Does the Catalog of California Earthquakes, With Aftershocks Included, Contain Information About Future Large Earthquakes?

Rundle, John B.; Donnellan, Andrea; Fox, Geoffrey; Ludwig, Lisa Grant; Crutchfield, James P.

doi:10.1029/2022ea002521

Cited by 7 publications

(11 citation statements)

References 56 publications

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“…We then defined a “signal”, which is the current value of the EPS above or below a selected threshold, and an “event”, which is the occurrence or non‐occurrence of a large earthquake within a selected future time window T W . In (Rundle et al., 2022a, 2023), the value of T W is typically 1–3 years.…”

Section: Nowcasting Methodsmentioning

confidence: 99%

“…To analyze the information content of the earthquake catalogs, we consider Shannon information entropy. As discussed in Rundle et al (2023), we use the Positive Predictive Value (PPV) of for large earthquakes (m ≥ 6.75) within a future time window of duration T W = 3 years as our probability value. The self-information I self is then defined by:…”

Section: Shannon Information Entropy Of Catalogsmentioning

confidence: 99%

“…As discussed in Rundle et al. (2023), we use the Positive Predictive Value (PPV) of for large earthquakes ( m ≥ 6.75) within a future time window of duration T W = 3 years as our probability value. The self‐information I self is then defined by:

{I}_{self}=-\text{Lo}{\mathrm{g}}_{2}p(\omega )

where p ( ω ) = PPV, and ω is the Earthquake Potential Score ( ω = EPS = value of the nowcast curve).…”

Section: Shannon Information Entropy Of Catalogsmentioning

confidence: 99%

“…Earthquake nowcasting (Rundle et al, 2019(Rundle et al, , 2021a(Rundle et al, , 2021b(Rundle et al, , 2021c(Rundle et al, , 2022a(Rundle et al, , 2023Rundle and Donnellan, 2020;Pasari, 2019Pasari, , 2020Pasari, , 2022Pasari and Mehta, 2018;Chouliaras et al, 2023) is a relatively new method that uses elements of machine learning to track the current state of the potential for large earthquakes, as well as the recent past and near future. Nowcasting is based on the Receiver Operating Characteristic (ROC) method that was developed with the invention of radar in 1941 relating to the observation of "signals" associated with reflections, or "events" (https://en.wikipedia.org/wiki/Receiver_operating_characteristic).…”

Section: Introductionmentioning

confidence: 99%

“…The fact that one can compute a probability for large earthquakes (Rundle et al., 2023) implies that there is information (Shannon, 1948) contained within seismic catalogs. An important question then is:

From where does this information arise?…”

Section: Introductionmentioning

confidence: 99%

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Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

Rundle,

Baughman,

Zhang

2024

Earth and Space Science

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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 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. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non‐Poisson” power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity.

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Section: Nowcasting Methodsmentioning

confidence: 99%

Section: Shannon Information Entropy Of Catalogsmentioning

confidence: 99%

{I}_{self}=-\text{Lo}{\mathrm{g}}_{2}p(\omega )

where p ( ω ) = PPV, and ω is the Earthquake Potential Score ( ω = EPS = value of the nowcast curve).…”

Section: Shannon Information Entropy Of Catalogsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From where does this information arise?…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

Rundle,

Baughman,

Zhang

2024

Earth and Space Science

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Seismic Nowcasting: A Holistic Artificial Neural Network Predictive Model

Bikos

2024

Preprint

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A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

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Nowcasting ETAS Earthquakes: Information Entropy of Earthquake Catalogs

Rundle,

Baughman,

Zhang

2023

Preprint

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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. So an important question remains, where does the information originate? In this paper, we examine this question using statistical simulations of earthquake catalogs computed using Epidemic Type Aftershock Sequence (ETAS) simulations. ETAS earthquake simulations are currently in widespread use for a variety of tasks, in modeling, analysis and forecasting. After examining several of the standard ETAS models, we propose a version of the ETAS model that conforms to the standard ETAS statistical relations of magnitude-frequency scaling, aftershock scaling, Bath’s law, and the productivity relation, but with an additional property. We modify the model to introduce variable non-Poisson aftershock clustering, inasmuch as we test the hypothesis that the information in the catalogs originates from aftershock clustering. We find that significant information in the catalogs arises from the non-Poisson aftershock clustering, implying that the common practice of de-clustering catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the the ETAS models as it does with observed seismicity.

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Does the Catalog of California Earthquakes, With Aftershocks Included, Contain Information About Future Large Earthquakes?

Abstract: Yes

Cited by 7 publications

References 56 publications

Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

Nowcasting Earthquakes With Stochastic Simulations: Information Entropy of Earthquake Catalogs

Seismic Nowcasting: A Holistic Artificial Neural Network Predictive Model

Nowcasting ETAS Earthquakes: Information Entropy of Earthquake Catalogs

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