Quantitatively determining the amount of chemical weathering within sedimentary rocks (and weathering profiles) took a major step forward with the creation of the chemical index of alteration (CIA) 40 years ago. The CIA relates the proportion of immobile aluminum to the mobile cations of calcium, sodium, and potassium and is grounded in empirical and modeled geochemical data for mineral reactions that occur during hydrolysis. However, the CIA should be applied cautiously because it is a one-dimensional value that in the most complex situations, as with clastic sedimentary rocks, homogenizes the compositional inputs of source, weathering, sorting, and diagenesis. Subsequently developed two-dimensional (2D) ternary diagrams (Al2O3–CaO*+Na2O–K2O; Al2O3–CaO*+Na2O+K2O–FeO+MgO) permitted the capacity to explore mineralogical-geochemical pathways in data sets that may separate those inputs, but interpreting the ternary diagrams may be complicated because they differentiate and group certain elements. Here we develop a three-dimensional tetrahedral diagram (Al2O3–CaO*+Na2O–K2O–FeO+MgO, A–CN–K–FM) that incorporates the same critical elements and permits the simultaneous assessment of felsic and mafic rocks and minerals on the same diagram while retaining the ability to separate plagioclase from alkali feldspar and monitor post-depositional potassium changes. Using the tetrahedral plot, we show that both the CIA value and positions on the 2D ternary diagrams can generate potentially misleading interpretations without properly budgeting the ferromagnesian components in parallel. We first show how the tetrahedron works, then use it with numerous previously published examples to identify how the competing mafic and felsic inputs shape the composition of source rocks, weathering profiles, actively transporting sediment, paleosols, and sedimentary rocks in sedimentary petrogenesis.

Since the invention of the chemical index of alteration (CIA) 40 years ago (Nesbitt and Young 1982, 1984, 1989), it has been used to address the composition of unlithified clastic sediments and sedimentary rocks that stretch as far back as nearly 4 billion years—on both Earth and Mars. At the time of this writing in 2022, these three papers have been cited more than 9000 times, indicating the transformational nature of the CIA concept and the derivative Al2O3–CaO*+Na2O–K2O (A–CN–K) ternary diagram (Fig. 1). The most widespread application of the CIA, an expression that quantifies the molar proportion of immobile aluminum to more mobile calcium, sodium, and potassium (Nesbitt and Young 1982), is as a tool to assess paleoweathering and paleoclimate. The effectiveness of the concept relies on a foundation of extensive field and geochemistry studies of weathering (soil) profiles (e.g., Fig. 1a) verified by kinetic and thermodynamic modeling of weathering reactions of mineral phases (Nesbitt and Young 1984; Nesbitt et al. 1997). The CIA, which is equivalent to the A value on the A–CN–K ternary diagram (Fig. 1b), is most applicable to the study of feldspar-dominated rocks of the continental crust and derived sediments because the elements in the equation center around plagioclase and alkali feldspar and their principal weathering products, kaolinite and illite (Nesbitt and Young 1984; Banfield and Eggleton 1990; Nesbitt et al. 1997). Generally speaking, sediments derived from continental sources that have CIA values near 50 are considered nearly unweathered, those with CIA values around 75 are considered moderately weathered, and those with CIA values near 100 are thought to have experienced extreme chemical weathering.

One limitation is that the CIA reduces the ratioed geochemical components to a one-dimensional (1D) value that homogenizes compositional input from different controlling factors dependent on the studied material. For example, in weathering profiles, the CIA is determined from a combination of source and chemical weathering; in actively transporting sediments, CIA incorporates source, chemical weathering (in all but polar or arid settings), and sorting effects; in paleosols, CIA incorporates source, chemical weathering, and diagenesis; in sedimentary rocks, CIA combines input from source, chemical weathering (in all but polar or arid settings), sorting effects, and diagenesis. Consequently, the 1D CIA values cannot simply be compared across time and space without careful consideration for the full set of events that shaped the composition of any specific sample set.

A second limitation of the CIA is that it does not address the role that mafic igneous (olivine, pyroxene, amphibole, biotite) and secondary clay minerals (e.g., smectites, vermiculite, chlorite) play in whole-rock sedimentary compositions. This shortcoming has been tempered by the development of the 1D mafic index of alteration (MIA; Babechuk et al. 2014), which adds the ferromagnesian component to the index value. However, like with the CIA, the MIA as a 1D value homogenizes all the inputs that shape sediment composition.

Expanding the CIA and MIA concepts to conventional two-dimensional (2D) A–CN–K (feldspar) and A–CNK–FM (mafics) ternary diagrams (Figs. 1b, 1c) opens the possibility to plot data and study mineralogical-elemental pathways that lead to whole-rock sample compositions. The A–CN–K (feldspar) ternary diagram primarily focuses on the effects of chemical weathering of felsic plutonic rocks (and derived sediments) that dominate the continental crust (Figs. 1a, 1b). While a large volume of sediments and sedimentary rocks come from the continents, a shortcoming of this 2D plot is that ferromagnesian components are not considered despite all but the most felsic rocks having mafic phases, such as biotite and amphibole. Studies that address mafic sources commonly focus attention on the original mafics ternary diagram (A–CNK–FM; McGlynn et al. 2012) or associated derivative plots (Babechuk et al. 2014; Babechuk and Fedo, in press) given the overall relative paucity of sodium and potassium. This approach adds the critical FM component, but results in the grouping of CN+K, thus losing the capacity to separate plagioclases from alkali feldspars, and track changes in potassium. Some studies take advantage of multiple ternary plots (e.g., Roy et al. 2008) to address the role of different phases, but the varying element combinations on poles still make reconciliation of competing mineralogical controls on whole-rock compositions difficult.

Even the combined use of different ternary diagrams comes with limitations of losing resolution in identifying individual mineralogical controls or deciphering all processes that influence whole-rock compositions. The A–CN–K plot, in separating the calcium and sodium components from potassium, differentiates between plagioclase and alkali feldspar, which weather at different rates (Nesbitt and Young 1984; Nesbitt et al. 1997). Further, some mafic minerals such as vermiculite and chlorite contain Al but no appreciable Ca, Na, or K, such that they inherently plot at the top of the A–CN–K ternary diagram with a CIA of ∼100. By not considering the ferromagnesian component of these minerals, there is potentially misleading inferences regarding the full extent of weathering and associated mineralogy of weathering profiles and clastic sediment/sedimentary rocks. Ternary diagrams expanded to include Fe and Mg allow the mafic component to be budgeted, but at the same time lose the capacity to resolve plagioclase from alkali feldspar by combining calcium, sodium, and potassium as a single component. Retention of K as a separate component is important in using the general approach to study the weathering of intermediate-to-felsic igneous rocks (where K minerals become increasingly abundant), mafic paleosols and sedimentary rocks sourced from basalt that experienced potassium metasomatism, and in sedimentary basins that have mixed mafic and felsic source rocks (e.g., Fig. 2a). An ideal solution to the forced combinations of elements as poles on ternary diagrams, which result in varying proportional weighting and producing different mineral transformation pathways, is to retain as much specific elemental separation on plotting diagrams as is reasonable.

The purpose of this paper is to present a novel three-dimensional (3D) tetrahedral A–CN–K–FM plot that retains the advantages of both the feldspar and mafics ternary diagrams while eliminating the principal shortcomings of each. This tetrahedron includes all major elements (except Si) that exert the greatest overall control on sediment and sedimentary rock compositions in a single plotting space with more resolution on poles than ternary diagrams. The elements (Al, Ca, Na, K, Fe, Mg) collectively make up >95% of mafic (basalt/gabbro) to felsic (granite/rhyolite) rock compositions, if recalculated without Si, and are separated on the tetrahedron in a way that separates most main mineral groups in the composition space. We develop use of the A–CN–K–FM tetrahedron following a source-to-sink approach that addresses the four principal controlling inputs that shape composition in siliciclastic petrogenesis: source (Fig. 2a), chemical weathering (Fig. 2b), sorting (Figs. 2c, 2d), and diagenesis (Figs. 2e, 2f) using previously published examples that range from felsic to mafic in composition.

