煤层瓦斯突出危险区综合预测方法

TD713; 常规的瓦斯突出预测技术,主要从单一角度出发,无法达到多因素影响下的瓦斯突出危险区域预测精度.以某研究区为例,利用基于遗传算法的支持向量机(SVM)网络,预测了瓦斯含量;将孔隙度作为构造煤的判别因子,并通过概率神经网络(PNN)反演方法,得到了构造煤分布情况;介绍了基于自然伽马曲线的拟密度反演方法,获得了煤层顶板岩性情况.综合瓦斯含量、构造煤分布及煤层顶板岩性3个方面特征,建立了一套瓦斯突出危险区域综合预测方法,为判断瓦斯突出危险区提供了理论基础.经过与实际突出位置做验证,预测结果吻合,说明了综合预测方法在此研究区具有较高的准确性....

Full description

Saved in:
Bibliographic Details
Published in煤炭学报 Vol. 43; no. 2; pp. 466 - 472
Main Authors 李冬, 彭苏萍, 杜文凤, 邢朕国, 李泽辰
Format Journal Article
LanguageChinese
Published 中国矿业大学(北京)地球科学与测绘工程学院,北京100083%中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083 01.02.2018
中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083
Subjects
gas content
瓦斯含量
瓦斯突出
comprehensive forecast
综合预测
tectonic coal
顶板岩性
roof lithology
构造煤
gas outburst
Online AccessGet full text
ISSN0253-9993
DOI10.13225/j.cnki.jccs.2017.1229

