Chemical heterogeneities in the mantle: progress towards a general quantitative description

Chemical heterogeneities in the mantle: progress towards a general quantitative description

Chemical heterogeneities in the mantle: progress towards a general quantitative descriptionChemical heterogeneities in the mantle: progress towards a general quantitative descriptionMassimiliano Tirone

Chemical equilibration between two different assemblages
(peridotite type and gabbro–eclogite type)
has been determined using basic thermodynamic principles and certain constraints and assumptions regarding
mass and reaction exchange.

When the whole system (defined by the sum of the two subsystems)
is in chemical equilibrium the two assemblages will not be homogenized, but they
will preserve distinctive chemical and mineralogical differences.
Furthermore, the mass transfer between the two subsystems
defines two petrological assemblages that separately are also in local thermodynamic equilibrium.
In addition, when two assemblages previously equilibrated as a whole in a certain initial mass ratio are
held together assuming a different proportion,
no mass transfer occurs and the two subsystems remain unmodified.

By modeling the chemical equilibration results of several systems
of variable initial size and different initial composition
it is possible to provide a quantitative
framework to determine the chemical and petrological evolution of two assemblages from an initial state,
in which the two are separately in chemical equilibrium, to a state of equilibration of the whole system.
Assuming that the local Gibbs energy variation follows
a simple transport model with an energy source at the interface, a complete petrological description of the two systems
can be determined over time and space. Since there are no data to constrain the kinetics of the processes involved,
the temporal and spatial scale is arbitrary. The evolution model should be considered only a semiempirical
tool that shows how the initial assemblages evolve while preserving distinct chemical and petrological features.
Nevertheless, despite the necessary simplification, a 1-D model illustrates how chemical equilibration
is controlled by the size of the two subsystems. By increasing the initial size of the first assemblage (peridotite like),
the compositional differences between the initial and the final equilibrated stage become smaller, while on the
eclogite-type side the differences tend to be larger.
A simplified 2-D dynamic model in which one of the two subsystems is allowed to move with a prescribed
velocity shows that after an initial transient state, the moving subsystem tends to preserve its original composition
defined at the influx side. The composition of the static subsystem instead progressively diverges from
the composition defining the starting assemblage.
The observation appears to be consistent for various initial proportions of the two assemblages, which
somehow simplify the development of potential tools for predicting the chemical equilibration process from real data
and geodynamic applications.

Four animation files and the data files of three 1-D and two 2-D numerical models are available following the instructions
in the Supplement.

Our understanding of the Earth and planetary interiors is based on the
underlying assumption that thermodynamic equilibrium is effectively achieved on a certain level,
which means that the system
under consideration is in thermal, mechanical and chemical equilibrium within a certain spatial and temporal domain.
Although this may appear to be just a formal definition, it affects the significance of geophysical, petrological and
geochemical interpretations of the Earth's interior.
While the assumption of thermodynamic equilibrium is not necessarily incorrect, the major uncertainty
is the size of the domain on which the assumption is expected to be valid.

The Earth and planetary interior as a whole could be defined to be in mechanical equilibrium when
the effect of the gravitational field is compensated for, within a close limit, by a pressure gradient
(for simplicity variations of viscous forces are neglected). Even when this is effectively the internal state
(one example could perhaps be the interior of Mars), thermodynamic equilibrium
most likely is not achieved because it also requires thermal equilibrium (i.e., uniform temperature)
and chemical equilibrium (for possible definitions of chemical equilibrium see, for example, Prigogine and Defay, 1954; Denbigh, 1971; Smith and Missen, 1991; Kondepudi and Prigogine, 1998).
On a smaller scale instead, local thermodynamic equilibrium could be a reasonable approximation.
If the system is small enough, the effect
of the gravitational field is negligible and a condition close to mechanical equilibrium is achieved by the
near balance between the gravitational force and pressure (locally both density and pressure are effectively uniform
and viscous forces are neglected for simplicity). Clearly a perfect balance will lead to static equilibrium.
On the other end, dynamic equilibrium makes it harder for chemical and
thermal equilibrium to be maintained. In studies of planetary solid bodies it is often reasonable to assume
dynamic equilibrium close to a quasi-static condition
in which the force balance is close to but not exactly zero.
At a smaller scale it is then easier to consider the temperature to also be nearly uniform.
The main uncertainty remains the chemical equilibrium condition.
On a planetary scale, whether the size of the system under investigation is defined to be
on the order of hundreds of meters or a few kilometers, it has little
effect on the variation of the gravity force and in most cases on the temperature gradient. But for chemical exchanges,
the difference could lead to a significant variation of the extent of the equilibration process.
For the Earth's mantle, in particular, this is the case because it is generally considered to be
chemically heterogeneous.
The topic has been debated for some time (Kellogg, 1992; Poirier, 2000; Schubert et al., 2001; van Keken et al., 2002; Helffrich, 2006) and
large-scale geodynamic models to study chemical heterogeneities in the Earth's mantle
have been refined over the years (Gurnis and Davies, 1986; Ricard et al., 1993; Christensen and Hofmann, 1994; Walzer and Hendel, 1999; Tackley and Xie, 2002; Zhong, 2006; Huang and Davies, 2007; Brandenburg et al., 2008; Li and al., 2014; Ballmer et al., 2015, 2017).
Geochemical (van Keken and Ballentine, 1998; van Keken et al., 2002; Kogiso et al., 2004; Blusztajn et al., 2014; Iwamori and Nakamura, 2014; Mundl et al., 2017)
and geophysical (van der Hilst et al., 1997; Trampert et al., 2004; Tommasi and Vauchez, 2015; Zhao et al., 2015; Tesoniero et al., 2016) data essentially support the idea
that the mantle develops and preserves chemical heterogeneities through the Earth's history.
Even though all the interpretations of the mantle structure are based on the assumption of
local thermodynamic equilibrium, the scale of chemical equilibration has never been investigated in much detail.
An early study (Hofmann and Hart, 1978) suggested that chemical equilibrium cannot be achieved
over geological time, even for relatively small systems (kilometer scale), and hence
it must preserve chemical heterogeneities on the same scale.
The conclusion was inferred based on volume diffusion data of Sr in olivine at 1000 ^{∘}C.
At that time the assessment was very reasonable, although the generalization was perhaps
an oversimplification of a complex multiphase multicomponent problem.
In any case, significant progress in the experimental methodology
to acquire kinetic data and a better understanding of the mechanisms involved suggest that the
above conclusion should at least be reconsidered.
Based on the aforementioned study, the only mechanism that was assumed to have some influence on
partially homogenizing the mantle was
mechanical thinning–mixing by viscous deformation (Kellogg and Turcotte, 1987).
In addition, there are very limited experimental data on specific chemical reactions relevant to mantle minerals
(Rubie and Ross II, 1994; Milke et al., 2007; Ozawa et al., 2009; Gardés et al., 2011; Nishi et al., 2011; Dobson and Mariani, 2014) to set the groundwork for a general reinterpretation of
chemical heterogeneities in the mantle.

Perhaps a common misconception is that chemical equilibrium between two lithologies implies
chemical homogenization. In other words, if the mantle is heterogeneous, chemical equilibration must not have been
effective. This is not necessarily true. A simple example may explain this point.
If we consider, for example, the reaction between quartz and periclase to form a variable amount of forsterite and enstatite,
$\mathrm{MgO}+n{\mathrm{SiO}}_{\mathrm{2}}\Rightarrow (\mathrm{1}-n){\mathrm{Mg}}_{\mathrm{2}}{\mathrm{SiO}}_{\mathrm{4}}+(\mathrm{2}n-\mathrm{1}){\mathrm{MgSiO}}_{\mathrm{3}}$, at equilibrium, homogenization would require
the formation of a bimineralic single layer made of a mixture of enstatite and forsterite crystals. However,
experimental studies (e.g., Gardés et al., 2011) have shown instead the formation
of two separate monomineralic layers, one made of polycrystalline enstatite and the other one made of forsterite.

