矿物成分快速鉴定是提高遥感矿产勘探、 遥感矿物填图以及诸多地学研究等工作效率的关键。 由于技术等各方面的限制, 国内外针对矿物快速分析的模型和软件较少。 20世纪90年代以来近红外光谱仪在技术上的突破和计算机的发展使得近红外光谱技术在矿物快速识别领域的应用变得可行, 先后出现了基于吸收位置的反演模型(模型一)和基于波形匹配的反演模型(模型二)。 文章提出了特征光谱线性反演模型。 经美国地质调查局矿物光谱库(USGS)端元混合实验数据验证, 该模型精度接近100%, 远优于模型一和二。 对新疆包古图地区地表随机所采23个样本分析, 该模型平均精度为64.6%, 另外两模型分别为: 33.8%和8.1%, 优于模型一和二。 虽精度尚低于传统镜下鉴定方法, 该模型具有高效、 方便、 工作量小、 人为误差小等优点, 已初步应用于新疆包古图地区遥感矿产勘探工作, 有较好的推广前景。

Rapid identification of minerals is the key point for enhancing the efficiency of mineral exploration by remote sensing, mineral mapping by remote sensing and many geological investigations. Because of the limitation of technology and other aspects, the amount of models and software concerning rapid identification of minerals is very small. Since 1990s the development in spectrometers and computers has made it possible to apply near infrared spectrum technology to identify minerals. Two models have emerged. Model Ⅰ is based on analyzing the position of absorption bands, while Model Ⅱ is founded on waveform matching. In the present paper, characteristic spectrum linear inversion modeling was built. Validated by the data gained from end-members of USGS mineral spectrum library by mixing randomly, this model with the accuracy being approximately 100% is much better than Model Ⅰ and Ⅱ. Used to analyze the 23 samples selected in Baogutu area in Xinjiang, the model we built with the accuracy of 64.6% is superior to Model Ⅰ (the accuracy is 33.8%) and Model Ⅱ (the accuracy is 8.1%). Though the accuracy of our model is not as high as that of identification by microscope at present, using our model is much more effective and convenient, and there also will be less artificial error and smaller workload. The good performance of our model in the mineral exploration work by remote sensing in Baogutu area in Xinjiang shows wide popularizing prospects.