TOPIC 4B Chemical Petrology I

Chemical Petrology I: Major and Minor Elements: 

TOPIC 4 Chemical Petrology I: Major and Minor Elements 1

Overview: 

Overview Chemical Analyses of Igneous Rocks Major, Minor and Trace Elements Rare Earth Elements Elemental Analyses Analytical Results Major and Minor Elements in the Earth’s Crust Studies of igneous rocks Classification Schemes (mode and norm) Types of Variation Diagrams Interpretation of Variation Diagrams and Magma Series 2

Variation Diagrams: 

Variation Diagrams How do we display chemical data in a meaningful way? The main objective of any research program on igneous rocks is to describe and display chemical variations for simplicity and to facilitate condensing information. The best way to simplify and condense analytical data is by graphical means. After fieldwork, mapping, sampling and analysis, you end up with hundreds of chemical analysis. We can show the results as a table. But still the relationships between components are not clear. A table of geochemical data from suite of rocks from a particular igneous province, metamorphic terrain or sedimentary succession may at first glance show a meaningless variation in the concentration of individual elements. Since the samples are probably geologically related , one major goal for geologists is to find a way to simplify the variation between individual rocks and condense this information so that relationships between the individual rocks may be identified. 3

Variation Diagrams: 

This table represents chemical data for a suite of volcanic rocks, from a single volcano, located in the Red Sea area. The results are listed, from 1 to 13, by increasing SiO 2 content varying from 45.5 to 61.22 wt%. 4 Variation Diagrams

Variation Diagrams: 

Variation Diagrams How do we display chemical data in a meaningful way? There is no single best way to display data and it is kind of an art form. The objective is to find the parameters that show systematic variation so that we can investigate the possible causes. The method most commonly used for graphically evaluating geochemical data is the variation diagram . 5

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6 Variation diagrams

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7 There are two main types of variation diagrams in particular that are used by geochemists: bivariate plots and triangular variation diagrams . Major element data is used to construct variation diagrams either an x-y graph or on a triangular graph . These types of diagrams are used to show the interrelationship between elements in a data set and then these relationships are used to infer geochemical processes . Variation Diagrams

Variation Diagrams: 

Variation Diagrams Types of variation diagrams: Bivariate Plots: This is a bivariate graph or scattergram where two selected variables or parameters are plotted on an x-y diagram. More than one components can be used for these parameters. For example, components that have similar behaviors or chemical properties (such as FeO , MgO and MnO ) can be combined. Triangular diagrams: Use 3 parameters with relative proportions In either of these diagrams, correlations and trends are shown in the pattern. 8

Variation Diagrams: 

For variation diagrams any element or oxide may be chosen, for example, as the x-axis) on a bivariate plot resulting in a similar set of diagrams. However, individual analysis would not appear in the same sequence on each diagram. SiO 2 is generally chosen because it is the most abundant oxide in igneous rocks and exhibits a wide variation in composition. This type of graphical presentation is useful for large quantities of analytical data and yields an approximation of inter-element variations for a group of samples. However, genetic links between samples can not be inferred directly from Harker diagrams, i.e. that the lowest SiO 2 content present on the diagram represents the original or first liquid, for the group of samples presented, from which all other liquids were derived. 9 Variation Diagrams

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10 What to plot: any component (major, minor, trace) combinations of elements and elemental ratios The main intent of a bivariate plot is to show variation between samples and to identify trends . Therefore, the element plotted along the x-axis of the diagram should be selected to show either the maximum variability between samples or to indicate a particular geochemical process . Also, for an extensive geochemical study of a problem, a very large number of variation diagrams may be used to define the possible geological processes involved. Bivariate Plots

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11 One particular type of variation diagram where SiO 2 is plotted along the x-axis is known as the 'Harker diagram '. Diagrams of this type were popularized as long ago as 1909 by Alfred Harker in his book Natural history of igneous rocks Bivariate Plots

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Bivariate (x-y) diagrams Harker diagrams for Crater Lake Harker variation diagram for 310 analyzed volcanic rocks from Crater Lake (Mt. Mazama), Oregon Cascades. Data compiled by Rick Conrey (personal communication). Note that the trends are clear but there is always some scattered data due to: Analytical errors Variations generally found in nature

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13 With larger data sets computer programs are used for the initial screening of the data to help decide on ‘correlations’ . If a correlation matrix is used, it should be noted that good correlations can occur from a cluster of data points and a single outlier . Also poor correlations can occur if the data set contains several populations that each have a different trend . Bivariate Plots

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14 Correlation matrices calculate the correlation coefficient (r) for each pair of variables in a data set . Most often there are as many as 30 variables to consider from one data set so there could be 435 scatter plots that can drawn from this one data set. The correlation matrix is a good way to identify highly correlated element pairs before plotting data on a conventional scatter plots. Bivariate Plots

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15 When r = +1 then there is a perfect positive linear relationship between x and y. When r = -1 there is a perfect negative linear relationship between x and y. If r = 0 then there is no relationship between x and y at all. Correlation Coefficient (r)

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17 However, geochemical investigations are more commonly designed to solve a particular problem and to test a hypothesis that is usually formulated from geological or other geochemical data. In this case, the plotting parameter for a variation diagram should be selected based on the process that is being tested . Bivariate Plots

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18 For example, if a crystal fractionation mechanism is expected for a suite of igneous rocks, then an element that is contained in the fractionating mineral or that will be either enriched or depleted in the melt should be selected. Bivariate Plots

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19 A binary eutectic phase diagram explains the chemical behavior of two immiscible (unmixable) crystals from a completely miscible (mixable) melt, such as diopside (CaMgSi 2 O 6 ) and anorthite (CaAl 2 Si 2 O 8 )