Since its original creation in the study of the Paleoproterozoic Huronian Supergroup (see Fig. 2f for an image of the Gowganda Formation, Huronian Supergroup), the CIA has been mostly applied to estimating the effects of chemical weathering in the study of paleoclimatic reconstructions. Subsequently, Nesbitt and Young (1989) expanded the CIA concept to the impact of diagenetic effects on paleosols. Rainbird et al. (1990) elegantly apply this approach in their study of the paleosol developed on the Ville Marie granite (also discussed below), which was later expanded on as a general consideration for interpreting paleosols and sedimentary rocks (Fedo et al. 1995). Although weathering and diagenesis are regularly used for interpreting the sedimentary archive, application of the CIA concept and derivatives may be used to assess all aspects of sedimentary petrogenesis, from source to sink.

Johnsson (1993) and Nesbitt (2003) provided excellent overviews that detail the roles and feedback loops of four major contributing inputs that shape sediment (and sedimentary rock) mineralogy and geochemistry: (1) source, (2) chemical weathering, (3) mechanical-physical sorting in transport, and (4) diagenesis. Compositional changes may occur in variable amounts with a net result that a final sedimentary rock composition represents a summation of all the inputs. To meaningfully frame composition then, each factor needs to be accounted for as best as possible.

Source, or provenance, dictates all original compositional (and textural) traits that sediments and sedimentary rocks inherit. For the sake of most siliciclastic sediments and sedimentary rocks, the feldspar-dominated bulk continental crust, essentially granodiorite on average (McLennan 1993; Rudnick and Gao 2005), represents a meaningful primary starting point. It is also recognized that volcanic rocks, including volcanic glass, comprise ∼12% of the exposed crust (Nesbitt and Young 1984), a significant part of which is likely to be mafic (e.g., Wolff-Boenisch et al. 2004). From the standpoint of sediment composition, not only will broad source compositional classifications matter (e.g., felsic vs. mafic), but so does the texture, such as crystal size, in the source. For example, the abundance of glass in volcanic rocks, which preferentially dissolves relative to a mineral counterpart (Wolff-Boenisch et al. 2006; Lo et al. 2017), will lead to different gravel, sand, and mud proportions relative to sediment derived from counterpart intrusive rocks. Primary igneous sources, in turn, get their composition in response to major tectonic settings, which may be interpreted based on the sedimentary rock composition (Bhatia and Crook 1986; McLennan et al. 1990; Verma and Altin-Armstrong 2013).

Weathering profiles developed on bedrock (Nesbitt and Markovics 1997; Figs. 1a, 2b) or on temporarily stored sediments between major transport episodes (Johnsson et al. 1988) play a crucial role in shaping sediment compositions, particularly where chemical weathering is intense because of solution chemistry (Nesbitt 2003), biologic system catalyzation (Bennett et al. 2001), and (or) extended times to develop mature soils (Weil and Brady 2017). A main compositional transformation that occurs during chemical weathering is the liberation of mobile elements to groundwater while converting primary minerals and glass to various clay minerals via hydrolysis (McLennan 1993; Nesbitt et al. 1997). The reactions result in weathering profile compositions forming along predictable pathways in conventional A–CN–K and A–CNK–FM space (Nesbitt and Young 1984, 1989; Nesbitt et al. 1997; Figs. 1b, 1c) and other mafic ternary spaces (Babechuk et al. 2014; Babechuk and Fedo, in press).

Once weathering profiles undergo mass wasting events, either as slope failures or though high-discharge flood events, their disaggregation through physical processes may also result in substantial compositional change though several pathways. At the highest level, silt- and clay-sized detritus will be carried as suspended load in fluvial systems decoupling from co-existing sand, which travels more as bedload (Johnsson 1993). At this point, substantial loss of compositional linkage to provenance may occur as mass balance across grain sizes becomes more difficult to reconcile. Even under bedload-only transport conditions with a point source (or limited source variability), mineral segregation (unmixing) based on mineral density, shape, and grain size will occur as a result of hydrodynamic sorting (e.g., Garzanti 1986; Nesbitt and Young 1996; Mangold et al. 2011; Fedo et al. 2015; Figs. 2c, 2d). As the geologic complexity and area of source regions increase, then the role of widespread source-composition mixing occurs even while local-scale unmixing via sorting takes place. The end result is that once sediment enters the transport system, mechanical processes drive sediment compositions away from the original source.

Diagenetic effects form the last major input in contributing to the evolution of sedimentary rock composition. Sedimentary layers begin interacting with basinal fluids directly after deposition, leading to the possibility that chemically reactive phases will begin to change while cements of varying compositions fill pore spaces and transform weathering profiles into paleosols (Fig. 2e) and unconsolidated sediment into rock (Fig. 2f). In sand and sandstone, feldspars (and rock fragments) are particularly susceptible to compositional modification via dissolution (loss of minerals) and replacement by multiple phases (Walker 1984; Milliken 1989; Cox et al. 2002; Parsons et al. 2005). Mudstones, or clay minerals in paleosols, are also susceptible to substantial changes whether it be from illitization of smectite (Foscolos 1984; Altaner and Ylagan 1997) or transformation of kaolinite to illite via potassium addition (Nesbitt and Young 1989; Fedo et al. 1995), processes that may occur long after the time of formation or deposition (e.g., Macfarlane and Holland 1991). The overwhelming increase in the abundance of illite in the sedimentary record through geologic time argues for the common occurrence of these transformations, both of which are accompanied by changes in major-element geochemistry.

The utility of the 1D CIA (Nesbitt and Young 1982) or MIA (Babechuk et al. 2014, Babechuk and Fedo, in press) and the 2D A–CN–K and various mafic-centric ternary plots are so well established that their use is nearly an expectation in any study seeking to understand paleoweathering, paleoclimate, and diagenesis using major-element geochemistry. The success of using the 1D indices and associated ternary diagrams, especially in tandem, centers on their inherent connection to mineralogy and geochemistry, where trends in geochemical data should be correlated to the compositions of mineral phases.

After first describing its potential in Modi et al. (2009) and later in Fedo (2021) and Babechuk (2021), here we present the A–CN–K–FM tetrahedron (Fig. 3), which provides a solution that retains the strengths of the A-CN-K and mafic-focused ternary plots simultaneously. While the combination of Fe and Mg on one pole comes with some limitations related to potential contrasting behavior of these elements in sedimentary systems (Babechuk et al. 2014; Babechuk and Fedo, in press), the A–CN–K–FM tetrahedron better permits the simultaneous study of source, chemical weathering, hydrodynamic setting, and diagenesis using the proportion of refractory to labile minerals and elements. Expanding this concept, a companion study (Babechuk and Fedo, in press) explores other element arrangements in tetrahedra to identify other geochemical patterns in sedimentary petrogenesis.

The Al2O3 (A) component is placed at the top of the tetrahedron, while the CaO*+Na2O (CN), K2O (K), and FeO(T)+MgO (FM) components are placed around the base (Fig. 3; all in molecular proportions). Following conventions established in Nesbitt and Young (1982, 1984, 1989), CaO* represents a transformation of the total CaO to that only in the silicate fraction by removing Ca linked to carbonates and phosphates (and sulfates; Gwizd et al. 2022) following the expression in Fedo et al. (1995). Iron data used in this paper are expressed as FeO(T). The A value on the tetrahedron is equivalent to the reduced MIA (MIA(R)) iteration where the iron component is expressed as FeO(T). We utilize two views to portray data: (1) a perspective view with the CN pole closest to the viewer and the A pole at the top (Fig. 3a) and (2) a nadir view with the A pole in the center and the CN, K, and FM poles around the outer edge (Fig. 3b). One face of the tetrahedron represents the A–CN–K (feldspar) ternary diagram, which has been shaded green on all tetrahedral plots as a reference frame throughout this paper. All tetrahedral plots used in this paper were made using TetLab (version 1.9) software (© Peter Appel). Each side of the tetrahedron is divided into 10% increments (small tick marks). We have avoided plotting all the percentage connecting lines for clarity.

To demonstrate how the A–CN–K–FM tetrahedron improves upon the present understanding of using the CIA, MIA, and (or) a combination of the feldspar and mafics ternary diagrams (Fig. 1), we first plot a number of common minerals critical to igneous and metamorphic source rocks and their weathered components that end up in the sedimentary environment in the tetrahedron (Fig. 3). Mineral abbreviations follow the recommended usage in Whitney and Evans (2010), where Pl = plagioclase, Kfs = alkali feldspar, Ms = muscovite, Bt = biotite, Ol = olivine, Aug = augite, Hbl = hornblende, Cal = calcite, FeOx = iron oxides, Non = nontronite, Vrm = vermiculite, Chl = chlorite, Mnt = montmorillonite, Ilt = illite, and Kln = kaolinite. Compositional data come from Deer et al. (1992), except for plagioclase (Fedo et al. 1997) and the generalized “iron oxide” composition. On the A–CN–K face, the line connecting plagioclase and alkali feldspar is known as the feldspar join (e.g., Nesbitt and Young 1984, 1989).

