# Earth's foundational geological events stored in the fundamental mantle source of hotspots

B J Peters, François Nauret, B Moine, P R Castillo

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B J Peters, François Nauret, B Moine, P R Castillo. Earth's foundational geological events stored in the fundamental mantle source of hotspots. Geochemical Perspectives Letters, 2025, 38, pp.34 - 39. 10.7185/geochemlet.2553. hal-05449863

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# Earth's foundational geological events stored in the fundamental mantle source of hotspots

B.J. Peters<fnref n="1" />*, F. Nauret<fnref n="2" />, B. Moine<fnref n="2" />, P.R. Castillo<fnref n="3" />

OPEN ACCESS

## Abstract

https://doi.org/10.7185/geochemlet.2553

TSNE-reduced isotopic compositions

<figure><img src="image_2.png" /></figure>

The geochemical signatures of nearly all Earth's volcanic hotspots converge on a common isotopic composition, which is often termed the 'focal zone' (FOZO) and has been associated with a shared deep mantle source. Yet, unlike other characteristic geochemical components of hotspots, the origin and nature of FOZO remain unclear. Geochemical evidence has been invoked to support both an ancient and a relatively young age for FOZO, and long lived radiogenic isotopic data support many potential geochemical relationships between geographically disparate hotspots. We examine this issue using the short lived <sup>146</sup>Sm-<sup>142</sup>Nd isotopic system, which can identify diverse geochemical origins in the Hadean Eon, more than four billion years ago.

Two hotspots with strong geochemical affinity to FOZO, the Crozet and Juan Fernandez hotspots, have statistically distinct <sup>142</sup>Nd compositions, meaning that despite the similarity in their long lived radiogenic isotopic compositions, they must reflect different Hadean-aged geological heritages. Detailed analysis of the isotopic compositions of global hotspots using machine learning further reveals that FOZO is most likely not singular but rather represents a spectrum of discrete domains that each have histories dating back to the first 10 % of Earth's history.

Received 15 January 2025 | Accepted 3 November 2025 | Published 23 December 2025

## Introduction

The emergence of complex biogeochemical cycles on Earth is largely owed to the development of plate tectonics, yet it is also tectonics that has almost entirely destroyed Earth's geochemical record of its origins. For example, the zircon age record implies that nearly half of Earth's total crust was produced more than 2.5 Gyr ago (Korenaga, 2018), yet only ~5 % of Earth's modern surface is older than this age (Goodwin, 1996). This means that our knowledge of Earth's formational geological processes is fundamentally limited. There has been profitable research aimed at understanding Earth's development through intensive study of these remnants of ancient geological material (Wilde et al., 2001; Bennett et al., 2007; Reimink et al., 2014); however, it is unknown whether these results present a complete perspective on Earth's evolution.

Earth's volcanic hotspots may represent a complementary view on early Earth processes that can partially remedy this preservation issue. It has long been recognised that the geochemical fingerprints of volcanic hotspots are among the most diverse of any terrestrial rock group. Some geochemical components of different volcanic hotspots have well constrained origins and may be more than 2.5 Gyr old, and therefore potential records of Earth's geological conditions before the emergence of complex life. The origins of one potential reservoir, variably termed the 'focus zone' (FOZO, used preferentially here; cf. Hart et al., 1992), 'common component' (C), or 'prevalent mantle' (PREMA), have evaded

conclusive agreement, but FOZO-like reservoirs have some remarkable geochemical characteristics. First, they have a geochemical signature that reflects less magmatic processing than the bulk upper mantle, but more magmatic processing than a theoretical chondritic primitive mantle—thus, FOZO reservoirs are said to be 'moderately depleted'. Second, nearly all volcanic hotspots appear to tap some form of FOZO, while other geochemical components are unique to one or more hotspots; thus, it has been hypothesised that FOZO reservoirs are widespread in the deep mantle sources of volcanic hotspots. Finally, only hotspot lavas (collectively termed ocean island basalts: OIBs) with geochemical affinity for FOZO reservoirs are known to possess elevated ³He/⁴He ratios (Class and Goldstein, 2005; Jackson et al., 2007) (Fig. 1) compared to young mid-ocean ridge basalts (MORBs). Such elevated ³He/⁴He ratios are a primary signature of ancient geological material (Class and Goldstein, 2005). Thus, it is possible that these FOZO reservoirs are older than any other geochemical component of hotspot volcanism.

Some of these unique geochemical characteristics have recently been linked to short-lived radiogenic isotope systems, which record the effects of early geological processes. Only OIBs with moderately depleted isotopic signatures preserve deficits in ¹²W, which must have been generated in the first 1 % of Earth's history (≥4.5 Gyr ago), compared to the bulk Earth (Fig. 1c). Similarly, heterogeneous ¹⁴Nd compositions, which were generated in the first 10 % of Earth's history (≥4.0 Gyr ago), are primarily observed in OIB samples with moderately

<footnote n="1">1. Institute of Geochemistry and Petrology, ETH Zürich, 8092 Zürich, Switzerland</footnote>

<footnote n="2">2. Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="3">3. Geosciences Research Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093-0244, United States</footnote>

