scholarly journals Editorial: Achievements and New Frontiers in Research Oriented to Earthquake Forecasting

2021 ◽  
Vol 9 ◽  
Author(s):  
Giovanni Martinelli ◽  
Antonella Peresan ◽  
Ying Li
Keyword(s):  
Earthquake Forecasting

scholarly journals Response to Zöller et al.'s critique on “Potential short‐term earthquake forecasting by farm‐animal monitoring”

Ethology ◽  
2021 ◽  
Vol 127 (3) ◽  
pp. 307-308
Author(s):  
Martin Wikelski ◽  
Uschi Mueller ◽  
Paola Scocco ◽  
Andrea Catorci ◽  
Lev V. Desinov ◽  
...  
Keyword(s):  
Farm Animal ◽  
Earthquake Forecasting ◽  
Short Term ◽  
Animal Monitoring

Repeating earthquakes follow afterslip gradient in the aftermath of the 16th April 2016 M7.8 Pedernales earthquake in Ecuador

2021 ◽  
Author(s):  
Caroline Chalumeau ◽  
Keyword(s):  
Template Matching ◽  
Earthquake Forecasting ◽  
Double Difference ◽  
Recurrence Time ◽  
Fault Mechanics ◽  
Aseismic Slip ◽  
Earthquake Triggering ◽  
Repeating Earthquakes ◽  
Waveform Cross Correlation ◽  
One Year

<p>Repeating earthquakes are earthquakes that repeatedly break a single, time-invariant fault patch. They are generally associated with aseismic slip, which is thought to load asperities, leading to repeated rupture. Repeating earthquakes are therefore useful tools to study aseismic slip and fault mechanics, with possible applications to earthquake triggering, loading rates and earthquake forecasting.</p><p>In this study, we analyze one year of aftershocks following the 16<sup>th</sup> April 2016 Mw 7.8 Pedernales earthquake in Ecuador to find repeating families, using data recorded by permanent and temporary seismological stations. In our area, seismicity during both the inter-seismic and post-seismic periods has been previously linked to aseismic slip. We calculate waveform cross-correlation coefficients (CC) on all available catalogue events, which we use to sort events into preliminary families, using a minimum CC of 0.95. These events were then stacked and used to perform template-matching on the continuous data. In total, 376 earthquakes were classified into 62 families of 4 to 15 earthquakes, including 8 from the one-year period before the mainshock. We later relocated these earthquakes using a double-difference method, which confirmed that most of them did have overlapping sources.</p><p>Repeating earthquakes seem to concentrate largely around the areas of largest afterslip release, where afterslip gradient is the highest. We also find an increase in the recurrence time of repeating events with time after the mainshock, over the first year of the postseismic period, which highlights a possible timeframe for the afterslip’s deceleration. Our results suggest that while most repeating aftershocks are linked to afterslip release, the afterslip gradient may play a bigger role in determining their location than previously thought.</p>

How prediction statistics can help us cope when we are shaken, scared and irrational

2021 ◽  
Author(s):  
Yavor Kamer ◽  
Shyam Nandan ◽  
Stefan Hiemer ◽  
Guy Ouillon ◽  
Didier Sornette
Keyword(s):  
Earthquake Prediction ◽  
Information Technologies ◽  
Predictive Performance ◽  
Earthquake Forecasting ◽  
Skill Assessment ◽  
Surveillance Network ◽  
The Public ◽  
Predictive Skill ◽  
Earthquake Predictability ◽  
Set Up

