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X-ray Micro-CT based characterization of rock cuttings with deep learning

Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process and can also be prone to subjectivity leading to errors. In this paper, a framework for the classification of multiple types of rock structures is introduced based on rock cutting images from X-ray micro-CT technology. The classification is performed using a simple yet effective deep learning model (a ResNet-18 architecture) to categorize five different lithologies: micritic limestone, bioclastic limestone, oolithic limestone, molassic sandstone and gneiss. The proposed network is trained on 2 datasets (laboratory and borehole) both containing the five lithologies and comprise over 10 000 images. The laboratory dataset consists of a well-controlled experiments with homogeneous samples and the borehole dataset with heterogeneous samples corresponding to a real case application. Among all the considered models, including ResNet-34, and SPP-CNN and human experts manual classification, ResNet-18 demonstrates superior performance across multiple evaluation metrics, including precision, recall, and F1-score. It is to our best knowledge, the first time a test comparing deep neural network and human performance is performed for this task. To optimize the performance of the proposed model, the transfer learning method is implemented. Furthermore, the experiments demonstrate that when employing transfer learning, the size of the dataset significantly impacts the performance of the model. In the studied design, the experimental results confirm that the proposed approach is a cost-effective and efficient method for automated rock cutting classification using the micro-CT technique, and it can be easily modified to adapt the rock cutting classification from various types and sources.

Ketersediaan
#
Perpustakaan BIG (Eksternal Harddisk) 551.136
245
Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
12 hlm PDF, 6.037 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.25, February 2025
Subjek
Deep learning
Transfer learning
Computer vision
Rock cuttings
X-ray computed tomography
Geological characterization
Info Detail Spesifik
-
Pernyataan Tanggungjawab
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Tidak tersedia versi lain

Lampiran Berkas
  • X-ray Micro-CT based characterization of rock cuttings with deep learning
    Rock cuttings from destructive boreholes are a common and cheaper source of drilling materials that can be used to determine underground geology compared to rock core samples. Classifying manually the series of cuttings can be a long and tedious process and can also be prone to subjectivity leading to errors. In this paper, a framework for the classification of multiple types of rock structures is introduced based on rock cutting images from X-ray micro-CT technology. The classification is performed using a simple yet effective deep learning model (a ResNet-18 architecture) to categorize five different lithologies: micritic limestone, bioclastic limestone, oolithic limestone, molassic sandstone and gneiss. The proposed network is trained on 2 datasets (laboratory and borehole) both containing the five lithologies and comprise over 10 000 images. The laboratory dataset consists of a well-controlled experiments with homogeneous samples and the borehole dataset with heterogeneous samples corresponding to a real case application. Among all the considered models, including ResNet-34, and SPP-CNN and human experts manual classification, ResNet-18 demonstrates superior performance across multiple evaluation metrics, including precision, recall, and F1-score. It is to our best knowledge, the first time a test comparing deep neural network and human performance is performed for this task. To optimize the performance of the proposed model, the transfer learning method is implemented. Furthermore, the experiments demonstrate that when employing transfer learning, the size of the dataset significantly impacts the performance of the model. In the studied design, the experimental results confirm that the proposed approach is a cost-effective and efficient method for automated rock cutting classification using the micro-CT technique, and it can be easily modified to adapt the rock cutting classification from various types and sources.
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