Cover

Abstract TD713; 常规的瓦斯突出预测技术,主要从单一角度出发,无法达到多因素影响下的瓦斯突出危险区域预测精度.以某研究区为例,利用基于遗传算法的支持向量机(SVM)网络,预测了瓦斯含量;将孔隙度作为构造煤的判别因子,并通过概率神经网络(PNN)反演方法,得到了构造煤分布情况;介绍了基于自然伽马曲线的拟密度反演方法,获得了煤层顶板岩性情况.综合瓦斯含量、构造煤分布及煤层顶板岩性3个方面特征,建立了一套瓦斯突出危险区域综合预测方法,为判断瓦斯突出危险区提供了理论基础.经过与实际突出位置做验证,预测结果吻合,说明了综合预测方法在此研究区具有较高的准确性.
AbstractList TD713; 常规的瓦斯突出预测技术,主要从单一角度出发,无法达到多因素影响下的瓦斯突出危险区域预测精度.以某研究区为例,利用基于遗传算法的支持向量机(SVM)网络,预测了瓦斯含量;将孔隙度作为构造煤的判别因子,并通过概率神经网络(PNN)反演方法,得到了构造煤分布情况;介绍了基于自然伽马曲线的拟密度反演方法,获得了煤层顶板岩性情况.综合瓦斯含量、构造煤分布及煤层顶板岩性3个方面特征,建立了一套瓦斯突出危险区域综合预测方法,为判断瓦斯突出危险区提供了理论基础.经过与实际突出位置做验证,预测结果吻合,说明了综合预测方法在此研究区具有较高的准确性.
Abstract_FL Conventional technology only considers one factor,which cannot achieve the same precision of gas outburst zone as multi-factor prediction methods.Taking an area as an example,the support vector machine (SVM) network based on genetic algorithm was used to predict the gas content.The porosity was used as the discriminant factor of tectonic coal.Distribution of tectonic coal was obtained by probabilistic neural network (PNN).The quasi-density inversion method based on natural gamma curve was in-troduced to obtain the lithology of coal seam roof.Characteristics of gas content,tectonic coal distribution and coal seam roof lithology was comprehensively considered to establish the gas outburst risk area comprehensive fore-casting method,which provided a theoretical basis to determine the gas outburst danger zone.The prediction results were consistent with actual prominent positions,which proved that this comprehensive forecasting method had high accuracy in this study area.
Author 李泽辰
邢朕国
杜文凤
李冬
彭苏萍
AuthorAffiliation 中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083;中国矿业大学(北京)地球科学与测绘工程学院,北京100083%中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083
AuthorAffiliation_xml – name: 中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083;中国矿业大学(北京)地球科学与测绘工程学院,北京100083%中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083
Author_FL XING Zhenguo
LI Dong
PENG Suping
LI Zechen
DU Wenfeng
Author_FL_xml – sequence: 1
  fullname: LI Dong
– sequence: 2
  fullname: PENG Suping
– sequence: 3
  fullname: DU Wenfeng
– sequence: 4
  fullname: XING Zhenguo
– sequence: 5
  fullname: LI Zechen
Author_xml – sequence: 1
  fullname: 李冬
– sequence: 2
  fullname: 彭苏萍
– sequence: 3
  fullname: 杜文凤
– sequence: 4
  fullname: 邢朕国
– sequence: 5
  fullname: 李泽辰
BookMark eNotj81Kw0AURmdRwbb6Cu5cJs6dyZ0kSyn-QcFNXZfJnYkYdQpG0a2iKFhBkBbcigi6kSIurL5OEvoWBnT1cTbn8LVYww2cZWwJuA9SCFzJfHL7e35GlPuCQ-iDEHGDNblA6cVxLOdZK88zzmUgFTaZV109F5OL6uGlHL9Xb-fF9bS4m8weX4vhtPr-Ke5vZk-X5edtOf4qP0YLbC7VB7ld_N8221lf63U2ve72xlZntevlwDl4CQVgY6sgMWhCNAYRldWklbaIhigNjFKQphQoIJKUYMRtKGqKhFShbLPlP--pdql2u_1scHLk6mL_8PgsqX9FXHAB8hfKiFdJ
ClassificationCodes TD713
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.13225/j.cnki.jccs.2017.1229
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
DocumentTitle_FL Comprehensive prediction method of coal seam gas outburst danger zone
EndPage 472
ExternalDocumentID mtxb201802021
GrantInformation_xml – fundername: 国家科技重大专项资助项目; 国家自然科学基金煤炭联合基金资助项目
  funderid: (2016ZX05066-001); (U1261203)
GroupedDBID -02
2B.
4A8
5XA
5XC
92H
92I
93N
ABJNI
ACGFS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CDRFL
CW9
FIJ
GROUPED_DOAJ
IPNFZ
PSX
RIG
TCJ
TGT
U1G
U5L
ID FETCH-LOGICAL-s1001-bc41e9e61bd5d75dd5556eaca6ae55dccf4d661ffc461cc3cb580e72461823673
ISSN 0253-9993
IngestDate Thu May 29 04:05:49 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 2
