{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T07:23:46Z","timestamp":1769930626750,"version":"3.49.0"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A2019"],"award-info":[{"award-number":["U21A2019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203106"],"award-info":[{"award-number":["62203106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hainan Province Science and Technology Special Fund of China","award":["ZDYF2022SHFZ105"],"award-info":[{"award-number":["ZDYF2022SHFZ105"]}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["LH2023D012"],"award-info":[{"award-number":["LH2023D012"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ph.D. Scientific Research and Innovation Foundation of Sanya Yazhou Bay Science and Technology City of China","award":["HSPHDSRF-2023-07-002"],"award-info":[{"award-number":["HSPHDSRF-2023-07-002"]}]},{"name":"New Era Longjiang Excellent Master and Doctor Dissertation Project of China","award":["LJYXL2022-034"],"award-info":[{"award-number":["LJYXL2022-034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2024.3436817","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T17:56:39Z","timestamp":1722534999000},"page":"1-15","source":"Crossref","is-referenced-by-count":2,"title":["A Meta-Learning-Based Approach for Automatic First-Arrival Picking"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7779-3772","authenticated-orcid":false,"given":"Hanyang","family":"Li","sequence":"first","affiliation":[{"name":"Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8616-9901","authenticated-orcid":false,"given":"Yuhang","family":"Sun","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4092-9004","authenticated-orcid":false,"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8531-6757","authenticated-orcid":false,"given":"Hongli","family":"Dong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Continental Shale Oil, the Artificial Intelligence Energy Research Institute, and Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"issue":"4","key":"ref1","first-page":"1708","article-title":"STA\/LTA algorithm analysis and improvement of Microseismic signal automatic detection","volume":"29","author":"Liu","year":"2014","journal-title":"Prog. Geophy."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1785\/BSSA0680051521"},{"key":"ref3","first-page":"26853","volume-title":"Akaike Information Criterion Statistics","volume":"81","author":"Sakamoto","year":"1986"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2307\/1390918"},{"issue":"1","key":"ref5","first-page":"150","article-title":"Determination of teleseismic relative phase arrival times using multi-channel cross-correlation and least squares","volume":"80","author":"VanDecar","year":"1990","journal-title":"Bull. Seismological Soc. Amer."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jappgeo.2016.09.024"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/HOST.1999.778766"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-246X.2010.04570.x"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1190\/geo2015-0598.1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1126\/science.abm4470"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2785834"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1190\/geo2023-0063.1"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3010541"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1190\/geo2019-0267.1"},{"key":"ref15","article-title":"MSSPN: Automatic first arrival picking using multi-stage segmentation picking network","author":"Wang","year":"2022","journal-title":"arXiv:2209.03132"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1190\/tle40060460.1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17591-w"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1190\/geo2019-0792.1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5548346"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1093\/jge\/gxac009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.107527"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1190\/geo2019-0437.1"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/bs.agph.2020.07.003"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1023\/A:1019956318069"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref29","first-page":"1369","article-title":"On data efficiency of meta-learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Al-Shedivat"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115011"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/atmos11050487"},{"key":"ref32","article-title":"Alpha MAML: Adaptive model-agnostic meta-learning","author":"Behl","year":"2019","journal-title":"arXiv:1905.07435"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01238"},{"key":"ref34","first-page":"1","article-title":"Towards fast adaptation of neural architectures with meta learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lian"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00621"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87240-3_7"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1190\/geo2022-0281.1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1190\/segam2020-3420195.1"},{"key":"ref39","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015","journal-title":"arXiv:1505.00853"},{"issue":"1","key":"ref40","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. ICML","volume":"30","author":"Maas"},{"key":"ref41","article-title":"Bayesian-deep-learning estimation of earthquake location from single-station observations","author":"Mostafa Mousavi","year":"2019","journal-title":"arXiv:1912.01144"},{"key":"ref42","first-page":"4334","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ren"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1190\/geo2022-0741.1"},{"key":"ref44","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10354519\/10620325.pdf?arnumber=10620325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T17:30:25Z","timestamp":1723570225000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10620325\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3436817","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}