From 3995a079ec6f64c943971b3cd753f2ada3ca4699 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Sat, 25 Nov 2023 12:06:41 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- sitemap.xml | 70 +++++++++++++++++++++++----------------------- visualisation.html | 36 ++++++++++++------------ 3 files changed, 54 insertions(+), 54 deletions(-) diff --git a/.nojekyll b/.nojekyll index a071b1e..261e210 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -be423ac5 \ No newline at end of file +8d02941d \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml index 6e6f809..9f4a173 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,142 +2,142 @@ https://archmetaldbm.github.io/GlobaLID-Edu/index.html - 2023-11-25T11:55:14.960Z + 2023-11-25T12:06:39.198Z https://archmetaldbm.github.io/GlobaLID-Edu/preface.html - 2023-11-25T11:55:14.968Z + 2023-11-25T12:06:39.206Z https://archmetaldbm.github.io/GlobaLID-Edu/intro.html - 2023-11-25T11:55:14.976Z + 2023-11-25T12:06:39.214Z https://archmetaldbm.github.io/GlobaLID-Edu/basics.html - 2023-11-25T11:55:14.984Z + 2023-11-25T12:06:39.218Z https://archmetaldbm.github.io/GlobaLID-Edu/isotope_system.html - 2023-11-25T11:55:14.992Z + 2023-11-25T12:06:39.226Z https://archmetaldbm.github.io/GlobaLID-Edu/sample_processing.html - 2023-11-25T11:55:15.008Z + 2023-11-25T12:06:39.246Z https://archmetaldbm.github.io/GlobaLID-Edu/measurement.html - 2023-11-25T11:55:15.020Z + 2023-11-25T12:06:39.254Z https://archmetaldbm.github.io/GlobaLID-Edu/exam1.html - 2023-11-25T11:55:15.024Z + 2023-11-25T12:06:39.262Z https://archmetaldbm.github.io/GlobaLID-Edu/deposit_formation.html - 2023-11-25T11:55:15.032Z + 2023-11-25T12:06:39.270Z https://archmetaldbm.github.io/GlobaLID-Edu/deposit_impact_PbIsos.html - 2023-11-25T11:55:15.044Z + 2023-11-25T12:06:39.278Z https://archmetaldbm.github.io/GlobaLID-Edu/exam2.html - 2023-11-25T11:55:15.048Z + 2023-11-25T12:06:39.282Z https://archmetaldbm.github.io/GlobaLID-Edu/metallurgy_basics.html - 2023-11-25T11:55:15.056Z + 2023-11-25T12:06:39.290Z https://archmetaldbm.github.io/GlobaLID-Edu/metallurgy_Pb-Ag.html - 2023-11-25T11:55:15.060Z + 2023-11-25T12:06:39.294Z https://archmetaldbm.github.io/GlobaLID-Edu/metallurgy_Cu.html - 2023-11-25T11:55:15.068Z + 2023-11-25T12:06:39.306Z https://archmetaldbm.github.io/GlobaLID-Edu/metallurgy_impact_PbIsos.html - 2023-11-25T11:55:15.076Z + 2023-11-25T12:06:39.310Z https://archmetaldbm.github.io/GlobaLID-Edu/exam3.html - 2023-11-25T11:55:15.084Z + 2023-11-25T12:06:39.318Z https://archmetaldbm.github.io/GlobaLID-Edu/summary_bascis.html - 2023-11-25T11:55:15.088Z + 2023-11-25T12:06:39.322Z https://archmetaldbm.github.io/GlobaLID-Edu/application.html - 2023-11-25T11:55:15.096Z + 2023-11-25T12:06:39.330Z https://archmetaldbm.github.io/GlobaLID-Edu/correction_QA.html - 2023-11-25T11:55:15.100Z + 2023-11-25T12:06:39.338Z https://archmetaldbm.github.io/GlobaLID-Edu/storage.html - 2023-11-25T11:55:15.112Z + 2023-11-25T12:06:39.346Z https://archmetaldbm.github.io/GlobaLID-Edu/reference_data.html - 2023-11-25T11:55:15.116Z + 2023-11-25T12:06:39.350Z https://archmetaldbm.github.io/GlobaLID-Edu/exam4.html - 2023-11-25T11:55:15.124Z + 2023-11-25T12:06:39.358Z https://archmetaldbm.github.io/GlobaLID-Edu/visualisation.html - 2023-11-25T11:55:15.184Z + 2023-11-25T12:06:39.418Z https://archmetaldbm.github.io/GlobaLID-Edu/matching.html - 2023-11-25T11:55:15.192Z + 2023-11-25T12:06:39.422Z https://archmetaldbm.github.io/GlobaLID-Edu/exam5.html - 2023-11-25T11:55:15.200Z + 2023-11-25T12:06:39.430Z https://archmetaldbm.github.io/GlobaLID-Edu/summary_application.html - 2023-11-25T11:55:15.204Z + 2023-11-25T12:06:39.434Z https://archmetaldbm.github.io/GlobaLID-Edu/interpretation.html - 2023-11-25T11:55:15.216Z + 2023-11-25T12:06:39.446Z https://archmetaldbm.github.io/GlobaLID-Edu/interpretation_additional.html - 2023-11-25T11:55:15.220Z + 2023-11-25T12:06:39.450Z https://archmetaldbm.github.io/GlobaLID-Edu/interpretation_potential.html - 2023-11-25T11:55:15.228Z + 2023-11-25T12:06:39.458Z https://archmetaldbm.github.io/GlobaLID-Edu/interpretation_combined.html - 2023-11-25T11:55:15.232Z + 2023-11-25T12:06:39.466Z https://archmetaldbm.github.io/GlobaLID-Edu/summary_interpretation.html - 2023-11-25T11:55:15.240Z + 2023-11-25T12:06:39.470Z https://archmetaldbm.github.io/GlobaLID-Edu/summary.html - 2023-11-25T11:55:15.248Z + 2023-11-25T12:06:39.494Z https://archmetaldbm.github.io/GlobaLID-Edu/references.html - 2023-11-25T11:55:15.256Z + 2023-11-25T12:06:39.514Z https://archmetaldbm.github.io/GlobaLID-Edu/appendix.html - 2023-11-25T11:55:15.264Z + 2023-11-25T12:06:39.526Z https://archmetaldbm.github.io/GlobaLID-Edu/about.html - 2023-11-25T11:55:13.416Z + 2023-11-25T12:06:38.238Z diff --git a/visualisation.html b/visualisation.html index e64413d..b36b845 100644 --- a/visualisation.html +++ b/visualisation.html @@ -425,8 +425,8 @@

