Microfossil analysis is important in subsurface mapping,for example to match strata between wells.This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical sam...Microfossil analysis is important in subsurface mapping,for example to match strata between wells.This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera.The current digitalization of large volumes of microfossil samples that is being conducted by the Norwegian Offshore Directorate,paired with AI development,opens up new opportunities for automating parts of the analysis to help the geologist in the analysis.Unsupervised representation learning is a research area in Artificial Intelligence(AI)that lies at the core of this challenge,as this way of learning can create useful image representations by utilizing large volumes of data without requiring labels.Previous work has presented good results for the classification of a limited number of classes,but there are still challenges related to classification in realistic settings where additional unknown species are present.In this paper,we connect unsupervised representation learning and uncertainty estimation and create a comprehensive tool to automate microfossil analysis.We present our methodology and results in three parts.In the first part,we train several AI models from scratch using state-of-the-art supervised self-learning methods,obtaining excellent results compared against state-of-the-art foundation models for image classification and content-based image retrieval.In the second part,we develop a method based on conformal prediction which enables our classifier to handle a large pool of images containing both distribution in-and out-of-distribution data,while at the same time allowing us to create error estimates to control the uncertainty of the prediction sets.In the third part,we use our method to create distribution charts of fossils for a range of genera in multiple wells.展开更多
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 project Transforming Ocean Surveying by the Power of Deep Learning Methods which is funded by the Norwegian Research Council,Norway(IKTPLUSS-IKT og digitalinnovasjon,project no.332901)。
文摘Microfossil analysis is important in subsurface mapping,for example to match strata between wells.This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera.The current digitalization of large volumes of microfossil samples that is being conducted by the Norwegian Offshore Directorate,paired with AI development,opens up new opportunities for automating parts of the analysis to help the geologist in the analysis.Unsupervised representation learning is a research area in Artificial Intelligence(AI)that lies at the core of this challenge,as this way of learning can create useful image representations by utilizing large volumes of data without requiring labels.Previous work has presented good results for the classification of a limited number of classes,but there are still challenges related to classification in realistic settings where additional unknown species are present.In this paper,we connect unsupervised representation learning and uncertainty estimation and create a comprehensive tool to automate microfossil analysis.We present our methodology and results in three parts.In the first part,we train several AI models from scratch using state-of-the-art supervised self-learning methods,obtaining excellent results compared against state-of-the-art foundation models for image classification and content-based image retrieval.In the second part,we develop a method based on conformal prediction which enables our classifier to handle a large pool of images containing both distribution in-and out-of-distribution data,while at the same time allowing us to create error estimates to control the uncertainty of the prediction sets.In the third part,we use our method to create distribution charts of fossils for a range of genera in multiple wells.
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.
