Volume 48 Issue 6
Dec.  2020
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CHENG Yan, ZHAO Pu, LIN Jiandong, ZHANG Xingping. Application of seismic waveform classification technology in interpretation of geological abnormal body[J]. COAL GEOLOGY & EXPLORATION, 2020, 48(6): 87-92,102. DOI: 10.3969/j.issn.1001-1986.2020.06.012
Citation: CHENG Yan, ZHAO Pu, LIN Jiandong, ZHANG Xingping. Application of seismic waveform classification technology in interpretation of geological abnormal body[J]. COAL GEOLOGY & EXPLORATION, 2020, 48(6): 87-92,102. DOI: 10.3969/j.issn.1001-1986.2020.06.012
shu

Application of seismic waveform classification technology in interpretation of geological abnormal body

  • 1. China National Administration of Coal Geology, Beijing 100038, China;

  • 2. Research Institute of Coal Geophysical Exploration, China National Administration of Coal Geology, Zhuozhou 072750, China

Funds: 

Science and Technology Innovation Fund of China National Administration of Coal Geology(ZMKJ-2019-B11，ZMKJ-2019-J11)

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  • Received Date: October 23, 2020
  • Revised Date: November 09, 2020
  • Published Date: December 24, 2020
    Graphical Abstract
    • Abstract
  • Seismic waveform classification technology has the characteristics of statistics of the overall change of the seismic signal and reflects the distribution of this change. It is an important extension of seismic attribute analysis technology. It has a good application effect on the reflected wave changes caused by geological anomalies, which is similar to the conventional single attribute prediction. Compared, it has the characteristics of sensitive reflection and reliable results. High density 3D seismic data have the characteristics of high signal-to-noise ratio, high resolution and high fidelity. This paper attempts to use waveform classification technology to predict the occurrence of coal seam and magmatic intrusion area reflected by high density 3D seismic data, and studies the interpretation method of collapse column. Through actual exposure and drilling verification in underground roadways, the prediction results have high accuracy and accurate bounds, which can provide accurate geological data for coal mining.
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    • References (21)
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