Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To ov...Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To overcome the challenges of subjectivity,low efficiency,and high expertise requirements in describing rock thin sections,we design a multimodal mapping network,ThinGPT,which aligns the feature spaces of the contrastive language-image pre-training(CLIP)and Generative Pre-trained(GPT-2)through network training.Given the high frequency of keywords and the structured sentence patterns in thin-section descriptions,we introduce a tokenization method tailored for rock thin sections.This approach enhances GPT-2's ability to effectively encode text and produce text feature vectors.We conducted comparative experiments using ThinGPT and other models on common sedimentary rocks.The results demonstrate that ThinGPT exhibits excellent potential in generating thin-section feature descriptions of rocks.Based on the geological expert evaluation criteria proposed in this study,ThinGPT achieved a score of 1.62 on the test set.For model complexity,ThinGPT avoids heavy initial training of large language models(LLMs).This training strategy makes the model lighter and improves the efficiency of rock thin section descriptions.As an innovative application of a LLMs within a lightweight architecture for rock thin section description,ThinGPT has significant implications for intelligent geology,geophysics,and petroleum exploration.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
基金supported by a grant from the National Natural Science Foundation of China(Grant No.42174156).
文摘Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To overcome the challenges of subjectivity,low efficiency,and high expertise requirements in describing rock thin sections,we design a multimodal mapping network,ThinGPT,which aligns the feature spaces of the contrastive language-image pre-training(CLIP)and Generative Pre-trained(GPT-2)through network training.Given the high frequency of keywords and the structured sentence patterns in thin-section descriptions,we introduce a tokenization method tailored for rock thin sections.This approach enhances GPT-2's ability to effectively encode text and produce text feature vectors.We conducted comparative experiments using ThinGPT and other models on common sedimentary rocks.The results demonstrate that ThinGPT exhibits excellent potential in generating thin-section feature descriptions of rocks.Based on the geological expert evaluation criteria proposed in this study,ThinGPT achieved a score of 1.62 on the test set.For model complexity,ThinGPT avoids heavy initial training of large language models(LLMs).This training strategy makes the model lighter and improves the efficiency of rock thin section descriptions.As an innovative application of a LLMs within a lightweight architecture for rock thin section description,ThinGPT has significant implications for intelligent geology,geophysics,and petroleum exploration.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