<footnote>* Corresponding author (email: bjpeters2@gmail.com)</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1** Long (He-Sr-¹⁴³Nd-²⁰⁶Pb) and short lived (¹⁸²W) isotopic compositions of global ocean island basalts. The value of D¹⁴Sr-Nd-Pb parameter refers to the deviation of sample isotopic compositions from a reference depleted composition (≡0) as defined in Jackson et al. (2020). Long lived radiogenic isotope data from GEOROC; W isotopic data from Mundl-Petermeier et al. (2020) and Herret et al. (2023). Dotted lines show approximate limits of present database.</figcaption></figure>

depleted isotopic signatures (Horan et al., 2018; Peters et al., 2018). However, the high precision ¹⁴²Nd database for global hotspots remains relatively small and, thus, small scale ¹⁴²Nd heterogeneity in modern ocean island basalts may remain undetected. The convergence of global OIBs toward a moderately depleted isotopic signature, as well as the selectivity of this signature for predicting elevated ³He/⁴He ratios and heterogeneous ¹⁴²Nd and ¹⁸²W signatures, have led to the common conclusion that FOZO is a singular, ancient reservoir in Earth's deep mantle. This interpretation is particularly attractive when attempting to reconcile an ancient FOZO with the presence of two discrete large low shear velocity provinces (LLSVPs) identified in Earth's lower mantle. Despite this, evidence for the existence of multiple mantle components with elevated ³He/⁴He has been found both within single hotspots (Willhite et al., 2019) and between global volcanic hotspots (Jackson et al., 2007). Further, many hotspots that are spatially associated with different LLSVPs share common correlations between He and W isotopic compositions, implying that they have a shared ancient heritage that is independent of LLSVPs. Further, the lack of global correlations between ¹⁴²Nd and either short (¹⁸²W) or long lived (⁴He, ⁸⁷Sr, ¹⁴³Nd, ¹⁸⁷Os, ²⁰⁶Pb) isotopic tracers has complicated interpretations of Hadean-aged processes preserved in both geologically young and ancient rocks. Thus, the timing and nature of the evolution of moderately depleted isotopic signatures akin to FOZO, as well as their global extent, remains unclear.

## The μ¹⁴²Nd Signatures of FOZO-Type Lavas From Volcanic Hotspots

We measured the μ¹⁴²Nd compositions (parts per million deviation of the ¹⁴²Nd/¹⁴⁴Nd ratio of a sample from that of the terrestrial standard) of OIBs from the Crozet (³He/⁴He ≤ 14 R<sub>A</sub>, n = 17) and Juan Fernandez (³He/⁴He ≤ 17 R<sub>A</sub>, n = 13) archipelagos, which display FOZO-like isotopic compositions and elevated ³He/⁴He ratios, and lack geochemical influence from non-FOZO end members. Notably, only hotspots that show clear geochemical influence from geochemical end member reservoirs, such as enriched mantle (EM), depleted mantle (DM), or high μ (high ²³⁸U/²⁰⁴Pb, HIMU), in addition to influence from FOZO, display very high ³He/⁴He (>30 R<sub>A</sub>; e.g., Jackson et al., 2017). Our strategy was instead to focus on hotspot islands that display tightly distributed Sr-¹⁴³Nd-Pb isotopic compositions that lie near the convergence point of global OIB isotopic arrays, without showing geochemical influence from any non-FOZO end member (Fig. 1; cf. Stracke et al., 2005, for different definitions of FOZO). We used high precision analytical methods (adapted from Garçon et al., 2018; Wang and Carlson, 2022) designed to resolve very small scale μ¹⁴²Nd heterogeneity, since observed variations in μ¹⁴²Nd values are very small among post-Archean rocks (Horan et al., 2018; Hyung and Jacobsen, 2020). We relied on intensive sample replication (more than 80 individual, high precision analyses) to constrain the μ¹⁴²Nd compositions of the two hotspots.

<figure><img src="image_4.png" /></figure>
Samples from both studied islands of the Juan Fernandez hotspot samples display homogeneous µ¹⁴²Nd compositions with an average of µ¹⁴²Nd = +1.0 ± 0.9 (all uncertainties represent 95 % c.i., *n* = 13; Fig. 2, Tables S-1, S-2). Among Crozet hotspot OIBs, samples from Penguin Island similarly display homogeneous compositions (µ¹⁴²Nd = -0.6 ± 0.9, *n* = 11), whereas samples from Possession Island have somewhat more heterogeneous and negative µ¹⁴²Nd values (µ¹⁴²Nd = -1.4 ± 1.7, *n* = 6), producing a hotspot average µ¹⁴²Nd of -0.8 ± 0.8 (Fig. 2). The µ¹⁴²Nd compositions of Crozet and Juan Fernandez OIBs are thus resolved at the 95 % confidence level and pass a t-test for the statistical resolution of their sample means (*p* = 0.006). Importantly, statistical resolution of µ¹⁴²Nd compositions between individual samples or between sample groups requires that these samples or groups recorded distinct Hadean-aged histories even when no samples or sample groups are statistically resolved from the JNdI-1 terrestrial standard (cf. Horan et al., 2018). Further, the presence of statistically resolvable µ¹⁴²Nd values in modern rocks implies that their mantle sources previously preserved much greater µ¹⁴²Nd heterogeneity, since this tends to be erased by mantle convection over time (e.g., Hyung and Jacobsen, 2020).

Although the Sr-¹⁴³Nd isotopic compositions of Crozet and Juan Fernandez OIBs fall into a global FOZO-type compositional range (Breton et al., 2013; Truong et al., 2018) (Fig. 1), there are also observable differences between the compositions of the two hotspots (Figs. 2b and 3). In particular, the Sr-¹⁴³Nd isotopic compositions of Penguin and Possession Island OIBs (Crozet) bracket those of both studied islands of Juan Fernandez, meaning that the Sr-¹⁴²,¹⁴³Nd isotopic compositions of the islands could potentially be attributed to discrete, shared compositional

domains that are variably mixed into the sources of these islands. We tested this possibility using several binary mixing models (Fig. 3). Model 1 assumes that Penguin and Possession represent unmixed FOZO-like components, and that Juan Fernandez OIBs represent an intermediate mixture of these components. This model can explain the Sr-¹⁴³Nd isotopic compositions of Juan Fernandez OIBs, albeit at comparatively extreme trace element abundances (Table S-3). However, mixing two components (Penguin and Possession) that both possess negative µ¹⁴²Nd values cannot produce an intermediate composition (Juan Fernandez) with a positive µ¹⁴²Nd value. Mixing model 2 instead assumes that Penguin Island represents a discrete FOZO component and that the isotopic compositions of Juan Fernandez and Possession OIB are explained by minor admixing of an EM component (cf. Breton et al., 2013). This model can successfully reproduce the Sr-¹⁴²,¹⁴³Nd compositions of Juan Fernandez lavas assuming that the EM end member possesses a µ¹⁴²Nd value of ≳ + 8. However, this assumption cannot simultaneously explain the negative µ¹⁴²Nd compositions of Possession OIBs, which would require the EM end member to have µ¹⁴²Nd ≲ -5. Further, there is no independent evidence that 1) such an EM component exists in Juan Fernandez lavas (Truong et al., 2018), and 2) that this EM component would possess a resolvable positive or negative µ¹⁴²Nd value.