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21 The oldest form of variation diagrams and one of the most frequently used ways of plotting major element data are called Harker diagrams where different oxides are plotted against SiO 2 . Harker Diagrams: bivariate plots using SiO 2 along the x-axis

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23 SiO 2 is commonly chosen as the plotting parameter for many igneous rock series and for suites of sedimentary rocks with a variable quartz content because it is the major constituent of the rock and shows greater variability than any of the other oxides. Harker Diagrams: bivariate plots using SiO 2 along the x-axis

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24 However, the fact that SiO 2 is the most abundant oxide means that there are a number of problems that should be considered such as: (1) a negative tendency (2) spurious correlations and (3) a reduced scatter of values as SiO 2 increases. Harker Diagrams: bivariate plots using SiO 2 along the x-axis

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25 The MgO plot is one of the most commonly used alternatives to the Harker diagram. It is most appropriate to use for rock suites that contain a lot of mafic minerals , because the range of SiO 2 concentrations may be small. MgO is an important component of the solid phases in equilibrium with mafic melts and shows a great deal of variation either as a consequence of the breakdown of magnesian phases during partial melting of their removal during fractional crystallization. Bivariate plots with MgO on the x-axis

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26 Another parameter, the magnesium-iron ratio or magnesium number, is a useful index of crystal fractionation in basaltic liquids because the Mg-Fe ratio changes noticeably in the early stages of crystallization. The magnesium-iron ratio is expressed in wt% as: 100*[MgO/(MgO + FeO)] or 100*[MgO/(MgO + FeO + Fe 2 O 3 )] or as an atomic fraction 100*[Mg 2+ /(Mg 2+ + Fe 2+ )] The inverse of this ratio is also used to measure iron enrichment . Bivariate plots using the magnesium number

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27 Triangular variation diagrams are used when it is useful to show simultaneous change between three variables. The plotting procedure for triangular diagrams is illustrated in Figure 3.17. This is most conveniently done by microcomputer and Topley and Burwell (1984) give an example of a versatile interactive program written in BASIC. Triangular Variation Diagrams

Triangular Representation of More Than Three Components: 

Triangular diagrams represent three components . These may be simple elements , compound oxides , or more complex components such as mineral formula components. To represent more than three components on one triangle, some components are combined , and some components may be excluded in the graphical representation . 29 Triangular Representation of More Than Three Components

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30 General pyroxene formula is XYSi 2 O 6

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33 The AFM diagram is the most commonly used triangular variation diagram and is named based on the oxides plotted at its apices: A lkalis (Na 2 O + K 2 O), F e oxides (FeO + Fe 2 O 3 ) and M gO. The igneous AFM diagram is different from the metamorphic diagram with the same name that is used to show changing mineral compositions in Al 2 O 3 -FeO-MgO space. The AFM diagram

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35 The plotting parameters are calculated by summing the oxides (Na 2 O + K 2 O) + [(FeO + Fe 2 O 3 ) recalculated as FeO] + MgO and then recalculating each as a percentage of the sum. There is some ambiguity in the way that the Fe- oxides should be treated and some of the ways that they are dealt with are: F = (FeO + 0.8998Fe 2 O 3 ), i.e. all Fe 2 O 3 converted to FeO is the same as F = total (FeO + Fe 2 O 3 ) expressed as FeO i.e. FeO[total] but different from F = (FeO + Fe 2 O 3 ) expressed as raw wt % The AFM diagram

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36 Some scientists also include MnO with the Fe-oxides which has been shown that the differences are minor and are not likely to result in serious misplotting. But it is recommended that a standard procedure is used and that F is calculated as total Fe , i.e. (FeO + Fe 2 O 3 ) recast as FeO. This accommodates XRF analytical data where the separate oxidation states of iron cannot be determined. The AFM diagram

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37 Most geochemists use oxide wt % when plotting data on an AFM diagram but sometimes atomic proportions are used and it is not always clear which method has been used. The shape of the trend is similar in each case but the position of the atomic proportions plot is shifted away from the Fe apex relative to the position of the oxide plot for the same data. The AFM diagram

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38 The AFM diagram is most commonly used to distinguish between tholeiitic and calc-alkaline differentiation trends in the subalkaline magma series . These are shown as dividing lines separating the rocks of the calc-alkaline series and rocks of the tholeiite series . The AFM diagram

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Alkali vs. Silica diagram for Hawaiian volcanics: Seems to be two distinct groupings: alkaline and subalkaline

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F A M Calc-alkaline T h o l e i i t i c AFM diagram: can further subdivide the subalkaline magma series into a tholeiitic and a calc-alkaline series

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41 Examples of the trends characteristic of the tholeiitic and calc-alkaline rock series can be plotted. The tholeiitic trend is shown by Thingmuli volcano in Iceland and the calc-alkaline trend is for the average compositions of the Cascades lavas. The AFM diagram

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43 It should be noted that the AFM diagram is limited by the extent to which petrogenetic information may be extracted. This is mainly a function of the trivariate plotting procedure, which does not use absolute values and only a part of the rock chemistry. In most rocks the A-F-M parameters make up less than 50% of the oxide weight percentages and cannot therefore fully represent the rock chemistry. Issues using the AFM diagram

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44 In addition, when plotting a rock series different proportions of each rock are normalized to 100 %. This distorts the plotted values. For example, in a series of volcanic rocks with a compositional range from basalt to dacite about 40 % of the basalt is used when plotting onto an AFM diagram whereas only about 15% of the dacite is used. Therefore, the interpretation of trends on triangular diagrams must be carried out with caution. Issues using the AFM diagram