A key observation about the mineral distribution in this space is that of major rock-forming minerals only plagioclase (Pl, of all compositions), alkali feldspar (Kfs), kaolinite (Kln), and calcite (Cal) sit on the A–CN–K face; other phases sit inside the tetrahedron or at the FM pole. As a result, the use of only A–CN–K does not monitor any whole-rock compositional effects resulting from changes in non-aluminous ferromagnesian minerals (e.g., Fe-oxides, olivine). The A–CN–K ternary diagram also does not entirely identify the role of aluminous ferromagnesian minerals (e.g., amphibole, biotite, chlorite, vermiculite), restricting their contribution to only the A, CN, or K, components of these minerals. The result of reducing the aluminous ferromagnesian-bearing minerals to only their projection on the A–CN–K face may result in a significant loss of comparative resolution of their mineralogical control on whole-rock composition. For example, the composition of biotite, a common mineral in felsic igneous rocks of the continental crust, sits deep inside the tetrahedron owing to abundant Fe and Mg as well as Al (Fig. 3). However, given the presence of aluminum and potassium, biotite will also plot in A–CN–K space in a position essentially on the A–K join at an A (equivalent to CIA) value of ∼61. The position in A–CN–K space results as a projection from the FM pole, through the data point in the tetrahedron, to the back side of the A–CN–K face (gray line through red-filled circle, to open red circle). The projected location gives a sense of biotite being much more aluminous than its actual composition by neglecting the FM component.

Even more extreme examples, such as chlorite and vermiculite, which because they lack calcium, sodium, and potassium but do have aluminum, plot at an A (CIA) value of ∼100, similar to kaolinite, along a projection line that connects the FM and A poles (Fig. 3; only the projection of chlorite shown). The significance of the high CIA of these minerals and projecting aluminous FM-bearing minerals to the A–CN–K face is made apparent in this study in terms of potentially misleading geological interpretations using mudstone. Utilization of the A–CN–K–FM tetrahedron overcomes these problems by allowing the FM components to contribute to the analysis while still preserving the separation of the CN and K components characteristic of plagioclase and alkali feldspar.

Igneous source compositions

In certain circumstances, such as when source rocks are in contact with, or adjacent to, derived sedimentary material direct assessment of the source is possible. This provides the greatest capacity to pair source and sediment compositions and document the processes that drive compositional departures. As established in Nesbitt and Young (1984, 1989) and Fedo et al. (1995) quartzo-feldspathic igneous rocks (and metamorphosed equivalents) ranging in composition from granite to tonalite have CIA values of approximately 50, which means that regardless of starting composition fresh rocks have the same starting value from which to compare subsequent alteration from weathering through diagenesis. However, in 3D A–CN–K–FM space, substantial whole-rock compositional variation controlled by the FM component becomes apparent. These variations are reflected in the MIA changes in igneous source rocks that are not captured by the CIA (see Babechuk and Fedo, in press).

Figure 4 illustrates a range of igneous rocks of differing compositions (points 1–5) similar to the range used in Fedo et al. (1995). In both the perspective (Fig. 4a) and nadir (Fig. 4b) views, the increasing amount of ferromagnesian mineral components from granite to diorite forms a trend towards the FM pole. As expected, basalt (point 1) plots closest to the FM pole. In both views, gray lines connect the FM pole and the data point (red-filled circles) in the tetrahedron to their projected positions along or near the feldspar join (open circles; especially in Fig. 4b) on the back side of the A–CN–K face. This sample trend towards FM in the tetrahedron somewhat resembles the one apparent in the A–CNK–FM ternary plot whereby igneous rock fall close to a line joining the composition of feldspars (halfway along the A–CNK join) to the FM pole. What is clear is that rocks with increasingly abundant ferromagnesian minerals show the greatest loss of compositional fidelity on the A–CN–K triangle. By contrast, igneous sources that are the most felsic (point 5) have the least amount of ferromagnesian phases such that the data in the tetrahedron plot close to the A–CN–K face.

Toorongo granodiorite—continental

A core aspect of the CIA (Nesbitt and Young 1982) and MIA (Babechuk et al. 2014) concepts and derivative ternary diagrams has been their application to deciphering weathering/paleoweathering and associated climate/paleoclimate conditions. A well-characterized, Holocene, deeply weathered profile is developed on top of the Toorongo granodiorite in southeastern Australia (Nesbitt and Markovics 1997). Fifteen samples originating at an unweathered corestone and going outward to material filling joints show CIA values that range from 50 to 95, indicative of incipient-to-advanced chemical weathering.

All data from the Toorongo weathering profile are plotted in Fig. 6, with five specific points (1–5) noted to show how composition varies from fresh to weathered. In A–CN–K–FM tetrahedral space, it is clear the unweathered granodiorite (point 1, filled blue circle) has a substantial FM component, lying well within the tetrahedron. Projecting this point (open blue circle) onto the A–CN–K face reveals that the CIA has the expected value of 50 (Nesbitt and Young 1984; Fedo et al. 1995) and sits on the feldspar join. Points 2–4 within the tetrahedron represent samples increasingly moving away from the unweathered source along a systematic linear trend directed mainly away from the CN pole that shows increasing residual accumulation of aluminum (formation of kaolinite) with the loss of calcium and sodium (Nesbitt and Markovics 1997). The pathway of samples between points 2–4 also shows a minor shift away from the FM pole, representing a very minor component of Mg loss alongside Ca and Na. However, most Mg is retained in vermiculite (similar to K in K-feldspar and illite) during these early weathering stages (Fig. 1a; Nesbitt and Markovics 1997). At the transition between points 4 and 5, approximately corresponding to the stage of near-complete Ca and Na loss, there is a shift in the sample trend to one of a steeper slope upwards towards the A apex. This shift is dominated by a vector away from the FM pole (Mg loss) but also has a minor shift away from the K pole.

With all of the data points projected (gray lines) onto the A–CN–K face (open red circles), the samples form the expected linear array (noted by red arrows, Fig. 5) from plagioclase hydrolysis during early to intermediate weathering stages, as well as the inflection towards the A pole after calcium and sodium exhaustion and subsequent potassium loss from alkali feldspar and illite hydrolysis. However, the A–CN–K projection neglects that the advanced weathering stage also includes the accumulation of Al from Mg loss (due to more advanced weathering of early formed vermiculite) in addition to the minor component of K loss (loss from illite) as two contributing pathways to kaolinite formation in addition to that formed after plagioclase. In contrast, the tetrahedral plot shows a singular compositional space whereby all of the major labile components removed during incipient to advanced weathering can be evaluated collectively, better revealing the advanced secondary mineral transformations that contribute to Al accumulation in the profile.

As pointed out in Nesbitt and Markovics (1997), a strict interpretation of major-element trends as arising from in situ mineral transformation at the advanced stages of weathering (near point 5) is not warranted due to the translocation of some highly aluminous clay minerals. This would result in some of the trends in A–CN–K–FM tetrahedral space (and A–CN–K space) possibly arising from what could be considered a mixing line between more advanced (kaolinite) and less advanced (vermiculite/illite) clays. Such an inference is also identified in the A–CN–K–FM tetrahedron in combination with mass-balance considerations and knowledge of Fe behavior. The fresh Toorongo granodiorite has an MIA(R) value of 35 (A value in the tetrahedron). If iron retention during oxidative weathering is either assumed or verified (as done in Nesbitt and Markovics 1997), then a maximum achievable MIA(R) value via only the processes of element leaching can be calculated as the Al2O3/(Al2O3+FeO(T)) ratio (see Babechuk and Fedo, in press). This maximum value, which is 69 for the Toorongo granodiorite (see reference plane at A = 69 in Fig. 5a), would reflect an advanced weathering substrate that is a mineralogical mixture of Al-oxides and Fe-oxides after complete Ca, Na, Mg, and K loss. The most highly weathered sample in the Toorongo profile (point 5) plots above this maximum at an MIA(R) value of 82, which can only be explained with an additional process of Al accumulation beyond what can be residually enriched from leaching of other elements from the parent rock. Such values (higher than the aforementioned maximum) could also be explained by Fe loss, but evidence for this being the dominant factor was absent for the Toorongo profile (Nesbitt and Markovics 1997). As such, a further advantage of the A–CN–K–FM tetrahedral plotting approach is revealed whereby the higher plotting positions are best explained by combined physical and chemical processes. Further quantitative analysis of the relative role of each process is beyond the scope of this contribution, but we note that the requirement for physical kaolinite addition in the most weathered samples is not possible in the A–CN–K plot, which can achieve a maximum value of 100 via physical addition of kaolinite or in situ loss of CNK from other minerals. Consequently, this also exposes the caution of needing to evaluate other characteristics of a weathering profile (e.g., Al/Ti ratios alone, Fe speciation data) in combination with mineralogical and physical observations for effective use of all chemical weathering ternary or tetrahedral plots.

