Timothy M. Lutz , Gomma Omar , Barry P. Kohn , Mary Emma Wagner
{"title":"Patterns of cooling in basement rocks — A bootstrap method to measure anomalous spatial dispersion of zircon fission-track ages","authors":"Timothy M. Lutz , Gomma Omar , Barry P. Kohn , Mary Emma Wagner","doi":"10.1016/1359-0189(93)90186-D","DOIUrl":null,"url":null,"abstract":"<div><p>Thermochronology depends on isotopic systems for which the age is related to the time that a specified mineral cooled through its closure temperature. In tectonic studies, it is often of interest to examine regional variations in cooling age. When the variation in age within a data set exceeds the analytical errors on the age determinations, many options are available to model the spatial variation in age and to correlate it with other data or with the predictions of hypotheses. For example, trend surfaces [e.g. Davis J.C. (1986) <em>Statistics and Data Analysis in Geology</em>, 2nd edn. Wiley, New York] could be used to explain the variation that exceeds experimental error. In the case of interest in this study it may appear that the variations in age originate entirely from random analytical error. We show how geologically significant patterns that may be present in such apparently random data can be detected. Our analysis is based on characterizing how the extreme ages (oldest and youngest) are distributed among the sample localities. In particular, we explore whether the extremes are more dispersed or more clustered than could be expected from a random assignment of ages to the localities, as deduced from bootstrap simulations. This mode of analysis is non-parametric and requires no assumptions about the distributional form of the errors or the ages. The proposed analysis is applied to 34 zircon fission-track ages from the central Appalachian Piedmont, eastern U.S.A. Our results show that the older ages are concentrated near the center of the sample region and are surrounded by younger ages. This age pattern suggests that rocks now at the surface in the central part of the study area cooled first, followed by rocks located toward the periphery of the area.</p></div>","PeriodicalId":82207,"journal":{"name":"Nuclear Tracks And Radiation Measurements","volume":"21 4","pages":"Pages 471-477"},"PeriodicalIF":0.0000,"publicationDate":"1993-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/1359-0189(93)90186-D","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nuclear Tracks And Radiation Measurements","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/135901899390186D","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Thermochronology depends on isotopic systems for which the age is related to the time that a specified mineral cooled through its closure temperature. In tectonic studies, it is often of interest to examine regional variations in cooling age. When the variation in age within a data set exceeds the analytical errors on the age determinations, many options are available to model the spatial variation in age and to correlate it with other data or with the predictions of hypotheses. For example, trend surfaces [e.g. Davis J.C. (1986) Statistics and Data Analysis in Geology, 2nd edn. Wiley, New York] could be used to explain the variation that exceeds experimental error. In the case of interest in this study it may appear that the variations in age originate entirely from random analytical error. We show how geologically significant patterns that may be present in such apparently random data can be detected. Our analysis is based on characterizing how the extreme ages (oldest and youngest) are distributed among the sample localities. In particular, we explore whether the extremes are more dispersed or more clustered than could be expected from a random assignment of ages to the localities, as deduced from bootstrap simulations. This mode of analysis is non-parametric and requires no assumptions about the distributional form of the errors or the ages. The proposed analysis is applied to 34 zircon fission-track ages from the central Appalachian Piedmont, eastern U.S.A. Our results show that the older ages are concentrated near the center of the sample region and are surrounded by younger ages. This age pattern suggests that rocks now at the surface in the central part of the study area cooled first, followed by rocks located toward the periphery of the area.
热年代学依赖于同位素系统,其年龄与特定矿物通过其闭合温度冷却的时间有关。在构造研究中,研究冷却年龄的区域差异常常引起人们的兴趣。当一组数据内的年龄变化超过年龄测定的分析误差时,可采用许多方法对年龄的空间变化进行建模,并将其与其他数据或假设预测相关联。例如,趋势面[如Davis J.C.(1986)《地质学中的统计和数据分析》,第2版]。Wiley, New York]可以用来解释超出实验误差的变异。在本研究感兴趣的情况下,年龄的变化似乎完全源于随机分析误差。我们展示了如何在这些明显随机的数据中发现具有重要地质意义的模式。我们的分析是基于表征极端年龄(最老和最年轻)如何分布在样本地区。特别是,我们探讨了极端是否比从随机分配年龄到地点所期望的更分散或更聚集,正如从bootstrap模拟中推断出来的那样。这种分析模式是非参数的,不需要对误差的分布形式或年龄进行假设。应用该方法对美国东部Appalachian Piedmont中部地区的34个锆石裂变径迹年龄进行了分析,结果表明,年龄较大的锆石径迹年龄集中在样品区域的中心附近,并被年龄较小的锆石径迹年龄所包围。这种年龄模式表明,目前在研究区域中部表面的岩石首先冷却,其次是位于该区域外围的岩石。