{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:08:28Z","timestamp":1775146108904,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271208"],"award-info":[{"award-number":["62271208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Key Project of Peng Cheng Laboratory","award":["PCL2023A09"],"award-info":[{"award-number":["PCL2023A09"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06664-y","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T18:44:02Z","timestamp":1731955442000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ICAT-net: a lightweight neural network with optimized coordinate attention and transformer mechanisms for earthquake detection and phase picking"],"prefix":"10.1007","volume":"81","author":[{"given":"Xue-Ning","family":"Li","sequence":"first","affiliation":[]},{"given":"Fang-Jiong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Ye-Ping","family":"Lai","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Xiao-Jun","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"6664_CR1","doi-asserted-by":"crossref","unstructured":"Shearer PM (2019) Introduction to seismology. Cambridge university press.","DOI":"10.1017\/9781316877111"},{"issue":"1","key":"6664_CR2","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1038\/s41467-020-17591-w","volume":"11","author":"SM Mousavi","year":"2020","unstructured":"Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat Commun 11(1):3952","journal-title":"Nat Commun"},{"key":"6664_CR3","doi-asserted-by":"publisher","unstructured":"Bormann P (Ed) (2012) New manual of seismological observatory practice (NMSOP-2). IASPEI, GFZ German Research Centre for Geosciences. https:\/\/doi.org\/10.2312\/GFZ.NMSOP-2","DOI":"10.2312\/GFZ.NMSOP-2"},{"issue":"5","key":"6664_CR4","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1785\/BSSA0680051521","volume":"68","author":"RV Allen","year":"1978","unstructured":"Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68(5):1521\u20131532","journal-title":"Bull Seismol Soc Am"},{"issue":"1","key":"6664_CR5","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1111\/j.1365-246X.2007.03650.x","volume":"172","author":"SJ Gibbons","year":"2008","unstructured":"Gibbons SJ, Ringdal F, Kv\u00e6rna T (2008) Detection and characterization of seismic phases using continuous spectral estimation on incoherent and partially coherent arrays. Geophys J Int 172(1):405\u2013421","journal-title":"Geophys J Int"},{"key":"6664_CR6","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.dsp.2013.12.009","volume":"26","author":"E-S Akhouayri","year":"2014","unstructured":"Akhouayri E-S, Agliz D, Atmani A et al (2014) Automatic detection and picking of p-wave arrival in locally stationary noise using cross-correlation. Digit Signal Process 26:87\u2013100","journal-title":"Digit Signal Process"},{"issue":"1\u20134","key":"6664_CR7","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/S0031-9201(99)00007-2","volume":"113","author":"R Sleeman","year":"1999","unstructured":"Sleeman R, Van Eck T (1999) Robust automatic p-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Phys Earth Planet Inter 113(1\u20134):265\u2013275","journal-title":"Phys Earth Planet Inter"},{"issue":"8","key":"6664_CR8","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.1109\/TGRS.2008.917272","volume":"46","author":"C Panagiotakis","year":"2008","unstructured":"Panagiotakis C, Kokinou E, Vallianatos F (2008) Automatic $$p$$-phase picking based on local-maxima distribution. IEEE Transact Geosci Remote Sens 46(8):2280\u20132287","journal-title":"IEEE Transact Geosci Remote Sens"},{"issue":"6","key":"6664_CR9","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TGRS.2002.800438","volume":"40","author":"CD Saragiotis","year":"2002","unstructured":"Saragiotis CD, Hadjileontiadis LJ, Panas SM (2002) Pai-s\/k: A robust automatic seismic p phase arrival identification scheme. IEEE Transact Geosci Remote Sens 40(6):1395\u20131404","journal-title":"IEEE Transact Geosci Remote