In summary, there are still unanswered questions regarding the chemical evolution of the Earth's mantle; for example,
at what spatial and temporal scale we can reasonably assume that a petrological system is at least close
to chemical equilibrium? And how does it evolve petrologically and mineralogically?

This study expands a previous contribution that aimed to provide an initial procedure to determine
the chemical equilibration between two lithologies (Tirone et al., 2015). The problem was
exemplified in an illustration (Fig. 1 in Tirone et al., 2015).
Because certain assumptions need to be made, the heuristic solution, further developed here,
is perhaps less rigorous than other approaches
based on diffusion kinetics that were applied mainly for contact metamorphism
problems (Fisher, 1973; Joesten, 1977; Nishiyama, 1983; Markl et al., 1998).
However, the advantage is that it is relatively easy to
generalize, and it leads towards a possible integration with large-scale geodynamic numerical models
while still allowing for a comparison with real petrological data.
At the same time it should be clear that to validate this model approach and to constrain the extent of the chemical
equilibration process, experimental data should be acquired on the petrological systems investigated here and in
the previous study.

The following section (Sect. 2) outlines the revised procedure to determine the two
petrological assemblages together forming a system in chemical equilibrium.
The revision involves the method used to determine the composition of the two assemblages when they are
in equilibrium together, the database of the
thermodynamic properties involved and the number of oxides considered in the bulk composition.
In addition, since the solids are nonideal solid mixtures (in the previous study all mixtures were ideal),
the chemical equilibration requires that the chemical potential of the same components in the two
assemblages must be the same (Prigogine and Defay, 1954; Denbigh, 1971).
The method is still semi-general in the sense that a similar approach can be used for
different initial lithologies with different compositions; however, some assumptions and certain specific restrictions
should be modified depending on the problem.
The simplified system discussed in the following sections assumes, on one side, a peridotite-like assemblage and a
gabbro–eclogite on the other side.
Both are considered at a fixed pressure and temperature (40 kbar and 1200 ^{∘}C) and their composition
is defined by nine oxides.
The general idea is to conceptually describe the proxy for a generic section of the mantle and a portion
of a subducting slab.
A more general scheme that allows for variations of the pressure and temperature should be considered in future studies.

The results of the equilibration method applied to 43 different systems are presented in Sect. 2.1.
The parameterization of the relevant information that can be used for various applications
is discussed in Sect. 2.2.

Section 3 presents the first application of a 1-D numerical model applied to pairs of assemblages
in variable initial proportions to determine the evolution over time towards a state of equilibration for the whole system.
The next section (Sect. 4) illustrates the results of a few simple 2-D dynamic models that assume
chemical and mass exchange when one side moves at a prescribed velocity while the other side remains fixed in space.
These simple models only serve to illustrate how
distinct mineralogical and petrological features are preserved after chemical equilibration has been reached.

All the necessary thermodynamic computations are performed in this study with the program AlphaMELTS (Smith and Asimow, 2005),
which is based on the thermodynamic modelization of Ghiorso and Sack (1995) and Ghiorso et al. (2002) for the melt phase,
the mixture properties of the solid and certain end-member solids. The thermodynamic properties of most of the
end-member solid phases are derived from an earlier work (Berman, 1988).
Even though melt is not present at the (P, T, X) conditions considered in this study
and other thermodynamic models are also available (Saxena, 1996; Stixrude and Lithgow-Bertelloni, 2005; Piazzoni et al., 2007; de Capitani and Petrakakis, 2010; Holland and Powell, 2011; Duesterhoeft and de Capitani, 2013),
AlphaMELTS proved to be a versatile tool to illustrate the method described in this work.
It also allows for a seamless transition to potential future investigations
in which it would be possible to study the melt products of two equilibrated,
or partially equilibrated, assemblages when the P–T conditions are varied.

2 Modeling chemical equilibration between two assemblages

This section describes in some detail the procedure to determine the transformations of two assemblages after
they are put in contact and the system as a whole reaches a condition of chemical equilibrium.
The bulk composition is described by nine oxides
(SiO_{2}, TiO_{2}, Al_{2}O_{3}, Fe_{2}O_{3}, Cr_{2}O_{3}, FeO, MgO, CaO, Na_{2}O).
Retaining the input format of the AlphaMELTS program, the bulk composition is given in grams.
Pressure and temperature are defined at the beginning of the process and they are kept constant.
Water (thermodynamic phase) is not considered simply
because the mobility of a fluid phase (or melt) cannot be easily quantified and incorporated in the model.
Three independent equilibrium assemblages are retrieved using AlphaMELTS.
These are standard equilibrium computations that consist of solving a constrained minimization of the Gibbs
free energy (van Zeggeren and Storey, 1970; Ghiorso, 1985; Smith and Missen, 1991).
The first two equilibrations involve the bulk compositions of the two assemblages separately.
The third one is performed assuming a weighted average of the bulk composition
of the two assemblages in a predefined proportion, for example 1:1, 5:1 or 100:1, also expressed as f:1, where f=1, 5, 100
(peridotite : gabbro–eclogite). This third computation applies to a whole system in which
the two assemblages are now considered subsystems.
The variable proportion essentially allows for increasingly larger portions of the subsystem mantle to be put in contact with the
subsystem gabbro–eclogite using the factor f to indicate the relative “size” or mass of the material involved.
By using AlphaMELTS
the mineralogical abundance and composition in moles are retrieved from the file phase_main_tbl.txt,
while the chemical potential
for each mineral component in the solid mixture is retrieved from the thermodynamic output file (option 15
in the AlphaMELTS program).
Knowing all the minerals components involved, an independent set of chemical reactions can be easily found (Smith and Missen, 1991).
For the problem at hand, the list of minerals and abbreviations are reported in Table 1,
and the set of independent reactions are listed in Table 2.

Table 1List of minerals and mineral components relevant for this study
with chemical formulas and abbreviations.

Given the above information, the next step is to determine the bulk composition and the mineralogical assemblages
of the two subsystems after they have been put together and equilibration of the whole system has been reached.
For this problem the initial amount of moles n of mineral components i in the two
assemblages is allowed to vary (Δn_{i}), provided that certain constraints are met.
The set of constraints can be broadly defined in two categories. The first group consists of relations
that are based on general mass, chemical or thermodynamic principles.
The second set of constraints is based on certain reasonable assumptions that should
be verified by future experimental studies.

The first and most straightforward set of constraints requires that the sum of the moles in the two assemblages
should be equal to the moles of the whole system:

where n_{i}(A_{0}) represents the initial number of moles of the mineral component in the
first assemblage (A) in equilibrium
before it is put in contact with the second assemblage (B). A similar definition applies to n_{i}(B_{0});
Δn_{i}(A) and Δn_{i}(B) are the variations of the number of moles
after the two assemblages are held together, and n_{i}(W) is the number of moles of the component in the
whole assemblage (A+B).
The size of the whole assemblage is defined by f+1, where f refers to the size of the first assemblage.

Another set of constraints imposes the condition of local chemical equilibrium (Prigogine and Defay, 1954; Denbigh, 1971; Kondepudi and Prigogine, 1998)
by requiring that the chemical potentials
of the mineral components in the two subsystems cannot differ from the chemical potentials found
from the equilibrium computation for the whole assemblage (W):

where μ_{i}(A) is the chemical potential of the mineral component in the assemblage A
whose number of moles is ${n}_{i}\left(A\right)={n}_{i}\left({A}_{\mathrm{0}}\right)+\mathrm{\Delta}{n}_{i}\left(A\right)$, and there is a similar expression for the second assemblage B.

