Soil Geochemistry Toward Lithium Pegmatite Exploration: Building a Machine-Learning Predictive Algorithm via Portable X-Ray Fluorescence Article Swipe
Luiza Maria Pereira Pierangeli
,
Mona-Liza C. Sirbescu
,
Sérgio Henrique Godinho Silva
,
David C. Weindorf
,
Thomas R. Benson
,
Nilton Curi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5382/econgeo.5166
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5382/econgeo.5166
As demand for lithium (Li) increases, cheaper, more sustainable, and faster methods are needed for the identification and characterization of new Li deposits. Lithium-rich pegmatites are major sources of Li, but their exploration is often hindered by soil cover. Portable X-ray fluorescence (pXRF) can rapidly and accurately quantify soil chemistry to determine the bedrock economic potential, but unfortunately, Li is undetectable via pXRF. Herein, pXRF data and random forest models were used to predict both Li contents in soil samples and Li-rich soil parent material based on abundances of 15 predictors (K, Rb, Al, Ba, Ca, etc.). For comparison, support vector regression and neural network deep learning were also conducted. The data set consisted of 112 soil samples collected over spodumene-rich pegmatites, barren granitic pegmatites, peraluminous granite, and metamorphic host rocks from forested, glaciated northern Wisconsin and Michigan, United States. Lithium abundances were independently measured using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The best Li prediction was achieved using neural networks, yielding a coefficient of determination (R2) of 0.90, a root mean square error (RMSE) of ~40 mg × kg–1, and residual prediction deviation of 3.2. The best parent material prediction model was achieved using random forest, with an overall accuracy of 0.88. Portable XRF analysis discriminates among soil samples formed on bedrock with distinct mineralogy. Using pXRF combined with appropriate machine learning models to predict the Li contents in the soil and the type of underlying bedrock could become an alternative, more efficient, and less invasive exploration method compared to traditional trenching.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5382/econgeo.5166
- https://pubs.geoscienceworld.org/segweb/economicgeology/article-pdf/doi/10.5382/econgeo.5166/7336060/5166_pierangeli_et_al_ep.pdf
- OA Status
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- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413924681Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5382/econgeo.5166Digital Object Identifier
- Title
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Soil Geochemistry Toward Lithium Pegmatite Exploration: Building a Machine-Learning Predictive Algorithm via Portable X-Ray FluorescenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-01Full publication date if available
- Authors
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Luiza Maria Pereira Pierangeli, Mona-Liza C. Sirbescu, Sérgio Henrique Godinho Silva, David C. Weindorf, Thomas R. Benson, Nilton CuriList of authors in order
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https://doi.org/10.5382/econgeo.5166Publisher landing page
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https://pubs.geoscienceworld.org/segweb/economicgeology/article-pdf/doi/10.5382/econgeo.5166/7336060/5166_pierangeli_et_al_ep.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://pubs.geoscienceworld.org/segweb/economicgeology/article-pdf/doi/10.5382/econgeo.5166/7336060/5166_pierangeli_et_al_ep.pdfDirect OA link when available
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Pegmatite, Geology, Lithium (medication), Fluorescence, X-ray fluorescence, Mineralogy, Geochemistry, Optics, Endocrinology, Physics, MedicineTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.5382/econgeo.5166 |
| publication_date | 2025-08-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4289336387, https://openalex.org/W3111129314, https://openalex.org/W2610170382, https://openalex.org/W3138542739, https://openalex.org/W2165976294, https://openalex.org/W1998053851, https://openalex.org/W4230978102, https://openalex.org/W2138973222, https://openalex.org/W2076845462, https://openalex.org/W4321375746, https://openalex.org/W2602228915, https://openalex.org/W2103999221, https://openalex.org/W3146097469, https://openalex.org/W94052953, https://openalex.org/W3198339393, https://openalex.org/W3137951894, https://openalex.org/W3203033308, https://openalex.org/W1831050183, https://openalex.org/W2164777277, https://openalex.org/W2789603996, https://openalex.org/W4318962860, https://openalex.org/W2335657501, https://openalex.org/W1536084664, https://openalex.org/W2325214818, https://openalex.org/W3038317215, https://openalex.org/W3167157040, https://openalex.org/W4389358362, https://openalex.org/W4409004035, https://openalex.org/W4210324263, https://openalex.org/W1984957490, https://openalex.org/W4283277514, https://openalex.org/W4312881442, https://openalex.org/W4385188491, https://openalex.org/W2030860739, https://openalex.org/W3217091817, https://openalex.org/W2169189930, https://openalex.org/W2092739804, https://openalex.org/W4411152878, https://openalex.org/W2009789540, https://openalex.org/W2905060367, https://openalex.org/W2969274637, https://openalex.org/W2261067204, https://openalex.org/W2293212777, https://openalex.org/W4402424979, https://openalex.org/W2770018638, https://openalex.org/W4318619700, https://openalex.org/W3100147273, https://openalex.org/W2100411277, https://openalex.org/W4205656784, https://openalex.org/W4391099338, https://openalex.org/W2947152641, https://openalex.org/W3175120962, https://openalex.org/W4200400075, https://openalex.org/W3186894947, https://openalex.org/W2490490808, https://openalex.org/W54143302, https://openalex.org/W4388660352, https://openalex.org/W4390227531, https://openalex.org/W4313855240, https://openalex.org/W3035455684, https://openalex.org/W2262611765, https://openalex.org/W3080155821, https://openalex.org/W1521320773, https://openalex.org/W1680797894 |
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