Sens"},{"key":"6664_CR10","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/s10950-018-9735-z","volume":"22","author":"Y Li","year":"2018","unstructured":"Li Y, Wang Y, Lin H, Zhong T (2018) First arrival time picking for microseismic data based on dwsw algorithm. J Seismol 22:833\u2013840","journal-title":"J Seismol"},{"key":"6664_CR11","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1007\/s11600-021-00636-z","volume":"69","author":"L Gao","year":"2021","unstructured":"Gao L, Liu D, Luo GF, Song GJ, Min F (2021) First-arrival picking through fuzzy c-means and robust locally weighted regression. Acta Geophysica 69:1623\u20131636","journal-title":"Acta Geophysica"},{"key":"6664_CR12","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"6664_CR13","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"6664_CR14","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"6664_CR15","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Info Process Syst 33:1877\u20131901","journal-title":"Adv Neural Info Process Syst"},{"key":"6664_CR16","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"issue":"17","key":"6664_CR17","doi-asserted-by":"publisher","first-page":"20561","DOI":"10.1007\/s10489-023-04571-y","volume":"53","author":"N Zacarias-Morales","year":"2023","unstructured":"Zacarias-Morales N, Hern\u00e1ndez-Nolasco JA, Pancardo P (2023) Full single-type deep learning models with multihead attention for speech enhancement. Appl Intell 53(17):20561\u201320576","journal-title":"Appl Intell"},{"key":"6664_CR18","doi-asserted-by":"publisher","first-page":"179464","DOI":"10.1109\/ACCESS.2019.2947848","volume":"7","author":"SM Mousavi","year":"2019","unstructured":"Mousavi SM, Sheng Y, Zhu W, Beroza GC (2019) Stanford earthquake dataset (stead): a global data set of seismic signals for ai. IEEE Access 7:179464\u2013179476","journal-title":"IEEE Access"},{"key":"6664_CR19","doi-asserted-by":"publisher","unstructured":"Ni Y, Hutko A, Skene F, Denolle M, Malone S, Bodin P, Hartog R, Wright A (2023) Curated Pacific Northwest AI-ready Seismic Dataset. Seismica 2(1). https:\/\/doi.org\/10.26443\/seismica.v2i1.368","DOI":"10.26443\/seismica.v2i1.368"},{"key":"6664_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eqs.2022.10.003","volume":"35","author":"M Zhao","year":"2022","unstructured":"Zhao M, Xiao Z, Chen S, Fang L (2022) Diting: a large-scale chinese seismic benchmark dataset for artificial intelligence in seismology. Earthq Sci 35:1\u201311","journal-title":"Earthq Sci"},{"issue":"7","key":"6664_CR21","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1029\/2018EA000466","volume":"6","author":"Y Chen","year":"2019","unstructured":"Chen Y, Zhang G, Bai M, Zu S, Guan Z, Zhang M (2019) Automatic waveform classification and arrival picking based on convolutional neural network. Earth Space Sci 6(7):1244\u20131261","journal-title":"Earth Space Sci"},{"issue":"1","key":"6664_CR22","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1121\/1.5116016","volume":"146","author":"H Niu","year":"2019","unstructured":"Niu H, Gong Z, Ozanich E, Gerstoft P, Wang H, Li Z (2019) Deep-learning source localization using multi-frequency magnitude-only data. J Acoust Soc Am 146(1):211\u2013222","journal-title":"J Acoust Soc Am"},{"issue":"2A","key":"6664_CR23","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1785\/0220180320","volume":"90","author":"M Kriegerowski","year":"2019","unstructured":"Kriegerowski M, Petersen GM, Vasyura-Bathke H, Ohrnberger M (2019) A deep convolutional neural network for localization of clustered earthquakes based on multistation full waveforms. Seismol Res Lett 90(2A):510\u2013516","journal-title":"Seismol Res Lett"},{"issue":"1","key":"6664_CR24","first-page":"261","volume":"216","author":"W Zhu","year":"2019","unstructured":"Zhu W, Beroza GC (2019) Phasenet: a deep-neural-network-based seismic arrival-time picking method. Geophys J Int 216(1):261\u2013273","journal-title":"Geophys J Int"},{"key":"6664_CR25","unstructured":"Li S, Yang X, Cao A, Wang C, Liu Y, Liu Y, Niu Q (2023) Seismogram transformer: a generic