Another constraint is given by the sum of the Gibbs free energy of the two subsystems
that should be equal to the total Gibbs free energy of the whole system:

where $G\left(A\right)={\sum}_{i}{n}_{i}\left(A\right){\mathit{\mu}}_{i}\left(A\right)$, and there are similar equations for B and W.

The list of reactions in Table 2 allows for the definition of a new set of equations that relates the extent of the reaction
ξ_{r} with the changes in the moles of the mineral components (Prigogine and Defay, 1954; Kondepudi and Prigogine, 1998). Consider,
for example, the garnet component almandine (Alm), which appears in Reactions (R1), (R3), (R10), (R12), (R13), (R14), (R15)
and (R16); the following relation can be established:

where the extents of the reactions are considered to be potential new variables.
However, not necessarily all the ξ_{r} should be treated as unknowns.
This can be explained by inspecting, for example, Table 3, which provides
the input data and the results of the equilibrium modeling for the study cases,
in particular the one that assumes an initial proportion 1:1 (f=1).
The second and third column on the upper side of the table report the input bulk composition on the two sides.
The second and fifth column on the lower part of the table show the results of the thermodynamic equilibrium
calculation applied separately to the two subsystems.
The last column shows the results for the whole system W.
This last column indicates, for example, that orthopyroxene is not present at equilibrium in the whole assemblage.
Considering the reactions in Table 2 and the data in Table 3,
the EN component in orthopyroxene appears only in Reaction (R2), and since no OEn is present on the B side,
the mole change in A can be locked ($\mathrm{\Delta}{n}_{\mathrm{OEn}}\left(A\right)=-\mathrm{0.0700777}$). Therefore,
ξ_{(R2)} is fixed to −0.0700777. The same is also true for
ξ_{(R3)}, which is uniquely coupled to Δn_{OEss}(A), furthermore ξ_{(R4)} coupled to Δn_{OHd}(A),
also ξ_{(R11)} coupled to −Δn_{OJd}(A), and finally ξ_{(R17)} fixed by Δn_{Coe}(B).

Table 3Summary of the results of one chemical equilibration procedure. The columns (A_{0}) and (B_{0}) describe
the initial bulk composition of the two subsystems and the Gibbs free energy G (joules) of the equilibrium
assemblages separately. Following the AlphaMELTS input format, the bulk compositions are given in grams.
The initial proportion of the whole system is f:1 (f=1) and the whole composition is reported in column (W).
Columns (A) and (B) in the upper portion of the table present the results of the chemical
equilibration in terms of oxides.
Note that the sum of the oxides is not 100, which indicates
a mass transfer between the two subsystems. The columns in the lower part of the table show the composition
of the mineral components at equilibrium before the two subsystems are put together (f×n(A_{0}) and n(B_{0})
and after equilibration of the whole system (f×n(A) and n(B)).
The change in moles (f×Δn(A), Δn(B) is also reported. The last
column is the composition of the whole system (W) after equilibration.

For the problem at hand the above set of relations does not allow for a unique definition of the changes in the moles
of the mineral components in the two subsystems.
Therefore, additional relations based on some reasonable assumptions have been added to the solution method.
Future experimental studies will need to verify the level of accuracy of such assumptions.
Certain constraints on the mass exchange can be imposed by comparing the equilibrium mineral assemblage of the
whole system (W) with the initial equilibrium assemblages in A_{0} and B_{0}.
For example, Table 3 shows that olivine is present in the whole assemblage W.
However, initially olivine is only located in subsystem A_{0}.
Therefore, rather than forming a completely new mineral in B, the assumption is that the moles of fayalite (Fa),
monticellite (Mtc) and forsterite (Fo) will change only in subsystem A to comply with the composition
found for the whole assemblage W.
Following this reasoning, the changes in the two subsystems could be set as
Δn_{Fa}(A)=0.0008090, $\mathrm{\Delta}{n}_{\mathrm{Mtc}}\left(A\right)=-\mathrm{0.0000555}$, $\mathrm{\Delta}{n}_{\mathrm{Fo}}\left(A\right)=-\mathrm{0.0726300}$
and $\mathrm{\Delta}{n}_{\mathrm{Fa}}\left(B\right)=\mathrm{\Delta}{n}_{\mathrm{Mtc}}\left(B\right)=\mathrm{\Delta}{n}_{\mathrm{Fo}}\left(B\right)=\mathrm{0}$.
In this particular case the same assumption is also applicable to the orthopyroxene components.
It is clear that by starting with different bulk compositions, proportions or T–P conditions,
alternative assemblages may be formed, and therefore different conditions may apply,
but the argument on which the assumption is based should be similar.

Additional constraints based on further assumptions can be considered. For example, garnet
appears on both sides A_{0} and B_{0}.
The components pyrope (Prp) and grossular (Grs) contribute only to two reactions, Reactions (R1) and (R12),
and in both cases the reactions involve only olivine components that have been fixed in subsystem A,
as previously discussed. The assumption that is made here is that the change in the moles of the garnet
components in subsystem B will be minimal because no olivine is available
in this subsystem. Therefore, the following relation is applied,

and similar relations can also be imposed to the other garnet components, Alm and Grs.
The same argument can be applied to the clinopyroxene
and spinel components. For example, the spinel component hercynite (Hc) appears only in Reaction (R13),
which involves olivine and orthopyroxene components (Fa, ODi) located in subsystem A,
and the garnet component Alm that has already been defined by the previous assumption.

The overall procedure is implemented with the use of Minuit (James, 1994), a program that is capable of performing a
minimization of multiparameter functions. Convergence is obtained making several calls of the Simplex and Migrad
minimizers (James, 1994). The procedure is repeated with different initial values for the parameters
Δn_{i}(A), Δn_{i}(B) and ξ_{r} to confirm that a unique global minimum has been found.

2.1 Results of the chemical equilibrium model between two assemblages

The procedure described in the previous section has been applied to 43 different cases, varying the
proportion of the two subsystems from 1:1 to 1000:1 and considering different, but related, initial compositions.
The initial bulk composition and the proportion factor f of the two subsystems for all 43 cases are
included in a table available in the Supplement.
For example, the initial compositions for A_{0} and B_{0} applied to case no. 11 are taken from
Table 4 (column A^{*}) and from Table 3 (column B_{0}); both tables are discussed in this section.
Tables 3–7 report the results of the procedure discussed in the previous section for a few cases.
Table 3 was briefly introduced earlier to show the initial bulk composition of the two subsystems
(upper portion of the table), the initial equilibrium assemblages and the mole changes after the chemical
equilibration (lower part of the table).
The table also includes the bulk composition in the two subsystems
after the chemical equilibration procedure is completed (upper part, columns 5 and 6).
These bulk compositions are calculated from the mole abundance of the mineral components shown in
the lower part (columns 4 and 7). The total mass of the subsystems is reported as well.
Note that a negative abundance of certain mineral components is permissible according to the
thermodynamic model developed by
Ghiorso and Carmichael (1980) and Ghiorso (2013) as long as the bulk abundance of the related oxides is greater than zero.

Table 4Normalized bulk composition (A^{*}) and (B^{*}) in the two subsystems taken from the results
of the model in Table 3, (A) and (B).
The lower part of the table shows the equilibrium mineral composition computed with the program AlphaMELTS
for each subsystem separately.

