age2exhume -一个MATLAB/Python脚本,用于计算热计时年龄的稳态垂直挖掘率,并应用于喜马拉雅地区

IF 2.7 Q2 GEOCHEMISTRY & GEOPHYSICS Geochronology Pub Date : 2023-01-16 DOI:10.5194/gchron-5-35-2023
P. A. van der Beek, T. Schildgen
{"title":"age2exhume -一个MATLAB/Python脚本,用于计算热计时年龄的稳态垂直挖掘率,并应用于喜马拉雅地区","authors":"P. A. van der Beek, T. Schildgen","doi":"10.5194/gchron-5-35-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Interpreting cooling ages from multiple thermochronometric systems and/or\nfrom steep elevation transects with the help of a thermal model can provide\nunique insights into the spatial and temporal patterns of rock exhumation.\nAlthough several well-established thermal models allow for a detailed\nexploration of how cooling or exhumation rates evolved in a limited area or\nalong a transect, integrating large, regional datasets in such models\nremains challenging. Here, we present age2exhume, a thermal model in the\nform of a MATLAB or Python script, which can be used to rapidly obtain a\nsynoptic overview of exhumation rates from large, regional\nthermochronometric datasets. The model incorporates surface temperature\nbased on a defined lapse rate and a local relief correction that is\ndependent on the thermochronometric system of interest. Other inputs include\nsample cooling age, uncertainty, and an initial (unperturbed) geothermal\ngradient. The model is simplified in that it assumes steady, vertical\nrock uplift and unchanging topography when calculating exhumation rates. For\nthis reason, it does not replace more powerful and versatile\nthermal–kinematic models, but it has the advantage of simple implementation\nand rapidly calculated results. We also provide plots of predicted\nexhumation rates as a function of thermochronometric age and the local\nrelief correction, which can be used to simply look up a first-order\nestimate of exhumation rate. In our example dataset, we show exhumation\nrates calculated from 1785 cooling ages from the Himalaya associated with\nfive different thermochronometric systems. Despite the synoptic nature of\nthe results, they reflect known segmentation patterns and changing\nexhumation rates in areas that have undergone structural reorganization.\nMoreover, the rapid calculations enable an exploration of the sensitivity of\nthe results to various input parameters and an illustration of the\nimportance of explicit modeling of thermal fields when calculating\nexhumation rates from thermochronometric data.\n","PeriodicalId":12723,"journal":{"name":"Geochronology","volume":"208 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Short communication: age2exhume – a MATLAB/Python script to calculate steady-state vertical exhumation rates from thermochronometric ages and application to the Himalaya\",\"authors\":\"P. A. van der Beek, T. Schildgen\",\"doi\":\"10.5194/gchron-5-35-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Interpreting cooling ages from multiple thermochronometric systems and/or\\nfrom steep elevation transects with the help of a thermal model can provide\\nunique insights into the spatial and temporal patterns of rock exhumation.\\nAlthough several well-established thermal models allow for a detailed\\nexploration of how cooling or exhumation rates evolved in a limited area or\\nalong a transect, integrating large, regional datasets in such models\\nremains challenging. Here, we present age2exhume, a thermal model in the\\nform of a MATLAB or Python script, which can be used to rapidly obtain a\\nsynoptic overview of exhumation rates from large, regional\\nthermochronometric datasets. The model incorporates surface temperature\\nbased on a defined lapse rate and a local relief correction that is\\ndependent on the thermochronometric system of interest. Other inputs include\\nsample cooling age, uncertainty, and an initial (unperturbed) geothermal\\ngradient. The model is simplified in that it assumes steady, vertical\\nrock uplift and unchanging topography when calculating exhumation rates. For\\nthis reason, it does not replace more powerful and versatile\\nthermal–kinematic models, but it has the advantage of simple implementation\\nand rapidly calculated results. We also provide plots of predicted\\nexhumation rates as a function of thermochronometric age and the local\\nrelief correction, which can be used to simply look up a first-order\\nestimate of exhumation rate. In our example dataset, we show exhumation\\nrates calculated from 1785 cooling ages from the Himalaya associated with\\nfive different thermochronometric systems. Despite the synoptic nature of\\nthe results, they reflect known segmentation patterns and changing\\nexhumation rates in areas that have undergone structural reorganization.\\nMoreover, the rapid calculations enable an exploration of the sensitivity of\\nthe results to various input parameters and an illustration of the\\nimportance of explicit modeling of thermal fields when calculating\\nexhumation rates from thermochronometric data.\\n\",\"PeriodicalId\":12723,\"journal\":{\"name\":\"Geochronology\",\"volume\":\"208 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geochronology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5194/gchron-5-35-2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geochronology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/gchron-5-35-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 7

