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Automatic classification of volcanic rocks from thin section images using transfer learning networks

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Abstract

In this study, efficient deep transfer learning models are proposed to classify six types of volcanic rocks, and this paper has a novelty in classifying volcanic rock types for the first time using thin section images. Convolutional neural network-based DenseNet121 and ResNet50 networks, which are transfer learning methods, are used to extract the features from thin section images of rocks, and the classification process is carried out with a single-layer fully connected neural network. The proposed models are trained and tested on 1200 thin section images using four different optimizers (Adadelta, ADAM, RMSprop, SGD). AUC, accuracy, precision, recall and f1-score are used as performance metrics. Proposed models are run 10 times for each optimizer. DenseNet121 classifies volcanic rock types using RMSprop with an average accuracy of 99.50% and a maximum of 100.00%, and ResNet50 classifies using ADAM with an average accuracy of 98.80% and a maximum of 99.72%. Thus, the applied deep transfer learning is promising in geosciences and can be used to identify rock types quickly and accurately.

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey

    Özlem Polat

  2. Sivas Provincial Disaster and Emergency Directorate, Sivas, Turkey

    Ali Polat

  3. Department of Geology Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey

    Taner Ekici

Authors
  1. Özlem Polat
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  2. Ali Polat
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  3. Taner Ekici
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Contributions

Özlem Polat designed the study, developed the software with Python, performed the experiments, analyzed the results and wrote the manuscript. Ali Polat conceived the presented idea and provided support in development of computer codes and writing manuscript. Taner Ekici prepared the image dataset by taking photograph and provided support in writing manuscript.

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Correspondence to Özlem Polat.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Conflicts of interest/Competing interests

We have no conflicts of interest/competing interests to disclose.

Availability of data

Datasets can be accessed from https://drive.google.com/drive/folders/12cVpuVQw6sS7ZXMChRdyQNht4b4E_Pvq

Code availability:

Computer codes are publicly available on https://drive.google.com/drive/folders/1UU0aiFzxjclEQfAo9VnHxXIiDuXsSxRE

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Polat, Ö., Polat, A. & Ekici, T. Automatic classification of volcanic rocks from thin section images using transfer learning networks. Neural Comput & Applic 33, 11531–11540 (2021). https://doi.org/10.1007/s00521-021-05849-3

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  • DOI: https://doi.org/10.1007/s00521-021-05849-3

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