Mixing models 3 and 4 take the alternative perspective that Juan Fernandez OIBs represent a discrete FOZO component, and that Penguin and Possession lavas reflect admixing of a depleted MORB mantle (DMM) or an EM component, respectively. These models can successfully reproduce the Sr-¹⁴²,¹⁴³Nd isotopic compositions of both Penguin and Possession lavas; however, they require that the bulk DMM

<figure><img src="image_5.png" /><figcaption>**Figure 2** Neodymium isotopic compositions of ocean island basalts measured in this study. Small circles represent individual measurements, larger coloured symbols and error bars represent sample averages and 95 % confidence intervals, respectively. Vertical lines and coloured fields represent the average and 95 % confidence intervals of each hotspot. Values of ε¹⁴³Nd represented as a kernel density estimate (KDE).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 3** Mixing models (labelled by circled numbers as described in the text) for Sr-Nd isotopic compositions of studied ocean island basalts. Stars represent regular intervals of mixing as defined on each mixing curve; the colour of each curve corresponds to the µ$^{142}$Nd composition of the mixing interval as defined by the colour bar in panel (a). The symbol size for each island covers the full range of its Sr-143Nd isotopic compositions. Model inputs are summarised in Table S-2.</figcaption></figure>

has µ$^{142}$Nd ≤ −4 and the Possession EM component has µ$^{142}$Nd < −100 (Table S-3). Although there is evidence that global MORBs possess slightly negative µ$^{142}$Nd values (ca. µ$^{142}$Nd = −1 to −2; Peters et al., 2024), there is no indication that the bulk DMM systematically possesses more negative µ$^{142}$Nd compositions. Similarly, there is no evidence that EM components have µ$^{142}$Nd compositions significantly more extreme than what has been observed in the Archean rock record (ca. −20 to +20). Thus, we infer that the µ$^{142}$Nd compositions of Crozet and Juan Fernandez OIBs cannot be reconciled by binary mixing processes. Instead, they must be explained by the existence of multiple mantle domains with similar, FOZO-like long lived isotopic compositions but heterogeneous µ$^{142}$Nd signatures. This conclusion expands the long held view of FOZO as a diffuse component without the clear, singular end member compositions exhibited by EM and HIMU (Hart et al., 1992; Stracke et al., 2005).

The Hadean-Aged Heritage of FOZO

The short half-life of the $^{146}$Sm-$^{142}$Nd system (recently updated to 92 ± 3 Ma; Chiera et al., 2024) requires that detectible µ$^{142}$Nd heterogeneity was generated before ca. 4 Ga ago, during the Hadean Eon. This means that the differing µ$^{142}$Nd compositions of Crozet and Juan Fernandez OIBs reflect distinct FOZO domains that experienced different geochemical histories during

the first ca. 10% of Earth's history. This finding implies that FOZO is not a singular component, but rather reflects different silicate differentiation histories that began in the Hadean Eon and have been partially preserved until the present. This result is particularly striking given the long lived isotopic characteristics of Crozet and Juan Fernandez OIBs. In particular, the $ ^{3}He/^{4}He $ ratios of lavas from both archipelagos do not extend to the very high values observed in other hotspots (Jackson et al., 2017). However, the He-Pb isotopic characteristics of Crozet and Juan Fernandez OIBs overlap with the trend toward high $ ^{3}He/^{4}He $ ratios defined by Galápagos and Samoa OIBs, implying that they have primitive components with comparable origins (cf. Jackson et al., 2007) (Fig. 1b). Further, Juan Fernandez lavas record some of the lowest known $ ^{182}W/^{184}W $ ratios among OIBs, well below OIBs from other hotspots with similar or higher $ ^{3}He/^{4}He $ ratios, such as Samoa (Mundl-Petermeier et al., 2020). Identifying hotspots with shared ancient heritage is therefore clearly complicated, and a better view may be obtained by combining multi-dimensional isotopic data to explore isotopic heterogeneity within FOZO-type OIBs. A baseline analysis, utilising the normalisation scheme of Jackson et al. (2020) (Fig. 1d) for the combined long lived radiogenic (He-Sr-$ ^{143}Nd-$ $ ^{206}Pb $) compositions of global OIBs, suggests that there are at least three distinct trends to high $ ^{3}He/^{4}He $ values. Some hotspots, such as Iceland, appear to transverse multiple trends. This raises the possibility that primitive mantle domains are not discrete and may rather be unique to each hotspot system.

We developed a machine learning model to approach the challenge of visualising and interpreting multi-dimensional isotopic data in this context. We initially compiled He-Sr-$ ^{143}Nd-Pb $ isotopic and trace element data from 33 hotspots using the individual pre-compiled files available on the GEOROC database (DIGIS, Geoscience Centre Göttingen). Due to the requirement of machine learning models for relatively large datasets, the small amount of available $ ^{142}Nd $ and $ ^{182}W $ data precluded their direct inclusion in our model. We used the t-distributed stochastic neighbor embedding (TSNE) method to reduce the dimensionality of the data to produce simpler visual representations of the multi-dimensional data (e.g., Fig. S-1). We found that the inclusion of He isotopic or trace element compositions made geological interpretation significantly more complex (Supplementary Information), and so we focused on the interpretation of results for Sr-$ ^{143}Nd-Pb $ isotopic data alone. We used the initial findings for all compiled hotspots to guide the development of a refined K-means cluster analysis for nine hotspots with the greatest data availability, plus Juan Fernandez and Crozet. This resulted in the appearance of clusters with clear association to established mantle end member compositions, such as EM and HIMU (Fig. 4a; e.g., White et al., 2025). We then used this cluster analysis as an objective criterion to filter out samples that represented significantly depleted or enriched isotopic compositions relative to FOZO, and subsequently repeated the TSNE dimensionality reduction on the filtered data alone. For a full overview of the model development, see the Supplementary Information.

