Share Email Print

Proceedings Paper

Geological characteristics in buried coalfields synthetically using remote sensing and non-remote sensing information
Author(s): Shifeng Dai; Silong Wang; Yurong Liu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

With the rapid development of coal industry in China, the emphasis of the geological exploration has been changed from the exposed area to the buried area. Because of the limitation of the geological condition and the exploration methods, it is very difficult to study the geological phenomena in buried coalfield. To the coal geologists in China, to search an effective and practical method has been the important tackle key problem for recent years. In this paper, the authors discussed the characteristics of remote sensing technology in the geological study, and the forming mechanism of remote sensing information in the buried area from the view of agrology and physics, so the important academic evidences were offered for the geological study using remote sensing image in the buried coalfield. The characteristics of the non-remote sensing information, the geophysics information and the basal geological information, were also introduced in the study of buried geological bodies. The authors expounded the general processing method in the investigation of buried geological bodies using remote sensing and non-remote sensing information. At last, the probable distribution area of buried igneous rocks, in Huaibei coalfield in China, were successfully forecasted synthetically using the remote sensing, and non-remote sensing information.

Paper Details

Date Published: 19 August 1998
PDF: 5 pages
Proc. SPIE 3504, Optical Remote Sensing for Industry and Environmental Monitoring, (19 August 1998); doi: 10.1117/12.319521
Show Author Affiliations
Shifeng Dai, China Univ. of Mining and Technology (China)
Silong Wang, China Univ. of Mining and Technology (China)
Yurong Liu, Ctr. for Space Science and Applied Research (China)

Published in SPIE Proceedings Vol. 3504:
Optical Remote Sensing for Industry and Environmental Monitoring
Upendra N. Singh; Huanling Hu; Gengchen Wang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?