黄宇奇¹,
查华胜^1,2,
高级^1,3,,,
令狐建设^4,,
宣金国²,
周建斌⁴,
董润平⁴,
霍晶晶⁴,
张海江^1,3
1. 中国科学技术大学地球和空间科学学院, 合肥 230026
2. 安徽万泰地球物理技术有限公司, 合肥 230026
3. 安徽蒙城地球物理国家野外科学观测研究站, 安徽蒙城 233527
4. 华阳新材料科技集团有限公司, 山西阳泉 045000

基金项目: 国家重点研发计划（2018YFC1504102），国家自然科学基金国际（地区）合作与交流项目（41961134001），华阳新材料科技集团有限公司（原阳泉煤业（集团）有限责任公司）科研项目（YM18005）资助


详细信息

作者简介: 黄宇奇, 男, 1995生, 博士研究生, 主要从事地球物理联合反演研究

通讯作者: 高级, 男, 1983年生, 副研究员, 主要从事近地表地球物理成像及联合反演研究.E-mail: gaoji617@ustc.edu.cn; 令狐建设, 男, 1965年生, 山西运城人, 正高级工程师, 华阳新材料科技集团有限公司(原阳泉煤业(集团)有限责任公司)总工程师, 从事煤矿安全技术及管理工作

中图分类号: P315

收稿日期:2020-12-15
修回日期:2021-05-29
上线日期:2021-11-10

Predicting the distribution of coalbed methane by ambient noise tomography with a dense seismic array

HUANG YuQi¹,
ZHA HuaSheng^1,2,
GAO Ji^1,3,,,
LINGHU JianShe^4,,
XUAN JinGuo²,
ZHOU JianBin⁴,
DONG RunPing⁴,
HUO JingJing⁴,
ZHANG HaiJiang^1,3
1. School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
2. Anhui Wantai Geophysical Technology Co., Ltd., Hefei 230026, China
3. Anhui Mengcheng National Geophysical Observatory, Anhui Mengcheng 233527, China
4. Huayang New Material Technology Group Co., Ltd., Shanxi Yangquan 045000, China


More Information

Corresponding authors: GAO Ji,E-mail:gaoji617@ustc.edu.cn ; LINGHU JianShe

MSC: P315

--> Received Date: 15 December 2020
Revised Date: 29 May 2021
Available Online: 10 November 2021


摘要
摘要:瓦斯突出是一种常见的煤矿动力灾害现象，随着煤矿矿井开采深度的增加，煤层瓦斯含量、压力都呈上升趋势，发生煤与瓦斯突出的危险性加大.传统的瓦斯测量方法只能测量局部离散点瓦斯含量，难以从矿井及采区尺度对瓦斯含量进行预测.因此需要寻求一种能够在采区及工作面布设前预测煤层瓦斯富集程度的高效地球物理方法.背景噪声成像方法已经在城市地下空间、矿产资源等近地表成像中得到广泛的运用.本文将该方法首次应用到阳泉寺家庄煤矿井田区域，采用96个台站记录的连续背景噪声数据，通过互相关方法获得了台站对之间的瑞利面波经验格林函数，并进一步提取了5 Hz~1.4 s的基阶瑞利面波的群速度和相速度频散曲线.本研究首先通过区域的平均频散曲线获得该区域的平均一维横波速度结构作为三维反演的初始模型；其次，利用基于射线追踪的面波频散直接成像方法获得研究区1.0 km以浅的三维横波速度结构；最后，结合获得的三维速度结构，以及岩石物理实验获得的瓦斯含量与地震波速度的经验关系，预测了寺家庄井田15号煤的瓦斯含量，预测的瓦斯含量与实际巷道揭露的瓦斯含量具有较好的一致性.本研究成果表明，对于煤矿瓦斯分布预测来说，背景噪声成像方法是一种潜在有效的全新的技术形式.
关键词: 背景噪声成像/
面波直接反演/
速度与瓦斯关系/
瓦斯含量预测

Abstract:Coalbed methane inrush is one of the commonly occurring dynamic disasters in coal mine. As the mining depth goes deeper, the coalbed methane concentration and pressure will also likely go up. As a result, the risks associated with coal and gas outburst increases. The traditional gas measurement method can only measure the gas content in local individual points, thus it is difficult to predict the gas content for the whole mining area. Therefore, it is necessary to find an effective geophysical method that can predict the concentration of coalbed methane before the mining process. The ambient noise tomography (ANT) method has begun to be widely used in near surface imaging for underground space development in the urban areas and for mineral exploration in mining areas. In this paper, we will show the first application of the ANT method to a dense array consisting of 96 stations deployed in the Sijiazhuang Coal Mine in Yangquan. The empirical Green's functions of Rayleigh waves between station pairs are obtained by the cross-correlation and stacking method. Furthermore, the dispersion curves of group velocity and phase velocity of fundamental Rayleigh surface waves for the period of 5 Hz ~1.4 s are extracted. In this study, the average one-dimensional shear wave velocity model of the region is firstly obtained from the average dispersion curve as the initial model for the three-dimensional inversion, and then the three-dimensional shear wave velocity model above 1.0 km is obtained by using the surface wave direct inversion method. Finally, the coalbed methane content of No.15 coal in the Sijiazhuang mine field is predicted from the velocity variations around the coal bed based on the empirical relationship between coalbed methane concentration and seismic wave velocity obtained from the laboratory petro-physical experiment. In general, the distribution of predicted coalbed methane concentrations is consistent with the actual gas content revealed during the roadway excavation. This study shows that the ANT method provides a new technique for the study of coal mine gas distribution, and is a potential method for effectively predicting the distribution of coalbed methane.
Key words:Ambient noise tomography/
Direct surface wave inversion/
Relationship between velocity and coalbed methane/
Prediction of coalbed methane concentration



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