Among the FOZO-filtered dataset, at least three visually resolved groups are apparent (Fig. 4b). One is comprised of nearly all Hawaii and a majority of Iceland OIBs, which show strong overlap and are distinct from other hotspots. A second group is comprised primarily of the remaining Iceland and Hawaii lavas, along with Galápagos, Pitcairn and most Marquesas lavas, along with all but one sample from Penguin Island (Crozet). The final group is comprised of a few Iceland lavas along with Mascarene (Réunion), Samoa, Juan Fernandez and the remaining Marquesas and Crozet lavas. Although these groups are clearly distinct from one another,
<figure><img src="image_7.png" /><figcaption>**Figure 4** TSNE-reduced Sr-¹⁴³Nd-Pb isotopic data for selected hotspots using (a) all data for each hotspot or (b) data filtered FOZO-like isotopic compositions according to a K-means cluster model. See text and **Supplementary Information** for model details.</figcaption></figure>

they each include OIBs that have differing compositions on binary isotope plots alone (Fig. 1). For example, the Sr-¹⁴³Nd-Pb and He-W isotopic systematics of Galápagos OIBs appear to be unlike those of Pitcairn or Marquesas OIBs; however, in the filtered TSNE analysis (Fig. 4b) these three hotspots mainly occupy the same cluster. Further, the TSNE analysis preserves apparent compositional differences between Penguin and Possession Island OIBs. However, Penguin Island lavas are not associated with either Juan Fernandez or Mascarene lavas, which share their FOZO-like Sr-¹⁴³Nd-Pb and moderately elevated ³He/⁴He compositions. Likewise, the association of Juan Fernandez and Possession OIBs, which have distinct µ¹⁴²Nd compositions, highlights that there must also be multiple independent geological histories preserved within a single TSNE group, since the TSNE reduction was done using only long lived isotopic compositions. One exception to these associations is lavas from Hawaii and Iceland OIBs, which were previously noted to have similar isotopic signatures (e.g., Jackson et al., 2007) and likewise have strongly overlapping TSNE compositions for a variety of multi-dimensional analyses, among them those including He isotopic compositions (Figs. 4, S-2), despite their geographic distance and association with different LLSVP.

Given these results, it is clear that the geological interpretation of machine learning models is not straightforward (cf. Stracke et al., 2022; White et al., 2025). In particular, given that the model associates OIBs with differing µ¹⁴²Nd compositions, the observed sample grouping must represent a minimum number of discrete FOZO components present in the mantle. Further, the presence of heterogeneous µ¹⁴²Nd values in a single TSNE cluster implies that Hadean-aged heterogeneity could be

present throughout global OIBs and more high precision data is required in order to detect it. Our results thus highlight an opportunity for further tests of the geochemical relationship between potential common components of global hotspots. Given the lack of clear association between FOZO compositional clusters and geographic position of LLSVPs, further spatial analysis is required to understand where Hadean-aged components are stored and protected within the mantle. Further, as the ¹⁴²Nd and ¹⁸²W datasets for modern OIBs are expanded (Mundl-Petermeier et al., 2020), the Hadean origins of modern volcanism can be better understood through multi-dimensional analysis and the geological processes responsible for Earth's development can be scrutinised more closely.

## Acknowledgements

This project was supported by ETH Zürich/Marie Skłodowska-Curie Actions COFUND (18-1 FEL-28 to BJP) and the Swiss National Science Foundation (PZ00P2_186064 to BJP). The authors thank editor Raúl Fonseca for editorial handling and for thoughtful reviews from Catherine Chauvel, Bill White, and Erik Scherer, which helped improve the manuscript.

Editor: Raúl O.C. Fonseca

## Data Availability Statement

All data generated in this study are available in the **Supplementary Tables** and have also been archived in the EarthChem database at https://doi.org/10.60520/IEDA/113771.

## Additional Information

**Supplementary Information** accompanies this letter at https://www.geochemicalperspectivesletters.org/article2553.

<figure><img src="image_8.png" /></figure>

© 2025 The Authors. This work is distributed under the Creative Commons Attribution 4.0 License, which permits unrestricted use,

distribution, and reproduction in any medium, provided the original author and source are credited. Additional information is available at http://www.geochemicalperspectivesletters.org/copyright-and-permissions.

**Cite this letter as:** Peters, B.J., Nauret, F., Moine, B., Castillo, P.R. (2025) Earth's foundational geological events stored in the fundamental mantle source of hotspots. *Geochem. Persp. Let.* 38, 34–39. https://doi.org/10.7185/geochemlet.2553

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<figure><img src="image_9.png" /></figure>
# Earth's foundational geological events stored in the fundamental mantle source of hotspots

B.J. Peters, F. Nauret, B. Moine, P. Castillo

## Supplementary Information

The Supplementary Information includes:

* Supplementary Materials and Methods Information

* Supplementary Information Regarding the Machine Learning Model

* Tables S-1 to S-6

* Figures S-1 to S-7

* Supplementary Information References

## Supplementary Materials and Methods Information

Ocean island basalts (OIB) from two islands of the Crozet hotspot (Possession and Penguin) and two islands of the Juan Fernandez hotspot (Alexander Selkirk/Más Afuera and Robinson Crusoe/Más a Tierra) were selected for analysis. Major and trace element and He-Sr-¹⁴³Nd-Pb isotopic data were previously published for most samples (Farley, 1993; Breton et al., 2013; Truong et al., 2018) and are supplemented in this study. Additionally, Os (Paquet et al., 2019) and W (Mundl-Petermeier et al., 2020) isotopic data are available for some of the studied OIB from Juan Fernandez (Table S-1). Juan Fernandez samples are primarily tholeiitic and represent the main stage of shield building on each island, with a minority of alkali basalts (MF-S1, PF-5, PV-2) and basanites (PF-16, PV-1). There is no systematic difference between the µ¹⁴²Nd compositions of tholeiitic and alkalic basalts (Fig. 2). Crozet samples with major element data are transitional tholeiitic to basanitic, however major element data are only available for 9 of the 17 studied samples.

