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Lithology identification method based on machine learning

A technology of lithology identification and machine learning, applied in machine learning, character and pattern recognition, instruments, etc., can solve the problems of high hardware cost investment, lack of sample data, and low recognition accuracy, so as to reduce cost investment and improve recognition efficiency , The effect of simplifying the identification steps

Pending Publication Date: 2022-05-10
NORTH BLASTING TECH
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Problems solved by technology

[0004] In order to solve the problems of low identification accuracy, lack of sample data, complicated steps, heavy workload, large hardware cost investment and low practicability existing in the prior art, the purpose of the present invention is to provide a lithology identification method based on machine learning

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  • Lithology identification method based on machine learning
  • Lithology identification method based on machine learning
  • Lithology identification method based on machine learning

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Embodiment 1

[0045] Such as figure 1 As shown, the present embodiment provides a method for lithology identification based on machine learning, including the following steps:

[0046] Obtain the historical operation data set of the drilling rig, and normalize the historical operation data set of the drilling rig to obtain the processed historical operation data set;

[0047] The operating data of the drilling rig includes drilling speed, rotation speed, wind pressure, pressurization pressure and rotation pressure difference, as shown in the historical operation data table of the drilling rig in Table 1:

[0048] Table 1

[0049]

[0050] The formula for normalization processing is:

[0051] Y=a+q(X-X min )

[0052] In the formula, Y is the data after normalization processing; a is the lower limit value of the normalization interval; X is the data before normalization processing; X min is the minimum value in the sample data; q is the normalization coefficient;

[0053] The formula...

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Abstract

The invention belongs to the technical field of lithology identification, and discloses a lithology identification method based on machine learning, comprising the following steps: acquiring a drilling machine historical operation data set, and normalizing the drilling machine historical operation data set to obtain a processed historical operation data set; according to the processed historical operation data set, an engineering blasting lithology identification model is established based on machine learning; real-time operation data of the drilling machine are obtained, normalization processing is conducted on the real-time operation data of the drilling machine, and processed real-time operation data are obtained; and inputting the processed real-time operation data into an engineering blasting lithology identification model for lithology identification to obtain a corresponding lithology identification result. The method solves the problems of low recognition precision, lack of sample data, complex steps, large workload, large hardware cost investment and low practicability in the prior art.

Description

technical field [0001] The invention belongs to the technical field of lithology identification, and in particular relates to a machine learning-based lithology identification method. Background technique [0002] Lithology refers to the sum of rock color, composition, structure, structure and other characteristics. Lithology identification refers to the process of recognizing and distinguishing lithology through some specific methods. important question. Especially in mine blasting engineering, it is necessary to identify rock properties to avoid safety accidents. [0003] The lithology identification of blasted rock formations currently has laboratory methods, field test methods, and part of it uses the micro-logging method to measure seismic wave inversion to obtain lithology data. The artificial intelligence algorithm used requires a large number of samples to support learning, and the key The core parameters have not been optimized, resulting in the problems of low re...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/10G06F18/24
Inventor 余德运李泽华王洪强王仲琦杨恩徐谦王旭耀杨威王金海郝成磊
Owner NORTH BLASTING TECH
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