Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

AI-based graptolite identification improves shale gas exploration

View ORCID ProfileZhi-Bin Niu, View ORCID ProfileHong-He Xu
doi: https://doi.org/10.1101/2022.01.17.476477
Zhi-Bin Niu
1State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology and Centre for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, 210008 Nanjing, China
2College of Intelligence and Computing, Tianjin University, 300354 Tianjin, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zhi-Bin Niu
  • For correspondence: zbniu@nigpas.ac.cn
Hong-He Xu
1State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology and Centre for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, 210008 Nanjing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Hong-He Xu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Graptolites are fossils from the mid-Cambrian to lower Carboniferous periods that inform both our understanding of evolution and the exploration of shale gas [1–4]. The identification of graptolite species remains a visual task carried out by experienced taxonomists because their fine-grained morphologies and incomplete preservation in multi-fossil samples have hindered automation. Artificial intelligence (AI) holds great promise for transforming such meticulous tasks, and has already proven useful in applications ranging from animal classification to medical diagnostics [5–15]. Here we demonstrate that graptolites can be identified with taxonomist accuracy using a deep learning model [16–18]. We develop a convolutional neural network to classify macrofossils, and construct a comprehensive dataset of >34,000 images of 113 graptolite species annotated at pixel-level resolution to train the model. We validate the model’s performance by comparing its ability to identify 100 images of graptolite species that are significant for rock dating and shale gas exploration with 21 experienced taxonomists from research institutes and the shale gas industry. Our model achieves 86% and 81% accuracy when identifying the genus and species of graptolites, respectively; outperforming taxonomists in terms of accuracy, time, and generalization. By investigating the decisions made by the neural network, we further show that it can recognise fine-grained morphological details better than taxonomists. Our AI approach, providing taxonomist-level graptolite identification, can be deployed on web and mobile apps to extend graptolite identification beyond research institutes and improve the efficiency of shale gas exploration.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • hhxu{at}nigpas.ac.cn

  • http://www.geobiodiversity.com

  • http://120.55.164.80:8089/

  • https://zenodo.org/record/5205216#.YeOFzmhBxok

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 20, 2022.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
AI-based graptolite identification improves shale gas exploration
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
AI-based graptolite identification improves shale gas exploration
Zhi-Bin Niu, Hong-He Xu
bioRxiv 2022.01.17.476477; doi: https://doi.org/10.1101/2022.01.17.476477
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
AI-based graptolite identification improves shale gas exploration
Zhi-Bin Niu, Hong-He Xu
bioRxiv 2022.01.17.476477; doi: https://doi.org/10.1101/2022.01.17.476477

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Paleontology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3505)
  • Biochemistry (7346)
  • Bioengineering (5323)
  • Bioinformatics (20263)
  • Biophysics (10016)
  • Cancer Biology (7743)
  • Cell Biology (11300)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9951)
  • Epidemiology (2065)
  • Evolutionary Biology (13322)
  • Genetics (9361)
  • Genomics (12583)
  • Immunology (7701)
  • Microbiology (19021)
  • Molecular Biology (7441)
  • Neuroscience (41036)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2137)
  • Physiology (3160)
  • Plant Biology (6860)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1896)
  • Systems Biology (5311)
  • Zoology (1089)