Remote sensing study on land subsidence in mining area

Large-scale exploitation of underground coal resources destroys the stability of overlying strata, resulting in crushing and caving, leading to surface subsidence and collapse. These ground collapses have caused the destruction of a large area of land resources, and even a large amount of accumulated water has appeared in areas with high groundwater level, which has led to changes in the regional ecological environment. Therefore, it is of great significance to reclaim and control the coal mine subsidence area according to local conditions to restore the destroyed land resources and maintain the regional ecological environment. Therefore, it is necessary to master the expansion law of subsidence land with mining activities, the area of subsidence area and the degree of destruction. The range and depth of water accumulation area, water pollution and other data. It is difficult to use conventional technical means, such as topographic survey, especially for stagnant water subsidence areas. Remote sensing images can provide a large number of information of subsidence areas quickly and periodically, and serve the land reclamation project in mining areas.

Study on dynamic expansion of surface subsidence area. The most effective remote sensing survey method is to use multi-temporal aerial remote sensing images of mining areas for superposition analysis. In a mine in Hebei Province, using multi-temporal and different kinds of aerial photography images, including panchromatic black-and-white photos, color infrared photos and multi-spectral images, these images were processed by computer to extract the expansion law of subsidence area. The processing flow is shown in Figure 3- 1 1.

Figure 3- Flow chart of image processing for dynamic expansion of surface subsidence in mining area +0 1

After the above processing, all the images are geometrically matched, and a band is extracted from each phase image and given different display colors to form a false color multi-phase matching image. The image clearly shows the shapes of subsidence areas in different periods. Therefore, combined with the geology and mining conditions of underground coal mining, the dynamic development law of subsidence area can be obtained.

Through the analysis of the surface subsidence area of this mine, it is concluded that the surface subsidence area will increase by 654.38+0.3 hm2 for every 6,543.8+0,000 tons of coal mined. The expansion direction of subsidence area is basically the same as that of underground mining, but the formation of subsidence area lags behind the formation of underground goaf, and the subsidence area will be stable after 1 ~ 2 years of coal mining.

The subsided waterlogged area appears dark gray-gray-black tone in black-and-white photos, but dark black in color infrared photos with clear boundaries. The depth of stagnant water shows different tones in color infrared photos. Through image analysis and field sampling, different types and depths of stagnant water can be delineated. This kind of accumulated water is usually divided into four types: deep water area, shallow water area, swamp area and seepage area. See Table 3-3 for the interpretation marks of color infrared aerial photographs.

Table 3-3 Classification and Interpretation Signs of Remote Sensing Images in a Coal Mine Subsidence and Water Accumulation Area in Hebei Province

The collapse boundary and shape of non-stagnant water subsidence area can also be extracted from color infrared photos. The extracted information and data can be converted into topographic maps or geographical maps to make thematic maps about the situation of subsidence areas, and the areas of various subsidence areas can also be calculated by computers or electronic orthogonal instruments. These maps and data are important basic data for formulating the treatment scheme of subsidence area. Figure 3- 12 is a remote sensing classification interpretation map of a coal mine subsidence area in Hebei Province, which uses the data of aerial remote sensing images and SPOT satellite images.

Figure 3- 12 Remote Sensing Classification Interpretation Map of Coal Mine Subsidence Area