Production of a homogeneous seismic catalog based on machine learning for northeast Egypt

This research presents a new approach which addresses the conversion of earthquake magnitude as a supervised machine-learning problem through a multistage approach. First, the moment magnitude (M w) calculations were extended to lower magnitude earthquakes using the spectral P-wave an...

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Autores principales:	Moustafa Sayed S. R., Mohamed Gad-Elkareem A., Metwaly Mohamed
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	regression source parameters ransac earthquake magnitude Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/306b0a3943234056b59abda6554f341c
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spelling	oai:doaj.org-article:306b0a3943234056b59abda6554f341c2021-12-05T14:10:49ZProduction of a homogeneous seismic catalog based on machine learning for northeast Egypt2391-544710.1515/geo-2020-0295https://doaj.org/article/306b0a3943234056b59abda6554f341c2021-09-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0295https://doaj.org/toc/2391-5447This research presents a new approach which addresses the conversion of earthquake magnitude as a supervised machine-learning problem through a multistage approach. First, the moment magnitude (M w) calculations were extended to lower magnitude earthquakes using the spectral P-wave analyses of the vertical component seismograms to improve the scaling relation of M w and the local magnitude (M L) of 138 earthquakes in northeastern Egypt. Second, using unsupervised clustering and regression analysis, we applied the k-means clustering technique to subdivide the mapped area into multiple seismic activity zones. This clustering phase created five spatially close seismic areas for training regression algorithms. Supervised regression analysis of each seismic area was simpler and more accurate. Conversion relations between M w and M L were calculated by linear regression, general orthogonal regression (GOR), and random sample consensus (RANSAC) regression techniques. RANSAC and GOR produced better results than linear regression, which provides evidence for the effects of outliers on regression accuracy. Moreover, the overall multistage hybrid approach produced substantial improvements in the measured-predicted dataset residuals when individual seismic zones rather than all datasets were considered. In 90% of the analyzed cases, M w values could be regarded as M L values within 0.2 magnitude units. Moreover, predicted magnitude conversion relations in the current study corresponded well to magnitude conversion relations in other seismogenic areas of Egypt.Moustafa Sayed S. R.Mohamed Gad-Elkareem A.Metwaly MohamedDe Gruyterarticleregressionsource parametersransacearthquake magnitudeGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 1084-1104 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	regression source parameters ransac earthquake magnitude Geology QE1-996.5
spellingShingle	regression source parameters ransac earthquake magnitude Geology QE1-996.5 Moustafa Sayed S. R. Mohamed Gad-Elkareem A. Metwaly Mohamed Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
description	This research presents a new approach which addresses the conversion of earthquake magnitude as a supervised machine-learning problem through a multistage approach. First, the moment magnitude (M w) calculations were extended to lower magnitude earthquakes using the spectral P-wave analyses of the vertical component seismograms to improve the scaling relation of M w and the local magnitude (M L) of 138 earthquakes in northeastern Egypt. Second, using unsupervised clustering and regression analysis, we applied the k-means clustering technique to subdivide the mapped area into multiple seismic activity zones. This clustering phase created five spatially close seismic areas for training regression algorithms. Supervised regression analysis of each seismic area was simpler and more accurate. Conversion relations between M w and M L were calculated by linear regression, general orthogonal regression (GOR), and random sample consensus (RANSAC) regression techniques. RANSAC and GOR produced better results than linear regression, which provides evidence for the effects of outliers on regression accuracy. Moreover, the overall multistage hybrid approach produced substantial improvements in the measured-predicted dataset residuals when individual seismic zones rather than all datasets were considered. In 90% of the analyzed cases, M w values could be regarded as M L values within 0.2 magnitude units. Moreover, predicted magnitude conversion relations in the current study corresponded well to magnitude conversion relations in other seismogenic areas of Egypt.
format	article
author	Moustafa Sayed S. R. Mohamed Gad-Elkareem A. Metwaly Mohamed
author_facet	Moustafa Sayed S. R. Mohamed Gad-Elkareem A. Metwaly Mohamed
author_sort	Moustafa Sayed S. R.
title	Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
title_short	Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
title_full	Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
title_fullStr	Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
title_full_unstemmed	Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
title_sort	production of a homogeneous seismic catalog based on machine learning for northeast egypt
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/306b0a3943234056b59abda6554f341c
work_keys_str_mv	AT moustafasayedsr productionofahomogeneousseismiccatalogbasedonmachinelearningfornortheastegypt AT mohamedgadelkareema productionofahomogeneousseismiccatalogbasedonmachinelearningfornortheastegypt AT metwalymohamed productionofahomogeneousseismiccatalogbasedonmachinelearningfornortheastegypt
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Production of a homogeneous seismic catalog based on machine learning for northeast Egypt

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