{"title":"中国内蒙古集宁浅层覆盖区草寺窑巨型斑岩钼矿床二维地震反射数据集","authors":"Kunqi Lin, Zhenjie Zhang, Jie Yang, Guoxiong Chen, Guopeng Wu, Yongzhi Wang, Qiuming Cheng","doi":"10.1002/gdj3.223","DOIUrl":null,"url":null,"abstract":"<p>Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world-class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans-North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"11 1","pages":"57-68"},"PeriodicalIF":3.3000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.223","citationCount":"0","resultStr":"{\"title\":\"A 2D seismic reflection dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China\",\"authors\":\"Kunqi Lin, Zhenjie Zhang, Jie Yang, Guoxiong Chen, Guopeng Wu, Yongzhi Wang, Qiuming Cheng\",\"doi\":\"10.1002/gdj3.223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world-class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans-North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas.</p>\",\"PeriodicalId\":54351,\"journal\":{\"name\":\"Geoscience Data Journal\",\"volume\":\"11 1\",\"pages\":\"57-68\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.223\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience Data Journal\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.223\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.223","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
A 2D seismic reflection dataset of the Caosiyao giant porphyry Mo deposit in the shallow coverage area in Jining, Inner Mongolia, China
Prospecting for and exploiting buried mineral deposits is currently challenging. Given their high precision and resolution, reflection seismic methods might be useful in such applications involving deep mineral deposits. However, there are few open seismic datasets available from mineral deposit exploration, especially in hardrock environments. The world-class Caosiyao porphyry Molybdenum deposit (1.76 Mt) in the Jining area of Inner Mongolia, China, is largely covered by loess layers, which poses challenges to its exploration. Seismic reflection surveys were conducted to help delineate the deep granite porphyry intrusions and associated orebodies. This paper presents the raw seismic reflection dataset from three profiles on the Caosiyao deposit area, which can be used as a standard dataset for reflection seismic processing in shallow coverage and hardrock areas. Situated at the juxtaposition of the Khondalite Belt and the Trans-North China Orogen in the northern North China Craton, the Jining region hosts considerable known porphyry Mo deposits. As such, this open dataset can assist in research on deep geological structures and hence increase prospecting efficiency in geologically similar areas.
Geoscience Data JournalGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍:
Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered.
An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices.
Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.