In the example shown in Table 3 there is a significant mass transfer from B to A:
mass(A_{0}) = 100, mass(A) = 146.36 and mass(B_{0}) = 100, mass(B) = 53.64 (grams).
The table also includes the total Gibbs energy for the subsystems before and after the equilibration
of the whole system, which is computed from the output of the program AlphaMELTS after combining the moles
of the components and the relative chemical potentials.
The total Gibbs free energy is relevant for the parameterization discussed in the next section.
Table 4 is a summary of a further analysis aiming to investigate whether there is any pattern
in the compositions of the two subsystems.
The bulk compositions in the upper portion
of the table (A^{*}, B^{*}) are obtained by normalizing the oxides in A and B
(upper part, column 5 and 6 of Table 3) to a total mass of 100 g.
For example, SiO_{2} in A^{*} from Table 4 (47.434) is 100 × (SiO_{2} in A) ∕ (sum of oxides in A) from
Table 3, which is equal to 100 × 69.428 ∕ 146.367.
The normalized oxides (A^{*}, B^{*}) represent the mass of the components in grams when
the total mass is 100 g, which is obviously also equivalent to the weight percent of the components.
These bulk compositions can be used for two new Gibbs free energy minimizations, one for each of
the two subsystems, to retrieve the correspondent equilibrium assemblages separately.
The interesting observation that can be made following the summary in the lower part of
Table 4 is that the abundance
of the mineral components remains unmodified after scaling the results for the total mass of the system.

For example, using the data from Table 3, the proportion relation ${n}_{\mathrm{alm}}\left(A\right):\mathrm{146.347}={n}_{\mathrm{alm}}\left({A}^{*}\right):\mathrm{100}$
gives
${n}_{\mathrm{alm}}\left({A}^{*}\right)={n}_{\mathrm{alm}}\left(A\right)\times \mathrm{100}/\mathrm{146.347}=\mathrm{0.01453}\times \mathrm{0.6833}=\mathrm{0.009928}$, which is remarkably close to the
moles of almandine found from the separate equilibration calculation reported
in Table 4, ${n}_{\mathrm{alm}}\left({A}^{*}\right)=\mathrm{0.0099353}$. In other words, the scaling factor used to define the input oxide bulk composition
can be also applied to the equilibrium mineral assemblage.

Based on this observation, some equilibration models have been carried out considering at least one
of the initial compositions from a previous model
(e.g., A^{*} from a previous equilibration model ⇒ input for a new model A_{0} or,
alternatively, ${B}^{*}\Rightarrow {B}_{\mathrm{0}}$),
while for the other subsystem the initial bulk composition from Table 3 is used again.
A special case is the one shown in Table 5 in which both A_{0} and B_{0} are taken from
the equilibrated and normalized data of the previous model, A^{*} and B^{*}, reported in Table 4.
If the proportion in the new model remains the same, 1:1,
then clearly no compositional changes are expected since the whole system is already in equilibrium.
If the proportion is changed, for example to 5:1 (f=5), the bulk composition of the whole system is different
from the bulk composition of the whole system with 1:1 proportion, and
the assemblages in the two subsystems may not remain unmodified after equilibration.
However, this does not appear to be the case, as shown in Table 5, where Δn_{i}(A) and Δn_{i}(B)
are very small. The results suggest that the moles of the mineral components remain unchanged.

Table 5Summary of the results of a chemical equilibration procedure in which the initial composition of the two subsystems
(A_{0}) and (B_{0}) is taken from the outcome of the previous model (A^{*} and B^{*} from Table 4).
The initial proportion of the whole system is f:1 (f=5).
The description of the results follows the outline of the caption of Table 3.

A more general case with f=5 is presented in Table 6. The model is essentially the same shown
in Table 3 but with the proportion of the two initial subsystems set to 5:1. As expected, the results
of the equilibration process are different from the results starting with an initial proportion of 1:1 (Table 3).
For example, with 1:1, n_{alm}(A)=0.01453, while with 5:1, ${n}_{\mathrm{alm}}\left(A\right)/\mathrm{5}=\mathrm{0.00737}$.
The question is whether the observation made for the first studied case with proportion 1:1 can be generalized.
In particular, there is the observation that the mineral abundance in the two subsystems from
the equilibration procedure of the whole system is equivalent to the one that is obtained
from two separate equilibration computations using the normalized
bulk compositions A^{*} and B^{*}.
Indeed, it appears that the same conclusion can be made for the model with 5:1 initial proportion (Table 7).
The number of moles of the almandine component is $\left({n}_{\mathrm{alm}}\right(A)/\mathrm{5})\times \mathrm{100}/\mathrm{110.064}=\mathrm{0.006698}$
(Table 6), which can be compared with ${n}_{\mathrm{alm}}\left({A}^{*}\right)=\mathrm{0.006695}$ from Table 7.
The similarity has been also observed for all the other models, with f ranging from 1 to 1000.

Table 6Results from a chemical equilibration model with the initial composition of the two subsystems
(A_{0}) and (B_{0}) analogous to the one presented in Table 3.
The only difference is that the initial proportion of the whole system is f:1 (f=5).

Table 7Normalized bulk composition (A^{*}) and (B^{*}) of the two subsystems taken from the results of the model in Table 6.
The lower part of the table shows the equilibrium mineral composition computed with the program AlphaMELTS
for each subsystem separately.

2.2 Parameterization of the equilibrium model results for applications

While interesting observations have been made about the mineralogical assemblages in the two subsystems
after chemical equilibration, it is still unclear how this type of model can be applied for studies
on the chemical evolution of the mantle. Figure 1 summarizes the relevant data
that allows for the determination of the bulk composition and the mineralogical assemblage in the two subsystems
after the chemical equilibration process is completed.

Figure 1Data and relative fitting of 43 study cases that are used to develop the chemical equilibration model.
(a) Relation between the ratio $G\left({A}^{*}\right)/G\left({B}^{*}\right)$ and G(B^{*}), which is applied to
constrain G(A^{*}) and G(B^{*}) at the interface.
Panels (b) and (c) illustrate the relation between G(A^{*}) and G(B^{*}) with MgO bulk abundance. Similar relations
are applied for all nine oxides defining the bulk composition. The normalized bulk abundance is intended as grams
with respect to a total mass of 100 g, which is equivalent to weight percent.
Knowing G(B), the total size of the assemblage at equilibrium can be found
assuming that (1) a relation between the mass change and the
change in G(B) is established (d). (2) The extension of the assemblage is proportional to the mass change
and it takes place along a direction perpendicular to the interface. The total length at equilibrium is then adjusted in
accordance with the difference between the spatial average G(B^{*}) of the assemblage and G(B^{*}) at the interface
(see the main text for a detailed explanation). The change in size of the second assemblage is also
applied on the first assemblage but with opposite sign.
Panel (e) allows for the determination of G(B) from the relation with G(B^{*}) at the interface.

The key quantity is the normalized Gibbs energy of the two subsystems after they have been equilibrated,
G(A^{*}) and G(B^{*}). The normalized Gibbs energy for an unspecified subsystem (either A^{*} or B^{*})
is defined by the symbol G(^{*}).
The quantity can be computed from the AlphaMELTS output after the Gibbs free energy minimization is applied
to A^{*} or B^{*}, or it can be simply obtained by scaling G(A) or G(B).
Figure 1a shows the relation between the ratio $G\left({A}^{*}\right)/G\left({B}^{*}\right)$ and G(B^{*}), which will be used later to define
G(^{*}) at the interface between the two assemblages. The data in the figure for the 43 models
have been fitted using a Chebyshev polynomial (Press et al., 1997).
By knowing G(^{*}), it is possible to retrieve the abundance of all the oxides defining
the bulk composition normalized to 100 g.
An example is shown in Fig. 1b and c, which illustrate the data points for MgO in (A^{*}) and (B^{*})
in the 43 study models and the fitting of the points using Chebyshev polynomials.

