煤岩识别研究可以定位煤层和岩层的分界面,确定截割位置,从而指导采煤机自动调高,实现更高水平的智能化无人开采。探地雷达是一种无损的探测手段,可以获取地下岩层的信息,识别岩性。但是探地雷达的分辨率受到信号频率和波长的限制,不同...煤岩识别研究可以定位煤层和岩层的分界面,确定截割位置,从而指导采煤机自动调高,实现更高水平的智能化无人开采。探地雷达是一种无损的探测手段,可以获取地下岩层的信息,识别岩性。但是探地雷达的分辨率受到信号频率和波长的限制,不同设备测量得到的数据存在差异,且信号容易被噪声干扰。本研究使用了一种U-Net结构的神经网络算法处理无线天线探地雷达数据,使其信号质量接近有线天线测得的信号水平,并使用改进的U-Net网络对信号进行去噪处理,实现了无线到有线转换和去噪后的图像。结果表明,经过U-Net处理,无线信号质量得到了较大的提升,雷达信号图像的平均峰值信噪比(PSNR)提高了约4 dB、平均结构相似性指数(SSIM)达到了0.935,证明了降低数据差异的GBR数据具有更好的去噪效果。The research on coal-rock identification can locate the interface between coal and rock seams and determine the cut-off position, thus guiding the coal miner to automatically adjust the height and realize a higher level of intelligent unmanned mining. Ground-penetrating radar (GPR) is a non-destructive means of detection, which can obtain the information about underground rock layers and identify the lithology. However, the resolution of GPR is limited by signal frequency and wavelength, the data obtained from different equipment measurements differ, and the signal is easily interfered by noise. In this study, a neural network algorithm with a U-Net structure is used to process the wireless antenna GPR data, so that the signal quality is close to the level of the signal measured by a wired antenna, and the signal is denoised using an improved U-Net network to realize the wireless-to-wired conversion and denoised image. The results show that after U-Net processing, the quality of the wireless signal is greatly improved, the average peak signal-to-noise ratio (PSNR) of the radar signal image is improved by about 4 dB and the average structural similarity index (SSIM) reaches 0.935, which proves that the GBR data with reduced data discrepancy has a better denoising effect.展开更多
文摘煤岩识别研究可以定位煤层和岩层的分界面,确定截割位置,从而指导采煤机自动调高,实现更高水平的智能化无人开采。探地雷达是一种无损的探测手段,可以获取地下岩层的信息,识别岩性。但是探地雷达的分辨率受到信号频率和波长的限制,不同设备测量得到的数据存在差异,且信号容易被噪声干扰。本研究使用了一种U-Net结构的神经网络算法处理无线天线探地雷达数据,使其信号质量接近有线天线测得的信号水平,并使用改进的U-Net网络对信号进行去噪处理,实现了无线到有线转换和去噪后的图像。结果表明,经过U-Net处理,无线信号质量得到了较大的提升,雷达信号图像的平均峰值信噪比(PSNR)提高了约4 dB、平均结构相似性指数(SSIM)达到了0.935,证明了降低数据差异的GBR数据具有更好的去噪效果。The research on coal-rock identification can locate the interface between coal and rock seams and determine the cut-off position, thus guiding the coal miner to automatically adjust the height and realize a higher level of intelligent unmanned mining. Ground-penetrating radar (GPR) is a non-destructive means of detection, which can obtain the information about underground rock layers and identify the lithology. However, the resolution of GPR is limited by signal frequency and wavelength, the data obtained from different equipment measurements differ, and the signal is easily interfered by noise. In this study, a neural network algorithm with a U-Net structure is used to process the wireless antenna GPR data, so that the signal quality is close to the level of the signal measured by a wired antenna, and the signal is denoised using an improved U-Net network to realize the wireless-to-wired conversion and denoised image. The results show that after U-Net processing, the quality of the wireless signal is greatly improved, the average peak signal-to-noise ratio (PSNR) of the radar signal image is improved by about 4 dB and the average structural similarity index (SSIM) reaches 0.935, which proves that the GBR data with reduced data discrepancy has a better denoising effect.
