Complete high resolution geochemical maps are strongly needed for mineral exploration; however, the previously proposed methods for making geochemical maps have low accuracy. In this research, we propose a new algorithm called sample density based mixture interpolation (SADBAMIN) for high resolution geochemical map completion using remote sensing data. In the SADBAMIN algorithm, first, according to the measured copper data density on the map, the map is classified into two parts: the area for training (T area) and the area waiting to be predicted (P area). The two areas are classified by the edge of the data point set’s alpha shape. In the T area, a triangle area among three neighbourhood points is interpolated by using the kriging model. Then, remote sensing data, including advanced spaceborne thermal emission and reflection radiometer (ASTER) data, digital elevation model (DEM) data, and geophysics (magnetic) data, and copper geochemical data at all measured and partial randomly selected interpolated points are applied as training data to construct a random forest regression model. By considering the relationship between interpolation reliability and distance, a penalty on data selection probability of going into training data is given. Finally, by inputting the remote sensing data in the P area to the model, the copper data in this area can be obtained, and the completed map comprises these two parts. We use 16,000 measured points, 10-fold cross-validation, and root mean squared error (RMSE) for model evaluation. We achieved an RMSE of 293 ppm, while the RMSE of the previously proposed method is 347 ppm.
In this report, mineral composition of rock samples including conglomerate, sandstone, and dolomite was analyzed by IR spectral imaging using QDIP focal plane arrays (FPAs) with a peak-responsivity wavelength of 6.5 μm (FPA 1) and 5.5 μm (FPA 2). The qualitative and quantitative analyses are presented, and the key factor that determines the quantitative precision is discussed. In the qualitative analysis, the luminance of the different components in the rock samples was compared in the image. In the FPA 1 images, the shell fossil in the conglomerate sample and the limestone in the sandstone sample were darker than the other parts of the rocks due to their low emittance at 6.5 μm. In contrast, the difference in the luminance is hardly observed in the FPA 2 images under the same conditions. In the quantitative analysis, the emittance of dolomite was measured. Ten points in the IR image were randomly selected and the average emittance was calculated. The obtained emittances were 0.544±0.012 (FPA 1) and 0.941±0.019 (FPA 2), which means the coefficient of variation of the emittance measurement is ±2.1%~2.2%. By calculating the propagation of error, the precision of thermocouples for monitoring the temperature of the rocks in the calibration contributes most significantly (73%) to the total error.
A spectral imaging system based on a bundle of hollow optical fibers transmitting infrared radiation image is
constructed. The system consists of an FT-IR spectrometer and a high-speed infrared camera and infrared transmission
spectra are obtained by carefully processing multiple interferograms. It is shown that infrared spectral images of a variety
of samples are measured by the system. By mapping transmission of the specific wavelengths in the spectrum, existence
maps of oil and fat of biological samples are obtained.
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