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Automatic classification of plutonic rocks with deep learning

Igneous rocks form when molten magma is cooled and solidified, either within the Earth’s crust (plutonic rocks), or from lava extruded onto the Earth’s surface in the atmosphere or underwater (volcanic rocks). The classification of igneous rocks can be done using data from different instrumental techniques. However, these approaches tend to be expensive and time-consuming. In this research work, several models for the classification of granitoids, which are the most abundant plutonic rocks in the Earth’s crust, were created with a convolutional neural network developed with TensorFlow. Specifically, several combinations of gabbro, diorite, tonalite, granodiorite, monzodiorite, and granite image samples were used in the experiments. The best result was obtained in the model that classifies images of gabbro, diorite, granodiorite, and granite with an accuracy value of 95%, an average precision value of 96%, an average recall value of 95%, and an average F1 score value of 95%.

Ketersediaan
112551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
8 h lm PDF, 2.214 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.10, June 2021
Subjek
Deep learning
Rock classification
Plutonic rocks
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Automatic classification of plutonic rocks with deep learning
    Igneous rocks form when molten magma is cooled and solidified, either within the Earth’s crust (plutonic rocks), or from lava extruded onto the Earth’s surface in the atmosphere or underwater (volcanic rocks). The classification of igneous rocks can be done using data from different instrumental techniques. However, these approaches tend to be expensive and time-consuming. In this research work, several models for the classification of granitoids, which are the most abundant plutonic rocks in the Earth’s crust, were created with a convolutional neural network developed with TensorFlow. Specifically, several combinations of gabbro, diorite, tonalite, granodiorite, monzodiorite, and granite image samples were used in the experiments. The best result was obtained in the model that classifies images of gabbro, diorite, granodiorite, and granite with an accuracy value of 95%, an average precision value of 96%, an average recall value of 95%, and an average F1 score value of 95%.
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