deep learning backbone network for multiple earthquake monitoring tasks. arXiv preprint arXiv:2310.01037"},{"issue":"2","key":"6664_CR26","doi-asserted-by":"publisher","first-page":"1700578","DOI":"10.1126\/sciadv.1700578","volume":"4","author":"T Perol","year":"2018","unstructured":"Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):1700578","journal-title":"Sci Adv"},{"issue":"7","key":"6664_CR27","doi-asserted-by":"publisher","first-page":"6612","DOI":"10.1029\/2019JB017536","volume":"124","author":"J Wang","year":"2019","unstructured":"Wang J, Xiao Z, Liu C, Zhao D, Yao Z (2019) Deep learning for picking seismic arrival times. J Geophys Res: Solid Earth 124(7):6612\u20136624","journal-title":"J Geophys Res: Solid Earth"},{"key":"6664_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10950-006-2296-6","volume":"10","author":"S Gentili","year":"2006","unstructured":"Gentili S, Michelini A (2006) Automatic picking of p and s phases using a neural tree. J Seismol 10:39\u201363","journal-title":"J Seismol"},{"issue":"3","key":"6664_CR29","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1785\/BSSA0890030670","volume":"89","author":"Y Zhao","year":"1999","unstructured":"Zhao Y, Takano K (1999) An artificial neural network approach for broadband seismic phase picking. Bull Seismol Soc Am 89(3):670\u2013680","journal-title":"Bull Seismol Soc Am"},{"issue":"1","key":"6664_CR30","doi-asserted-by":"publisher","first-page":"10267","DOI":"10.1038\/s41598-019-45748-1","volume":"9","author":"SM Mousavi","year":"2019","unstructured":"Mousavi SM, Zhu W, Sheng Y, Beroza GC (2019) Cred: a deep residual network of convolutional and recurrent units for earthquake signal detection. Sci Rep 9(1):10267","journal-title":"Sci Rep"},{"key":"6664_CR31","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhang L, Cheng M-M, Feng J (2020) Strip pooling: rethinking spatial pooling for scene parsing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4003\u20134012","DOI":"10.1109\/CVPR42600.2020.00406"},{"issue":"10","key":"6664_CR32","doi-asserted-by":"publisher","first-page":"1976","DOI":"10.3390\/sym14101976","volume":"14","author":"W Khan","year":"2022","unstructured":"Khan W, Raj K, Kumar T, Roy AM, Luo B (2022) Introducing urdu digits dataset with demonstration of an efficient and robust noisy decoder-based pseudo example generator. Symmetry 14(10):1976","journal-title":"Symmetry"},{"key":"6664_CR33","doi-asserted-by":"crossref","unstructured":"Si X, Wu X, Sheng H, Zhu J, Li Z (2024) SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2024.3354456"},{"issue":"2","key":"6664_CR34","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1093\/gji\/ggab139","volume":"226","author":"J M\u00fcnchmeyer","year":"2021","unstructured":"M\u00fcnchmeyer J, Bindi D, Leser U, Tilmann F (2021) Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophys J Int 226(2):1086\u20131104","journal-title":"Geophys J Int"},{"issue":"18","key":"6664_CR35","doi-asserted-by":"publisher","first-page":"6290","DOI":"10.3390\/s21186290","volume":"21","author":"A Stepnov","year":"2021","unstructured":"Stepnov A, Chernykh V, Konovalov A (2021) The seismo-performer: a novel machine learning approach for general and efficient seismic phase recognition from local earthquakes in real time. Sensors 21(18):6290","journal-title":"Sensors"},{"key":"6664_CR36","doi-asserted-by":"crossref","unstructured":"Sunkara R, Luo T (2022) No more strided convolutions or pooling: a new cnn building block for low-resolution images and small objects. