{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:57:12Z","timestamp":1772243832992,"version":"3.50.1"},"reference-count":26,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:p>Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA\/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net\u2019s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage.<\/jats:p>","DOI":"10.3233\/jifs-211792","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:11:37Z","timestamp":1638886297000},"page":"5227-5236","source":"Crossref","is-referenced-by-count":2,"title":["Automatic phase identification of earthquake based on the UBDN deep network"],"prefix":"10.1177","volume":"42","author":[{"given":"Jianxian","family":"Cai","sequence":"first","affiliation":[{"name":"Institute of Disaster Prevention, Yanjiao Economic Development Zone, Sanhe, Hebei, China"},{"name":"Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology, Sanhe, China"}]},{"given":"Xun","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Control Engineering, Institute of Disaster Prevention, Sanhe, China"}]},{"given":"Zhitao","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Disaster Prevention, Yanjiao Economic Development Zone, Sanhe, Hebei, China"},{"name":"Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology, Sanhe, China"}]},{"given":"Yan","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Disaster Prevention, Yanjiao Economic Development Zone, Sanhe, Hebei, China"},{"name":"Hebei Key Laboratory of Seismic Disaster Instrument and Monitoring Technology, Sanhe, China"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/JIFS-211792_ref2","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1785\/BSSA0680051521","article-title":"Automatic earthquake recognition and timing from single traces[J]","volume":"68","author":"Allen","year":"1978","journal-title":"Bulletin of the Seismological Society of America"},{"issue":"4","key":"10.3233\/JIFS-211792_ref3","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1785\/BSSA0770041437","article-title":"An automatic phase picker for local and teleseismic events[J]","volume":"77","author":"Baer","year":"1987","journal-title":"Bulletin of the Seismological Society of America"},{"issue":"1","key":"10.3233\/JIFS-211792_ref4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1785\/BSSA0880010095","article-title":"A comparison of select trigger algorithms for automated global seismic phase and event detection","volume":"88","author":"Withers","year":"1998","journal-title":"Bulletin of the Seismological Society of America"},{"issue":"6","key":"10.3233\/JIFS-211792_ref5","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Transactions on Automatic Control"},{"issue":"1\u20134","key":"10.3233\/JIFS-211792_ref6","first-page":"265","article-title":"Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings","volume":"113","author":"Sleeman","year":"1998","journal-title":"Physics of the Earth and Planetary Interiors"},{"key":"10.3233\/JIFS-211792_ref7","unstructured":"Akazawa T. , A technique for automatic detection of onset time of P-and S-phases in strong motion records, In Proc of the 13th World Conf on Earthquake Engineering. Vancouver, Canada, 1\u20136 August (2004)."},{"issue":"5","key":"10.3233\/JIFS-211792_ref8","first-page":"1660","article-title":"On micro-seismic first arrival identification: A case study","volume":"56","author":"Liu","year":"2013","journal-title":"Journal of Geophysics"},{"issue":"4","key":"10.3233\/JIFS-211792_ref9","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1190\/1.1444030","article-title":"A fractal-based algorithm for detecting first arrivals on seismic trances","volume":"61","author":"Boschetti","year":"1996","journal-title":"Geophysics"},{"issue":"06","key":"10.3233\/JIFS-211792_ref10","first-page":"839","article-title":"Generalized fractal dimension of seismic records and its application","volume":"2002","author":"Chang","year":"2002","journal-title":"Journal of Geophysics"},{"issue":"1","key":"10.3233\/JIFS-211792_ref11","first-page":"60","article-title":"An improved algorithm for picking up seismic first arrivals by using fractal dimension","volume":"37","author":"Han","year":"2002","journal-title":"Petroleum Geophysical EXploration"},{"issue":"05","key":"10.3233\/JIFS-211792_ref12","first-page":"509","article-title":"Length fractal dimension method for picking seismic first break","volume":"2004","author":"Cao","year":"2004","journal-title":"Petroleum Geophysical Exploration"},{"key":"10.3233\/JIFS-211792_ref13","unstructured":"Tang Y.L. , The application of fractal theory in P-wave detection, Dissertation, Southwest Jiaotong University. (2017)."