Probabilistic Prediction of the Next Earthquake in the NAFZ (North Anatolian Fault Zone), Turkey

Yılmaz, Veysel; Erişoğlu, Murat; Çelik, Hakan

doi:10.31671/dogus.2019.301

Cited by 13 publications

(8 citation statements)

References 7 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of candidate statistical distributions have been proposed for computation of conditional probabilistic of future earthquakes, including the Laplace or double exponential (Utsu [5]), Gamma (Utsu [6]), Lognormal (Nishenko and Buland [7]), and Weibul (Hagiwara [8]; Rikitak [9]) distributions. Veysel Yilmaz et al [10] obtained a reasonably good fit to a Weibull distribution for earthquake data. In this paper, the earthquake data which occurred in the area coordinated 23˚ -26˚ North latitudes and 89˚ -91˚ East longitudes between 1918-2007 years and whose magnitude equals 7 or higher were used.…”

Section: S Roy 109mentioning

confidence: 97%

See 1 more Smart Citation

Probabilistic Prediction for Earthquake in Bangladesh: Just How Big Does the Earthquake Have to Be Next Years?

Roy¹

2014

OJER

View full text Add to dashboard Cite

Earthquakes are more deadly than any other form of natural hazard. Because of this, scientists have been searching for accurate ways to predict earthquakes so that lives can be saved. Statistical analysis is useful methods of predicting earthquakes. They provide additional insights to the seismic hazard. In this work, the statistical analysis has been described through the study of "Region of Bangladesh". A sample result from the statistical analysis which gives intermediate term prediction of earthquakes is given. At the end of the study, the prediction of near future earthquakes is also investigated through the pictorial and tabular behaviors.

show abstract

Section: S Roy 109mentioning

confidence: 97%

“…But by 2007, 10 years had elapsed since the last severe earthquake, the one in 1997. Again according to Vevsel Yilmaz et al [10], in modeling earthquake data the most appropriate distribution was established as Weibull distribution. Henceforth, to determine earthquakes risk we also consider Weibull distribution.…”

Section: Statistical Analysis Of Earthquakementioning

confidence: 99%

Probabilistic Prediction for Earthquake in Bangladesh: Just How Big Does the Earthquake Have to Be Next Years?

Roy¹

2014

OJER

View full text Add to dashboard Cite

show abstract

“…(d) Log-logistic distribution [Yilmaz et al 2004, Yilmaz andCelik 2008] Loglogistic distribution is a probability distribution of random variables, if logarithm of the random variable is logistically distributed. The loglogistic distribution is generally used to model events that experience an initial increase, followed by a rate decrease.…”

Section: Probability Distribution Modelsmentioning

confidence: 99%

“…In the past, several statistical distri-bution models have been proposed for forecasting of future earthquakes including double exponential [Utsu 1972b], Gaussian [Rikitake 1974], Weibull [Hagiwara 1974, Rikitake 1974, Gamma [Utsu 1984] and Lognormal [Nishenko and Buland 1987]. This type of study has been carried by many researchers for different areas Ram 1997, 1999;Yilmaz et al 2004, Yilmaz and Celik 2008, Yadav et al 2008, Sil et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Zarola

Sil²

2017

Annals of Geophysics

View full text Add to dashboard Cite

The paper presents the probability of earthquake occurrences and forecasting of earthquake magnitudes size in northeast India, using four stochastic models (Gamma, Lognormal, Weibull and Log-logistic) and artificial neural networks, respectively considering updated earthquake catalogue of magnitude Mw ≥ 6.0 that occurred from year 1737 to 2015 in the study area. On the basis of past seismicity of the region, the conditional probabilities for the identified seismic source zones (12 sources) have been estimated using their best fit model and respective model parameters for various combinations of elapsed time (T) and time interval (t). It is observed that for elapsed time T=0 years, EBT and Kabaw zone shows highest conditional probability and it reaches 0.7 to 0.91 after about small time interval of 3-6 years (2014-2017; since last earthquake of Mw ≥ 6.0 occurred in the year 2011) for an earthquake magnitude Mw ≥ 6.0.Whereas, Sylhet zone shows lowest value of conditional probability among all twelve seismic source zones and it reaches 0.7 after about large time interval of 48 years (year 2045, since last event of Mw ≥ 6.0 occurred in the year 1999). While for elapsed time up to 2016 from the occurrence of the last earthquake of magnitude Mw ≥ 6.0, the MBT and MCT region shows highest conditional probability among all twelve seismic source zones and it reaches 0.88 to 0.91 after about 6-7 (2022-2023) years and in the same year (2022-2023) Sylhet zone shows lowest conditional probability and it reaches 0.14-0.17. However, we have proposed Artificial Neural Network (ANN) technique, which is based on feedforward backpropagation neural network model with single hidden layer to estimate the possible magnitude of future earthquake in the identified seismic source zones. For conditional probability of earthquake occurrence above 0.8, the neural network gives the probable magnitude of future earthquake as Mw 6.6 in Churachandpur-Mao fault (CMF) region in the years 2014 to 2017, Mw 6.8 in Myanmar Central Basin (MCB) region in the years 2013 and Kabaw region in the years 2015-2018, respectively. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in Churachandpur-Mao fault (CMF) region Myanmar Central Basin (MCB) region and EBT and Kabaw region, respectively and that are the identified seismic source zones in the study area which show that the ANN model yields good forecasting accuracy.

show abstract

“…One of the prediction methods conducted by the scientific community to predict the recurrence time of earthquake is probabilistic analysis. It has proved to be an appropriate method to predict the recurrence time of earthquake in several seismically active zones such as in Mexico, Turkey, Teheran, Italy, Taiwan and Los Angeles [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Java Earthquake Program (JEP) to Predict the Recurrence Time of Significant Earthquake in Indonesia

Muhammad¹,

Anggraini²

2013

IJCA

View full text Add to dashboard Cite

This paper developed the Java Earthquake Program (JEP) to predict the recurrence time of significant earthquake in Indonesia. The 59 events of significant earthquake in Indonesia were taken from the National Geophysical Data Center (NGDC) of United States. Five probabilistic models were used to represent the significant earthquake data in Indonesia. The models were tested by two goodness fit tests, Kolmogorov-Smirnov (K-S) and Chi-Square test. The goodness of fit tests showed that gamma distribution is an appropriate model to represent the significant earthquake data in Indonesia. The JEP result shows that the next earthquake will be occurred on 64 days after the last hit earthquake. With the 7 days of error, it was confirmed that the range period of the next significant earthquake in Indonesia was on January 26 to February 9, 2013. This work successfully proved the reliability of the use of JEP to predict the Indonesia significant earthquake.

show abstract

Probabilistic Prediction of the Next Earthquake in the NAFZ (North Anatolian Fault Zone), Turkey

Cited by 13 publications

References 7 publications

Probabilistic Prediction for Earthquake in Bangladesh: Just How Big Does the Earthquake Have to Be Next Years?

Probabilistic Prediction for Earthquake in Bangladesh: Just How Big Does the Earthquake Have to Be Next Years?

Artificial neural networks (ANN) and stochastic techniques to estimate earthquake occurrences in Northeast region of India

Java Earthquake Program (JEP) to Predict the Recurrence Time of Significant Earthquake in Indonesia

Contact Info

Product

Resources

About