Preface to the Global Earthquake Forecasting System (GEFS) special issue: Towards using non-seismic precursors for the prediction of large earthquakes

Abstract We conclude this special issue on the Global Earthquake Forecasting System (GEFS) by briefly reviewing and analyzing the claims of non-seismic precursors made in the present volume, and by reflecting on the current limitations and future directions to take. We find that most studies presented in this special volume, taken individually, do not provide strong enough evidence of non-seismic precursors to large earthquakes. The majority of the presented results are hampered by the fact that the task at hand is susceptible to potential biases in data selection and possible overfitting. The most encouraging results are obtained for ground-based geoelectric signals, although the probability gain is likely small compared to an earthquake clustering baseline. The only systematic search on satellite data available so far, those of the DEMETER mission, did not find a robust precursory pattern. The conclusion that we can draw is that the overall absence of convincing evidence is likely due to a deficit in systematically applying robust statistical methods and in integrating scientific knowledge of different fields. Most authors are specialists of their field while the study of earthquake precursors requires a system approach combined with the knowledge of many specific characteristics of seismicity. Relating non-seismic precursors to earthquakes remains a challenging multidisciplinary field of investigation. The plausibility of these precursors predicted by models of lithosphere-atmosphere-ionosphere coupling, together with the suggestive evidence collected here, call for further investigations. The primary goal of the GEFS is thus to build a global database of candidate signals, which could potentially improve earthquake predictability (if the weak signals observed are real and false positives sufficiently uncorrelated between different data sources). Such a stacking of disparate and voluminous data will require big data storage and machine learning pipelines, which has become feasible only recently. This special issue compiled an eclectic list of non-seismic precursor candidates, which is in itself a valuable source of information for seismologists, geophysicists and other scientists who may not be familiar with such types of investigations. It also forms the foundation for a coherent, multi-disciplinary collaboration on earthquake prediction.

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Earthquake forecasting and its verification

Nonlinear Processes in Geophysics ◽

10.5194/npg-12-965-2005 ◽

2005 ◽

Vol 12 (6) ◽

pp. 965-977 ◽

Cited By ~ 75

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.

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The Establishment of an Operational Earthquake Forecasting System in Italy

Seismological Research Letters ◽

10.1785/0220130219 ◽

2014 ◽

Vol 85 (5) ◽

pp. 961-969 ◽

Cited By ~ 29

Author(s):

W. Marzocchi ◽

A. M. Lombardi ◽

E. Casarotti

Keyword(s):

Earthquake Forecasting ◽

Forecasting System

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EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-823-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 823-827 ◽

Cited By ~ 8

Author(s):

A. Bhatia ◽

S. Pasari ◽

A. Mehta

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Network Architecture ◽

Short Term Memory ◽

Economic Loss ◽

Earthquake Forecasting ◽

Accuracy Evaluation ◽

Neural Network Architecture ◽

Forecasting System ◽

Artificial Neural

Abstract. Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences.

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Geo-Chemical Techniques for Earthquake Forecasting in Nigeria

Asian Journal of Geographical Research ◽

10.9734/ajgr/2021/v4i394 ◽

2021 ◽

pp. 29-45

Author(s):

Lungfa Collins Wuyep ◽

Umar Afegbua Kadiri ◽

Isogun Adeyemi Monday ◽

Nanshin Emmanuel Nansak ◽

Lumi Zakka ◽

...

Keyword(s):

Continuous Monitoring ◽

Physical Mechanism ◽

Earthquake Forecasting ◽

Theoretical Knowledge ◽

Seismic Record ◽

Earthquake Forecast ◽

Forecasting System ◽

Chemical Techniques ◽

Precursory Phenomena ◽

The Relationship

Regardless of the doubt caused by some rounds on the impossibility of earthquake forecast, more and more countries, even at the highest governmental levels, realize that doing nothing is the ostrich position of dread before the real difficulties associated with the creation of a real forecasting system. Nigeria in times past was believed to be aseismic. However, the seismic record of Nigeria from 1933-2021 have demonstrated in contrast to the idea, numerous quakes have been recorded in Nigeria throughout the years. With the development of observation techniques and theoretical knowledge of geochemistry, geochemical observation of faults gas has become a hotspot once more in recent years. Rn, Hg, H2, etc., are used for geochemical observations. 222Rn has a half-life of 3.825 days, a magnitude 5.0 earthquake will be detected through precursory phenomena at a distance not greater than 142 km. Mercury and other elements are used as important detectors for earthquake prediction and they play an important role in revealing the relationship between fluid in the fault zone and the occurrence of earthquakes, the range for a magnitude 5.0 earthquake is limited to 200 km. Hydrogen concentrations have been monitored for precursory variations in many fault systems, using either discrete sampling and laboratory analysis or continuous monitoring of ground gas, using hydrogen-sensitive fuel cells. Precursory changes in groundwater chemistry are often attributed to the mixing of fluids from two or more chemically distinct aquifers, the physical mechanism responsible for the mixing of fluids is, however, not well established.