In summary, the use of the A–CN–K–FM tetrahedral plot allows compositional analysis that includes all main igneous silicate to secondary mineral transformations and can aid with using major-element data to support physical observations of mineral translocation. This is possible in a way that is not achievable with the A–CN–K with or without a companion A–CNK–FM plot due to the inclusion of all major-element components that contribute chemically to Al accumulation and Fe that generally follows an independent pathway to Fe-oxides in the same compositional space. Nevertheless, like in the A–CN–K ternary diagram, if guided by geological context, the major-element geochemistry in the A–CN–K–FM tetrahedral diagram still follows a systematic pathway indicative of a predictable set of circumstances resulting from hydrolysis (Nesbitt et al. 1997).

Baynton basalt—mafic

A Holocene weathering profile developed on the Baynton basalt is exposed in southeastern Australia (Nesbitt and Wilson 1992). This profile, in combination with other well-characterized basaltic profiles (e.g., Babechuk et al. 2014), provides context to evaluate the effects of chemical weathering on mafic bedrock. While less common for Earth, such context is particularly important for interpreting sediment (Ehlmann et al. 2017) and sedimentary rock (Mangold et al. 2019) data from Mars, whose crust is overwhelmingly basaltic in composition (McSween 2015).

Nine samples (sample set A1–A9) of the Baynton profile were analyzed by Nesbitt and Wilson (1992) from the center of an unweathered corestone outward to advanced weathered saprolite with CIA values ranging from 39 to 95. The data are indicative of progressive weathering from incipient to advanced stages. Assessing chemical weathering effects using CIA and ternary diagrams for mafic samples functions the same as with more felsic igneous sources, but with an understanding that mafic sources initially may be strongly depleted in both sodium and potassium in the source, which are critical in calculating CIA. Consequently, CIA values for many mafic weathering profiles are calculated with an understanding that calcium and aluminum primarily control the value. In contrast to behavior in felsic rocks, Mg is a more significant labile component for mafic rocks, which was a major justification for expanding the elements in the CIA to formulate the MIA (Babechuk et al. 2014; Babechuk and Fedo, in press). The MIA(R) values that project into the A–CN–K–FM tetrahedron range from ∼20 to 22 in the fresh Baynton basalt to 43 in the most weathered sample.

The nine samples for the Baynton profile are plotted in A–CN–K–FM tetrahedral space (Fig. 6), where unweathered basalt (point 1) is shown with a filled blue circle. Two points are identified that highlight compositional changes from the intermediate (point 2) advanced (point 3) parts of the profile. As expected for compositions with a considerable amount of ferromagnesian components, the source and profile samples plot deep within the tetrahedron near the FM pole (Fig. 6), corresponding to lower MIA(R) values than for that of the Toorongo granodiorite (Fig. 5). Similar to the Toorongo profile, the Baynton profile samples define a systematic path away from the source rock that is largely controlled by calcium (and lesser sodium) loss accompanied by residual aluminum accumulation through the formation of clays during hydrolysis. The loss of FM component across the lower part of the weathering profile, where calcium and sodium loss dominates (between points 1 and 2), is generally minimal apart from the first samples away from the source that show a greater deflection away from FM in the tetrahedron (below point 2, Fig. 6a). This deflection has also been noted in A–CNK–FM ternary space (Nesbitt and Wilson 1992) and was interpreted by McGlynn et al. (2012) to represent an initial loss of olivine followed by the domination of clay production at the expense of aluminous silicates like pyroxene and plagioclase. Rather than a general expectation in basaltic profiles, this early FM component loss is likely to be a site-specific (i.e., mineralogically and/or texturally controlled) feature as it is not recognized in other basaltic weathering profiles (e.g., Babechuk et al. 2014). Between points 2 and 3 (sample A9 in Nesbitt and Wilson 1992), there is a transition in the sample trend in the A–CN–K–FM tetrahedron that corresponds to enhanced Mg loss after near-complete Ca and Na loss.

Projection of the samples from source (blue) through profile (red) samples to the A–CN–K face (red open circles) produces the expected chemical weathering trend near the A–CN join ending in the very high CIA value of 95 (point 3). However, by working only with the A–CN–K plot and neglecting the FM component, it is possible to reach the conclusion that the profile is much more chemically weathered and that there has been much more residual accumulation of kaolinite than is realistic. While smectite does drop in abundance relative to kaolinite in the most weathered samples (Nesbitt and Wilson 1992), the tetrahedron better reveals the important role of the FM component, which in this case is controlled by both the development of Fe-oxides and the retention of Mg in smectites.

Following a similar exercise as for the Toorongo granodiorite, the maximum MIA(R) achievable by leaching of labile Ca, Na, Mg, and K in the Baynton profile is ∼50 (see reference plane at A = 50 in Fig. 6a). The final plotting position of point 3 at an MIA(R) of 43 is consistent with the following observations: (1) all of the products being an in situ chemical residuum (i.e., no physical clay mineral translocation), (2) the retention of iron in Fe-oxides during weathering, and (3) the appreciable amount of Mg remaining in smectites (Nesbitt and Wilson 1992). The latter minerals contribute to the mineral-chemical budget controlling Al in addition to kaolinite, but their influence on whole-rock compositions is only exposed if the FM component is included. If Fe behaves conservatively and concentrates in secondary minerals in parallel with Al, the whole-rock composition of the final weathered product evolves towards a mixture of both Al and Fe phases, but the CIA and A–CN–K plot captures only the aluminous secondary component. Use of the mafics ternary diagram (A–CNK–FM; Fig. 1c), and additional more newly developed ternary diagrams with a FM component (A–CNKM–F, AF–CNK–M; Babechuk et al. 2014), helps to resolve the Fe-redox and Mg behaviour in combination with the Ca, Na, and K from the CIA and A–CN–K diagram. However, tetrahedral compositional space provides the advantage of retaining more correspondence of individual poles to specific processes during mafic rock weathering and post-depositional alteration, which becomes particularly important when examining for later diagenetic/metasomatic effects in lithified paleosols (e.g., Babechuk and Fedo, in press).

Modern rivers and the present continental crust source

In many circumstances, sedimentary rocks have been detached from their source for several potential reasons, including (1) the sedimentary succession has been later structurally severed from the basement terrane (e.g., Yan et al. 2003), a common feature in fold-and-thrust belts; (2) the source region may have been thousands of kilometers distant and never really connected to the depositional basin (e.g., Rainbird et al. 1992; Muhlbauer et al. 2017); or (3) the source has been deeply eroded (peneplain) and subsequently buried by younger strata (Japsen et al. 2016). The end result is that the sedimentary rocks of interest may have no obvious connection to their source region, leaving that to be reconstructed from the sedimentary rocks themselves.

Except in certain environments (e.g., glacial), siliciclastic sediment is typically drawn from weathering profiles rather than directly from bedrock sources prior to entering the transport system. Nesbitt and Young (1984) and Nesbitt et al. (1997) demonstrated that a systematic pattern from fresh bedrock to the top of the profile occurs in A–CN–K and A–CNK–FM space (Figs. 1b, 1c). A consequence of this relationship is that sediments, particularly clay through fine sand, drawn from such a profile may lie along this trend so that a line fit through the data will project back to the feldspar join at the position of bulk source composition in A–CN–K space (e.g., Schoenborn et al. 2011).