<p>Nature is scary. You can be sitting at your home and next thing you know you are trapped under the ruble of your own house or sucked into a sinkhole. For millions of years we have been the figurines of this precarious scene and we have found our own ways of dealing with the anxiety. It is natural that we create and consume prophecies, conspiracies and false predictions. Information technologies amplify not only our rational but also irrational deeds. Social media algorithms, tuned to maximize attention, make sure that misinformation spreads much faster than its counterpart.</p><p>What can we do to minimize the adverse effects of misinformation, especially in the case of earthquakes? One option could be to designate one authoritative institute, set up a big surveillance network and cancel or ban every source of misinformation before it spreads. This might have worked a few centuries ago but not in this day and age. Instead we propose a more inclusive option: embrace all voices and channel them into an actual, prospective earthquake prediction platform (Kamer et al. 2020). The platform is powered by a global state-of-the-art statistical earthquake forecasting model that provides near real-time earthquake occurrence probabilities anywhere on the globe (Nandan et al. 2020). Using this model as a benchmark in statistical metrics specifically tailored to the prediction problem, we are able to distill all these voices and quantify the essence of predictive skill. This approach has several advantages. Rather than trying to silence or denounce, we listen and evaluate each claim and report the predictive skill of the source. We engage the public and allow them to take part in a scientific experiment that will increase their risk awareness. We effectively demonstrate that anybody with an internet connected device can make an earthquake prediction, but that it is not so trivial to achieve skillful predictive performance.</p><p>Here we shall present initial results from our global earthquake prediction experiment that we have been conducting on www.richterx.com for the past two years, yielding more than 10,000 predictions. These results will hopefully demystify the act of predicting an earthquake in the eyes of the public, and next time someone forwards a prediction message it would arouse more scrutiny than panic or distaste.<br><br>Nandan, S., Kamer, Y., Ouillon, G., Hiemer, S., Sornette, D. (2020). <em>Global models for short-term earthquake forecasting and predictive skill assessment</em>. European Physical Journal ST. doi: 10.1140/epjst/e2020-000259-3<br>Kamer, Y., Nandan, S., Ouillon, G., Hiemer, S., Sornette, D. (2020). <em>Democratizing earthquake predictability research: introducing the RichterX platform.</em> European Physical Journal ST. doi: 10.1140/epjst/e2020-000260-2 </p>

scholarly journals Earthquake forecasting and its verification

2005 ◽  
Vol 12 (6) ◽  
pp. 965-977 ◽  
Author(s):  
J. R. Holliday ◽  
K. Z. Nanjo ◽  
K. F. Tiampo ◽  
J. B. Rundle ◽  
D. L. Turcotte
Keyword(s):  
Temporal Variations ◽  
Earthquake Forecasting ◽  
Earthquake Risk ◽  
Decision Threshold ◽  
New Approach ◽  
Pi Method ◽  
Time Prediction ◽  
Short Time ◽  
Large Earthquakes ◽  
Proven Method

Abstract. No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ("hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver) operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

scholarly journals Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model

2012 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
David Alan Rhoades ◽  
Paul G. Somerville ◽  
Felipe Dimer de Oliveira ◽  
Hong Kie Thio
Keyword(s):  
Long Range ◽  
Information Gain ◽  
Tectonic Setting ◽  
Depth Resolution ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Testing Period ◽  
Crustal Earthquakes ◽  
Average Information ◽  
Better Than

The Every Earthquake a Precursor According to Scale (EEPAS) long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are vertically separated. However, when depth resolution is ignored, the model formed by aggregating the optimal forecasts for each tectonic category performs no better than the raw EEPAS model.

The Establishment of an Operational Earthquake Forecasting System in Italy

2014 ◽  
Vol 85 (5) ◽  
pp. 961-969 ◽  
Author(s):  
W. Marzocchi ◽  
A. M. Lombardi ◽  
E. Casarotti
Keyword(s):  
Earthquake Forecasting ◽  
Forecasting System

scholarly journals Earthquake forecasting: statistics and information

2016 ◽  
Vol 58 (6) ◽  
Author(s):  
Vladimir Gertsik ◽  
Mark Kelbert ◽  
Anatoly Krichevets
Keyword(s):  
Confidence Intervals ◽  
Decision Rule ◽  
Random Fields ◽  
Axiomatic Approach ◽  
Earthquake Forecasting ◽  
Alarm System ◽  
Conditional Probabilities ◽  
Mathematical Basis ◽  
Strong Earthquakes ◽  
Point Estimates

<div class="abstract"><div class="abstract_item"><p>The paper presents a decision rule forming a mathematical basis of earthquake forecasting problem. We develop an axiomatic approach to earthquake forecasting in terms of multicomponent random fields on a lattice. This approach provides a method for constructing point estimates and confidence intervals for conditional probabilities of strong earthquakes under conditions on the levels of precursors. Also, it provides an approach for setting a multilevel alarm system and hypothesis testing for binary alarms. We use a method of comparison for different algorithms of earthquake forecasts in terms of the increase of Shannon information. ‘Forecasting’ (the calculation of the probabilities) and ‘prediction’ (the alarm declaring) of earthquakes are equivalent in this approach.</p></div></div>

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