• Email Us: [email protected]
  • Contact Us: +1 718 874 1545
  • Skip to main content
  • Skip to primary sidebar

Medical Market Report

  • Home
  • All Reports
  • About Us
  • Contact Us

New Machine Learning Technique May Revolutionize Research Into 500 Million-Year-Old Microfossils

June 29, 2024 by Deborah Bloomfield

Have you ever heard of Palynomorphs, “microfossils” that are abundant pretty much everywhere? They’re microscopic fossils that appear in sedimentary rocks across the world and are invaluable for geologists and paleontologists researching the planet’s evolutionary history. However, their tiny size and sheer numbers can be a challenge to work with, so researchers have now created a new machine learning technique to make this otherwise arduous task more manageable.

Advertisement

Palynomorphs really are small; they can range from 5 to 500 micrometres in size. If you consider the diameter of a human hair measure between 17 to 181 micrometres, then you get a sense for just how small they can be. Even grains of pollen tend to be larger than the smallest  Palynomorphs.

Advertisement

These tiny fragments are made of compounds that are extremely resistant to most forms of decay, as they are often made up of sporopollenin, dinosporin, or similar compounds. They were formed at any point between a couple of million years ago to over 500 million years ago. As such, they are valuable for researchers looking to age a rock layer or reconstruct a long-lost environment – such as whether the layer formed underwater or was a terrestrial feature.

Analysis of this variations tell us a lot about how the Earth has changed and can also offer insights into past climate conditions and geological events.

Previously, scientists would spend tedious hours manually classifying these microfossils by staring into microscopes where they may see billions of samples across multiple slides. It is a painstaking and frustrating process, but new advances in AI assisted techniques may make this significantly easier.

Researcher led by a team from the University of Tromsø, Norway, has introduced a two-stage AI-driven system that detects and classifies microfossils from microscope images.

Advertisement

“We propose an automatic pipeline for microfossil extraction and classification from raw microscope pictures. The method is fast and efficient and does not require intensive computing power”, the team wrote.

“We show that our approach improves the state-of-the-art for fossil extraction. The identification of individual species with machine learning is new and promising.”

The team achieved this in stages. Firstly, they used a pre-trained object detection model – YOLOv5 – to examine, identify and extract individual Palynomorphs from slide images. This process creates bounding boxes that appear around each microfossil, saving dozens of hours of work.

Two microscope images side by side. Each one shows dozens of tiny pieces of microfossil - which look like pieces of amber - scattered on slides. Each slide also has red boxes places around each piece, but the boxes are more numerous and accurate in the left hand image. The right hand image has red boxes that overlap more or are so large they cover multiple fragments.

The image on the left shows the results of the machine learning method introduced in this research. It is more precise than the one on the right, which was created with the pipeline of standard image processing methods.

Then, in the second stage, the team used a self-supervised learning system (SSL), which is a relatively new learning paradigm that is increasingly popular. The technique can essentially be trained to extract specific features from the samples it processes. It relies on self-supervised models to generate implicit labels from unstructured data.

Advertisement

Within this study, the team compared two SSL frameworks – SimCLR and DINO – both of which were found to be invaluable means for speeding up the classification process.

“This work shows that there is great potential in utilizing AI in this field,” Iver Martinsen, first and co-corresponding author of the study said in a statement. “By using AI to automatically detect and recognize fossils, geologists might have a tool that can help them better utilize the enormous amount of information that wellbore samples provide”.

The team used the AI to detect Palynomorphs using data obtained by the Norwegian Offshore Directorate, which came from the Norwegian continental shelf. In order to test its accuracy, the team then tested the model by classifying several hundred previously labels fossils from the same well.

“We are very happy with our results. Our model exceeds previous benchmarks available out there. We hope that the present work will be beneficial for geologists both in industry and academia,” adds Martinsen.

Advertisement

The paper is published in Artificial Intelligence in Geosciences.

Deborah Bloomfield
Deborah Bloomfield

Related posts:

  1. Financial comparison “super app” Jeff raises $1.5M seed extension
  2. Toyota’s Woven Planet acquires vehicle operating system developer Renovo Motors
  3. U.S., Russia lift targeted sanctions to allow Nuland visit – Moscow
  4. 72-Foot Fusion Gun Fires Projectiles At 4.3 Miles Per Second Trying To Create Limitless Energy

Source Link: New Machine Learning Technique May Revolutionize Research Into 500 Million-Year-Old Microfossils

Filed Under: News

Primary Sidebar

  • Why Do Some Toilets Have Two Flush Buttons?
  • 130-Year-Old Butter Additive Discovered In Danish Basement Contains Bacteria From The 1890s
  • Prehistoric Humans Made Necklaces From Marine Mollusk Fossils 20,000 Years Ago
  • Zond 5: In 1968 Two Soviet Steppe Tortoises Beat Humans To Orbiting Around The Moon
  • Why Cats Adapted This Defense Mechanism From Snakes
  • Mother Orca Seen Carrying Dead Calf Once Again On Washington Coast
  • A Busy Spider Season Is Brewing: Why This Fall Could See A Boom Of Arachnid Activity
  • What Alternatives Are There To The Big Bang Model?
  • Magnetic Flip Seen Around First Photographed Black Hole Pushes “Models To The Limit”
  • Something Out Of Nothing: New Approach Mimics Matter Creation Using Superfluid Helium
  • Surströmming: Why Sweden’s Stinky Fermented Fish Smells So Bad (But People Still Eat It)
  • First-Ever Recording Of Black Hole Recoil Captured During Merger – And You Can Listen To It
  • The Moon Is Moving Away From Earth At A Rate Of About 3.8 Centimeters Per Year. Will It Ever Drift Apart?
  • As Solar Storm Hits Earth NASA Finds “The Sun Is Slowly Waking Up”
  • Plate Tectonics And CO2 On Planets Suggest Alien Civilizations “Are Probably Pretty Rare”
  • How To Watch The “Awkward” Partial Solar Eclipse This Weekend
  • World’s Oldest Pots: 20,000-Year-Old Vessels May Have Been Used For Cooking Clams Or Brewing Beer
  • “The Body Is Slowly And Continuously Heated”: 14,000-Year-Old Smoked Mummies Are World’s Oldest
  • Pizza Slices, Polaroid Pictures, And Over 300 Hats: What’s Left Behind In Yellowstone’s Hydrothermal Areas?
  • The Mathematical Paradox That Lets You Create Something From Nothing
  • Business
  • Health
  • News
  • Science
  • Technology
  • +1 718 874 1545
  • +91 78878 22626
  • [email protected]
Office Address
Prudour Pvt. Ltd. 420 Lexington Avenue Suite 300 New York City, NY 10170.

Powered by Prudour Network

Copyrights © 2025 · Medical Market Report. All Rights Reserved.

Go to mobile version