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Smarter grain selection for reliable sediment provenance

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  • Choosing the largest or smallest mineral grains for geochronological analysis can skew age profiles, especially in poorly sorted sediment.
  • Representative grain size sub-sampling or truly random grain selection reduces potential operator bias and strengthens comparisons between sediment provenance studies.
  • About 240 well-selected grains capture facies-level age variability, supporting faster, reproducible mineral fingerprinting without sacrificing geological confidence.

Zametzer, A., Dröllner, M., Barham, M., Kirkland, C. L. & Norris, C. A. (2025). Grain selection for representative detrital zircon age populations. Earth and Planetary Science Letters, 671, 119619. https://doi.org/10.1016/j.epsl.2025.119619.

Geochronology
Regolith and Sedimentology
Resource Geoscience

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Particle Analysis
Particle Analysis
U–Pb Geochronology
U–Pb Geochronology
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Mineral Geochronology

Ocean Listening for Antarctic Minke Whales

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  • A bespoke convolutional neural network detected Antarctic minke whale “bio-duck” calls across nine East Antarctic and Western Australian soundscapes.
  • Performance was strongest in quieter Antarctic winter recordings, where lower ambient noise helped distinguish whale calls more reliably.
  • Industrial and biological sounds, including vessels, seismic airguns, humpback whales and dwarf minke whales, drove false detections in northern sites.

Darias-O’Hara, A. K., Nguyen Hong Duc, P., Madhusudhana, S., McCauley, R., Tollefsen, C., Erbe, C. & Miller, B. S. (2026). Machine learning methods for the detection of Antarctic Minke Whales (Balaenoptera bonaerensis) in East Antarctica and Western Australia. Marine Mammal Science, 42, e70118. https://doi.org/10.1111/mms.70118.

Acoustics

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Marine mammal habitat modelling
Marine mammal habitat modelling
Soundscape measurement
Soundscape measurement
Underwater signature measurement
Underwater signature measurement
Underwater noise modelling
Underwater noise modelling

Tracking Columbia's Slow Dance: Ancient magnetic clues reveal supercontinent stabilisation

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  • High-precision magnetic signatures from 1.70–1.68 billion-year-old mafic dykes in the North China Craton offer rare insights into Earth’s ancient continental positions.
  • Subtle shifts between the North China and North Australian cratons between 1.73 and 1.65 billion years ago mark a key transitional phase in Columbia's formation.
  • Findings suggest Columbia's tectonic configuration became more stable during the Statherian period, refining our models of early supercontinent assembly.

Wang, C., Peng, P., Mitchell, R.N., Deng, C., Salminen, J., Liu, Y. & Kirscher, U. (2025). Toward a more stable supercontinent Columbia in the Statherian. Geophysical Research Letters, 52, e2025GL116097. https://doi.org/10.1029/2025GL116097.

Magnetism and Palaeomagnetism

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Magnetic Remanence Analysis
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Rock Magnetic Characterisation

Earth’s Mineral Pulse: Reading supercontinent cycles through critical metal systems

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  • Mineral deposit ages reveal a repeating tectonic rhythm of about 750 million years, linked to supercontinent assembly and breakup.
  • Pegmatite-hosted critical metals increasingly formed on faster cycles as Earth’s mantle cooled and plate tectonics matured.
  • Crustal source complexity appears about 300 million years before pegmatite mineralisation, offering an early signal of future metal fertility.

Kirkland, C. L. (2026). Pacing supercontinent rhythms from the metallogenic record. Geology, 54 (6), 507–511. https://doi.org/10.1130/G54471.1.

Geochronology
Resource Geoscience
Geoinformatics and Geocomputing

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Machine Learning & AI for Geological Materials
Machine Learning & AI for Geological Materials
Mineral Geochronology
Mineral Geochronology

Zircon Knows: Smarter Ore Exploration Through Machine Learning

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  • Using the trace chemistry of zircon, researchers accurately predicted the source rock type—even without visible geological context.
  • Random Forest models achieved over 90% accuracy in classifying both igneous rocks and ore deposits from zircon element data.
  • Just eight elements in zircon—including hafnium, uranium, and yttrium—are enough to map mineral systems and guide exploration.

Wen, Z.-H., Li, L., Kirkland, C.L., Li, S.-R., Sun, X.-J., Lei, J.-L., Xu, B. & Hou, Z.-Q. (2024). A machine learning approach to discrimination of igneous rocks and ore deposits by zircon trace elements. American Mineralogist, 109(6), 1129–1142. https://doi.org/10.2138/am-2022-8899.

Geochemistry
Geoinformatics and Geocomputing
Resource Geoscience

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Machine Learning & AI for Geological Materials
Machine Learning & AI for Geological Materials
Mineral Trace Element Analysis
Mineral Trace Element Analysis

How heavy minerals reveal climate and weathering histories

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  • A continental-scale heavy mineral data set captures both geological origin and environmental overprint, allowing distinction between source rock signals and climate-driven mineral alteration across Australia.
  • Weathering and sediment storage significantly reshape heavy mineral signatures, with labile minerals like apatite rapidly lost in warm, wet, or low-relief environments.
  • Mineral indices provide a tool to map palaeo-weathering intensity, offering a way to evaluate sediment reworking and upgrading of Ti–Zr–REE placer deposits in ancient landscapes.

Dröllner, M., Barham, M., Kirkland, C.L., Zametzer, A. & Schulz, M. (2025). Australian continental‐scale heavy mineral patterns track climate, weathering and erosion. Sedimentology, 72(5), 1430–1452. https://doi.org/10.1111/sed.70008.

Regolith and Sedimentology
Resource Geoscience

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Particle Analysis
Particle Analysis

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