Search Results (3)

Carbonate-hosted clay-type lithium deposits have emerged as strategic resources critical to the global energy transition, yet their exploration faces the dual challenges of technical complexity and environmental sustainability. Traditional methods often entail extensive land disruption, particularly in ecologically sensitive ecosystems where vegetation coverage and weathered layers hinder mineral detection. This study presents a case study of the San Dan lithium deposit in central Yunnan, where we propose a hierarchical anomaly extraction and multidimensional weighted comprehensive analysis. This comprehensive method integrates multi-source data from GF-3 QPSI SAR, GF-5B hyperspectral, and Landsat-8 OLI datasets and is structured around two core parts, as follows: (1) Hierarchical Anomaly Extraction: Utilizing principal component analysis, this part extracts hydroxyl and iron-stained alteration anomalies. It further employs the spectral hourglass technique for the precise identification of lithium-rich minerals, such as montmorillonite and illite. Additionally, concealed structures are extracted using azimuth filtering and structural detection in radar remote sensing. (2) Multidimensional Weighted Comprehensive Analysis: This module applies reclassification, kernel density analysis, and normalization preprocessing to five informational layers—hydroxyl, iron staining, minerals, lithology, and structure. Dynamic weighting, informed by expert experience and experimental adjustments using the weighted weight-of-evidence method, delineates graded target areas. Three priority target areas were identified, with field validation conducted in the most promising area revealing Li₂O contents ranging from 0.10% to 0.22%. This technical system, through the collaborative interpretation of multi-source data and quantitative decision-making processes, provides robust support for exploring carbonate-clay-type lithium deposits in central Yunnan. By promoting efficient, data-driven exploration and minimizing environmental disruption, it ensures that lithium extraction meets the growing demand while preserving ecological integrity, setting a benchmark for the sustainable exploration of clay-type lithium deposits worldwide. Full article

In recent years, geological disasters have frequently occurred on basarlt highway slopes. Studying the stability of highway slopes in this type of area is of great significance for traffic safety. However, due to the high cost and low efficiency of traditional monitoring and experimental methods for slope engineering, these methods are not conducive to the quick and comprehensive identification of regional slope stability. Due to the high iron content of basalt, iron staining anomalies in the ore prospecting field are reinterpreted from an engineering perspective in this study. Taking the S3K section of a highway in Changbai County, China, as an example, Landsat8 remote sensing (RS) images from 2014, 2016, 2018, 2020, and 2021 are selected, and principal component analysis is used to extract iron staining anomalies in the region. Combined with field investigation and evidence collection, the corresponding rock mass fragmentation is distinguished via iron staining anomalies. Then, according to previous research results, eight indexes including annual rainfall, slope, topographic relief, surface roughness, vegetation index, leaf area index (LAI), root depth of vegetation, and human activity intensity are selected for investigation. The artificial neural network–cellular automata (ANN-CA) model is established, and the rock fragmentation classification data obtained based on iron staining anomalies are used to simulate the area. Next, the calculation formula of slope stability is determined based on the simulation results, and the stability of a high and steep slope in the area is calculated and analyzed. Finally, a comparison with an actual field investigation shows that the effect of the proposed method is good. The research findings reveal that it is feasible to judge the stability of a high and steep slope in a basalt area via the use of iron staining anomalies as an indicator. The findings are tantamount to expanding the application scope of RS in practical engineering. Full article

There are many small and medium-sized orogenic copper deposits in the Jinman–Lanping area of Yunnan. In order to standardize mining, long-term planning, and unified management, it is necessary to further delineate prospecting areas. In order to improve the efficiency of prospecting, a data-driven approach is established. This paper uses the weight of evidence model to make prospecting predictions, and it then delineates the prospective prospecting area. The relevant evidence layers in the weight of evidence model are geochemical anomalies and remote sensing iron staining anomalies. Among them, the geochemical anomaly layer mainly uses the concentration-area (C-A) fractal model to separate the geochemical background and anomaly acquisition. The remote sensing iron-stained anomaly layer mainly uses bands (1, 4, 5, 7), and bands (1, 3, 4, 5) were combined for principal component analysis to extract abnormal iron staining. Finally, using the weight of evidence model, the spatial element layers (evidence layers) from different sources were combined, and the interaction between them was analyzed. It is pointed out that the area has good prospects for prospecting, and the prospective prospecting area was thus delineated. Full article