Oil shale can produce oil and shale gas by heating the oil shale at 300–500 °C. The high temperature and the release of organic matter can change the physical and mechanical properties of rocks and make the originally tight impervious layer become a permeable layer under in situ exploitation conditions. To realize the potential impact of the in situ exploitation of oil shale on groundwater environments, a series of water–rock interaction experiments under different temperatures was conducted. The results show that, with the increase of the reaction temperature, the anions and cations in the aqueous solution of oil shale, oil shale–ash, and the surrounding rock show different trends, and the release of anions and cations in the oil shale–ash solution is most affected by the ambient temperature. The hydrochemical type of oil shale–ash solution is HCO₃-SO₄-Na-K at 80 °C and 100 °C, which changes the water quality. The main reasons are that (1) the high temperature (≥80 °C) can promote the dissolution of FeS in oil shale and (2) the porosity of oil shale increases after pyrolysis, making it easier to react with water. This paper is an important supplement to the research on the impact of the in situ exploitation of oil shale on the groundwater environment. Therefore, the impacts of in situ mining on groundwater inorganic minerals should be taken into consideration when evaluating in situ exploitation projects of oil shale.

Traditional supervised band selection (BS) methods mainly consider reducing the spectral redundancy to improve hyperspectral imagery (HSI) classification with class labels and pairwise constraints. A key observation is that pixels spatially close to each other in HSI have probably the same signature, while pixels further away from each other in the space have a high probability of belonging to different classes. In this paper, we propose a novel discriminative feature metric-based affinity propagation (DFM-AP) technique where the spectral and the spatial relationships among pixels are constructed by a new type of discriminative constraint. This discriminative constraint involves chunklet and discriminative information, which are introduced into the BS process. The chunklet information allows for grouping of spectrally-close and spatially-close pixels together without requiring explicit knowledge of their class labels, while discriminative information provides important separability information. A discriminative feature metric (DFM) is proposed with the discriminative constraints modeled in terms of an optimal criterion for identifying an efficient distance metric learning method, which involves discriminative component analysis (DCA). Following this, the representative subset of bands can be identified by means of an exemplar-based clustering algorithm, which is also known as the process of affinity propagation. Experimental results show that the proposed approach yields a better performance in comparison with several representative class label and pairwise constraint-based BS algorithms. The proposed DFM-AP improves the classification performance with discriminative constraints by selecting highly discriminative bands with low redundancy.

Sandstone type uranium is the most valuable and has the most potential for mining among the known uranium deposits. In the process of forming, the hydrolytic migration and enrichment of uranium require special basin sedimentary environment and tectonic background. Therefore, the mineralization process of sandstone type uranium deposits has certain layering characteristics and distribution rules in the underground vertical depth space. It is important to mine the spatial distribution characteristics of vertical uranium-bearing layers, and thus, reconstruct the three-dimensions of uranium orebodies. In this paper, according to the metallogenic law and distribution characteristics of sandstone type uranium in the underground vertical space, a nonlinear uranium-bearing layers identification (NULI) method of sandstone type uranium is proposed by using different types, resolutions and scales of borehole data. Then, the depth of uranium mineralization for the Daying uranium deposit within northern Ordos Basin is identified accurately and the spatial distribution characteristics of the uranium-bearing layer on the exploration line are obtained. Finally, the occurrence mode of the underground uranium orebodies are presented by using three-dimensional reconstruction analysis. It provides a basis for the prediction, exploration and mining of sandstone type uranium deposits within the Ordos Basin.

Because of the interference of mass spectrum and non-mass spectrum, the tracing accuracy of rare earth elements in the surrounding rock of geological formation is low. Pretreatment of test sample reagent, dissolution of test sample residue, characterization of rare earth element doped materials, analysis of mass spectrometry and non-mass spectrometry interference in rare earth element tracking, using three-dimensional positioning algorithm to track rare earth elements in geological strata surrounding rock. In the experiment, five samples of surrounding rock of geological strata are selected as experimental indexes. The experimental results show that the tracking accuracy of four rare earth elements is high. Debido a la interferencia del espectro de masas y del espectro no masivo, la precisión de la traza de elementos de tierras raras en la roca circundante de formación geológica es baja. En este trabajo se realizó el pretratamiento del reactivo de la muestra de prueba, disolución del residuo de la muestra de prueba, caracterización de materiales dopados con elementos de tierras raras, análisis de espectrometría de masas e interferencia sin espectrometría de masas en el seguimiento de elementos de tierras raras, con un algoritmo de posicionamiento tridimensional para rastrear elementos de tierras raras en los estratos geológicos que rodean la roca. En el experimento se seleccionaron cinco muestras de rocas circundantes de estratos geológicos como índices experimentales. Los resultados experimentales muestran que la precisión de seguimiento de cuatro elementos de tierras raras es alta.