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- W4308499075 abstract "An efficient algorithm, tc-cylinder, has been developed to obtain terrain corrections by considering computational efficiency, data storage and use, spherical curvature effects, water bodies, etc. High resolution Digital Elevation Models (DEMs) are fundamental data sources for accurate terrain corrections. But a high-resolution DEM like SRTM1 poses some difficulties during processing, such as data storage, handling, efficiency and computation speed over large areas. In order to overcome these issues, a shortened computation area radius (e.g., 50 km) is preferred for practical studies. This kind of limitation may cause an accuracy loss of over 20 mGal in steep topography. Similarly, spectral methods used to speed up the computations have drawbacks in areas with high slopes. The presented algorithm for segmenting cylinder bodies proves more realistic and effective terrain correction results in all physical conditions. An optimization approach that belongs to the Intel’s processor and compiler architecture improved processing efficiency by a factor of 2.5 times with non-temporal stores, which copies large data directly to memory. Synthetic masses based on known geometric bodies were considered to test program efficiency and numerical results. The contribution was examined at gravity stations in the Konya Closed Basin, which has a sophisticated topography. Additionally, an investigation was conducted in extreme conditions, such as Everest Mountain and the Ağrı Mountain in Turkey, with abrupt elevation profile changes. The numerical analyses of the accuracy of terrain correction values is performed on different combinations of DEM resolutions. Consequently, it is verified that 1′′ inner and 15′′ outer grid resolutions lead to sub-mGal accuracy with less processing time, even in rough topography." @default.
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- W4308499075 date "2023-01-01" @default.
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- W4308499075 title "tc-cylinder: An optimized algorithm for accurate topography effect from high-resolution digital elevation models" @default.
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- W4308499075 doi "https://doi.org/10.1016/j.cageo.2022.105264" @default.
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