},{"issue":"B7","key":"10.3233\/JIFS-211792_ref14","doi-asserted-by":"crossref","first-page":"15105","DOI":"10.1029\/97JB00625","article-title":"The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings","volume":"102","author":"Dai","year":"1997","journal-title":"Journal of Geophysical Research: Solid Earth"},{"issue":"04","key":"10.3233\/JIFS-211792_ref15","first-page":"43","article-title":"The seismic signal and phase recognition by using artificial neural network theory","volume":"1998","author":"Zhang","year":"1998","journal-title":"Journal of Northwest Seismology"},{"issue":"1","key":"10.3233\/JIFS-211792_ref16","first-page":"14","article-title":"The improved bp neural network and its application in seismic first breaks picking","volume":"28","author":"Wang","year":"2006","journal-title":"Calculation Technology of Geophysical and Geochemical Exploration"},{"issue":"3","key":"10.3233\/JIFS-211792_ref17","first-page":"41","article-title":"An automatic seismic event detection method based on artificial neural network","volume":"29","author":"Wang","year":"2008","journal-title":"Seismogeomagnetic Observation and Research"},{"issue":"6","key":"10.3233\/JIFS-211792_ref18","doi-asserted-by":"crossref","first-page":"5120","DOI":"10.1029\/2017JB015251","article-title":"P wave arrival picking and first-motion polarity determination with deep learning","volume":"123","author":"Ross","year":"2018","journal-title":"Journal of Geophysical Research: Solid Earth"},{"issue":"08","key":"10.3233\/JIFS-211792_ref19","first-page":"3034","article-title":"Earthquake phase arrival auto-picking based on U-shaped convolutional neural network","volume":"62","author":"Zhao","year":"2019","journal-title":"Journal of Geophysics"},{"key":"10.3233\/JIFS-211792_ref20","doi-asserted-by":"crossref","unstructured":"Perol T. , Gharbi M. and Denolle M.A. , Convolutional neural network for earthquake detection and location, Science Advances 4(2) (2018).","DOI":"10.1126\/sciadv.1700578"},{"issue":"5A","key":"10.3233\/JIFS-211792_ref21","doi-asserted-by":"crossref","first-page":"2894","DOI":"10.1785\/0120180080","article-title":"Generalized seismic phase detection with deep learning","volume":"108","author":"Ross","year":"2018","journal-title":"Bulletin of the Seismological Society of America"},{"issue":"12","key":"10.3233\/JIFS-211792_ref22","first-page":"4873","article-title":"Pick onset time of P and S phase by deep neural network","volume":"61","author":"Yu","year":"2018","journal-title":"Journal of Geophysics"},{"issue":"1","key":"10.3233\/JIFS-211792_ref23","first-page":"374","article-title":"Waveform classification and seismic recognition by convolution neural network","volume":"62","author":"Zhao","year":"2019","journal-title":"Journal of Geophysics"},{"key":"10.3233\/JIFS-211792_ref24","unstructured":"Zhu L.L. , Research on heart sound recognition of congenital heart disease based on Bi-LSTM network. Dissertation, Yunnan University. (2019)."},{"issue":"07","key":"10.3233\/JIFS-211792_ref25","first-page":"89","article-title":"Quality Control Chart Pattern Recognition Based on Bidirectional LSTM","volume":"40","author":"Wu","year":"2019","journal-title":"Software"},{"issue":"4","key":"10.3233\/JIFS-211792_ref27","doi-asserted-by":"crossref","first-page":"R2693","DOI":"10.1103\/PhysRevE.51.R2693","article-title":"Effects of the chaotic noise on the performance of a neural network model for optimization problems","volume":"51","author":"Hayakawa","year":"2693","journal-title":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics"},{"key":"10.3233\/JIFS-211792_ref28","first-page":"815","article-title":"Facenet: A unified embedding for face recognition and clustering","volume":"2015","author":"Schroff","year":"2015","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211792","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:28:02Z","timestamp":1769776082000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":26,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211792","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-417923\/v1","asserted-by":"object"}]},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}