1887

Abstract

Summary

We present the Scampi system, a digital solution designed to automate and accelerate species identification in palynology. Traditional biostratigraphy is hindered by slow, manual fossil identification and a shrinking pool of specialists. Scampi addresses these challenges by leveraging content-based image retrieval (CBIR) using vision transformers, enabling efficient and accurate search of microfossil images.

The software allows experts to scour large image databases, refine results through active learning, and visualize depth distributions of identified specimins. A case study on 23 slide scans from a North Sea well showed that Scampi gave a 96% match to traditional methods, delivering interpretations ten times faster and halving the overall workflow time. The approach is expected to scale well for larger studies.

Scampi’s integration of advanced machine learning and user-friendly interfaces promises to transform biostratigraphy, making analyses faster and more cost-effective.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202639022
2026-03-09
2026-02-08

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References

  1. Caron et al. [2021] Emerging Properties in Self-Supervised Vision Transformers. IEEE/CVF International Conference on Computer Vision (ICCV)
     [Google Scholar]
  2. Daood, A., Ribeiro, E., Bush, M. [2016]. Pollen Grain Recognition Using Deep Learning. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science, vol 10072
     [Google Scholar]
  3. Martinsen, I., Wade, D., Ricaud, B. and Godtliebsen, F. [2024] The 3-billion fossil question: How to automate classification of microfossils. Artificial Intelligence in Geosciences, 5, 100080.
     [Google Scholar]
  4. RomeroIC, KongS, FowlkesCC, JaramilloC, UrbanMA, Oboh-IkuenobeF, D’ApolitoC, PunyasenaSW. [2020] Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy. Proc Natl Acad Sci U S A. 117(45):28496–28505
     [Google Scholar]
  5. Sanches, A., Ağbulut, B., Castro, L. and Vieira, M. [2025] Machine learning applied to recognition of dinoflagellate cysts: Type study with the species Batioladinium longicornutum. Artificial Intelligence in Geosciences, 6(2), 100150.
     [Google Scholar]
  6. Wade, D., Stefanowicz, S., Anthonissen, E. and Cullum, A. [2024] Species Classification Automation for Microfossil Photomicrograph Images (Scampi). 85th EAGE Conference & Exhibition, 2024(1) 1–5.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202639022
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Scampi - Exploring The Fossil Frontier
Conference Proceedings 2026, 1 (2026); https://doi.org/10.3997/2214-4609.202639022
/content/papers/10.3997/2214-4609.202639022
/content/papers/10.3997/2214-4609.202639022
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