A global survey detailing the suspended-load sediment geochemistry from almost 20 large rivers on Earth shows a remarkable pattern similar to what would be expected if having been sourced from a complete weathering profile (Fig. 1 in McLennan 1993). CIA values for the rivers range from 50 to 95. Figure 7 shows these data in the A–CN–K–FM tetrahedron (closed red circles) and projections of the data to the A–CN–K face (red open circles), including a fit through the data (red arrow) that projects back to the feldspar join. The projected position for granodiorite (open blue circle) is plotted as a proxy for the average upper continental crust. Given that the rivers sampled in this compilation scatter across latitudes and climates, there is little surprise that the data appear to represent all degrees of chemical weathering. Furthermore, on the A–CN–K face, the intersection of the approximate fit line through the data and granodiorite reinforces the concept of a present-day upper crust is dominated by granodiorite on average. However, there is some scatter on the A–CN–K face with data on both sides of the line projecting to granodiorite, particularly at higher CIA values. Viewing the river data within the A–CN–K–FM tetrahedron (Fig. 7), more compositional scatter in the data becomes particularly apparent, with many river examples plotting much closer to the FM pole than would be expected for weathering of a granodiorite. Such positions likely require the involvement of non-weathering processes that concentrate ferromagnesian phases and result in whole-rock compositions that can affect the determined CIA value independent of chemical weathering.

We highlight the positions of four rivers (point 2 = Columbia, point 3 = Indus, point 4 = Nile, point 5 = Niger) to illustrate the complexity in connecting increasing CIA and prevailing climate conditions. Across this span of rivers, the CIA ranges from a low of 57 (Columbia) to a high of 95 (Niger), with the Indus (66) and Nile (76) sitting in between. The difference in CIA between the Columbia (∼45°–52° N) and Niger (∼4°–16° N) rivers makes sense given their prevailing boreal-warm humid and equatorial-arid climates (Beck et al. 2018) at higher and lower latitudes, respectively. At ∼31 wt.%, the Al2O3 in the Niger River is nearly double that of the Columbia (∼17 wt.%) consistent with intense chemical weathering in the Niger watershed.

Both the Indus and Nile rivers (points 3 and 4, Fig. 7) have CIA values indicative of more chemical weathering than the Columbia (point 2), yet in terms of their Al2O3 all are very similar (17–19 wt.%); however, in terms of FM components they are comparatively much different, which shows up in their positions in A–CN–K–FM tetrahedral space (Fig. 7). Unlike the Niger River (point 5), which has the highest CIA value in this assessment (CIA = 95) and approximately lies along a predicted granodiorite weathering trend (light blue dashed arrow), the Nile River (point 4) has a higher CIA value, but plots deep within the tetrahedron (enriched in FM) in a position far removed from the reference granodiorite weathering trend in this tetrahedral space. Thus, the relationship between source rock, extent of total element loss during weathering, residual aluminous clay abundance, and climate is not as clear.

We explore three possibilities to explain why the data scatter away from the granodiorite weathering trend on the A–CN–K triangle. First, if the addition of FM component occurred in the form of adding chlorite or vermiculite (ferromagnesian clays with no CNK component), which projects to the A pole on the A–CN–K triangle (CIA = 100), then it can ultimately lead to an increase in CIA along a mixing trajectory towards the A pole independent of weathering extent. Second, concentrating biotite can produce a trend towards FM in the A–CN–K–FM tetrahedron that would project as a trend away from a granodiorite weathering line on the A–CN–K triangle and towards the A–K join. The result would be generating scatter to the right of a granodiorite weathering trend (see Fig. 3). Third, adding a non-aluminous ferromagnesian phase (e.g., Fe-oxide), which is highly likely in fluvial sediment, would add more FM component and produce trends in tetrahedral space that do not correspond to a change in CIA because it would be equivalent to a mixing line between the A–CN–K face and FM pole. The tetrahedral plotting space provides the ability to investigate these possible hydrodynamic effects in a manner that can potentially relate climate/weathering information to the amount of pure ferromagnesian mineral availability/mixing.

Plotting in A–CN–K–FM tetrahedral space illustrates that there is much less overall geochemical coherence between the world river sediments when compared to their positions on the A–CN–K ternary diagram. Examining trends in the A–CN–K plot alone cannot differentiate between the three possible causes related to the FM component. The tetrahedral plot provides a solution that better relates all major element components together. Although a connection to a generalized “granodioritic” source is consistent with the totality of the sediment data, sufficient geochemical variability exists to suggest that exposed crust includes mafic rocks as is expected and that sedimentary processes, such as mineral sorting and concentration during transport along with any and minerals precipitated in the transport environment, considerably increase data scatter within the tetrahedron. The purpose of this exercise is not to explicitly identify the specific processes in the major rivers generating the data scatter. Instead, what is revealed is that each individual river and samples from within each river should be evaluated independently, and using more textural (e.g., grain size) and elemental/mineralogical components in the sedimentary system, to isolate controls that cannot be identified completely using 1D values or 2D ternary diagrams.

Genoa River—continental weathered

Sediments derived from felsic-to-intermediate continental bedrock and recycled sedimentary sources are being transported >60 km along the Genoa River to the Tasman Sea in an area experiencing advanced chemical weathering in southeastern Australia (Nesbitt et al. 1996). Eight samples of mud and nine samples of sand were collected from the headwaters area down to the Mallacoota estuary (Nesbitt et al. 1996), providing a comprehensive collection of material along the course of the river. That mud samples have a higher CIA and plot closer to the A pole than corresponding sands in the A–CN–K ternary plot (Figs. 8a, 8b, open pink and purple circles), led Nesbitt et al. (1996) to conclude that mud samples contain more secondary aluminous phases relative to sand, an expected conclusion with sub-sand-sized clay minerals being concentrated as suspended load during transport.

Mud and sand data from the Genoa River are plotted as filled pink circles (mud) and filled purple circles (sand) in the A–CN–K–FM tetrahedron in Fig. 8. Out of the total plotted data, four specific samples, two sand (points 1, 2) and two mud (points 3, 4), have been highlighted to illustrate the importance of assessing the data with the A–CN–K–FM tetrahedron. Sand samples have a minimal compositional range in terms of Al2O3 from about 6.5–9 wt.% and virtually identical abundances of CaO, Na2O, and K2O, indicating there should be minimal variation in the degree of chemical weathering, and consistent with the inferred loss of Ca-plagioclase controlling changes in sand composition (Nesbitt et al. 1996). The lack of increasing aluminous components is particularly evident in the perspective view of the tetrahedron (Fig. 8a) where point 1 represents the point closest to the A–CN–K face and point 2 follows a linear path at about the same A value towards the inside of the tetrahedron. The linear trend is also clearly seen in the nadir view (Fig. 8b). Mud samples, bracketed by points 3 and 4, plot more as a group as seen in the nadir view (Fig. 8b), but like with the sands have relatively consistent Al2O3, CaO, Na2O, and K2O abundances, suggesting that weathering intensity should not vary much within muds nor be significantly different from sand because these components do not differ significantly across the data set. However, as seen on the A–CN–K face, where A = CIA value, sands range from A = 53 to 62 and muds range from 63 to 71, consistent with the notion of clay formation during moderate chemical weathering followed by sorting into sand and mud fractions.

Linking the data in the tetrahedron with the corresponding data for only the A–CN–K face shows how the apparent discrepancy between samples possessing relatively consistent Al2O3, CaO, Na2O, and K2O abundances and yet different CIA values may lead to potentially overestimated inferences of source weathering. One aspect that changes dramatically within the sand and mud samples, and collectively for all samples, is the progressively increasing FM component, which was observed on the A–CNK–FM ternary diagram as a horizontal line (Nesbitt et al. 1996). Sample points 1 and 2 represent the minimum and maximum CIA values for sand; sample points 3 and 4 represent the minimum and maximum CIA values for mud on the A–CN–K face. The importance of the A–CN–K–FM tetrahedral plot in further evaluating this trend is apparent through the horizontal trend in the tetrahedron (especially apparent in perspective view, Fig. 8a) being a significant departure from an igneous rock weathering trend that climbs on a slope towards the A apex (e.g., Fig. 5). The horizontal trend instead likely indicates that there is a strong control from the physical mixing of an aluminous FM component that increases in abundance from sand to mud. Alternatively, the A component value could be matched by the mixing of a non-aluminous ferromagnesian component (e.g., Fe-oxides) with more aluminous clays, which would have the net effect of lowering the A component (MIA(R)) in the tetrahedron. Mineralogically, kaolinite is documented in these sediments (Reinson 1973; Nesbitt et al. 1996), which indicates the role of chemical weathering as an influence increasing the CIA. However, vermiculite–smectite still dominates the mineralogical budget (secondary minerals after biotite that have retained most of their Fe and Mg budget) in the highest CIA samples. These aluminous ferromagnesian minerals, which have lost some FM component relative to original igneous FM minerals (amphibole, biotite), can increase the Al slightly (and thus the CIA), but when examining only A–CN–K trends the role of kaolinite accumulation and chemical weathering could be overestimated. In other words, the mafic component weathered from the source granite (amphibole, biotite) preferentially produces clay-sized particles and (or) mafic mineral-derived clays concentrate preferentially via sorting in the finer fraction of the sediment relative to the feldspar and quartz in the sand fraction.