The mass transfer between the two subsystems can be related to the total Gibbs free energy variation in
each of the two subsystems G(A) and G(B). The two relations are almost linear, as shown in Fig. 1d.
For practical applications, once a relation is found between G and the normalized G(^{*}), then the mass transfer can
be quantified.
Figure 1e shows the data points and the data fitting with the Chebyshev polynomial
of the function $G\left(B\right)\left[G\right({B}^{*})-G({B}_{\mathrm{0}}\left)\right]$ versus $\left[G\right({B}^{*})-G({B}_{\mathrm{0}}\left)\right]$. More details on the use of the
fitting polynomial functions are provided in the next section.

3 Application to the evolution of a 1-D static model with variable extension

The chemical and petrological evolution of two assemblages can be investigated with a 1-D numerical model, assuming
that the two subsystems always remain in contact and they are not mobile.
The problem is assumed to follow a simple conduction–diffusion coupled-type model with variable size
for the local variation of G(^{*}), which can be expressed by the following equation for each subsystem:

where S(^{*}) is a scaling factor and G(^{*}) and S(^{*}) refer to either A^{*} or B^{*}.
Time t, distance d_{x}(^{*}) and the scaling factor S(^{*}) have no specific
units since we have no knowledge of the kinetics of the processes involved.
At the moment these quantities are set according to arbitrary units, S(A^{*}) and S(B^{*}) are set to 1,
and t, d_{x}(A^{*}) and d_{x}(B^{*}) have different values depending on the numerical simulation.
It should be clear that the dynamic model provides only a semiempirical quantitative description
of a complex process. The main purpose is to illustrate the general concept and
to show that the two assemblages could develop distinct regions evolving
towards the condition of chemical equilibrium, while far from the interface area the initial compositions can be preserved
for a certain amount of time.
A detailed description of how the two subsystems will eventually reach chemical equilibration is
beyond the scope of this study.

The numerical solution with grid spacing Δd_{x}(^{*}), uniform on both sides, is obtained
using the well-known Crank–Nicolson method (Tannehill et al., 1997).
At the interface (defined by the symbol if) the polynomial of the function shown in Fig. 1a is used together with the flux conservation equation,

to retrieve G(A^{*})_{if} and G(B^{*})_{if} assuming that $S\left({A}^{*}\right)=S\left({B}^{*}\right)$. The external
boundaries defining the limits of the
whole system (symbol l) are assumed to be of closed type or symmetric type. Both are
obtained by the condition $G({A}^{*}{)}_{l}=G({A}^{*}{)}_{{n}_{A}-\mathrm{1}}$ and $G({B}^{*}{)}_{l}=G({B}^{*}{)}_{{n}_{B}-\mathrm{1}}$, where
n_{A} and n_{B} are the total number of grid points on each side (excluding the boundary points).
G(A^{*})_{l} and G(B^{*})_{l} define the outside boundary limits of the whole system, which represent either
the closed end of the system or the middle point of two mirrored images.

To determine the mass transfer and how it affects the length of the two subsystems, the following
steps are applied. The polynomial of the relation shown in Fig. 1e is used at the interface point
to find G(B)_{if} (from the relation with $G({B}^{*}{)}_{\mathrm{if}}-G({B}_{\mathrm{0}})$). Defining $\mathrm{\Delta}G=\left[G\right({B}_{\mathrm{0}})-G(B{)}_{\mathrm{if}}]/G({B}_{\mathrm{0}})$,
the length of subsystem B at complete equilibrium would be ${D}_{x,\mathrm{eq}}\left({B}^{*}\right)={D}_{x}\left({B}_{\mathrm{0}}\right)+{D}_{x}\left({B}_{\mathrm{0}}\right)\mathrm{\Delta}G$,
where D_{x}(B_{0}) is the total length of the subsystem at the initial time.
The spatial average of G(B^{*}), defined as G(B^{*})_{av}, can be easily computed.
The quantity G(B^{*})_{av} is needed in the following relation to find the current total length of the
subsystem at a particular time:

The same change in length is applied with opposite sign on the other subsystem.
The new dimensions ${D}_{x,t}\left({A}^{*}\right)$ and ${D}_{x,t}\left({B}^{*}\right)$ also define new constant grid step sizes,
Δd_{x}(A^{*}) and Δd_{x}(B^{*}).
The final operation is to re-mesh the values of G(^{*}) at the previous time step onto the new uniform spatial grid.

It is worth mentioning that in the procedure outlined above,
converting the change in G to the change in the total length of the subsystem is a two-step process.
The first step makes use of the relation between the change in G and the change in the total mass, which
is illustrated in Fig. 1d.
In the next step the assumption is that the change in mass (and G) is
proportional to the change in the total length of the subsystem.

To summarize the numerical procedure,
at every time step the complete solution on both sides is obtained by solving Eq. (6) for G(A^{*}) and
G(B^{*}) with the boundary conditions imposed for the limits of the whole system
and preliminary values for the interface points.
Then the interface points are updated using the polynomial function and Eq. (7). The total length is then
rescaled to account for the mass transfer and the numerical grid size is updated. This procedure is iterated until
the variation between two iterations becomes negligible (typically convergence is set by
$\left|G\right({A}^{*}{)}_{\mathrm{if}}^{\mathrm{no}.\phantom{\rule{0.25em}{0ex}}\mathrm{1}}-G\left({A}^{*}{)}_{\mathrm{if}}^{\mathrm{no}.\phantom{\rule{0.25em}{0ex}}\mathrm{2}}\right|+\left|G\right({B}^{*}{)}_{\mathrm{if}}^{\mathrm{no}.\phantom{\rule{0.25em}{0ex}}\mathrm{1}}-G\left({B}^{*}{)}_{\mathrm{if}}^{\mathrm{no}.\phantom{\rule{0.25em}{0ex}}\mathrm{2}}\right|<\mathrm{1}e-\mathrm{4}$, where the labels no. 1 and no. 2
refer to two iterative steps).

Once convergence has been reached, the oxide abundance can be found easily using the
Chebyshev polynomial parameterization in which each oxide is related to a function of G(A^{*}) or G(B^{*})
(e.g., for MgO see Fig. 1b and c).
For convenience the composition is identified in weight percent since
the normalized oxides (^{*}) represent the grams of the components with respect to a total mass of 100 g.
Finally, knowing temperature, pressure and the variation of the bulk oxide compositions in space and time,
a thermodynamic equilibrium calculation can be performed at every grid point
using the program AlphaMELTS to determine the local mineralogical assemblage.

