Underground shallow detonation point positioning method based on deep reinforcement learning

A technology of reinforcement learning and positioning method, applied in the field of blasting vibration testing technology and passive positioning, it can solve the problems of low positioning accuracy and poor stability of underground shallow seismic sources, and achieves reduction of positioning parameter extraction, improvement of quantity and quality, and improved positioning. The effect of efficiency

Pending Publication Date: 2020-12-08
ZHONGBEI UNIV
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Problems solved by technology

[0007] The invention provides a method for locating the initiation point of underground shallow layers based on deep reinforcement learning. The technical problem to be solved is: to solve the problems of low positioning accuracy and poor stability of underground shallow seismic sources

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  • Underground shallow detonation point positioning method based on deep reinforcement learning
  • Underground shallow detonation point positioning method based on deep reinforcement learning
  • Underground shallow detonation point positioning method based on deep reinforcement learning

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Embodiment Construction

[0028] In order to make the purpose, content and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.

[0029] The present invention proposes a method for locating an underground shallow detonation point based on deep reinforcement learning, which is characterized in that it comprises the following steps:

[0030] S1. Lay out the shock sensor array

[0031] Select a point at the center of the monitoring area as the coordinate origin, establish a Cartesian coordinate system, and place n=168 sensors, centered on the coordinate origin, with a distance of 1m, and arrange the vibration sensors on the ground to form an equidistant square array, using high-precision Beidou obtains the coordinate information X of each sensor i =(x i ,y i ,z i )(i=1,2,3,...,n);

[0032] S2. Generate learning samples based on energy information, specifically as follows:

[0033] S2.1 Obtain the preset s...

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Abstract

The invention relates to a deep reinforcement learning-based underground shallow detonation point positioning method, which comprises the following steps of: arranging a vibration sensor array, generating an energy information-based learning sample, designing a deep learning network, directly setting an initial search box in a three-dimensional energy field diagram, and inputting the initial search box into a trained deep decision network through up-sampling, and outputting an action corresponding to the maximum value, finding a new region corresponding to the action in the three-dimensional energy field diagram, taking the new region as an initial search box again, inputting the new region into the deep decision network again through up-sampling, and so on, until the action is stopped, taking the central point of the last region as the predicted seismic source position. According to the method, the positioning precision and the positioning stability are improved. And meanwhile, the steps of positioning parameter extraction, positioning model modeling, positioning model calculation and the like in the traditional shallow seismic source positioning process are greatly reduced, and the seismic source positioning efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the field of blasting vibration testing technology and passive positioning technology, and in particular relates to a method for locating an underground shallow detonation point based on deep reinforcement learning. Background technique [0002] Underground shallow distributed seismic source positioning technology is a new method of position measurement that integrates sensing, networking, transmission, and positioning. In this method, a large number of wireless vibration sensor nodes are arranged on the ground surface, and the vibration signals generated by the explosion are obtained by using the node group. Feature extraction, positioning modeling, positioning calculation and other processes, finally realize the source positioning. The method can realize the location of the underground explosion point, the measurement of the position of the fuze initiation point, the advanced prediction of rockburst, water inrush, etc., and ...

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Application Information

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IPC IPC(8): G01V1/30
CPCG01V1/307G01V2210/65Y02A90/30
Inventor 李剑李传坤曹凤虎韩焱王黎明韩星程
Owner ZHONGBEI UNIV
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