This consequence also raises the capacity to resolve between steady- and non-steady-state weathering (Nesbitt et al. 1997). Samples that record non-steady-state weathering are derived from multiple levels in weathering profiles and so show compositional variation that mimics the path for a weathering profile (e.g., Fig. 5). In the Genoa River example, data form a line on the A–CN–K face consistent with non-steady-state weathering, but this line does not make it apparent that some of the aluminum accumulation may be the result of mixing in minimally weathered aluminous ferromagnesian phases rather than sampling different parts of a weathering profile (Figs. 1a, 5).

Evaluating this concept further, we plot data from bulk and sieved fractions of Genoa River sample GR5 in expanded perspective (Fig. 8c upper) and nadir (Fig. 8c lower) views. The sieve fractions represent detritus that is coarser and finer than 63 µm, which represents the cutoff between silt and sand. Compositions for data plotted in the A–CN–K–FM tetrahedron are filled pink circles, while compositions plotted on the A–CN–K ternary diagram are in open pink circles; the bulk GR5 sample composition is represented by the larger circle and sits between the two sieved fractions. The data on the A–CN–K face show that the finer sieve fraction has a much higher A value (CIA = 78) relative to the sand fraction (CIA = 58), suggestive of more highly weathered clay phases accumulating in the finest fraction and being compositionally differentiated because of grain-size sorting (Nesbitt et al. 1996). However, the positions of the sieve fraction within the tetrahedron clearly show that while there is some increase in the A component in the <63 µm fraction, there is also a considerable increase in the FM component (particularly evident in Fig. 8c upper) relative to the >63 µm fraction. Plotting the data in the tetrahedron demonstrates that the much higher CIA in the <63 µm fraction corresponds also to increasing FM, which is likely explained by a combination of preferential accumulation of Fe-oxide (hematite documented in samples) and aluminous ferromagnesian minerals (vermiculite–smectite). In the case of the Genoa River, concentrating ferromagnesian phases in the finer grain sizes plays a vital role in compositional differentiation, which then carries significant consequences for interpreting weathering conditions using CIA. These relationships are best visualized and interpreted using the A–CN–K–FM tetrahedron.

Guys Bight basin—continental unweathered

Holocene sediments located within the ∼8 km2 Guys Bight basin on Baffin Island, Nunavut, Canada, were derived from crustal sources (consistent with a bulk granodiorite composition) that are presently undergoing extreme comminution as a result of glaciation (Nesbitt and Young 1996). After emanating from glacial termini, sediments enter a fluvial-dominated transport system that is capable of naturally sorting detritus into separate gravel, sand, and mud accumulations. Given the polar climate and overall compositional immaturity of the sediment, Nesbitt and Young (1996) concluded that chemical weathering is at a minimum in this basin and specifically chose this location to study the effects of abrasion and sorting in the absence of significant chemical weathering.

Fifty samples representing six grain-size fractions (silt = purple, mud = blue, fine sand = light blue, medium sand = teal, coarse sand = orange, gravel = red) are plotted in the A–CN–K–FM tetrahedron (filled circles) and projected on to the A–CN–K face (open circles, Fig. 9). Two sample points (1, 2) are highlighted as reference guides. Finer grain sizes are shown in cooler colors; coarser grain sizes are shown in warmer colors. On the A–CN–K face, all data regardless of grain size plot as a tight, overlapping, data cluster (Fig. 9a) with A (CIA) values just above 50, and a slight spread along the feldspar join (Fig. 9b). The simplest interpretation of the data on the A–CN–K face is that the sediments have approximately the same composition and that chemical weathering is minimal. Furthermore, despite tremendous mechanical breakdown, there is little noticeable compositional change.

Including the ferromagnesian component to make the A–CN–K–FM tetrahedron, the data plot along a linear array where coarser grained deposits sit closer to the A–CN–K face and where finer grained deposits show an increasing FM component, as also previously identified in the A–CNK–FM ternary diagram (Figs. 9a, 9b; Nesbitt and Young 1996). There appears to be an inflection in the data separating two sub-parallel trends defined by grain size: (1) silt-fine sand and (2) medium sand-gravel (Figs. 9a, 9b). Figure 9c shows part of the tetrahedron from the nadir view with only the finer grain sizes in the upper plot and coarser grain sizes in the lower plot. A line passing through the finer grained samples (gray arrow) points to biotite in the tetrahedron, strongly suggesting that it concentrates in the finer fractions (Fig. 9c upper) as Nesbitt and Young (1996) and Young and Nesbitt (1998) previously identified. A line passing through the coarser grained data (Fig. 9c lower) defines a different trend more focused on the different proportions of mixed plagioclase and alkali feldspar. Dashed gray lines in both images represent the orientation of the second data set as a reference guide. Details in sediment composition as a result of sorting are clearly evident in the A–CN–K–FM tetrahedron and contrast with a pure chemical weathering trend from granodiorite (Fig. 5) and the mixed chemical weathering plus sorting example from the Genoa River (Fig. 8). In the Guys Bight basin example, the data scatter in a line almost parallel to the projection lines from the FM pole or the composition of biotite solely because of sorting, essentially forming mixing lines with biotite and (or) Fe-oxides as an endmember. In the absence of chemical weathering, data essentially cluster as a single group on the A–CN–K face with a CIA value near fresh bedrock. Working in the A–CN–K–FM tetrahedron thus provides a clear way to identify both chemical weathering (if any exists) and mixing effects associated with specific mineral phases (e.g., biotite) by deflections towards a range of minerals with variable C–N–K–FM proportions.

Kilauea synthetic sediment—mafic unweathered

Sediment derived from a sample of crushed, unweathered, Kilauea basalt was studied by Fedo et al. (2015) as a way to test mineralogic and geochemical variation across grain sizes as a proxy for size sorting. Although mafic point sources and derived sediment are not overly common on Earth, they do occur in a number of places, such as hot spot island chains, in large igneous provinces, and as part of Archean greenstone belt terranes. By contrast, studies of sediments and sedimentary rocks on Mars almost uniquely use basalt as source given its widespread nature as the principle composition of the crust (McSween 2015). As a result, investigating sediment composition trends from mafic sources is essential to understanding the range of compositional starting points.

A sample of original olivine phyric basalt (blue filled and open circles) and 13 samples of synthetic sediment (made by crushing; orange and red filled and open circles) are plotted in A–CN–K–FM tetrahedral space (Fig. 10). On the A–CN–K face, all the data plot as a very tight cluster, where sediments expectedly have the same A (CIA) value given the complete absence of chemical weathering (Figs. 10a, 10b). However, in the tetrahedron, there is a considerable amount of compositional variation that essentially lies on the blue line from the FM pole, through the bedrock point (blue-filled circle), and to the bedrock position on the A–CN–K face (blue open circle). This very coherent linear array results from the concentration of olivine, which plots at the FM pole, in the finer grain-size fractions (Fedo et al. 2015). Essentially, the composition of each grain-size fraction lies on a mixing line between the FM pole where olivine and iron oxides plot and whole-rock composition. In this example, as with the Guys Bight basin where chemical weathering is essentially absent, the data scatter does not result in a change of the CIA.