Keywords gas content
瓦斯含量
瓦斯突出
comprehensive forecast
综合预测
tectonic coal
顶板岩性
roof lithology
构造煤
gas outburst
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1001-bc41e9e61bd5d75dd5556eaca6ae55dccf4d661ffc461cc3cb580e72461823673
PageCount 7
ParticipantIDs wanfang_journals_mtxb201802021
PublicationCentury 2000
PublicationDate 2018-02-01
PublicationDateYYYYMMDD 2018-02-01
PublicationDate_xml – month: 02
  year: 2018
  text: 2018-02-01
  day: 01
PublicationDecade 2010
PublicationTitle 煤炭学报
PublicationTitle_FL Journal of China Coal Society
PublicationYear 2018
Publisher 中国矿业大学(北京)地球科学与测绘工程学院,北京100083%中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083
中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083
Publisher_xml – name: 中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083
– name: 中国矿业大学(北京)地球科学与测绘工程学院,北京100083%中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京,100083
SSID ssj0034365
ssib048394982
ssib023167597
ssib012291397
ssib051374103
ssib001105247
ssib046784615
Score 2.2373493
Snippet TD713;...
SourceID wanfang
SourceType Aggregation Database
StartPage 466
Title 煤层瓦斯突出危险区综合预测方法
URI https://d.wanfangdata.com.cn/periodical/mtxb201802021
Volume 43
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9VAEA-lXvQgfuJn6cE9pr5sdpPd4yYvj-LBUwu9lWST-AVPsO2lN0VRqIIgFryKCHqRIh6s_jvvPfpfODPZ9xJtwQ_kQdg3Ozs7v5lNMhOSWc-7lhfwq4qe36vL0BfSal_HgfRLqq5WC1hWVO3zZrS8Km6sybW5-Z3OW0tbm8WS3T7yu5J_8SrQwK_4lexfeHYmFAjQBv_CETwMxz_yMctipiQzgmWSJQFTHCk6ZCZiWcR0xMwAKcZAwIg8kPknhhp95M8005oZTZSUuoAhYUmKFN1jSiGP4UwJFJgAW-IkJ5ooIWt2r5wGuB2VYtTH9FEUHBuVlGFGTp1MkvpMZTQ_KJu2PYCnT4MVTqkG2ECF-r8M1qnTB6A1AI1oWTSpwIklBU0JFuDrdx92BGr6frRbniwTLFFOdWInsw5YMnBdmowIIE3cwuPK2VHHxAZQU8bJuDB50iMhACEkl0A76JiGxKIlZlYmT2iSmQC_pFFkjXZU4z8O58nhqQMKfBmX_xvOYQcr0rlZIgYxtnCAM2OqWWES9Uc4Kb5fg86JnducVsScRNOuvpsi0a5LZ7QcDWrVUECNo7ADsYHfuc9xGfqQp4Tdm7IIOxcf3rnDiijqBGui2ffpUByAtykKBOzw3p2lu9ZiZf4gXgq4e772c411OLMLXG6QO2FRimM8jiLeeT5DuQVkIrzNvVESZk_T_xxLSsj2PwQgENq3sbaATETotpCUDEII5nuzsDEUIe17O7OGK2eASK4fiYM-MxzW-fBWJyJeOeWddKnsommuS6e9ue3bZ7wTnQKnZz1_8uTdaO_R5NX78e6nyceHo6f7oxd7B28-jJ7vT759H718dvD28fjLznj36_jz63Pe6iBbSZd9t0GLv4Gl2_zCiqDSVRQUpSxjWZZSyggiuTzKKylLa2tRQvxf1xYsYW1oC6l6VYwlLBVWjgzPe_PD-8Pqgrdo41oVVU_keVyKnNu8LqyOdCCstJZX1UVvwYFddxfgjfWfvHbpdwyXvePtFeWKN7_5YKu6CgnFZrFAjv4BoZ3Gyg
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E7%85%A4%E5%B1%82%E7%93%A6%E6%96%AF%E7%AA%81%E5%87%BA%E5%8D%B1%E9%99%A9%E5%8C%BA%E7%BB%BC%E5%90%88%E9%A2%84%E6%B5%8B%E6%96%B9%E6%B3%95&rft.jtitle=%E7%85%A4%E7%82%AD%E5%AD%A6%E6%8A%A5&rft.au=%E6%9D%8E%E5%86%AC&rft.au=%E5%BD%AD%E8%8B%8F%E8%90%8D&rft.au=%E6%9D%9C%E6%96%87%E5%87%A4&rft.au=%E9%82%A2%E6%9C%95%E5%9B%BD&rft.date=2018-02-01&rft.pub=%E4%B8%AD%E5%9B%BD%E7%9F%BF%E4%B8%9A%E5%A4%A7%E5%AD%A6%28%E5%8C%97%E4%BA%AC%29%E5%9C%B0%E7%90%83%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%B5%8B%E7%BB%98%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E5%8C%97%E4%BA%AC100083%25%E4%B8%AD%E5%9B%BD%E7%9F%BF%E4%B8%9A%E5%A4%A7%E5%AD%A6%28%E5%8C%97%E4%BA%AC%29%E7%85%A4%E7%82%AD%E8%B5%84%E6%BA%90%E4%B8%8E%E5%AE%89%E5%85%A8%E5%BC%80%E9%87%87%E5%9B%BD%E5%AE%B6%E9%87%8D%E7%82%B9%E5%AE%9E%E9%AA%8C%E5%AE%A4%2C%E5%8C%97%E4%BA%AC%2C100083&rft.issn=0253-9993&rft.volume=43&rft.issue=2&rft.spage=466&rft.epage=472&rft_id=info:doi/10.13225%2Fj.cnki.jccs.2017.1229&rft.externalDocID=mtxb201802021
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fmtxb%2Fmtxb.jpg