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b-value of what? Complex behavior of the magnitude distribution during and within the 2016–2017 central Italy sequence

10.21203/rs.3.rs-1210699/v1 ◽

2022 ◽

Author(s):

Marcus Herrmann ◽

Ester Piegari ◽

Warner Marzocchi

Keyword(s):

Relative Rate ◽

Central Italy ◽

B Value ◽

Earthquake Forecasting ◽

Earthquake Catalog ◽

Spatiotemporal Variations ◽

Earthquake Occurrence ◽

Tectonic Structures ◽

Magnitude Distribution ◽

Large Earthquakes

Abstract The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present unexpected MFD variability using a high-resolution earthquake catalog of the 2016–2017 central Italy sequence. Isolation of seismicity clusters reveals that the MFD differs in nearby clusters, varies or remains constant in time depending on the cluster, and features an unexpected b-value increase in the cluster where the largest event will occur. These findings suggest a strong influence of the heterogeneity and complexity of tectonic structures on the MFD. Our findings raise the question of the appropriate spatiotemporal scale for resolving the b-value, which poses a serious obstacle to interpreting and using the MFD in earthquake forecasting.

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Identification of seismic precursors before large earthquakes: Decelerating and accelerating seismic patterns

Journal of Geophysical Research Atmospheres ◽

10.1029/2007jb005112 ◽

2008 ◽

Vol 113 (B4) ◽

Cited By ~ 12

Author(s):

Panayotis Papadimitriou

Keyword(s):

Seismic Precursors ◽

Large Earthquakes

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Numerical Modeling of Coseismic Tropospheric Disturbances Arising from the Unstable Acoustic Gravity Wave Energetics

Atmosphere ◽

10.3390/atmos12060765 ◽

2021 ◽

Vol 12 (6) ◽

pp. 765

Author(s):

Esfhan A. Kherani ◽

Saul A. Sanchez ◽

Eurico R. de Paula

Keyword(s):

Numerical Modeling ◽

Gravity Waves ◽

Earthquake Forecasting ◽

Relative Role ◽

Acoustic Gravity Wave ◽

Strong Earthquakes ◽

Epicentral Distribution ◽

Large Earthquakes ◽

Origin Mechanism

Numerous recent studies report the Coseismic Tropospheric Disturbances (CTDs) during large earthquakes. Their presence suggests the importance of atmospheric seismology in a possible earthquake forecasting scenario. The origin mechanism and associated energetics of CTDs are not well understood though the observations associate them with the atmospheric waves. We present the numerical modeling of coupled dynamics of Gravity waves (GWs) and convective instability (CI) in the dry troposphere that produces the CTDs, in the form of pressure disturbances, of observed magnitudes. The study reveals the altitude and epicentral distribution of CTDs and elaborates the relative role of GWs and CI in the generation and intensification of CTDs. The study finds that mega and strong earthquakes disturb the troposphere to a similar level as the severe meteorological weather.

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Parametric time series analysis of geoelectrical signals: an application to earthquake forecasting in Southern Italy

Annals of Geophysics ◽

10.4401/ag-3947 ◽

1996 ◽

Vol 39 (1) ◽

Author(s):

G. Di Bello ◽

V. Lapenna ◽

M. Macchiato ◽

C. Satriano ◽

C. Serio ◽

...

Keyword(s):

Time Series ◽

Autoregressive Model ◽

Southern Italy ◽

Earthquake Forecasting ◽

Seismic Events ◽

Statistical Methodology ◽

Climatic Effects ◽

Seismic Precursors ◽

Automatic Station ◽

Filtering Procedure

An autoregressive model was selected to describe geoelectrical time series. An objective technique was subsequently applied to analyze and discriminate values above (below) an a priorifixed threshold possibly related to seismic events. A complete check of the model and the main guidelines to estimate the occurrence probability of extreme events are reported. A first application of the proposed technique is discussed through the analysis of the experimental data recorded by an automatic station located in Tito, a small town on the Apennine chain in Southern Italy. This region was hit by the November 1980 Irpinia-Basilicata earthquake and it is one of most active areas of the Mediterranean region. After a preliminary filtering procedure to reduce the influence of external parameters (i.e. the meteo-climatic effects), it was demonstrated that the geoelectrical residual time series are well described by means of a second order autoregressive model. Our findings outline a statistical methodology to evaluate the efficiency of electrical seismic precursors.

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The prognostic value of foreshocks - a statistical reevaluation

10.5194/egusphere-egu21-8691 ◽

2021 ◽

Author(s):

Ester Manganiello ◽

Marcus Herrmann ◽

Warner Marzocchi

Keyword(s):

Physical Process ◽

Large Earthquake ◽

Aftershock Sequence ◽

Earthquake Forecasting ◽

Earthquake Triggering ◽

Epidemic Type Aftershock Sequence ◽

Sequence Selection ◽

Short Time ◽

Scientific Questions ◽

Large Earthquakes

The ability to forecast large earthquakes on short time scales is strongly limited by our understanding of the earthquake nucleation process. Foreshocks represent promising seismic signals that may improve earthquake forecasting as they precede many large earthquakes. However, foreshocks can currently only be identified as such after a large earthquake occurred. This inability is because it remains unclear whether foreshocks represent a different physical process than general seismicity (i.e., mainshocks and aftershocks). Several studies compared foreshock occurrence in real and synthetic catalogs, as simulated with a well-established earthquake triggering/forecasting model called Epidemic-Type Aftershock Sequence (ETAS) that does not discriminate between foreshocks, mainshocks, and aftershocks. Some of these studies show that the spatial distribution of foreshocks encodes information about the subsequent mainshock magnitude and that foreshock activity is significantly higher than predicted by the ETAS model. These findings attribute a unique underlying physical process to foreshocks, making them potentially useful for forecasting large earthquakes. We reinvestigate these scientific questions using high-quality earthquake catalogs and study carefully the influence of subjective parameter choices and catalog artifacts on the results. For instance, we use data from different regions, account for the short-term catalog incompleteness and its spatial variability, and explore different criteria for sequence selection and foreshock definition.

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