Closer inspection of the data array reveals that, similar to the naturally sorted deposits in the Guys Bight basin, grain size controls where compositions plot inside the tetrahedron. Inset plots show sieve fractions less than 1 mm as red-filled circles and fractions coarser than 1 mm as orange-filled circles (Figs. 10c, 10d). Coarser than 1 mm, sediment samples remain dominated by rock fragments that contain the essential mineralogy of the host bedrock with data making a cluster near the bedrock composition. Finer than 1 mm, sediment samples scatter much more significantly as individual mineral phases (phenocrysts of different compositions) break out of the host and reproportion elements leaving only glass and very small phenocrysts in the residual rock fragments (Fedo et al. 2015). We note these data only show the sieve fractions and not sorting driven by mineral density (e.g., Fig. 2C), which would drive further mineral segregation and spread in the data as recognized in Martian sediment (Siebach et al. 2017). Consequently, even in this simple example that explores the composition of sediment derived from crushing an olivine phyric basalt, enough variation exists to produce trends similar to naturally segregated sediment.

Paleoproterozoic Ville Marie paleosol—continental

Rainbird et al. (1990) studied the geochemistry of an approximately 12 m thick paleosol developed on top of the Archean Ville Marie granite, which is exposed near the community of Ville Marie, Québec, Canada (Rainbird et al. 1990). In this location, a hematitic breccia followed by quartzites of the Paleoproterozoic Lorrain Formation (Cobalt Group, Huronian Supergroup) overlie the paleosol, which by nature of its age provides data about weathering conditions at the onset of the “Great Oxidation Event” (Gumsley et al. 2017) coupled with the important effects of diagenesis after burial by the Lorrain Formation and subsequent strata.

Twenty-three samples inclusive of unweathered bedrock, variably weathered saprolite, and overlying brecciated material show the extensive weathering and diagenesis preserved in the profile (Rainbird et al. 1990). Here we plot the 19 samples on the A–CN–K–FM tetrahedron (Fig. 11) inclusive of all zones except the brecciated material overlying the saprolite. An example of unweathered bedrock (point 1, blue-filled circle) shows the location of the granite, as well as its location (open blue circle) on the A–CN–K face following the projection from the FM pole (blue line). A true granite composition can be inferred from the plagioclase to alkali feldspar ratio on the feldspar join (Figs. 11a, 11b) and the near absence of FM components (Fig. 11a). One example from the top of the paleosol is highlighted for reference (point 2). Data for the paleosol samples projected onto the A–CN–K face (open red circles) describe a trend (solid red arrow) that deviates from the expected trend (red dashed line, also see Fig. 1b) for chemical weathering of a bedrock (e.g., Figs. 1a, 6) with the composition of the Ville Marie granite.

Projections (gray lines) of all the data from within the tetrahedron to the A-CN-K face are shown in the expanded plot of the nadir view (Fig. 11c). The deviation between the actual (solid red arrow) and expected (dashed red arrow) locations of data on the A–CN–K face is more clearly observed in the expanded view (Fig. 11c) and results from the diagenetic addition of potassium after burial (Rainbird et al. 1990; Fedo et al. 1995), which may occur at any time after burial, even at times tens to hundreds of millions of years after the known time of weathering (e.g., Macfarlane and Holland 1991; Roscoe et al. 1992). The expanded plot (Fig. 11c) also shows that there is a substantial variation in FM components in the tetrahedron (red solid circles) that is lost when data are projected on the A–CN–K face.

In minimally weathered samples (close to fresh bedrock), FM components are depleted and enriched relative to fresh bedrock, both compositional changes that could be related to Fe mobility within the profile during weathering or later burial (Rainbird et al. 1990). Expanding the plot to focus on data from only the upper part of the profile (Fig. 11d; see inset in Fig. 11c for location) in the tetrahedron shows a path from lower (left side) to the top (right side) reveals a “loop” trend in successively higher samples in the profile. These samples plot in positions more complicated than would be expected from simple CN loss followed by K addition. Specifically, in the uppermost weathered samples (Fig. 11d), there is a minor but clear loss of FM component in the samples with the greatest K addition (e.g., point 2). This trend away from FM is also apparent in the A–CNK–FM ternary plot (Rainbird et al. 1990), but the tandem effect of K addition in the samples showing FM loss is not readily apparent in the ternary configuration where K is combined with CN. In contrast, the tetrahedron retains K on its own such that its more apparent how the FM loss and shift of samples downwards towards the K apex both contribute to the paleosol composition. The addition of K is a diagenetic/metasomatic effect that is well documented as a process producing deviations from predicted weathering trend arrays in the A–CN–K ternary diagram (see Fedo et al. 1995; Nesbitt and Young 1989). The advantage of using the tetrahedron is in the better identification of weathering trends associated with calcium, sodium, and magnesium loss prior to potassium addition by keeping K as its own component (unlike the A–CNK–FM ternary). Similar effects of K addition are apparent in other paleosols of the Huronian Supergroup, such as the Cooper Lake paleosol developed on a mafic source (Babechuk et al. 2019) and the use of tetrahedral plots to expose these combined weathering-diagenetic/metasomatic effects in mafic paleosols is further explored in Babechuk and Fedo (in press).

Archean Buhwa Greenstone Belt mudstone—continental and mafic

Mesoarchean (3 Ga) shales from the Buhwa Greenstone Belt, Zimbabwe, accumulated on the margin of the Paleoarchean Zimbabwe Archean Craton (Fedo and Eriksson 1996). Modeling of trace and rare earth elements led Fedo et al. (1996) to propose a mixed source dominated by felsic plutonic rocks (tonalite, granite) and lesser basalt, all common elements of granite-greenstone terranes. The stratigraphic succession in the variably metamorphosed Buhwa Greenstone Belt (Fedo et al. 1995) consists of interstratified quartz arenite and shale that transitions basinward to shale interbedded with banded iron formation (Fedo and Eriksson 1996).

Here we plot data from 17 shale (and phyllite) samples in the A–CN–K–FM tetrahedron (pink-filled circles), highlighting three samples (points 1–3) for reference. Projection lines (gray) from the FM pole show their locations on the A–CN–K face (pink open circles, Fig. 12). We also estimate the position for the mixed felsic-mafic source based on the modeling Fedo et al. (1996) in both A–CN–K–FM tetrahedral (dashed, filled, light-blue circle) and A-CN-K ternary diagram (dashed, open, light-blue circle) compositional spaces.

The projected shale data on the A–CN–K face all lie around the location of idealized muscovite (black open box), which is used as a proxy for idealized illite (Fig. 12). Points 2 (CIA = 72) and 3 (CIA = 79) bracket the maximum and minimum CIA values on the A–CN–K face, although both are the least aluminous of all the samples at ∼11 wt.% Al2O3 (note their positions in Fig. 12a). Based on the trace- and rare earth-element geochemistry (Fedo et al. 1996), the source was interpreted as dominated by tonalite that underwent extreme chemical weathering. Dashed blue arrow “a” shows a general expected weathering trend in the A–CN–K–FM tetrahedron (see Fig. 5) for a starting point approximating tonalite; dashed blue arrow “b” represents the same weathering trend but plotted on only the A–CN–K face. With plagioclase hydrolysis producing abundant kaolinite, not illite, as the major weathering end product (Nesbitt and Young 1984, 1989; Banfield and Eggleton 1990), both arrows “a” and “b” head towards kaolinite in Fig. 12.

“Present day” compositions for shale samples from the Buhwa Greenstone Belt lie on the line connecting the A and K poles on the A–CN–K face astride the composition for idealized illite (black open box, Fig. 12). Given that illite is not the primary weathering product of plagioclase, the present shale compositions were interpreted to be the result of diagenetic potassium addition to kaolinite to form illite (Fedo et al. 1996) at a time determined to be hundreds of millions of years younger than the time of deposition (Krogstad et al. 2004). Potassium enrichment attributed to this pathway has also been also been recognized in rocks from Earth (Yang et al. 2022) and Mars (Mangold et al. 2019) with mafic sources that initially have low abundances of potassium. The solid blue arrow “c” on the A–CN–K face traces a path from the estimated source position to the idealized illite position (open black box) in the middle of the shale data guided by the path of progressive potassium addition in the Ville Marie paleosol (cf., Figs. 11a11c).

Plotting these data in the A–CN–K–FM tetrahedral helps reveal several important compositional features with significant implications for inferred mixing/sorting and degree of weathering. All but one shale sample (point 1) form a data array in the A–CN–K–FM tetrahedron that is approximately between the positions of the reference (not idealized) illite (Ilt; which projects to a CIA of ∼72) and chlorite (which projects to a CIA of ∼100) compositions (Fig. 12). Thus, while most of the samples cluster near the composition of idealized illite on the A–CN–K face, they have a significant compositional trend towards the FM pole (deep into the tetrahedron). The process of potassium addition to kaolinite, as implied when interpreting the data on the A–CN–K face, oversimplifies the likely diagenetic reactions. A direct path from kaolinite to the reference illite composition (Ilt, Fig. 12, black arrow) would also require addition of iron and magnesium either through diagenetic addition and (or) mixing with clays of chloritic composition for example.