The Sr-¹⁴³Nd-Pb isotopic compositions of all samples overlap with the broadly-defined compositional range of focus zone (FOZO) OIB (see comparison of interpreted FOZO compositional ranges in Stracke et al., 2005) with each island defining a relatively homogeneous composition (Fig. 3). Possession Island OIB have slightly higher ⁸⁷Sr/⁸⁷Sr (average: 0.7042 ± 0.0003, 2 s.d.) than Penguin or Juan Fernandez OIB and thus lie on the outer edge of previously-defined FOZO ranges (e.g., Hart et al., 1992). These samples were previously interpreted to contain a minor enriched mantle (EM) component compared to Penguin Island samples (Breton et al., 2013) and this perspective is evaluated in our mixing models (Fig. 3).

New Sr and Pb isotopic data were obtained for select Crozet samples to complement published data (Breton et al., 2013) and are summarized in **Table S-1** and tabulated in full in **Tables S-4 and S-5**. Lead was separated from sample matrix, including Sr, on BioRad® AG1-X8, 100-200 mesh anion exchange resin using mixed HBr-HNO₃ solutions. Strontium, Rb, and other minor elements were then separated from sample matrix on BioRad® AG50-X8, 200-400 mesh cation exchange resin using 2M HCl. Finally, Sr was purified using Eichrom® Sr resin using solutions with
variable HNO₃ concentrations. Strontium isotopic compositions were measured on the Triton TIMS in the Isotope Geochemistry and Cosmochemistry group at ETH Zürich using a static acquisition method with amplifier rotation. Data were normalized to an ⁸⁸Sr/⁸⁶Sr value of 8.375209 using the exponential law. Repeated measurements of the NBS987 standard in the same analytical session yielded an average ⁸⁷Sr/⁸⁶Sr value of 0.710270 ± 0.000011 (2 s.d.) and samples were re-normalized using a reference ⁸⁷Sr/⁸⁶Sr value for NBS987 of 0.710245 (mean of published values in the GeoREM database).

Lead isotopic compositions were measured on a Thermo Fisher NeptunePlus inductively coupled plasma mass spectrometer (ICP-MS) in the Isotope Geochemistry and Cosmochemistry group at ETH using a static acquisition method with amplifier rotation. Correction for instrumental mass bias was done using Tl doping and a ²⁰³Tl/²⁰⁵Tl ratio that was iteratively optimized in order to eliminate the summed offset between average measured ²⁰⁶,²⁰⁷,²⁰⁸Pb/²⁰⁴Pb and ²⁰⁷,²⁰⁸Pb/²⁰⁶Pb values of standard NBS981 and the reference values of (Baker et al., 2004). The 2σ standard deviations of measured NBS981 across all analytical sessions were 0.007–0.0019 for ²⁰⁶Pb/²⁰⁴Pb, 0.0007–0.0022 for ²⁰⁷Pb/²⁰⁴Pb, and 0.0020–0.0059 for ²⁰⁸Pb/²⁰⁴Pb. Repeated measurements (n = 15) of the BHVO-2 reference material (United States Geological Survey) produced an average ²⁰⁶Pb/²⁰⁴Pb of 18.6195 ± 0.0002 (95% c.i., GeoREM preferred: 18.634 ± 0.068), average ²⁰⁷Pb/²⁰⁴Pb of 15.5417 ± 0.0001 (95% c.i., GeoREM preferred: 15.524 ± 0.050), and average ²⁰⁸Pb/²⁰⁴Pb of 38.2378 ± 0.0004 (95% c.i., GeoREM preferred: 38.146 ± 0.646).

Neodymium isotopic compositions were determined on separate sample aliquots from those used for Sr-Pb isotopic measurements. Neodymium was separated from matrix material using three different methods: (1) a four-step method adapted from (Garçon *et al.*, 2018) (designated LN Chrom in **Table S-2**), (2) a two-step protocol utilizing BioRad AG50-X8 resin (Garçon *et al.*, 2018) and a tandem LN resin-DGA resin column adapted from (Chu *et al.*, 2019) and (Wang and Carlson, 2022) (designated cation-DGA in **Table S-2**), or (3) a three-step protocol with matrix separation on DGA resin (Chu, 2020), Ce separation using a tandem LN-DGA column (Wang and Carlson, 2022), and a final step to separate La, Ce, and Pr on a long-aspect (6 mm inner diameter x 6 cm length) Teflon column. Replicate measurements of the BHVO2 reference material using these three methods produced indistinguishable results (**Table S-2**), meaning that no method produced appreciable isotopic fractionation relative to another. Yields for method (1) were typically 70–90 % and yields for methods (2) and (3) were typically 80–100 %. Both methods produced similar Ce-Nd and Nd-Sm separation, but method (3) produced substantially better Pr-Nd separation than either method (1) or method (2).

