Paper
19 August 1998 Geological characteristics in buried coalfields synthetically using remote sensing and non-remote sensing information
Shifeng Dai, Silong Wang, Yurong Liu
Author Affiliations +
Proceedings Volume 3504, Optical Remote Sensing for Industry and Environmental Monitoring; (1998) https://doi.org/10.1117/12.319521
Event: Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Environment, and Space, 1998, Beijing, China
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shifeng Dai, Silong Wang, and Yurong Liu "Geological characteristics in buried coalfields synthetically using remote sensing and non-remote sensing information", Proc. SPIE 3504, Optical Remote Sensing for Industry and Environmental Monitoring, (19 August 1998); https://doi.org/10.1117/12.319521
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KEYWORDS
Remote sensing

Image processing

Magnetism

Soil science

Image analysis

Image enhancement

Statistical analysis

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