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A method for automatic identification and classification of rock lithology under deep learning mode

An automatic recognition and deep learning technology, applied in character and pattern recognition, biological neural network models, instruments, etc., can solve problems such as large influence of human factors, many interactive operations, ignoring textures, etc.

Active Publication Date: 2021-05-25
TIANJIN UNIV
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  • Application Information

AI Technical Summary

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

Method used

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  • A method for automatic identification and classification of rock lithology under deep learning mode
  • A method for automatic identification and classification of rock lithology under deep learning mode
  • A method for automatic identification and classification of rock lithology under deep learning mode

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Experimental program
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Effect test

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 automatic identification and classification of rock lithology in a deep learning mode, which is used to analyze rock lithology in geological engineering, comprising the following steps: step A, collecting different types of rock images according to the required rock types, And divide it into a training set and a test set; Step B, use the convolutional neural network Inception‑v3 model as a pre-training model, and use its feature extraction model to obtain image features; Step C, establish a Softmax regression model; Step D, train rock Image automatic recognition and classification model; Step E, testing the rock image automatic recognition and classification model. The invention can automatically and intelligently analyze the geological conditions in engineering by establishing an automatic recognition and classification model of rock images, greatly saving manpower and material resources, and reducing costs.

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...

Claims

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Application Information

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