A Comprehensive Earthquake Catalog for Iraq in Terms of Moment Magnitude

Onur, Tuna; Gök, R.; Abdulnaby, Wathiq; Mahdi, Hanan; Numan, Nazar M. S.; Al‐Shukri, Haydar; Shakir, Ammar M.; Chlaib, Hussein Khalefa; Ameen, Taher H.; Najah, A.

doi:10.1785/0220160078

Cited by 20 publications

(4 citation statements)

References 31 publications

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“…Measurable speculation testing strategies and performing machine learning approaches, to be specific polynomial calculated relapse and bolster Vector Machine (SVM) for seismic tremor information, may be utilized in classification and relapse investigation to decide the likelihood of a seismic tremor (Onur et al, 2017).…”

Section: Earthquake Predictionmentioning

confidence: 99%

A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Jarah,

Alasadi,

Hashim

2024

Journal of Computer Science

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Seismic tremors are among the foremost perilous normal fiascos individuals confront due to their event without earlier caution and their effect on their lives and properties. In expansion, to consider future disaster prevention measures for major earthquakes, it is necessary to predict earthquakes using Neural Networks (NN). A machine learning technique has developed a technology to predict earthquakes from ground controller data by measuring ground vibration and transmitting data by a sensor network. Devices to process this data and record it in a catalog of seismic data from 1900-2019 for Iraq and neighboring regions, then divide this data into 80% training data and 20% test data. It gave better results than other prediction algorithms, where the NN model performs better Seismic prediction than other machine learning methods.

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Section: Earthquake Predictionmentioning

confidence: 99%

A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Jarah,

Alasadi,

Hashim

2024

Journal of Computer Science

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“…The tectonic activity was recorded after 2014 by relying on seismic monitoring stations, and to this day, which is spread in most governorates of Iraq [15], where three sensor networks represent these stations: -…”

Section: Seismic Data In Iraq 61 Seismic Monitoring Stationsmentioning

confidence: 99%

Earthquake prediction in Iraq using machine learning techniques

Jarah

Hashim

Alasadi

2022

IJEECS

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This study deals with addressing the scientific achievements and the history of earthquake prediction in Iraq, in addition to attempting to discuss the possibility of machine learning to predict earthquakes from a theoretical perspective. The idea of predicting earthquakes gives at least a little time to protect people and reduce earthquake damage. In Iraq, we notice an increase in the occurrence of earthquakes, especially in the southern regions, where they form a strange phenomenon because they are plain areas and far from the seismic fault line, due to the errors that accompany excessive oil extraction and in random and unstudied ways, and geological studies raise fears in predicting an increase in earthquakes for the coming years. We have explored the possibility of applying machine learning technology to predict earthquakes in Iraq, and follow-up recording of tremors at different stations in Iraq through three centers of seismic sensor networks. In addition to the earthquake catalog in Iraq (1900-2019). This study may pave the way for more research to develop an integrated and accurate earthquake prediction system using machine-learning technologies.

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“…The effect of the tectonic and structural processes will possibly pass on different densities of dendritic drainage patterns in the same fold and semi-identical rock hardness. The region undergoes intense earthquakes of varying magnitudes due to the pressure built up between the colliding of the Arabian plate with the Eurasian plate (Jackson, et al, 1981;Hessami and Jamali, 2006;Onur, et al, 2017). Due to rock hardness differences and high folding, the region is exposed to different weathering grades, badlands, and topographical conditions, which create densely dendritic drainage patterns (Sissakian, 2013).…”

Section: Introductionmentioning

confidence: 99%

Three Dimensional Terrain Modeling for Tectonic Geomorphology of Chinara Anticline, Northern Iraq

Thannoun

Al-Jawadi

Adeeb

2021

IGJ

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Analysis of the terrain using three-dimensional models offers a deep insight view of ground surface topography and terrain representation. The Chinara anticline is one of the main structures of NW-SE trends for the highly folded zone in northeastern Iraq. The objective of this study is to understand the interrelationship between topography and morphotectonic features using three-dimensional models. This research employed fourth generates principal raster derivative products from the DEM using ArcGIS. To understand the undulating of this anticline with the morphotectonic style, the adaptive equation has been suggested to determine the direction and amount of the main tectonic forces, which can be applied to other undulated anticlines. The values of northeastern and southwestern limbs undulating index UI are 11.7 and 7.8 respectively that indicates the strong tectonic force towards the northeast. Two listric faults have been conducted via the field survey that confirmed by remotely sensed interpretation and DEM products. These listric faults had an intensive impact in comparison with concluded strike-slip faults, and then the Chinara anticline would be less structural undulating in a region of vicinity syncline to Perat undulation. The morphotectonic landscapes reveal that the listric fault has branched into two parts, the first one extending to form the anticline and the other comprises the structural dilemma.

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A Comprehensive Earthquake Catalog for Iraq in Terms of Moment Magnitude

Cited by 20 publications

References 31 publications

A New Algorithm for Earthquake Prediction Using Machine Learning Methods

A New Algorithm for Earthquake Prediction Using Machine Learning Methods

Earthquake prediction in Iraq using machine learning techniques

Three Dimensional Terrain Modeling for Tectonic Geomorphology of Chinara Anticline, Northern Iraq

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