A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning
The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquake...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923000872 |
| _version_ | 1797867592994521088 |
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| author | Hamzeh Mohammadigheymasi Nasrin Tavakolizadeh Luís Matias S. Mostafa Mousavi Yahya Moradichaloshtori Seyed Jalaleddin Mousavirad Rui Fernandes |
| author_facet | Hamzeh Mohammadigheymasi Nasrin Tavakolizadeh Luís Matias S. Mostafa Mousavi Yahya Moradichaloshtori Seyed Jalaleddin Mousavirad Rui Fernandes |
| author_sort | Hamzeh Mohammadigheymasi |
| collection | DOAJ |
| description | The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository [1]. |
| first_indexed | 2024-04-09T23:43:41Z |
| format | Article |
| id | doaj.art-5ccf7405eb3049fe9a847035c05252e8 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-09T23:43:41Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-5ccf7405eb3049fe9a847035c05252e82023-03-18T04:41:47ZengElsevierData in Brief2352-34092023-04-0147108969A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learningHamzeh Mohammadigheymasi0Nasrin Tavakolizadeh1Luís Matias2S. Mostafa Mousavi3Yahya Moradichaloshtori4Seyed Jalaleddin Mousavirad5Rui Fernandes6Instituto Dom Luiz (IDL), Universidade da Beira Interior, Covilha, 6201-001, Portugal; Corresponding author.Departamento de Informatica, Universidade da Beira Interior, Covilha, 6201-001, PortugalDepartment of Geophysics, Stanford University, Stanford, CA 94305-2215, United StatesInstituto Dom Luiz, Faculdade de Ciencias, Universidade de Lisboa, Lisboa, 1749-016, PortugalInstitute of Geophysics, University of Tehran, Tehran, 14359-44411, IranDepartamento de Informatica, Universidade da Beira Interior, Covilha, 6201-001, PortugalInstituto Dom Luiz (IDL), Universidade da Beira Interior, Covilha, 6201-001, PortugalThe Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository [1].http://www.sciencedirect.com/science/article/pii/S2352340923000872Earthquake waveformsDeep learningSeismic catalogLive Matlab figures |
| spellingShingle | Hamzeh Mohammadigheymasi Nasrin Tavakolizadeh Luís Matias S. Mostafa Mousavi Yahya Moradichaloshtori Seyed Jalaleddin Mousavirad Rui Fernandes A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning Data in Brief Earthquake waveforms Deep learning Seismic catalog Live Matlab figures |
| title | A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning |
| title_full | A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning |
| title_fullStr | A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning |
| title_full_unstemmed | A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning |
| title_short | A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning |
| title_sort | data set of earthquake bulletin and seismic waveforms for ghana obtained by deep learning |
| topic | Earthquake waveforms Deep learning Seismic catalog Live Matlab figures |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923000872 |
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