The bi-plot (Figure 20.1) is by far the most common option to display lead isotope data. Since there are four isotopes of Pb, twelve combinations of isotopic ratios can be derived. The use of paired ratios depends on the instruments used and the scientific disciplines of the studies. In the early days, Pb isotopic ratios were often reported based on 206Pb-based ratios as 204Pb could not be measured precisely. However, in the 2000s, the advent of the multi-collector mass spectrometer (MC-ICP-MS) and the double- or triple-spiked technique created a huge amount of Pb isotope data with precisely measured 204Pb. Conventionally, environmental science tends to use the ratios based on 206Pb, which however generates plots with linear patterns and thus a low discrimination power (Ellam 2010). In geological literature, ratios based on 204Pb are commonplace which enable a better visualisation of system closure time (or model age) and U-Th-Pb composition (or µ and κ) of parental source(s) (Albarède et al. 2012). However, it has to be kept in mind that all two-dimensional plots incompletely represent a dataset. All twelve combination plots are suggested to be tested to view the full isotopic extent of ore deposits (Albarede et al. 2020). Ideally, the Pb isotopic ratios should be considered in a three-dimensional space.

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Figure 20.1: A binary plot of Roman lead artefacts in comparison with ore districts in Germany.
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Figure 20.1: A binary plot of Roman lead artefacts in comparison with ore districts in Germany.

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Instead of isotopic ratios, Albarède et al. (2012) advocate the use of calculated geological model parameters, namely the model age (T), U/Pb (μ), and Th/U (κ) to discriminate potential ore sources in provenance studies (Figure 20.2). As shown in chapter 3, 206Pb, 207Pb, and 208Pb are generated by radioactive decay of their parental isotopes 238U, 235U, and 232Th, respectively. We can therefore calculate the model age, 238U/204Pb and 232Th/238U from the Pb isotope ratios determined for a given sample using the equations provided in Albarède et al. (2012) or any other of the Pb isotope models mentioned in chapter 3 by using, e.g., an R script.

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Figure 20.2: A binary plot of Roman lead artefacts using geological parameters of model age (T), U/Pb (μ), and Th/U (κ).
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Figure 20.2: A binary plot of Roman lead artefacts using geological parameters of model age (T), U/Pb (μ), and Th/U (κ).

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(a) Ellipses based on 206Pb vs 207Pb
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(a) Ellipses based on 206Pb vs 207Pb
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(b) Ellipses based on 206Pb vs 208Pb
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(b) Ellipses based on 206Pb vs 208Pb

Figure 20.3: Binary scatter plots with 90% confidence ellipses around data points.

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(a) KDEs based on 206Pb vs 207Pb
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(a) KDEs based on 206Pb vs 207Pb
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(b) KDEs based on 206Pb vs 208Pb
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(b) KDEs based on 206Pb vs 208Pb

Figure 20.4: Binary plots with the KDEs of mining regions. Note that three different color gradients from light to dark represent respective intervals of 95%, 75%, and 50%.

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\] Figure 20.5 displays a ternary scatter plot.

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Figure 20.5: A ternary plot of lead isotope data visualising the same dataset.
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Figure 20.5: A ternary plot of lead isotope data visualising the same dataset.

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The use of any single bivariate plot is insufficient for provenancing and is visually confusing when the ratios overlap. Therefore, additional diagrams are needed to show other combinations of isotopes. Three-dimensional plots represent the distribution of data in a three dimensional space (Figure 20.7) which has a higher discrimination power and is therefore better suited for provenance studies. The downside is that it is inherently difficult to read a 3D diagram and, therefore, a rotatable version is highly recommended.

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Figure 20.7: The same dataset visualised as a three-dimensional plot.
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Figure 20.7: The same dataset visualised as a three-dimensional plot.

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Warning: no DISPLAY variable so Tk is not available
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Figure 20.8: Three-dimensional plot with the KDEs of the mining regions.
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Figure 20.8: Three-dimensional plot with the KDEs of the mining regions.