Method for automatically identifying and classifying rock lithology in deep learning mode

An automatic recognition and deep learning technology, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve the problems of large influence of human factors, ignoring textures, and many interactive operations, so as to achieve a more automatic and intelligent training process. , the effect of reducing the influence of subjective factors

Active Publication Date: 2018-01-26
TIANJIN UNIV
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Problems solved by technology

Many scholars have obtained the characteristic information of rocks through the analysis of rock images, and have made some progress. However, in this process, manual selection of features is required, and there are many interactive operations and human factors. Sampling, but ignores other features such as texture, its method has limitations, and the use of neural networks to achieve rock image classification requires a good ability to adjust network parameters

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  • Method for automatically identifying and classifying rock lithology in deep learning mode
  • Method for automatically identifying and classifying rock lithology in deep learning mode
  • Method for automatically identifying and classifying rock lithology in deep learning mode

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Embodiment

[0142] The rock image samples used in the experiment were collected through different means such as photos, rock databases, and network searches. Three rock images of granite, phyllite, and breccia were mainly selected for test identification and analysis. The rock types are mainly composed of three types of images: laboratory rock specimens, field rock specimens and field large-scale rock images. In general, images of granites are mostly granular structures, images of phyllites show phyllite structures, and images of breccias show porphyritic structures; some image samples of these three types of rocks are as follows: figure 1 shown. A total of 571 images of the three types of rock images were collected, including 173 images of granite, 152 images of phyllite, and 246 images of breccia. The classification and quantity of their training set and test set are shown in Table 1. The training set is obtained from the respective total samples. Randomly selected, the rest is the tes...

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Abstract

The invention discloses a method for automatically identifying and classifying rock lithology in a deep learning mode, aiming at analyzing rock lithology in geological engineering. The method includesthe following steps: A. based on the kinds of rocks, acquiring rock images of different types, and grouping the rock images into a training group and a test group; B. taking a convolutional neural network Inception-v3 model as a pre-training model, acquiring image features by using a feature extraction model of the pre-training model; C. establishing a Softmax regression model; D. training a model for automatically identifying and classifying rock images; and E. testing the model for automatically identifying and classifying rock images. According to the invention, the method herein, by establishing the model for automatically identifying and classifying rock images, can analyze the geological conditions in an automatic and intelligent manner, greatly save labor and materials, and reducescost.

Description

technical field [0001] The invention relates to rock lithology classification, in particular to a method for automatic identification and classification of rock lithology in a deep learning mode. Background technique [0002] The classification of rock lithology has always been a key analysis problem in geology. Many scholars have obtained the characteristic information of rocks through the analysis of rock images, and have made some progress. However, in this process, manual selection of features is required, and there are many interactive operations and human factors. Sampling, but ignores other features such as texture, its method has limitations, and the use of neural networks to achieve rock image classification requires a good ability to adjust network parameters. Contents of the invention [0003] The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a method for automatic identification and classification of rock lithol...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/66G06N3/04
Inventor 李明超张野韩帅
Owner TIANJIN UNIV
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