Isotopic compositions of Nd were measured on the Thermo Fisher Triton thermal ionisation mass spectrometer in the Isotope Geochemistry and Cosmochemistry group at ETH using a 4-step multi-dynamic method adapted from (Garçon *et al.*, 2018). All Nd isotopic ratios were normalized to a ¹⁴⁶Nd/¹⁴⁴Nd ratio of 0.7219 using the exponential law and were corrected to account for the difference in time at which ¹⁴⁶Nd/¹⁴⁴Nd ratios were measured (e.g., lines 1 and 2 for ¹⁴²Nd/¹⁴⁴Nd) and the times at which the Nd isotope ratios of interest were measured (e.g., lines 3 and 4 for ¹⁴²Nd/¹⁴⁴Nd). Data were thoroughly checked for signs of non-exponential (Andreasen and Sharma, 2009) or anomalously fast (e.g., Roth *et al.*, 2013) mass fractionation and runs were filtered or rejected when these effects could not be adequately constrained by the reduction (ca. 10 % of all runs). The medium-term precision of µ¹⁴²Nd is estimated as the drift-corrected 2 s.d. of all JNdi-1 standard runs from all instrument users between replacements of Faraday cup liners and was 5.2-6.9 ppm over three sets of cup liners. Uncertainty on single measurements was estimated to be the largest of (1) the medium-term uncertainty, (2) the 2 s.e.m. of the measurement (typically ca. 2 ppm), or (3) the 2σ standard deviation of all JNdi-1 standard runs in the same barrel as the sample (0.8–6.9 ppm, average 4.5 ppm). Uncertainty on replicate measurements of the same sample was calculated using Isoplot (Ludwig, 2003) as the weighted 95 % confidence interval (c.i.) of all replicate measurements with input uncertainties for individual measurements as defined above. Systematic effects on data accuracy were monitored through repeated dissolutions of the BHVO-2 reference material, which produced an average µ¹⁴²Nd of +0.7 ± 1.7 (n = 11) over the timeframe of this project, a value that is consistent with published ICPMS (e.g., Saji *et al.*, 2016) and TIMS (e.g.,
Wang and Carlson, 2022) data. Further, these data show no correlation between different Nd isotopic ratios and no correlation between post-separation Nd yield and Nd isotopic ratios.

**Supplementary Information Regarding the Machine Learning Model**

Our clustering model utilized data accessed from the pre-compiled data files (December 2023 version) on GEOROC (DIGIS Team, Geoscience Centre Göttingen). No short-lived radiogenic data were used in the model, since the available number of samples with complete long-lived and short-lived isotopic data is too small to permit quantitative analysis with machine learning models. The data were first filtered to include only bulk rock analyses for rock names corresponding to intermediate-mafic volcanic rocks. In cases where the rock name was not given, rocks with SiO₂ contents between 40 and 65 wt. % were utilized. Following this, duplicate entries for the same sample (e.g., multiple analyses of the same analyte) were aggregated and unweighted averages were taken for duplicated data. All forms of He (³He/⁴He, ⁴He/³He, and these ratios normalized to those of air) and Nd (¹⁴³Nd/¹⁴⁴Nd and ε¹⁴³Nd) isotopic data were reconciled. Sub-datasets were then created for samples that possessed Sr-¹⁴³Nd-Pb isotopic data (n = 4340), He-Sr-¹⁴³Nd-Pb isotopic data (n = 444), and Sr-¹⁴³Nd-Pb isotopic data plus abundances for Nb, Ba, La, Ce, Pb, and U (n = 2571). These trace element abundances were used to calculate Nb/U, Ce/Pb, and Ba/La ratios for use in the model, which are often considered to be relatively unchanged by partial melting and magma differentiation processes and are thus utilized to investigate OIB mantle sources (e.g., Cordier et al., 2021). Each sample in each sub-dataset must possess data for all analytes in a given sub-dataset in order to be used in the analysis. The data were then normalized on a linear scale of 0 to 1 with 0 representing the minimum value for a given analyte present in a sub-dataset and 1 representing the maximum.

Dimensionality reduction was accomplished using a t-distributed stochastic neighbour embedding (t-SNE) model to produce two-dimensional data from five- to eight-dimensional data in the original sub-datasets. A random state of 23 was employed for all models. Input perplexity was optimized using the Kullback-Leibler divergence over a perplexity range of 10-110 (Fig. S-1). The calculated divergence decreased with increasing perplexity over the entire tested range of perplexity, even well above the generally recommended maximum perplexity of 50. In general, higher perplexities caused the data to be divided into clusters that were increasingly further apart from each other. At intermediate perplexities, more discrete clusters were visible, but with less separation between clusters. We were unable to reproduce cluster separation to the degree observed by (Stracke et al., 2022), who reported that they used perplexity values between 25 and 45. There are many possible reasons for this discrepancy, since t-SNE is also sensitive to other inputs such as random state and data normalisation. For our models, we selected a perplexity of 70 as a compromise between the recommended maximum and the desire to minimise divergence as much as possible.

When applied to each sub-dataset, the t-SNE dimensionality reduction produced distinct results (Fig. S-2). The Sr-¹⁴³Nd-Pb isotopic sub-dataset produced the clearest visually-identified groups at a given input perplexity. By contrast, including trace element ratios Ce/Pb, Nb/U, and Ba/La decreased separation between groups. However, HIMU-type samples from the Austral-Cook hotspot formed a discrete group with greater separation compared to results from the Sr-¹⁴³Nd-Pb isotopic dataset. The sub-dataset including He isotopic compositions produced two broad groups, one of which was comprised of Hawaii and Iceland OIB while the other represented most other hotspots together. Samples from the Galápagos Islands formed a 'bridge' between these two broad groups (Fig. S-2c), with the intersection point between this bridge and the Hawaii-Iceland sample group consisting primarily of the highest ³He/⁴He samples from these three hotspots and Samoa. Due to the complexity in interpreting these results, we elected to proceed with only the Sr-¹⁴³Nd-Pb sub-dataset, however we provide comparative results for each sub-dataset in each figure for guidance. At an input perplexities ≥30, t-SNE reduction on this sub-dataset produced groups with broad relationships to recognized mantle geochemical components and endmembers (e.g., Fig. S-3). Samples with the most depleted isotopic compositions plotted near the t-SNE variable origin, where some of the groups come together. Samples with progressively more enriched isotopic compositions plotted further away from the origin, with distinct radial pathways for samples with EM and HIMU affinity. Hawaii and Iceland OIB with EM affinity, however, did not plot along the
same radial pathway as other EM-type lavas. Instead, these two hotspots defined their own large group with the most EM-like samples plotting furthest away from the origin.