Because the modeled source (Fedo et al. 1996) carries a mafic–ultramafic component, a more likely explanation of the shale data is that the samples never essentially reached the level of 100% kaolinite (plotting at the A pole, where both CIA and MIA(R) = 100), but rather consisted of a mixture of kaolinite, other clays (e.g., nontronite, vermiculite, montmorillonite), and iron oxides (of different generations) from mafic phases in the tonalite and alteration of basaltic sources. Regardless, the amount of K2O in the shale samples (average ∼6 wt.%) requires potassium addition via diagenesis. The mafic clays would be currently represented by chlorite and intermediate mixed-layer clay compositions between chlorite and illite. The interbedding of some shale samples with banded iron formation also raises the potential that data array lies on a mixing line between illite and the FM pole, which could result from iron addition at the time of deposition or from variable diagenetic addition of iron sourced from adjacent lithologies. However, apart from points 1 and 2, the three samples that plot deep in the tetrahedron lie on a mixing line between illite and chlorite (purple dashed line Fig. 12; originally kaolinite and smectites, respectively). Such a compositional mixture carries significance for the CIA values and their relationship to climate and extent of weathering. First, because chlorite plots with a CIA of 100 on the A–CN–K face, it becomes apparent that the composition of sample point 3 (highest CIA), which plots deepest in the tetrahedron, is best explained as a sample with the greatest mafic component in a mixture rather than the most weathered sample with a greater felsic component. Second, the original clay mineral array prior to metasomatism is likely to have retained a significant FM component despite both Fe and Mg being leachable from mafic materials under anoxic conditions. This would also be consistent with a lower extent of overall leaching from chemical weathering than would be assumed from the A–CN–K plot alone that does not accurately budget the fate of mafic minerals.

In summary, although plotting the shale data in the A–CN–K–FM tetrahedron does not fully resolve the exact mechanisms for generating the shale compositions, only considering their CIA values or positions on an A–CN–K triangle loses considerable compositional information needed to address their petrogenetic history. Further, as with the Ville Marie paleosol example, the use of a tetrahedron relative to the A–CNK–FM ternary diagram provides the ability to better assess the combined effects of provenance, sorting, and diagenesis by retaining K as a separate pole.

Extracting quantitative information about paleoweathering and paleoclimate in siliciclastic sedimentary systems has utilized the 1D CIA and accompanying A–CN–K ternary diagram for four decades, taking advantage of the ratio of immobile aluminum to mobile calcium, sodium, and potassium during chemical weathering. Expanding on the CIA and A–CN–K concept to produce ternary plots with Fe+Mg and the MIA, allows consideration of mafic components in petrogenetic pathways. However, the 2D space with FM added comes with the penalty of grouping CNK together and thus losing feldspar resolution and the ability to properly examine K-diagenetic/metasomatic effects. In this paper, we have developed a new 3D tetrahedral plotting approach that uses molar proportions of aluminum, calcium+sodium, potassium, and iron+magnesium as the poles. The tetrahedral plot takes advantage of the strengths of conventional 2D ternary plots while eliminating the shortcomings of each. Examination of source rocks, weathering profiles, actively transporting sediment, paleosols, and sedimentary rocks using the A–CN–K–FM tetrahedron reveals a number of important conclusions pertaining to the petrogenesis of sedimentary systems:

  1. The plotting position of rocks and minerals on a conventional A–CN–K (feldspar) ternary diagram comes with the shortcoming of not properly examining the role of aluminous ferromagnesian minerals on controlling plotting positions in A–CN–K space or in evaluating the mixing of ferromagnesian minerals along the sedimentary petrogenesis continuum. Adding pure FM component alone (e.g., not increasing aluminum) will not change the CIA, but adding an aluminous ferromagnesian component such as vermiculite or chlorite (which project to a CIA value of 100) could give the illusion of recording more intense chemical weathering than actually experienced in generating the sediment.

  2. While felsic plutonic sources all have CIA values of about 50, they still vary more significantly in aluminous ferromagnesian components that make them plot at different levels within the A–CN–K–FM tetrahedron. Across the igneous compositional spectrum, the influence of the FM component increases from felsic (highest MIA; highest plotting position in tetrahedron) to mafic (lowest MIA; lowest plotting position in tetrahedron), as explained further in Babechuk and Fedo (in press).

  3. The compositional trends of weathering profiles in tetrahedral space still generally follow predictable linear pathways that increase the A component on the tetrahedron, but the competing roles of different labile element loss are better exposed than in using ternary diagrams and provide an integrative link with variations of the MIA. The highest achievable plotting positions in the tetrahedron at the most extreme levels of weathering are dependent on the starting rock composition. Weathering profiles developed on felsic plutonic bedrocks generate substantial kaolinite and so reside closer to the position of kaolinite on the tetrahedron, which corresponds with their high CIA and MIA(R) values. However, under oxic conditions, the retention of Fe still anchors positions to a lower value (e.g., an MIA(R) of ∼69 for the Toorongo granodiorite). Weathering profiles on mafic rocks, such as basalt, do not extend beyond the halfway point in the tetrahedron (MIA(R) of ∼50) due to their higher abundance of Fe if it is fully retained during weathering (thought of as a mixing line along the A–FM join between kaolinite and iron oxides).

  4. Hydrodynamic sorting in active sediment transport is effective at segregating between bedload and suspended load and even differentiating grain-size populations within bedload deposits. Suspended load sediments are generally thought to concentrate aluminous clays made during chemical weathering and have higher CIA values as a result. When plotting the data on the A–CN–K–FM tetrahedron, examples studied here in natural systems show that in addition to concentrating more aluminous clays by mixing or density sorting, the clays may also preferentially include ferromagnesian phases as a component of suspended sediments relative to bedload. This can have the effect of raising the CIA without strictly increasing illite or kaolinite if the ferromagnesian phases are aluminous. Mixing or unmixing of FM components in the form of non-aluminous ferromagnesian minerals (e.g., olivine or Fe-oxide) does not influence the CIA. The full effects of hydrodynamic sorting may play a critical role in determining the CIA value, but assessing this role relative to provenance and extent of weathering is best done using the A–CN–K–FM tetrahedron, which retains resolution of feldspar compositions (plagioclase vs. K-feldspar) while documenting the compositional variability related to the FM component.

  5. After deposition, sediments may rapidly begin to change composition as a result of interactions with burial fluids during diagenesis. Aside from the smectite-to-illite transformation, among the most common diagenetic changes recognized within paleosols and mudrocks is the addition of potassium to make abundant illite. These processes are commonly inferred from the A–CN–K ternary plot, but this plot alone does not allow for the recognition of the role of smectites and their ferromagnesian component prior to metasomatism. The data sets evaluated here show that without proper evaluation of the ferromagnesian mineral component, overestimation of the original extent of weathering and underestimation of the amount of mafic mineral mixing are possible outcomes from using the 1D CIA and 2D A–CN–K ternary diagram alone. However, plotting data in the A–CN–K–FM tetrahedron shows that diagenetic/metasomatic processes also significantly influence FM-bearing clays, which could include transformation of smectites to chlorite and mixed-layer clays, and also helps with identifying cases where Mg and Fe may have been removed or added by fluids in combination with K.

CMF thanks Grant Young and Wayne Nesbitt for countless discussions about sedimentary petrogenesis, which have strongly shaped the ideas developed here. The development of the tetrahedral plot benefitted from discussions with past students Aubrey Modi and Alex Aust. MGB benefitted from the support of a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant #RGPIN-2017–05028 during the preparation of this work. We thank Darrel G.F. Long for encouragment and support through the manuscript process and organizing the volume dedicated to Grant Young. Scott McLennan and anonymous reviewer are thanked for providing constructive comments that improved the manuscript.

No new data were collected for this study. All data used are taken as reported from previous studies, all of which are cited in the text.

Conceptualization: CMF

Data curation: MGB

Formal analysis: CMF, MGB

Investigation: CMF

Methodology: CMF, MGB

Validation: MGB

Visualization: CMF

Writing – original draft: CMF

Writing – review & editing: MGB

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Competing Interests

The authors declare there are no competing interests.