陆斌. 基于孔间地震细分动态探测的透明工作面方法[J]. 煤田地质与勘探, 2019, 47(3): 10-14. DOI: 10.3969/j.issn.1001-1986.2019.03.002
引用本文: 陆斌. 基于孔间地震细分动态探测的透明工作面方法[J]. 煤田地质与勘探, 2019, 47(3): 10-14. DOI: 10.3969/j.issn.1001-1986.2019.03.002
LU Bin. Method of transparent working face based on dynamic detection of cross hole seismic subdivision[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 10-14. DOI: 10.3969/j.issn.1001-1986.2019.03.002
Citation: LU Bin. Method of transparent working face based on dynamic detection of cross hole seismic subdivision[J]. COAL GEOLOGY & EXPLORATION, 2019, 47(3): 10-14. DOI: 10.3969/j.issn.1001-1986.2019.03.002

基于孔间地震细分动态探测的透明工作面方法

Method of transparent working face based on dynamic detection of cross hole seismic subdivision

  • 摘要: 透明工作面是煤矿智能开采的重要组成部分,为智能开采提供工作面的详细地质构造信息,提出一种基于孔间地震密集动态探测的透明工作面方法,能够实现工作面的渐进式精细探测。该方法是以采煤机为震源的随采地震方法的进一步发展,主要利用平行于切眼的一系列水平孔对工作面进行细分探测,随着采煤工作的逐渐推进,利用孔间地震方法对细分区域进行精细成像。与已有的随采地震方法比较,本方法具有明显的优点。首先,射线覆盖更加均匀且无盲区。第二,探测区域被细分所以探测精度更高。第三,因为利用地震干涉的"虚"震源方法能得到高信噪比的单炮,可进一步提高探测精度。该方法能够适应智能开采透明工作面的目标要求,有望成为智能开采的重要组成部分。

     

    Abstract: Transparent working surface is an important part of intelligent coal mining. It provides detailed geological structure information of working face for intelligent coal mining. This paper proposes a working surface exploration method based on segmented and dynamic cross-hole seismic, which can realize progressive fine detection of coal working face. The method is a new development with seismic while mining, which is using the coal cutter as seismic source. The main idea is to image the working face segmented by deploying some detectors in a series of horizontal holes parallel to the long wall. The method performs fine imaging on the subdivided area. Compared with the existing seismic while mining, this method has obvious advantages. Firstly, the ray coverage is more uniform without blind zone. Secondly, the detection area is subdivided, so the detection accuracy is higher. The "virtual" source method, seismic interferometry, can be used to obtain shot gathers with high SNR, which can further improve the detection accuracy. The study believes that this method can adapt to the target requirements of transparent working face, and it is expected to become an important part of intelligent mining.

     

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