摘要

摘要在热模型的帮助下,从多个热时计系统和/或陡峭海拔样带解释冷却年龄,可以为岩石发掘的时空模式提供独特的见解。尽管一些成熟的热模型允许详细探索在有限区域或沿样带的冷却或挖掘速率如何演变,但在这些模型中整合大型区域数据集仍然具有挑战性。在这里,我们提出了age2exhume,这是一个以MATLAB或Python脚本形式的热模型,可用于从大型区域热时序数据集快速获得挖掘率的渐近概述。该模型结合了基于确定的递减率的地表温度和依赖于感兴趣的热时系统的局部地形起伏校正。其他输入包括样品冷却年龄、不确定性和初始(未受干扰的)地热梯度。在计算挖掘速率时,该模型被简化为假设稳定、垂直的岩石隆起和不变的地形。因此,它不能取代功能更强大、功能更广泛的热运动学模型,但它具有实现简单、计算结果迅速的优点。我们还提供了预测挖掘率随热时年龄和局部地形校正的函数图,可以用来简单地查找挖掘率的一阶估计值。在我们的示例数据集中,我们显示了从喜马拉雅山脉的1785年冷却时代计算出的与五种不同的热时计系统相关的挖掘率。尽管结果是概要性的,但它们反映了已知的分割模式和在经历结构重组的地区不断变化的挖掘率。此外,快速计算可以探索结果对各种输入参数的敏感性,并说明在从热时学数据计算挖掘率时明确建模热场的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Short communication: age2exhume – a MATLAB/Python script to calculate steady-state vertical exhumation rates from thermochronometric ages and application to the Himalaya
Abstract. Interpreting cooling ages from multiple thermochronometric systems and/or from steep elevation transects with the help of a thermal model can provide unique insights into the spatial and temporal patterns of rock exhumation. Although several well-established thermal models allow for a detailed exploration of how cooling or exhumation rates evolved in a limited area or along a transect, integrating large, regional datasets in such models remains challenging. Here, we present age2exhume, a thermal model in the form of a MATLAB or Python script, which can be used to rapidly obtain a synoptic overview of exhumation rates from large, regional thermochronometric datasets. The model incorporates surface temperature based on a defined lapse rate and a local relief correction that is dependent on the thermochronometric system of interest. Other inputs include sample cooling age, uncertainty, and an initial (unperturbed) geothermal gradient. The model is simplified in that it assumes steady, vertical rock uplift and unchanging topography when calculating exhumation rates. For this reason, it does not replace more powerful and versatile thermal–kinematic models, but it has the advantage of simple implementation and rapidly calculated results. We also provide plots of predicted exhumation rates as a function of thermochronometric age and the local relief correction, which can be used to simply look up a first-order estimate of exhumation rate. In our example dataset, we show exhumation rates calculated from 1785 cooling ages from the Himalaya associated with five different thermochronometric systems. Despite the synoptic nature of the results, they reflect known segmentation patterns and changing exhumation rates in areas that have undergone structural reorganization. Moreover, the rapid calculations enable an exploration of the sensitivity of the results to various input parameters and an illustration of the importance of explicit modeling of thermal fields when calculating exhumation rates from thermochronometric data.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geochronology
Geochronology Earth and Planetary Sciences-Paleontology
CiteScore
6.60
自引率
0.00%
发文量
35
审稿时长
19 weeks
期刊最新文献
Geochronological and geochemical effects of zircon chemical abrasion: insights from single-crystal stepwise dissolution experiments The marine reservoir age of Greenland coastal waters Late Neogene terrestrial climate reconstruction of the central Namib Desert derived by the combination of U–Pb silcrete and terrestrial cosmogenic nuclide exposure dating Early Holocene ice retreat from Isle Royale in the Laurentian Great Lakes constrained with 10Be exposure-age dating Technical note: Darkroom lighting for luminescence dating laboratory
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1