{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:41:51Z","timestamp":1742996511488,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453151"},{"type":"electronic","value":"9783031453168"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-45316-8_5","type":"book-chapter","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:03:14Z","timestamp":1699228994000},"page":"51-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classical Artificial Neural Networks and\u00a0Seismology, Basic Steps for\u00a0Training Process"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7895-802X","authenticated-orcid":false,"given":"Israel","family":"Reyes-Ram\u00edrez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6854-6796","authenticated-orcid":false,"given":"Eric G\u00f3mez","family":"Serrano","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0220-2500","authenticated-orcid":false,"given":"Octavio Sebasti\u00e1n Hern\u00e1ndez","family":"P\u00e9rez-Riveroll","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1627-0323","authenticated-orcid":false,"given":"\u00c1lvaro Anzueto","family":"R\u00edos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3309-5782","authenticated-orcid":false,"given":"Jorge Fonseca","family":"Campos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"issue":"11","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/PROC.1977.10770","volume":"65","author":"J Allen","year":"1977","unstructured":"Allen, J., Rabiner, L.: A unified approach to short-time Fourier analysis and synthesis. Proc. IEEE 65(11), 1558\u20131564 (1977). https:\/\/doi.org\/10.1109\/PROC.1977.10770","journal-title":"Proc. IEEE"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Arrowsmith, S.J., Trugman, D.T., MacCarthy, J., Bergen, K.J., Lumley, D., Magnani, M.B.: Big data seismology. Rev. Geophys. 60(2), e2021RG000769 (2022)","DOI":"10.1029\/2021RG000769"},{"key":"5_CR3","unstructured":"Berzal, F.: Redes neuronales & deep learning: Volumen I. Independently published (2018)"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Chen, C.H., Lin, P.H., Hsieh, J.G., Cheng, S.L., Jeng, J.H.: Robust multi-class classification using linearly scored categorical cross-entropy. In: 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII), pp. 200\u2013203 (2020). https:\/\/doi.org\/10.1109\/ICKII50300.2020.9318835","DOI":"10.1109\/ICKII50300.2020.9318835"},{"key":"5_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-90-481-8697-6","volume-title":"Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software","author":"J Havskov","year":"2010","unstructured":"Havskov, J., Ottemoller, L.: Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software. Springer, Dordrecht (2010). https:\/\/doi.org\/10.1007\/978-90-481-8697-6"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR7","unstructured":"IBM Cloud Education: Neural networks (2020). https:\/\/www.ibm.com\/cloud\/learn\/neural-networks"},{"key":"5_CR8","unstructured":"Incorporated Research Institutions for Seismology: mseed. https:\/\/ds.iris.edu\/ds\/nodes\/dmc\/data\/formats\/miniseed\/"},{"key":"5_CR9","unstructured":"Instituto Geof\u00edsico - EPN: Descarga de datos. https:\/\/www.igepn.edu.ec\/descarga-de-datos"},{"key":"5_CR10","unstructured":"International Federation of Digital Seismograph Networks: Seed reference manual. http:\/\/www.fdsn.org\/pdf\/SEEDManual_V2.4.pdf"},{"issue":"2","key":"5_CR11","doi-asserted-by":"publisher","first-page":"365","DOI":"10.5194\/gi-9-365-2020","volume":"9","author":"O Kafadar","year":"2020","unstructured":"Kafadar, O.: A geophone-based and low-cost data acquisition and analysis system designed for microtremor measurements. Geosci. Instrum. Methods Data Syst. 9(2), 365\u2013373 (2020)","journal-title":"Geosci. Instrum. Methods Data Syst."},{"issue":"3","key":"5_CR12","doi-asserted-by":"publisher","first-page":"800","DOI":"10.3390\/s20030800","volume":"20","author":"I Khan","year":"2020","unstructured":"Khan, I., Choi, S., Kwon, Y.W.: Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method. Sensors 20(3), 800 (2020)","journal-title":"Sensors"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3161017","volume":"19","author":"I Khan","year":"2022","unstructured":"Khan, I., Kwon, Y.W.: P-detector: real-time P-wave detection in a seismic waveform recorded on a low-cost MEMS accelerometer using deep learning. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"5_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"5_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"5_CR16","first-page":"2303","volume":"157","author":"O Kulhanek","year":"2011","unstructured":"Kulhanek, O., Persson, L.: Seismogram interpretation. Geophysics 157, 2303\u20132322 (2011)","journal-title":"Geophysics"},{"key":"5_CR17","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"10","author":"WS McCulloch","year":"1943","unstructured":"McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 10, 115\u2013133 (1943)","journal-title":"Bull. Math. Biophys."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Mousavi, S.M., Beroza, G.C.: Deep-learning seismology. Science 377(6607), eabm4470 (2022)","DOI":"10.1126\/science.abm4470"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1146\/annurev-earth-071822-100323","volume":"51","author":"SM Mousavi","year":"2023","unstructured":"Mousavi, S.M., Beroza, G.C.: Machine learning in earthquake seismology. Annu. Rev. Earth Planet. Sci. 51, 105\u2013129 (2023)","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"5_CR20","unstructured":"Raspberry Shake, S.A.: How it works. https:\/\/raspberryshake.org\/about\/technology\/"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhou, S., Wang, J., Yang, S., Liu, C.: Research on identification of natural and unnatural earthquake events based on AlexNet convolutional neural network. Wireless Commun. Mob. Comput. 2022, 6782094 (2022)","DOI":"10.1155\/2022\/6782094"},{"key":"5_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5_CR23","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.sna.2017.11.047","volume":"269","author":"JL Soler-Llorens","year":"2018","unstructured":"Soler-Llorens, J.L., et al.: Design and test of Geophonino-3D: a low-cost three-component seismic noise recorder for the application of the H\/V method. Sens. Actuators, A 269, 342\u2013354 (2018)","journal-title":"Sens. Actuators, A"},{"issue":"1","key":"5_CR24","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1785\/BSSA0850010308","volume":"85","author":"J Wang","year":"1995","unstructured":"Wang, J., Teng, T.L.: Artificial neural network-based seismic detector. Bull. Seismol. Soc. Am. 85(1), 308\u2013319 (1995)","journal-title":"Bull. Seismol. Soc. Am."},{"issue":"8","key":"5_CR25","doi-asserted-by":"publisher","first-page":"7076","DOI":"10.1109\/TGRS.2020.3030324","volume":"59","author":"H Zhu","year":"2020","unstructured":"Zhu, H., Sun, M., Fu, H., Du, N., Zhang, J.: Training a seismogram discriminator based on ResNet. IEEE Trans. Geosci. Remote Sens. 59(8), 7076\u20137085 (2020)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Communications in Computer and Information Science","Telematics and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45316-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:04:51Z","timestamp":1699229091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45316-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453151","9783031453168"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45316-8_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"6 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WITCOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Congress of Telematics and Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Puerto Vallarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"witcom2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.witcom.upiita.ipn.mx\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"88","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}