IDENTIFICATION OF THE HOST LITHOLOGY OF TOURMALINE USING LASER-INDUCED BREAKDOWN SPECTROSCOPY FOR APPLICATION IN SEDIMENT PROVENANCE AND MINERAL EXPLORATION

被引:15
|
作者
McMillan, Nancy J. [1 ]
Curry, John [1 ]
Dutrow, Barbara L. [2 ]
Henry, Darrell J. [2 ]
机构
[1] New Mexico State Univ, Dept Geol Sci, Las Cruces, NM 88003 USA
[2] Louisiana State Univ, Dept Geol & Geophys, Baton Rouge, LA 70803 USA
来源
CANADIAN MINERALOGIST | 2018年 / 56卷 / 04期
基金
美国国家科学基金会;
关键词
LIBS; tourmaline; detrital; provenance; chemometrics; multivariate analysis; composition; MASSIVE SULFIDE DEPOSITS; FIBROUS TOURMALINE; BORON; SANDSTONES; EXAMPLE; CONSTRAINTS; INDICATOR; SYSTEM; BRAZIL; BASIN;
D O I
10.3749/canmin.1800004
中图分类号
P57 [矿物学];
学科分类号
070901 ;
摘要
Indicator minerals play a critical role in understanding Earth processes and in exploration for mineral resources. Tourmaline, a chemically complex boro-cyclosilicate, is a useful indicator mineral for provenance studies because its chemical signature reflects the environment during formation. Coupled with its lack of significant volume diffusion, even at high temperatures, and mechanical stability in clastic detrital environments, it retains textural and chemical fingerprints that may permit identification of the host rock lithology. To advance provenance determination by taking advantage of the complex chemistry of tourmaline, this study employs multivariate analysis of spectra acquired from Laser-Induced Breakdown Spectroscopy (LIBS). LIBS spectra contain information on the concentrations of nearly all elements and thus are ideal tools for modeling the complex compositional variations in tourmaline. The LIBS technique is especially sensitive to light elements such as Li, Be, and H and requires minimal sample preparation, allowing collection of data from many samples. A total of 189 spectra from 59 samples representing Li-rich pegmatites, Li-poor pegmatites, calcareous metamorphic rocks, pelitic metamorphic rocks, and hydrothermal deposits were modeled using the multivariate techniques Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). A matching algorithm consisting of a series of four binary PLSR models was used to sequentially identify spectra belonging to the five lithologic groups. Success rates, defined as the percent of correctly classified spectra during test-set validation, range from 92% to 99%; the overall success rate is 96%. The success rate will likely improve with additional samples and more lithologic groupings. This approach is a promising new direction in analyzing large mineral data sets for determination of provenance. Such a tool could have widespread use for analyzing detrital tourmaline to establish sediment provenance and lithologic correlation as well as to utilize tourmaline to explore for mineral resources.
引用
收藏
页码:393 / 410
页数:18
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