ICELEARNING · Artificial Intelligence techniques for ice core analyses

Artificial Intelligence techniques for ice core analyses

Earth System scientists can investigate the Earth past climate by studying natural archives where information can be frozen in time. Examples of such archives are the bed floor of the oceans or ice from polar regions and glaciers. Ice cores have provided us with some of the most pristine records of the Earth’s past climate. While the oldest Antarctic and Greenland cores date back to up to 800,000 and 125,000 year ago respectively and register variability of climate parameters at hemispheric scales, ice stored in glaciers and small ice caps located at lower latitudes typically contain a fingerprint or local to regional climate. Understanding climate variability is important to bring forward our knowledge of Earth system climate, understand the past and refine models to better constrain future scenarios. Different types of climate information can be trapped in ice cores and can be retrieved using many methods and analytical techniques. The climate proxies that have been targeted in this project are insoluble particles, i.e. visible particles that do not dissolve in water. The broad category of insoluble particles include mineral dust, volcanic particles, biological matter like pollen grains and marine particulates like diatoms and foraminifera. The investigation of these particles in ice cores allow to produce past climate records which scientific importance are far reaching. Dust records allow to investigate the Earth past aridity, vegetation cover and atmospheric circulation. Volcanic particles tell us about past volcanic eruptions thereby allowing to investigate past volcanic activity. Biological matter and marine particles tell climate scientists crucial information about past vegetation and sea level. At present, by far the most common technique deployed to find and classify insoluble particles in ice cores is manual microscopy. Despite bench microscopy can provide precise information to the operator, its use is extremely laborious, time consuming and a limited number of samples can be processed. The overall objective of the ICELEARNING project is to develop a methodology that would be able to support human researchers in finding and at the same time automatically classifying different types of insoluble particles in ice cores. We do so by deploying a Flow Imaging Microscope, called FlowCam, an instrument capable to continuously acquire images of particles in liquid ice core samples. The images are then processed by neural networks, specifically trained to recognize and count autonomously different types of particles at the same time. The developed methodology would not only benefit ice core scientists but would positively impact different branches of science where investigations by manual bench microscopy are still the standard approach. The scientific project objectives include: characterization of the FlowCam instrument for ice core samples, selection and acquisition of the training datasets necessary to train classification models, development and training of the models, application of the models to real ice core samples and comparison with human microscopy to access the potentials and limitations of the developed methodology.

Data: CORDIS, © European Union