Comment on “Potential short‐term earthquake forecasting by farm animal monitoring” by Wikelski, Mueller, Scocco, Catorci, Desinov, Belyaev, Keim, Pohlmeier, Fechteler, and Mai

AbstractWhether changes in animal behavior allow for short-term earthquake predictions has been debated for a long time. During the 2016/2017 earthquake sequence in Italy, we instrumentally observed the activity of farm animals (cows, dogs, sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct-Nov 2016) and over a subsequent longer observation period (Jan-Apr 2017). Relating 5304 (in 2016) and 12948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a stable, but not during their time on a pasture. We detect these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1-20hrs) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous instrumental monitoring of animal collectives has the potential to provide statistically reliable patterns of pre-seismic activity that could allow for short-term earthquake forecasting.One Sentence SummaryA collective of domestic animals repeatedly showed unusually high activity levels before earthquakes, with anticipation times (1-20h) negatively related to distance from epicenters (5-28km).

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Short-term earthquake forecasting experiment before and during the L’Aquila (central Italy) seismic sequence of April 2009

Annals of Geophysics ◽

10.4401/ag-6583 ◽

2015 ◽

Vol 57 (6) ◽

Author(s):

Maura Murru ◽

Jiancang Zhuang ◽

Rodolfo Console ◽

Giuseppe Falcone

Keyword(s):

Central Italy ◽

Aftershock Sequence ◽

Earthquake Forecasting ◽

Model Parameters ◽

Short Term ◽

Productivity Function ◽

Epidemic Type Aftershock Sequence ◽

Forecasting Performance ◽

The One ◽

Lower Magnitude

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In this paper, we compare the forecasting performance of several statistical models, which are used to describe the occurrence process of earthquakes in forecasting the short-term earthquake probabilities during the L’Aquila earthquake sequence in central Italy in 2009. These models include the Proximity to Past Earthquakes (PPE) model and two versions of the Epidemic Type Aftershock Sequence (ETAS) model. We used the information gains corresponding to the Poisson and binomial scores to evaluate the performance of these models. It is shown that both ETAS models work better than the PPE model. However, in comparing the two types of ETAS models, the one with the same fixed exponent coefficient (<span>alpha)</span> = 2.3 for both the productivity function and the scaling factor in the spatial response function (ETAS I), performs better in forecasting the active aftershock sequence than the model with different exponent coefficients (ETAS II), when the Poisson score is adopted. ETAS II performs better when a lower magnitude threshold of 2.0 and the binomial score are used. The reason is found to be that the catalog does not have an event of similar magnitude to the L’Aquila mainshock (M<sub>w</sub> 6.3) in the training period (April 16, 2005 to March 15, 2009), and the (<span>alpha)</span>-value is underestimated, thus the forecast seismicity is underestimated when the productivity function is extrapolated to high magnitudes. We also investigate the effect of the inclusion of small events in forecasting larger events. These results suggest that the training catalog used for estimating the model parameters should include earthquakes of magnitudes similar to the mainshock when forecasting seismicity during an aftershock sequence.</p></div></div></div>

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Some Challenges of Short-Term Earthquake Forecasting and Possible Solutions

Doklady Earth Sciences ◽

10.1134/s1028334x20120041 ◽

2020 ◽

Vol 495 (2) ◽

pp. 910-913

Author(s):

V. G. Bondur ◽

M. B. Gokhberg ◽

I. A. Garagash ◽

D. A. Alekseev

Keyword(s):

Earthquake Forecasting ◽

Short Term

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Integration of geological and seismological data in earthquakes occurrence models for Italy: towards a unified model for different forecast perspectives

Geophysical Journal International ◽

10.1093/gji/ggz419 ◽

2019 ◽

Vol 219 (3) ◽

pp. 2148-2164

Author(s):

A M Lombardi

Keyword(s):

