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Related papers: Earthquake Prediction: Probabilistic Aspect

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Recent studies in the literature have introduced a new approach to earthquake forecasting based on representing the space-time patterns of localized seismicity by a time-dependent system state vector in a real-valued Hilbert space and…

We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert…

Applications · Statistics 2019-05-28 P. Proskura , A. Zaytsev , I. Braslavsky , E. Egorov , E. Burnaev

We study the spatio-temporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatio-temporal prediction is extensively studied in Machine Learning literature due to its critical real-life applications such…

Machine Learning · Statistics 2021-03-17 Oguzhan Karaahmetoglu , Suleyman S. Kozat

Recently, attempts have been made to take into account the fractal properties of seismicity when mapping the long-term rate of earthquakes. The paper touches upon the theoretical aspects of fractality and provides a critical analysis of its…

Geophysics · Physics 2019-05-08 G. M. Molchan

We propose a new method to test the effectiveness of a spatial point process forecast based on a log-likelihood score for predicted point density and the information gain for events that actually occurred in the test period. The method…

Data Analysis, Statistics and Probability · Physics 2010-11-24 Yan Y. Kagan

We study the predictability of large events in self-organizing systems. We focus on a set of models which have been studied as analogs of earthquake faults and fault systems, and apply methods based on techniques which are of current…

Condensed Matter · Physics 2009-10-22 S. L. Pepke , J. M. Carlson

This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties…

Geophysics · Physics 2014-08-26 Didier Sornette , Ivan Osorio

Estimates of seismic wave speeds in the Earth (seismic velocity models) are key input parameters to earthquake simulations for ground motion prediction. Owing to the non-uniqueness of the seismic inverse problem, typically many velocity…

A prominent feature of earthquakes is their empirical laws including memory (clustering) in time and space. Several earthquake forecasting models, like the EpidemicType Aftershock Sequence (ETAS) model, were developed based on earthquake…

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to…

We review the present status of our research and understanding regarding the dynamics and the statistical properties of earthquakes, mainly from a statistical physical viewpoint. Emphasis is put both on the physics of friction and fracture,…

Statistical Mechanics · Physics 2012-05-22 Hikaru Kawamura , Takahiro Hatano , Naoyuki Kato , Soumyajyoti Biswas , Bikas K. Chakrabarti

Extending the central concept of recurrence times for a point process to recurrent events in space-time allows us to characterize seismicity as a record breaking process using only spatiotemporal relations among events. Linking record…

Geophysics · Physics 2015-06-26 Joern Davidsen , Peter Grassberger , Maya Paczuski

Immediately following a disaster event, such as an earthquake, estimates of the damage extent play a key role in informing the coordination of response and recovery efforts. We develop a novel impact estimation tool that leverages a…

Applications · Statistics 2025-01-15 Max Anderson Loake , Hamish Patten , David Steinsaltz

Endurance time method is a time history dynamic analysis in which structures are subjected to predesigned intensifying excitations. This method provides a tool for response prediction that correlates structural responses to the intensity of…

Computational Engineering, Finance, and Science · Computer Science 2020-08-27 Homayoon E. Estekanchi , Mohammadreza Mashayekhi , Hassan Vafai , Goodarz Ahmadi , S. Ali Mirfarhadi , Mojtaba Harati

The ranking problem of earthquake forecasts is considered. We formulate simple statistical requirements to forecasting quality measure R and analyze some R-ranking methods on this basis, in particular, the pari-mutuel gambling method by…

Geophysics · Physics 2016-04-21 G. Molchan

Forecasting fault failure is a fundamental but elusive goal in earthquake science. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with…

We develop and implement a new type of global earthquake forecast. Our forecast is a perturbation on a smoothed seismicity (Relative Intensity) spatial forecast combined with a temporal time-averaged (Poisson) forecast. A variety of…

Geophysics · Physics 2013-07-23 James R Holliday , William R Graves , John B Rundle , Donald L Turcotte

Testing earthquake forecasts is essential to obtain scientific information on forecasting models and sufficient credibility for societal usage. We aim at enhancing the testing phase proposed by the Collaboratory for the Study of Earthquake…

Applications · Statistics 2026-02-03 Jonas R. Brehmer , Kristof Kraus , Tilmann Gneiting , Marcus Herrmann , Warner Marzocchi

The successful prediction of earthquakes is one of the holy grails in Earth Sciences. Traditional predictions use statistical information on recurrence intervals, but those predictions are not accurate enough. In a recent paper, a machine…

Geophysics · Physics 2020-11-16 Silke van Klaveren , Ivan Vasconcelos , Andre Niemeijer

Simple models for ruptures along a heterogeneous earthquake fault zone are studied, focussing on the interplay between the roles of disorder and dynamical effects. A class of models are found to operate naturally at a critical point whose…

Disordered Systems and Neural Networks · Physics 2009-10-30 Daniel S. Fisher , Karin Dahmen , Sharad Ramanathan , Yehuda Ben-Zion