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, pp 443\u2013459","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"6664_CR37","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"6664_CR38","unstructured":"Wang S, Li BZ, Khabsa M, Fang H, Ma H (2020) Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768"},{"key":"6664_CR39","doi-asserted-by":"crossref","unstructured":"Wu H, Xiao B, Codella N, Liu M, Dai X, Yuan L, Zhang L (2021) Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 22\u201331","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"6664_CR40","first-page":"41","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Machine learning. Multitask Learn 28:41\u201375","journal-title":"Multitask Learn"},{"key":"6664_CR41","first-page":"30392","volume":"34","author":"T Xiao","year":"2021","unstructured":"Xiao T, Singh M, Mintun E, Darrell T, Doll\u00e1r P, Girshick R (2021) Early convolutions help transformers see better. Adv Neural Info Process Syst 34:30392\u201330400","journal-title":"Adv Neural Info Process Syst"},{"key":"6664_CR42","volume-title":"Sigmoid functions: some approximation and modelling aspects","author":"N Kyurkchiev","year":"2015","unstructured":"Kyurkchiev N, Markov S (2015) Sigmoid functions: some approximation and modelling aspects. LAP LAMBERT Academic Publishing, Saarbrucken"},{"key":"6664_CR43","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"6664_CR44","doi-asserted-by":"publisher","first-page":"5781","DOI":"10.1007\/s00024-020-02617-7","volume":"177","author":"H Zhang","year":"2020","unstructured":"Zhang H, Ma C, Pazzi V, Li T, Casagli N (2020) Deep convolutional neural network for microseismic signal detection and classification. Pure Appl Geophys 177:5781\u20135797","journal-title":"Pure Appl Geophys"},{"issue":"1","key":"6664_CR45","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3390\/electronics13010229","volume":"13","author":"S Choi","year":"2024","unstructured":"Choi S, Lee B, Kim J, Jung H (2024) Deep-learning-based seismic-signal p-wave first-arrival picking detection using spectrogram images. Electronics 13(1):229","journal-title":"Electronics"},{"issue":"1","key":"6664_CR46","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s13677-022-00378-3","volume":"12","author":"Y Sang","year":"2023","unstructured":"Sang Y, Peng Y, Lu M, Zhao C, Li L, Ma T (2023) Seisdenet: an intelligent seismic data denoising network for the internet of things. J Cloud Comput 12(1):34","journal-title":"J Cloud Comput"},{"issue":"4","key":"6664_CR47","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1504\/IJEDPO.2013.059667","volume":"3","author":"J Cha","year":"2013","unstructured":"Cha J, Cho BR, Sharp JL (2013) Rethinking the truncated normal distribution. Int J Exp Des Process Optim 3(4):327\u2013363","journal-title":"Int J Exp Des Process Optim"},{"key":"6664_CR48","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"6664_CR49","doi-asserted-by":"crossref","unstructured":"Smith LN (2017) Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 464\u2013472. IEEE","DOI":"10.1109\/WACV.2017.58"},{"key":"6664_CR50","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp 233\u2013240","DOI":"10.1145\/1143844.1143874"},{"key":"6664_CR51","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D (2020) Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, pp 79\u201391","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"key":"6664_CR52","unstructured":"Error MA (2016) Mean absolute error. Retrieved September 19, 2016"},{"issue":"3","key":"6664_CR53","doi-asserted-by":"publisher","first-page":"220","DOI":"10.4097\/kjae.2015.68.3.220","volume":"68","author":"DK Lee","year":"2015","unstructured":"Lee DK, In J, Lee S (2015) Standard deviation and standard error of the mean. Korean J Anesth 68(3):220\u2013223","journal-title":"Korean J Anesth"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06664-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06664-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06664-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T19:09:46Z","timestamp":1731956986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06664-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6664"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06664-y","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"29 October 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors certify that there is no conflict of interest with any individual or organization for this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"191"}}