Aftershock Sequence ◽

Earthquake Forecasting ◽

Short Term ◽

Background Rate ◽

Etas Model ◽

Term Forecast ◽

Earthquake Predictability ◽

Background Seismicity ◽

Different Types

SUMMARY The operational earthquake forecasting (OEF) is a procedure aimed at informing communities on how seismic hazard changes with time. This can help them live with seismicity and mitigate risk of destructive earthquakes. A successful short-term prediction scheme is not yet produced, but the search for it should not be abandoned. This requires more research on seismogenetic processes and, specifically, inclusion of any information about earthquakes in models, to improve forecast of future events, at any spatio-temporal-magnitude scale. The short- and long-term forecast perspectives of earthquake occurrence followed, up to now, separate paths, involving different data and peculiar models. But actually they are not so different and have common features, being parts of the same physical process. Research on earthquake predictability can help to search for a common path in different forecast perspectives. This study aims to improve the modelling of long-term features of seismicity inside the epidemic type aftershock sequence (ETAS) model, largely used for short-term forecast and OEF procedures. Specifically, a more comprehensive estimation of background seismicity rate inside the ETAS model is attempted, by merging different types of data (seismological instrumental, historical, geological), such that information on faults and on long-term seismicity integrates instrumental data, on which the ETAS models are generally set up. The main finding is that long-term historical seismicity and geological fault data improve the pseudo-prospective forecasts of independent seismicity. The study is divided in three parts. The first consists in models formulation and parameter estimation on recent seismicity of Italy. Specifically, two versions of ETAS model are compared: a ‘standard’, previously published, formulation, only based on instrumental seismicity, and a new version, integrating different types of data for background seismicity estimation. Secondly, a pseudo-prospective test is performed on independent seismicity, both to test the reliability of formulated models and to compare them, in order to identify the best version. Finally, a prospective forecast is made, to point out differences and similarities in predicting future seismicity between two models. This study must be considered in the context of its limitations; anyway, it proves, beyond argument, the usefulness of a more sophisticated estimation of background rate, inside short-term modelling of earthquakes.

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Mixture Models for Improved Short-Term Earthquake Forecasting

Bulletin of the Seismological Society of America ◽

10.1785/0120080063 ◽

2009 ◽

Vol 99 (2A) ◽

pp. 636-646 ◽

Cited By ~ 22

Author(s):

D. A. Rhoades ◽

M. C. Gerstenberger

Keyword(s):

Mixture Models ◽

Earthquake Forecasting ◽

Short Term

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Earthquake trend prediction using long short-term memory RNN

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i2.pp1304-1312 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1304 ◽

Cited By ~ 3

Author(s):

Tanvi Bhandarkar ◽

Vardaan K ◽

Nikhil Satish ◽

S. Sridhar ◽

R. Sivakumar ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Future Trend ◽

Earthquake Forecasting ◽

Short Term ◽

Feed Forward Neural Network ◽

Trend Prediction ◽

Term Memory ◽

Area Of Interest ◽

Long Short Term Memory

<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>

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Analysis of Pre-earthquake Signals Using ANN: Implication for Short-Term Earthquake Forecasting

Lecture Notes in Electrical Engineering - Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication ◽

10.1007/978-981-16-0289-4_45 ◽

2021 ◽

pp. 619-630

Author(s):

Ramya Jeyaraman ◽

M. Senthil Kumar ◽

N. Venkatanathan

Keyword(s):

Earthquake Forecasting ◽

Short Term

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Earthquake forecasting, public policy and earthquake forecasting

Bulletin of the New Zealand Society for Earthquake Engineering ◽

10.5459/bnzsee.12.4.328-333 ◽

1979 ◽

Vol 12 (4) ◽

pp. 328-333

Author(s):

G. J. Lensen

Keyword(s):

Public Policy ◽

Public Awareness ◽

Earthquake Forecasting ◽

Short Term ◽

The Public ◽

Prone Area ◽

Socio Economic Impact ◽

Civil Defence ◽

Precursory Phenomena

The geodetic, geophysical and seismological approaches to earthquake forecasting are summarised and illustrated with examples of precursory phenomena, which can be categorised into long term, intermediate term, short term and imminent. The results from at least two, but preferably more, disciplines are needed to reliably identify a precursory phase before a forecast can be made. The socio-economic impact of earthquake forecasting on business and the community is discussed and it is recommended that for at least the present such forecasts should not be made public. Instead Civil Defence and related exercises should be held in several areas including the "prone" area, in a realistic manner, ensure that manpower is organised, equipment etc. upgraded, and public awareness sharpened in order to reduce the imminent effects on safety both in public and at home. Once the public has gained confidence, not only in the reliability of such forecasts, but more specifically confidence in the authorities concerned, and once their preparedness has become an accepted fact, only then will a public forecasting have a beneficial effect.

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