Finding a probability density function for time intervals between earthquake occurrences

Sadeghian, Ramin

doi:10.1007/s12517-018-3777-y

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…The result obtained for x indicates that the average x of the interest group (Projects to be analyzed) is above the average of the percentage of the population resulting from (2). This calculation was made with the mean and standard deviation of each population group to be compared against the curricular project [19][20][21][22][23][24][25][26]. The results of the percentage indicate that the average of the Students of the curricular Project is located above the results of that percentage of the population with each comparative curve.…”

Section: Methodsmentioning

confidence: 99%

Resultados 2013-2019 en las saber-pro: revisión para consolidar estrategias en la formación tecnológica

2020

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En el siguiente documento se realiza un análisis de resultados de las pruebas Saber T y T en el ciclo tecnológico y Saber Pro en el ciclo de Ingeniería, con datos emanados por el Instituto Colombiano Para la Evaluación de la Educación (Icfes), de los estudiantes de Tecnología en Electrónica, Ingeniería en Control e Ingeniería en Telecomunicaciones de la Universidad Distrital Francisco José de Caldas, durante el periodo de 2013-2019. Inicialmente se extrae la información de resultados publicada para cada cohorte de Estudiantes durante el intervalo de tiempo descrito anteriormente. Posteriormente con la ayuda de herramientas estadísticas se realiza un balance en cuanto al desempeño en las diferentes competencias genéricas y específicas, que permite comparar los desempeños en el tiempo respecto al programa, a los programas afines que constituyen un grupo de referencia, a los programas de la Institución y a la población nacional. En la parte final se hace un análisis de las tendencias obtenidas, con el cual se resaltan los desempeños altos, identifican las oportunidades de mejora y se proponen estrategias que buscan incrementar la calidad de la formación desde el nivel tecnológico de los estudiantes de estas carreras.

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Section: Methodsmentioning

confidence: 99%

Resultados 2013-2019 en las saber-pro: revisión para consolidar estrategias en la formación tecnológica

2020

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“…Some scholars have focused on the exploration of magnitude time series [3][4][5]. There have been studies on the time interval sequence of earthquake occurrence, most of which have analyzed the probability distribution of the earthquake time interval [6][7][8][9][10]. There have also been studies on the prediction of time intervals.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of the SARIMA Model for a Time Interval of Earthquakes in the Longmenshan Fault Zone

Yuan¹,

Huang²,

Qiuyin³

et al. 2023

Natural Hazards - New Insights

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Based on the catalog data of earthquakes with Ms ≥ 2.5 in the Longmenshan fault zone from January 2012 to September 2021, we establish an earthquake time interval series grouped by earthquake magnitude and then use the SARIMA model to predict the series in different periods. By analyzing the fitting effect of the models, the optimal model parameters of different magnitude sequences and the corresponding period values are obtained. Among them, the adjusted R2 values of each model with Ms ≥ 2.5 and Ms ≥ 3.0 sequences are more than 0.86, up to 0.911; the short-time prediction effects are good, and the values of predicted RMSE are 10.686 and 8.800. The prediction results of the models show that the overall trend of the subsequent earthquake time interval in the Longmenshan fault zone is stable, and the prediction results of the Ms ≥ 3.0 sequence have a weak fluctuating growth trend; that is, the number of earthquakes with the Ms ≥ 3.0 in this area will decrease slightly, and the seismicity will decrease in a period of time. The analysis results and method can provide a scientific basis for earthquake risk management and a feasible way to predict earthquake occurrence times.

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“…Many researchers studied the field of earthquake. Sadeghian and Jalali-Naini [4] found a new probability density function (PDF) for forecasting the time of earthquake occurrence. Rusnardi et al [5] constructed area earthquake source model and estimated the frequency magnitude relationship by using the catalogs compiled.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

Barakbah¹,

Harsono²,

Sudarsono³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability.

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Finding a probability density function for time intervals between earthquake occurrences

Cited by 4 publications

References 37 publications

Resultados 2013-2019 en las saber-pro: revisión para consolidar estrategias en la formación tecnológica

Resultados 2013-2019 en las saber-pro: revisión para consolidar estrategias en la formación tecnológica

Analysis and Prediction of the SARIMA Model for a Time Interval of Earthquakes in the Longmenshan Fault Zone

Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

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