The t-SNE reduced data where then subjected to a K-means clustering model to provide a quantitative division between visually identified clusters, with no specified number of data points per cluster. K-means clustering models are significantly less computationally expensive than agglomerative clustering models (e.g., Stracke et al., 2022; White et al., 2025) and rely on Euclidian distance between data points, which is independent of the size or composition of input datasets. However, they have the disadvantage that the number of clusters must be specified in advance, which decreases the objectivity of the model in evaluating the presence of discrete geochemical compositions. However, the selected number of clusters can be optimized for example using the silhouette score, which evaluates how similar each data point is to its assigned cluster compared to its similarity to other clusters. Low silhouette scores indicate that data points generally display high spatial affinity to their assigned clusters, whereas high scores reflect more ambiguous cluster assignments. We calculated the silhouette scores for models with 2 to 40 clusters (Fig. S-4). In general, we found that a local minimum in silhouette scores occurred around a number of clusters that roughly corresponded to the number of well-defined mantle geochemical components (n = 3–5: FOZO, DM, EM-1, EM-2, HIMU). The silhouette scores for all sub-datasets then increased after this point before reaching broad minimum plateaux at ≥15 clusters. This finding is similar to a recent study using agglomerative clustering (White et al., 2025) but differs from a prior study that identified many more clusters using agglomerative clustering (Stracke et al., 2022).

For visual inspection, cluster analysis was then performed using either 5 or 15 clusters (Fig. S-5). When the t-SNE reduced Sr-143Nd-Pb isotopic sub-dataset is divided into five clusters (labelled 0–4), the K-means algorithm produces two large clusters for Hawaii and Iceland samples (clusters 0 and 3), with one cluster containing nearly exclusively Hawaii OIB and the second covering mainly the area of overlap between these two hotspots, plus one visually distinct sample group that contains Hawaii, Iceland, and Galápagos OIB. Cluster 2 contains most remaining Galápagos OIB, plus most Pitcairn and Penguin Island OIB as well as a radially protruding arm representing HIMU-type OIB from the Austral-Cook hotspot. Cluster 1 contains most Mascarene (Réunion), Samoa, and Possession Island samples and is the closest representative of a broad FOZO-like composition. Finally, cluster 4 is comprised primarily of Kerguelen and remaining Pitcairn OIB and thus roughly defines an EM-1 endmember cluster. Together, these assigned clusters reflect a relatively high-level view of global OIB compositions, with variable grouping of OIB traditionally assigned to different mantle geochemical components. However, there are some clear shortfalls to this relatively simplified clustering analysis. For example, HIMU-type OIB are clustered together with clearly non-HIMU OIB, and cluster 0 includes two TSNE sample groups with clear visual separation. Thus, it is most likely that five clusters underestimate the dispersion present in the data.

When instead fifteen clusters are assumed, many of these divisions are further granulated (Fig. S-5). The cluster consisting nearly entirely of Hawaii OIB is split into four distinct clusters, and the cluster representing Hawaii plus Iceland and Galápagos OIB is likewise split into four separate clusters. One of these clusters represents the visually distinct group of samples that comprises overlapping t-SNE variable values for Hawaii, Iceland, and Galápagos data together. The HIMU-type OIB are split into a separate cluster, but the former EM-1 cluster is likewise split into two clusters that do not correspond to the two primary hotspots comprising this group. Similarly, the cluster corresponding most closely to FOZO-type hotspots was split into two clusters, with some Juan Fernandez samples present in each cluster. It is therefore evaluated that assigning fifteen clusters to the data overestimates the spatial distinction between global OIB data. However, this granularity is useful to identify and filter out clusters not clearly associated with FOZO-like compositions.

Using this baseline clustering analysis, we removed clusters representing OIB with isotopic compositions clearly more depleted or enriched than a broadly defined FOZO range. Of the fifteen originally defined clusters, eight were removed on the basis of isotopic affinity to non-FOZO mantle endmembers like EM and HIMU (e.g., Stracke et al.,
2005). This selection enabled filtering on a per-sample basis, which avoids excluding entire hotspots because some lavas from that hotspot have isotopic affinity to a non-FOZO endmember. However, this meant that two samples from the Juan Fernandez hotspot with slightly more enriched isotopic compositions (Gerlach *et al.*, 1986) than those measured in this study were selectively removed because they were quantitatively clustered with other samples possessing isotopic compositions trending toward an EM1-like endmember. All samples measured for their µ¹⁴²Nd compositions in this study were retained by this filtering, presenting a semi-objective confirmation that they are close representatives of global FOZO compositions. Following this, t-SNE dimensionality reduction was repeated on the filtered database. Perplexity was again optimized but the result was similar to the full dataset and a perplexity value of 70 was thus maintained. The number of quantitatively identified clusters was again optimized, which revealed an absence of the local minima of silhouette scores previously identified and broad plateaux of silhouette scores with *n*<sub>clusters</sub> ≥15 and especially for *n*<sub>clusters</sub> ≥ 35.