Several 1-D numerical simulations have been carried out with the initial proportion ranging from 1:1 to 100:1.
Some results from a test case with proportion 1:1 are shown in Fig. 2. The initial total length on both sides is set to
${D}_{x}\left({A}_{\mathrm{0}}\right)={D}_{x}\left({B}_{\mathrm{0}}\right)=\mathrm{100}$ (arbitrary units), and the initial spatial grid step is $\mathrm{\Delta}{d}_{x}\left({A}_{\mathrm{0}}\right)=\mathrm{\Delta}{d}_{x}\left({B}_{\mathrm{0}}\right)=\mathrm{1}$.
The time step is set to 4 (arbitrary units) and $S\left({A}^{*}\right)=S\left({B}^{*}\right)=\mathrm{1}$.
The initial bulk compositions of the two assemblages, with separately are in complete thermodynamic equilibrium,
are the same as reported in Table 3 for A_{0} and B_{0}:
SiO_{2}=45.2, TiO_{2}=0.20, Al_{2}O_{3}=3.94, Fe_{2}O_{3}=0.20, Cr_{2}O_{3}=0.40, FeO=8.10, MgO=38.40,
CaO=3.15, Na_{2}O=0.41 wt % (peridotite side)
SiO_{2}=48.86, TiO_{2}=0.37, Al_{2}O_{3}=17.72, Fe_{2}O_{3}=0.84, Cr_{2}O_{3}=0.03, FeO=7.61, MgO=9.10,
CaO=12.50 and Na_{2}O=2.97 wt % (gabbro–eclogite side).
Figure 2a illustrates the variation of G(^{*}) on both sides at the initial time (black line) and at
three different times: 80, 4000 and 20 000 (arbitrary units). Note the increase in the length on the A side
and decrease on the B side.
Bulk oxide abundance is also computed at every grid point. The bulk MgO (wt %) is reported in Fig. 2b, which
shows the progressive decrease on the A side, while MgO increases on the B side.
The bulk composition can be used with the program AlphaMELTS to determine the local equilibrium assemblage.
Figure 2c–h show the amount of the various minerals in weight percent (solid lines) and
the MgO content in each mineral in weight percent (dotted lines), with the exception of coesite in Fig. 2h (SiO_{2}).
The complex mineralogical evolution during the chemical equilibration process can be studied in some detail. For example,
one can observe the progressive disappearance of orthopyroxene on the peridotite side and the exhaustion of coesite
on the gabbro–eclogite side.

Figure 2Solution of a 1-D model simulation. The initial proportion of the two assemblages is 1:1.
(a)G(A^{*}) and G(B^{*}) at three different times and at time zero when the two assemblages
separately are considered in chemical equilibrium.
(b) Local bulk MgO (wt %) retrieved from the relation with G(^{*}). All the other
oxides are retrieved with similar relations. The units of the oxides is weight percent (wt %), which is equivalent to the
mass of the components in grams with respect to a total mass of 100 g.
(c–g) Mineral abundance (solid lines)
and MgO content (dotted lines) in the corresponding minerals.
(h) Distribution of coesite.
Local mineral abundances and compositions shown in panels (c)–(h) are retrieved after performing
thermodynamic computations at every spatial location with the program AlphaMELTS
using the bulk oxide abundance exemplified in (b) for MgO.
An animation file and complete data for all nine oxides
are available following the instructions in the Supplement.
Time and distance in arbitrary units. Pressure and temperature are fixed at 40 kbar and 1200 ^{∘}C.
The rest of the parameters for the model are defined in the main text.

Similar results are shown in Figs. 3 and 4 for models with the initial proportion set to 5:1 and 50:1, respectively.
Differences in the numerical setup of the new test cases can be summarized as follows. For the 5:1 case,
D_{x}(A_{0})=500, D_{x}(B_{0})=100, $\mathrm{\Delta}{d}_{x}\left({A}_{\mathrm{0}}\right)=\mathrm{\Delta}{d}_{x}\left({B}_{\mathrm{0}}\right)=\mathrm{1}$ and the time step is set to 40;
for the 50:1 case, D_{x}(A_{0})=5000, D_{x}(B_{0})=100, Δd_{x}(A_{0})=5, Δd_{x}(B_{0})=1 and the time step is set to 800.

Figure 3Solution of a 1-D model simulation. The initial proportion of the two assemblages is 5:1 (f=5).
The description of the panels follows the caption provided for Fig. 2.

Figure 4Solution for a 1-D model. The initial proportion of the two assemblages is 50:1 (f=50).
The description of the panels follows the caption provided for Fig. 2.
The raw data file for all nine oxides can be retrieved online, but no animation file is available for this simulation.

A few observations can be made by comparing the three simulations. For example, orthopyroxene on the peridotite
side becomes more resilient and the total amount of Opx increases with the size of the initial subsystem.
On the other side, it appears that the MgO content in garnet (pyrope component) is
greater for the model with starting proportion 5:1
compared to the 1:1 case. However, with initial proportion 50:1, the MgO content does not seem to change any further.

The Supplement provides a link to access the raw data (all nine oxides) for the three test cases with
initial proportion 1:1, 5:1 and 50:1. In addition, two animations (1:1 and 5:1 cases) should help to visualize
the evolution of the numerical models over time.

4 Application to the evolution of a 2-D model with one dynamic assemblage and variable extension

A 2-D numerical model makes it possible to study cases in which at least one of the two assemblages
becomes mobile. The simplest design explored in this section considers a rectangular box
with a vertical interface dividing the two subsystems. The dynamic condition is simply enforced in the model
by assuming that one of the two assemblages moves downwards with a certain velocity,
replaced by new material entering from the top side,
while the other assemblage remains fixed in the initial spatial frame.
The whole system evolves over time following the same principles introduced in the previous section.
The numerical solution of the 2-D model is approached at every time step in two stages. In the first stage
the following equation is applied to both subsystems:

where d_{x}(^{*}) is the general spacing in the x direction representing either d_{x}(A^{*}) or d_{x}(B^{*}),
and the vertical spacing d_{y} is assumed to be the same on both sides.
This equation is solved numerically using the alternating-direction implicit method (ADI) (Peaceman and Rachford, 1955; Douglas Jr., 1955), which
is unconditionally stable with a truncation error $O(\mathrm{\Delta}{t}^{\mathrm{2}},\mathrm{\Delta}{d}_{x}^{\mathrm{2}},\mathrm{\Delta}{d}_{y}^{\mathrm{2}})$(Tannehill et al., 1997).
Similar to implicit methods applied for 1-D problems, the ADI method requires only the solution of a tridiagonal matrix.

The numerical procedure described in Sect. 3 to determine G(^{*}) at the interface is also applied here
to the 2-D model.
The limits of the whole system opposite to the interface (left–right)
are also treated similarly, assuming either a closed-type or symmetric-type boundary.
For the other two boundaries (top, bottom)
the zero flux condition is imposed, $G({A}^{*}{)}_{l}^{(\mathrm{t},\mathrm{b})}=G({A}^{*}{)}_{y=(\mathrm{1},{n}_{y})}$ and $G({B}^{*}{)}_{l}^{(\mathrm{t},\mathrm{b})}=G({B}^{*}{)}_{y=(\mathrm{1},{n}_{y})}$,
where n_{y} is the total number of grid points in the y direction (excluding the boundaries).

In the previous section a procedure was developed to account for the mass transfer between the two subsystems.
The same method is applied for the 2-D problem. The conceptual difference is that in a 2-D problem
the mass change in principle should affect the area defined around a grid point. For practical purposes, however,
in this study it only affects the length in the horizontal x direction; hence, re-meshing due to the change in mass
is applied only to determine ${D}_{x,t}\left({A}^{*}\right)$ and ${D}_{x,t}\left({B}^{*}\right)$ as well as the two uniform grid step sizes in the x direction,
Δd_{x}(A^{*}) and Δd_{x}(B^{*}).

Up to this point the evolution of the system is not different than what was described for the 1-D case.
The dynamic component is included at every time step in the second stage of the procedure.
It is activated at a certain time assuming
that the chosen subsystem moves downwards with a fixed predefined vertical velocity (y component).
The material introduced from the top side is assumed to have the same composition of the initial assemblage
as defined for the 1-D models in Table 3 (and the same G(A_{0}) and G(B_{0}) values).
This is accomplished by assigning G(A_{0}) or G(B_{0}) at a location near the interface, which is defined by the
imposed velocity. Then the G(^{*}) points are also shifted according to the prescribed velocity.
Values of G(^{*})_{y} on the original orthogonal grid are obtained by linear interpolation of the shifted G(^{*}) points.

