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 val...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.展开更多
基金supported by the Research Council of Norway,through its Centre for Research-based Innovation funding scheme (grant no.309439),and Consortium Partners.
文摘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.