The t-SNE reduction of FOZO hotspots revealed three visually distinct groups (Fig. S-6). One group again corresponds to nearly all Hawaii and most Iceland samples. The persistence of this unique group in all TSNE analyses (e.g., Figs. 4, S-2) is remarkable and implies that the commonality of the FOZO component of these hotspots should be investigated in more detail. The second group is comprised primarily of Galápagos and Pitcairn samples, along with remaining Iceland samples, all but one of the Penguin Island samples, and FOZO-type samples from the Cook-Austral hotspot. The third group consists of Juan Fernandez samples, the remaining Penguin Island sample, all Possession Island samples, Mascarene OIB, and FOZO-type Samoa OIB. Quantitative clustering analysis assuming 15 clusters splits each of these groups into 4–6 clusters without clear relationship to sample origin. Nevertheless, the use of at least 15 clusters is justified by decreasing silhouette scores with increasing cluster counts, implying that the spatial separation of these clusters in five dimensions (i.e. the number of isotope analytes) is quantitatively justified. However, regardless of whether one considers the visual separation (3 groups) or the quantitative separation (≥15 clusters), it is clear that FOZO-type OIB have multi-nodal long-lived radiogenic isotopic compositions. The short-lived radiogenic isotopic compositions of these samples implies that their distinctness is still greater than what is implied by this analysis. For example, the He-W isotopic compositions of Hawaii and Samoa OIB plot on the same trend (Mundl-Petermeier et al., 2020), but their Sr-¹⁴³Nd-Pb isotopic compositions are visually and quantitatively distinct after t-SNE reduction. Further, Crozet samples display statistically indistinct µ¹⁴²Nd compositions (Fig. 2) but plot in visually and quantitatively distinct groups or clusters. However, the apparently greater internal µ¹⁴²Nd heterogeneity among Penguin Island OIB may imply that this similarity represents the analytical limitations still associated with µ¹⁴²Nd analysis. Future incorporation of short-lived radiogenic isotopic compositions into machine learning models like this one may reveal that the division of FOZO-like OIB into many clusters, as implied by our analysis, is further justified and that FOZO-type OIB represent many diverse geological histories stored in Earth's mantle.

The diverse isotopic compositions of FOZO-like OIB are not easy to reconcile with the two well-defined large low shear wave velocity provinces (LLSVP) in Earth's deep mantle. This is even clearer when considering the clear geochemical association of FOZO-type OIB, such as those from the Hawaii and Iceland hotspots, that are spatially near different LLSVP. Independent analysis of hotspots surrounding LLSVP have revealed a closer spatial association of LLSVP with isotopic compositions indicative of long-term tectonic recycling (Jackson et al., 2018), rather than FOZO-like compositions. If the quantitative clustering model we employ is correct in predicting the existence of many FOZO-like compositional domains, it may be difficult to store these compositions in singular domains over geological timescales without mixing them together. The implication of our machine learning model is instead that FOZO-like domains are discrete, numerous, and small enough to only be sampled over short spatial scales by a limited number of hotspots.
Supplementary Tables

**Tables S-1 to S-6** are available for download (.xlsx) from the online version of this article at
[https://doi.org/10.7185/geochemlet.2553](https://doi.org/10.7185/geochemlet.2553).

**Table S-1** Per-sample isotopic data.

**Table S-2** Per-run TIMS Nd isotopic data.

**Table S-3** Medium-term precision of Nd isotopic ratios.

**Table S-4** TIMS Sr isotopic data.

**Table S-5** ICP-MS Pb isotopic data.

**Table S-6** Inputs to binary mixing models in Figure 2.
Supplementary Figures

<figure><img src="image_10.png" /><figcaption>**Figure S-1** Optimisation of perplexity in t-SNE dimensionality reduction and examples of t-SNE reduced data with variable perplexity.</figcaption></figure>
<figure><img src="image_11.png" /></figure>

**Figure S-2** Comparison of t-SNE reduction for sub-datasets including only Sr-¹⁴³Nd-Pb isotopic compositions (a) and those additionally including (b) trace element (Nb/U, Ce/Pb, and Ba/La ratios) or (c) He isotopic compositions. For all models, perplexity is set at 70.
<figure><img src="image_12.png" /></figure>

**Figure S-3** Reduced-dimension OIB data for selected hotspots (as in **Fig. S-2**) with a perplexity of 30 to emphasise visual grouping (cf., **Fig. S-2e**). Individual data points are colour coded for their $D^{Sr-Nd-Pb}$ values, which index their Sr-$^{143}$Nd-$^{206}$Pb isotopic compositions against a reference depleted composition (≡0) (after Jackson *et al.*, 2020). The most isotopically depleted samples plot near the origin (t-SNE variable 1 = 0, t-SNE variable 2 = 0) and $D^{Sr-Nd-Pb}$ values increase radially, with the closest associations to EM- and HIMU-type OIB on the outside of the diagram. However, the spatial distance between samples with moderate $D^{Sr-Nd-Pb}$ values at different radial locations implies that they have differing multi-dimensional isotopic compositions even when their isotopic compositions appear to overlap on binary isotope plots (e.g., **Fig. 1a**).
<figure><img src="image_13.png" /><figcaption>**Figure S-4** Optimisation of input cluster counts for K-means cluster analysis. Silhouette score (y-axis) measures the average affinity of each data point for its assigned cluster with higher values indicating less affinity.</figcaption></figure>
<figure><img src="image_14.png" /></figure>

**Figure S-5** Comparison of clustering behavior in 9 key hotspots for different sub-datasets (rows, except for first row) and different defined numbers of clusters (columns, except for first column). Data in the first row and first column are colour coded according to their hotspot whereas data in the remaining panels are colour coded according to their assigned cluster. Reducing the data analysis to 9 key hotspots significantly decreased the computational expense of the model and also simplifies visualisation (cf., **Figure S-1**).
<figure><img src="image_15.png" /><figcaption>**Figure S-6** Filtering of data according to assigned cluster (n = 15) to keep only data from clusters that strongly overlap with a broad FOZO-like isotopic range. In panels (a) and (b) data are colour coded by hotspot, and in panels (c) and (d) they are colour coded by cluster (cf., **Fig. S-5**).</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>**Figure S-7** Result of re-calculated t-SNE and K-means models for data filtered for FOZO affinity. In panel (a), symbol colouring corresponds to that in Fig. S-5. In panel (b), the same colours are used for each cluster as in Fig. S-5, but note that due to the recalculation after filtering these now represent different clusters.</figcaption></figure>
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