Oxide bulk composition is then retrieved at each grid point over time using the same polynomial functions
applied for the 1-D problem. The complete mineralogical assemblage can also be computed using
AlphaMELTS as part of a post-process step after the numerical simulation is completed.

Only a few 2-D simulations have been performed specifically considering the initial proportions 1:1, 5:1 and 50:1,
assuming that one of the two assemblages is moving downward.
Figure 5 summarizes some of the results for the case 5:1 (A), i.e., with moving subsystem A.
Initial grid specifications are D_{x}(A_{0})=500, D_{x}(B_{0})=100, $\mathrm{\Delta}{d}_{x}\left({A}_{\mathrm{0}}\right)=\mathrm{\Delta}{d}_{x}\left({B}_{\mathrm{0}}\right)=\mathrm{2}$,
${D}_{y}\left({A}_{\mathrm{0}}\right)={D}_{y}\left({B}_{\mathrm{0}}\right)=\mathrm{50}$ and $\mathrm{\Delta}{d}_{y}\left({A}_{\mathrm{0}}\right)=\mathrm{\Delta}{d}_{y}\left({B}_{\mathrm{0}}\right)=\mathrm{1}$ (arbitrary units). The time step is set to 16 (arbitrary units).
The scaling coefficients S_{x}(^{*}) and S_{y}(^{*}) are set to 0.01 (arbitrary units). The dynamic component is activated
at time 100 000 with vertical velocity set to 0.00625 (arbitrary units).
The figure is a snapshot of the whole system soon after subsystem A has been activated downwards (time 102 400).
Figure 5a shows the variation of G(^{*}), while Fig. 5b illustrates the bulk MgO distribution (wt %).
The other panels, (c)–(h), present an overview of the mineralogical distribution (flood contour type) and the
MgO content in each mineral phase (line contour type), with the exception of Fig. 5h for coesite (SiO_{2}).
The panels clearly illustrate the variations introduced by the mobile subsystem A. On the other side, there is apparently
no immediate effect on the assemblage B; however, the long-term effect is significant and becomes
visible in a later figure (Fig. 7).

Figure 5Solution of a 2-D model simulation at time 102 400 (arbitrary units).
The starting proportion of the two assemblages is 5:1 (f=5).
In the initial setup the two assemblages are separately in chemical equilibrium.
At time 100 000 a new assemblage A enters from the top side with velocity 0.00625 (arbitrary units).
The new assemblage is assumed to have been equilibrated but never previously in contact
with assemblage B (the composition of the new assemblage is the same as the assemblage in the initial setup).
(a) Spatial variation of G(^{*}).
(b) Local distribution of MgO in the bulk assemblage. Similar results are obtained for all the other oxides
defining the bulk composition.
An animation file and raw data for all nine oxides are available online
following the instructions provided in the Supplement.
(c–g) Local mineral distribution (color map) and contour lines for the abundance of MgO in the
associated minerals. (h) Spatial distribution of coesite.
Time and distance in arbitrary units. Pressure and temperature are fixed at 40 kbar and 1200 ^{∘}C.
The rest of the parameters for the numerical model are defined in the main text.

Figure 6Solution of a 2-D model simulation at time 102 400 (arbitrary units).
The starting proportion of the two assemblages is 5:1 (f=5).
In this model it is assumed that at time 100 000 a new assemblage B enters from the top with velocity 0.00625 (arbitrary units).
The description of the panels follows the caption of Fig. 5.

Figure 7Summary of the results for all the 1-D and 2-D numerical models at conditions close to chemical equilibrium for
the whole system. The models consider different initial proportions of the two assemblages.
In addition, for the 2-D models it is assumed that either assemblage A or B enters from the top side
at time 100 000 (arbitrary units) with velocity 0.00625 (arbitrary units).
For the 2-D models the profiles represent a horizontal section at the vertical middle point (D_{y}∕2).
(a) Spatial variation of G(^{*}). For clarity, the plot of the 2-D model with 50:1 (B) is truncated at x∼500.
(a2) Enlarged view of G(^{*}) near the interface.
(b) Variation of bulk MgO (wt %). (b2) Enlarged view of bulk MgO near the interface.
(c–g) Spatial variation of mineral abundance.
(c2–g2) Mineral abundance zoomed near the interface.
(c3–g3) MgO content in the associated minerals near the interface.
(h, h2) Distribution of coesite (SiO_{2}).

Figure 6 provides a similar overview for the case assuming 5:1 (B) with subsystem B moving downward.
The same numerical conditions described for the previous case apply for this case as well.
This figure, which shows only one time frame soon after the subsystem is mobilized,
does not appear to reveal new remarkable features. However, advancing the simulation,
a clear effect becomes more evident near the interface. In particular, changes in the
chemical and mineralogical properties moving away from the top entry side are quite significant.
An animation related to Fig. 6 is best suited to illustrate this point.
This movie file and another file for the animation related to Fig. 5
can be downloaded following the link provided in the Supplement.
The raw data files, which include all nine oxides for both simulations, are also available online.

5 Summary of the 1-D and 2-D models approaching chemical equilibration

Figure 7 summarizes the results of all the 1-D and 2-D numerical test models
when the whole system approaches or is close to chemical equilibration.
In the static scenario, exemplified by the 1-D models (solid lines), by increasing the initial size of subsystem A, the
mineralogical and compositional variations tend to be smaller
(see panels c–h and enlarged view around the interface, panels c2–h2).
It is the expected behavior since any change is distributed over a larger space of the subsystem.
The variations of the mineral abundance in assemblage B (gabbro–eclogite type) instead remain quite
independent of the initial size of subsystem A.
However, the abundance of the minerals is not necessarily the same found in the
initial assemblage. In particular, the amount of garnet, clinopyroxene and coesite is quite different from
the amount of these minerals in the initial assemblage.
This difference is rather unaffected by the initial proportion of the two assemblages, which has been varied
from 1:1 (f=1) to 100:1 (f=100).

The composition of the minerals in assemblage A (e.g., MgO illustrated in panels c3–g3)
follows a pattern similar to the mineral abundance.
As the size of the initial subsystem increases, MgO tends to approach the oxide amount in the initial composition.
A different result is observed for the composition of the minerals in assemblage B.
Regardless of whether the mineral
abundance changes or remains close to the initial amount, the oxide composition varies quite significantly and in most
minerals the difference is larger when f is set to higher values.

When one of the subsystems is allowed to move (2-D models),
the general observation in the long run is that the dynamic subsystem
tends to preserve the assemblage that enters the model. In this study this assemblage
is set to be equal to the initial assemblage.
Note that the 2-D data plotted in Fig. 7 refer to a horizontal section of extracted
points at the middle vertical distance D_{y}∕2.
When subsystem A is mobile (dotted lines), the behavior of assemblage B is similar to the
static case, with some minerals changing their initial abundance: garnet, clinopyroxene, coesite and partly spinel.
In the reverse case, with B set as the dynamic subsystem,
the mineralogical abundance of A differs from the initial assemblage (dashed lines).
But unlike the static cases, no significant variations can be noted with the increase in the initial proportion.

In terms of mineral composition (e.g., MgO, panels c3–g3
in Fig. 7), the dynamic subsystem
preserves the composition of the entering assemblage. The immobile assemblage instead shows a compositional
variation that is larger than any change observed for the static cases.
This variation still somehow remains independent of the initial proportion of the two assemblages, at least with $f=\mathrm{1},\mathrm{5},\mathrm{50}$.

Complete data for the bulk composition, which include all nine oxides, are available
for three 1-D models and two 2-D simulations following the instructions in the Supplement.

The main objective of this work was to show that a chemically heterogeneous mantle does not necessarily
mean that different lithologies are in chemical disequilibrium (at least not entirely).

