{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:27:36Z","timestamp":1743049656902,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819991181"},{"type":"electronic","value":"9789819991198"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9119-8_31","type":"book-chapter","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T13:03:04Z","timestamp":1706878984000},"page":"347-358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lithology Identification Method Based on CNN-LSTM-Attention: A Case Study of Huizhou Block in South China Sea"],"prefix":"10.1007","author":[{"given":"Zhikun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuedong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhong","family":"She","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongdong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liupeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"issue":"06","key":"31_CR1","first-page":"2290","volume":"68","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Lithology identification: method, current situation and intelligent development trend. Geolog. Rev. 68(06), 2290\u20132304 (2022)","journal-title":"Geolog. Rev."},{"issue":"12","key":"31_CR2","first-page":"1999","volume":"38","author":"H Cai","year":"2019","unstructured":"Cai, H., et al.: Classification of metallogenic prospect areas based on convolutional neural network model: a case study of gold polymetallic ore field in Daqiao Area, Gansu Province. Geolog. Bull. China 38(12), 1999\u20132009 (2019)","journal-title":"Geolog. Bull. China"},{"issue":"03","key":"31_CR3","first-page":"1001","volume":"20","author":"Y Duan","year":"2020","unstructured":"Duan, Y., Wang, Y., Sun, Q.: Application of selective ensemble learning model in lithology-porosity prediction. Sci. Technol. Eng. 20(03), 1001\u20131008 (2020)","journal-title":"Sci. Technol. Eng."},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Xu, T., et al.: Evaluation of active learning algorithms for formation lithology identification. J. Petrol. Sci. Eng. 206, 108999 (2021)","DOI":"10.1016\/j.petrol.2021.108999"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Arn\u00f8, M., Morten, J., Morten Aamo, O.: Real-time classification of drilled lithology from drilling data using deep learning with online calibration. In: SPE\/IADC International Drilling Conference and Exhibition (2021)","DOI":"10.2118\/204093-MS"},{"issue":"11","key":"31_CR6","first-page":"3372","volume":"19","author":"M Lei","year":"2022","unstructured":"Lei, M., et al.: Research on intelligent recognition method and application of Rock lithology Mask R-CNN. J. Railway Sci. Eng. 19(11), 3372\u20133382 (2022)","journal-title":"J. Railway Sci. Eng."},{"issue":"10","key":"31_CR7","first-page":"4044","volume":"23","author":"Z Yue","year":"2023","unstructured":"Yue, Z., et al.: Research progress of machine learning algorithms for lithology identification based on LWD data. Sci. Technol. Eng. 23(10), 4044\u20134057 (2023)","journal-title":"Sci. Technol. Eng."},{"key":"31_CR8","unstructured":"Wei, Y., Gong, J.: Rolling bearing fault diagnosis based on CNN-LSTM-attention. J. Shenyang Univ. Technol. (08) (2022)"},{"key":"31_CR9","unstructured":"Liu, W., Liu, W., Gu, J.: Prediction of daily oil production of oil Wells based on machine learning method. Oil Drill. Prod. Technol. 421, 70\u201375 (2020)"},{"issue":"06","key":"31_CR10","first-page":"316","volume":"41","author":"Z Ma","year":"2022","unstructured":"Ma, Z., Ma, L., Li, K., Yao, W., Wang, P., Wang, X.: Multi-scale lithology recognition based on deep learning of rock images. Geolog. Sci. Technol. Bull. 41(06), 316\u2013322 (2022)","journal-title":"Geolog. Sci. Technol. Bull."},{"issue":"12","key":"31_CR11","first-page":"109","volume":"26","author":"J Yang","year":"2006","unstructured":"Yang, J., Zhang, H.: Research on neural network method of formation lithology identification while drilling. Nat. Gas. Ind. 26(12), 109\u2013111 (2006)","journal-title":"Nat. Gas. Ind."},{"key":"31_CR12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"8","author":"SJ Hochreiters","year":"1997","unstructured":"Hochreiters, S.J.: Longshort-termmemory. Neural Comput. 8, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"31_CR13","doi-asserted-by":"publisher","first-page":"107306","DOI":"10.1016\/j.petrol.2020.107306","volume":"192","author":"QS Yin","year":"2020","unstructured":"Yin, Q.S., Yang, J., Hou, X.X., et al.: Drilling performance improvement in offshore batch Wells based on rig state classification using machine learning. J. Petrol. Sci. Eng. 192, 107306 (2020)","journal-title":"J. Petrol. Sci. Eng."},{"issue":"19","key":"31_CR14","first-page":"84","volume":"39","author":"H Zhu","year":"2020","unstructured":"Zhu, H., Ning, Q., Lei, Y.J., et al.: Fault classification of rolling bearing based on attention mechanism-inception CNN model. J. Vib. Shock 39(19), 84\u201393 (2020)","journal-title":"J. Vib. Shock"},{"key":"31_CR15","unstructured":"Roger, Z.L., et al.: Research on construction of deep prospecting prediction model based on PSO-CNN. J. Chengdu Univ. Technol. (Nat. Sci. Edn.) (09) (2020)"},{"issue":"561","key":"31_CR16","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","volume":"6","author":"JF Zhang","year":"2018","unstructured":"Zhang, J.F., Zhu, Y., Zhang, X.P., et al.: Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J. Hydrol. 6(561), 918\u2013929 (2018)","journal-title":"J. Hydrol."},{"issue":"3","key":"31_CR17","first-page":"120","volume":"33","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Zhang, X., Zhang, C., et al.: Lithologic reservoir identification method based on LSTM recurrent neural network. Litholog. Reserv. 33(3), 120\u2013128 (2021)","journal-title":"Litholog. Reserv."},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Arps, J.J., Arps, J.L.: The subsurface telemetry problem - a practical solution. Soc. Petrol. Eng. (1964)","DOI":"10.2118\/710-PA"},{"key":"31_CR19","first-page":"1","volume":"06","author":"W Tong","year":"2022","unstructured":"Tong, W., Zhao, R., Guo, C.: Limestone slurry density prediction based on grey relational analysis and mutual information theory. China Testing 06, 1\u20137 (2022)","journal-title":"China Testing"},{"key":"31_CR20","first-page":"109","volume":"06","author":"D Yan","year":"2021","unstructured":"Yan, D., Chen, B., Song, L., Wang, B.: Drilling fluid system of deep Wells in Huizhou area, South China Sea. Chem. Eng. Equip. 06, 109\u2013110 (2021)","journal-title":"Chem. Eng. Equip."},{"issue":"02","key":"31_CR21","first-page":"410","volume":"40","author":"A Wang","year":"2012","unstructured":"Wang, A., et al.: Characteristics and controlling factors of physical properties of deep tight sandstone reservoirs: a case study of the second lower Member of Xuerang Formation in Yuanba West area, Northeast Sichuan Basin. Acta Sedimentol. Sinica 40(02), 410\u2013421 (2012)","journal-title":"Acta Sedimentol. Sinica"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9119-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T13:09:53Z","timestamp":1706879393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9119-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819991181","9789819991198"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9119-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"22 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"376","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":"101","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":"16","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":"27% - 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":"2.9","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":"1.9","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)"}}]}}