The 3-billion fossil question:How to automate classification of microfossils  

作　　者：Iver Martinsen David Wade Benjamin Ricaud Fred Godtliebsen 

机构地区：[1]UiT-The Arctic University of Norway,Tromsø,Norway [2]Equinor ASA,Stavanger,Norway

出　　处：《Artificial Intelligence in Geosciences》2024年第1期137-145,共9页地学人工智能（英文）

基　　金：supported by the Research Council of Norway,through its Centre for Research-based Innovation funding scheme (grant no.309439),and Consortium Partners.

摘　　要：Microfossil classification is an important discipline in subsurface exploration,for both oil&gas and Carbon Capture and Storage(CCS).The abundance and distribution of species found in sedimentary rocks provide valuable information about the age and depositional environment.However,the analysis is difficult and consuming,time-as it is based on manual work by human experts.Attempts to automate this process face two key challenges:(1)the input data are very large-our dataset is projected to grow to 3 billion microfossils,and(2)there are not enough labeled data to use the standard procedure of training a deep learning classifier.We propose an efficient pipeline for processing and grouping fossils by genus,or even species,from microscope slides using self-supervised learning.First we show how to efficiently extract crops from whole slide images by adapting previously trained object detection algorithms.Second,we provide a comparison of a range of self-supervised learning methods to classify and identify microfossils from very few labels.We obtain excellent results with both convolutional neural networks and vision transformers fine-tuned by self-supervision.Our approach is fast and computationally light,providing a handy tool for geologists working with microfossils.

关 键 词：Self-supervised learning PALYNOLOGY Deep learning MICROFOSSILS 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]