then deSine a transformer model and train it using a long catalog of ERAS simulations, then apply it to an ERAS validation dataset with the transformer model. In this paper, we describe this new method and assess the applicability to observed earthquake catalogs for use in nowcasting/forecasting. Supplementary Material File (nowcasting earthqakes with quakegpt.pdf) Download 5.12 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keyword geophysics Authors Affiliations John B. Rundle 0000-0002-1966-4144 [email protected] View all articles by this author John B Rundle University of California Santa Fe Institute View all articles by this author Geoffrey C Fox University of Virginia View all articles by this author Andrea Donnellan Jet Propulsion Laboratory, California Institute of Technology View all articles by this author Lisa Grant Ludwig University of California View all articles by this author Funding Information U.S. Department of Energy DE- SC0017324 John Rundle, University of California, Davis, CA U.S. Department of Energy DE- SC0017324 John Rundle University of California Metrics & Citations Metrics Article Usage 81 views 79 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation John B. Rundle, John B Rundle, Geoffrey C Fox, et al. Nowcasting Earthquakes with "QuakeGPT": A Generative Earthquake Pretrained Transformer. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463802.23514503/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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