Often geochemical and petrological interpretations of the Earth interior rely on
the achievement of thermodynamic equilibrium on a certain scale.
The use of phase equilibrium data and partition coefficients, for example, implies
that chemical equilibrium has been achieved and is maintained.
Curiously, while this assumption is tacitly imposed on the most convenient dimension
to interpret observed data,
chemical equilibration is ignored when it comes to discussing the presence or the extent
of chemical heterogeneities (i.e., chemical equilibration, in this regard, is considered ineffective)
(e.g., Morgan, 2001; Ito and Mahoney, 2005a, b; Strake and Bourdon, 2009; Brown and Lesher, 2014).

Geophysical interpretations usually require the specification of certain properties,
such as the density for the Earth materials under consideration.
For example, when the density is considered representative of real rock assemblages,
the system has to be sufficiently small
that the gravitational force is almost completely balanced by the pressure effect (viscous forces are
ignored for simplicity), effectively establishing a quasi-static or static condition. Under this condition,
thermodynamic equilibrium can be achieved when the system is also equilibrated chemically
so that petrological constraints can be applied to determine the density of the assemblage.
When different lithologies are considered in geophysical applications, it is assumed that
chemical equilibrium is never achieved among them, regardless of the size of the system or the temporal scale.
For studies whose conclusions are based on geological processes lasting
for hundreds or billions of years, such an assumption should be carefully verified considering that
chemical and mass exchange are always effective to a certain extent.

The results from 43 study models (Sect. 2.1) suggest that
the imposed condition of thermodynamic equilibrium for the whole system (sum of two subsystems) defines
two new assemblages that are not homogenized compositionally or mineralogically, and
their equilibrated compositions are different from those in the two initial assemblages.
The two new assemblages not only define a condition of chemical equilibrium for the whole system but they
also represent the equilibration within each separate subsystem.
In addition, mass exchange between these equilibrated assemblages does not progress any further
when the initial mass proportion of the two is varied and a new equilibration model is imposed to the newly defined
whole system.

The results of the study models have been condensed in a series of parameterized functions that can be
used for various applications (Sect. 2.2).

A semiempirical quantitative forward model was also developed to describe
the evolution of the chemical equilibration process in the mantle. The model has been restricted to one
set of values for the pressure and temperature and one pair of bulk compositions indicative of
a peridotite type and a gabbro–eclogite type.
The gabbro–eclogite type can be interpreted as a portion of a subduction slab.
Ignoring a thin sedimentary layer that could possibly peel off during subduction,
a large portion of the slab consists also of a depleted peridotite.
Three lithologies (mantle peridotite, gabbro, depleted slab peridotite) can probably also be approached
with a chemical equilibration model similar to the one presented here.
However, it remains to be seen whether the
difference in composition with respect to the generic peridotite assumed in this study would lead
to significant new results that would justify the additional modeling effort.

Priority was given here to understanding the influence on the final assemblages
of various initial proportions of the two subsystems and, to a limited extent, the effect of the initial compositions.
The spatial and temporal evolution necessarily assumes arbitrary units.
The reason behind this is that a comprehensive approach to study chemical heterogeneities that
would include time-dependent experiments and suitable models for the interpretation of the
experimental results has not been developed yet.
Experimental data are also necessary to validate certain assumptions that were made to model the composition
of the two equilibrated assemblages (Sect. 2).

The choice made to describe the variation of G(^{*}) using the transport model presented
in Sects. 3 and 4 may seem rather arbitrary.
While details of the transition towards chemical equilibration should be investigated by experimental studies,
the main point of the models in Sects. 3 and 4
(and of this study) is to show that different lithologies can evolve while preserving distinct chemical and
mineralogical features.
The idea of using the concept of local Gibbs free energy variations over time and space (Kondepudi and Prigogine, 1998) to describe
the chemical changes is a practical means to simplify a problem that otherwise becomes intractable for complex
systems. The choice is not a complete abstraction; it is approximately based on the consideration that the
mass exchange is not governed by the compositional gradient but by the differences in the chemical
potential of the various components in the various phases (e.g., Denbigh, 1971).
Ultimately, only extensive experimental studies could determine whether the simple evolution model for G(^{*}) applied
in this work to a heterogeneous system can be considered
a reasonable approximation for describing the chemical evolution in practical geodynamic mantle models.

Two aspects of the numerical applications presented in the previous sections perhaps deserve further consideration.
The assumption made for the composition of the entering assemblage in the 2-D models perhaps should be
reconsidered in future studies.
The other consideration concerns the boundary condition imposed on the opposite side of the interface
between the two assemblages.
The assumption is that the whole system is either close to mass exchange or mirror
images exist outside the boundary limits. From a geological perspective the first scenario is probably the more
difficult to realize. On the other hand, the possibility that
periodic repetitions of the same model structure are replicated over a
large portion of the mantle, if not the entire mantle, seems more reasonable.
Assuming that the timescale is somehow constrained, an investigation of the temporal evolution
would still require some kind of assessment of the periodic distribution of the thermodynamic system as a whole.

The 2-D simulations in which one of the assemblages is allowed to move have shown that in the long run
the mineralogical abundance and compositional variations are
approximately independent of the size of the two subsystems.
This observation suggests the possibility of implementing large geodynamic models with evolving petrological systems
once the temporal and spatial scale of the chemical changes have been constrained.

At the moment the spatial and temporal variations are arbitrarily defined, but this study shows that
the petrological and mineralogical changes may still be approximately quantified,
at least at the P–T conditions that have been considered.
It would be useful, for example, to select a few bulk compositions for the two subsystems
and apply them to the dynamic equilibrium melting (DEM) and dynamic fractional melting (DFM) models
that have been developed combining 1-D multiphase flow with AlphaMELTS (Tirone and Sessing, 2017; Tirone, 2018).
Perhaps even a simplified model for nonequilibrium fractional crystallization could be applied
to try to reproduce observed 3-D chemical zoning in minerals and multicomponent chemical zoning in melts (Tirone et al., 2016).
More in general the results should be compared with
existing data on melt products and residual solids observed in various geological settings to investigate
indirectly, but from a quantitative perspective, the presence of chemical heterogeneities in the mantle should be taken into account.
It also becomes possible to determine the variation of physical properties, such as bulk density, and relate
them to certain observables, such as seismic velocities. At least on a relative scale, the effect of the
compositional variations could be associated with seismic velocity variations, providing in this way more
indirect evidence of heterogeneities in the mantle based on a quantitative forward description.

Thanks to Paula Antoshechkina for taking the time to answer many questions regarding the use of
AlphaMELTS (version 1.7) and for fixing on-the-fly minor issues of the program.
The commitment of Topical Editor Mike Heap has been truly appreciated.
Nothing but sincere appreciation goes to Topical Editor Mike Heap for the hard work he put
into handling the review process.
Three anonymous reviewers helped to clarify several points in the paper.
The work was carried out while visiting the Department of Mathematics and Geosciences at the
University of Trieste, Italy.
This study was part of a larger comprehensive project aiming to investigate chemical heterogeneities in the mantle
by providing a first set of experimental data to determine the kinetics of the equilibration process,
establishing a modeling procedure and developing geodynamic numerical applications.

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The prevalent assumption in solid Earth science is that if we have different lithologies in the mantle they are separately in chemical equilibrium and together in chemical disequilibrium; this is the condition that at the moment defines a chemically heterogeneous mantle. The main contribution of this study is to show that this may not be the case. We can have (partial) chemical equilibration between the two and still observe a chemically heterogeneous mantle.

The prevalent assumption in solid Earth science is that if we have different lithologies in the...