Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model

Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.

     

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

     

Characteristics of Permeability Changes in Bituminous Coal Under Conditions of Stress Variation Due to Repeated Mining Activities

The factors affecting permeability change under repeated mining of coal seams are important study aspects that need to be explored. This study combined various stress variation characteristics of protective seam mining and simplified the stress path of repeated mining in protective seam mines. Based on the results from the bespoke gas flow and displacement testing apparatus, seepage tests for simulated repetitive mining were carried out. The results simulated the actual behavior very well. With any drastic increase in the mining influence, the axial deviation stress in the stress path increased, and the greater the difference in coal permeability during the unloading and stress recovery stage, the more substantial the increase in permeability. The change in coal permeability was significantly influenced by the severity of simulated repeated mining cycles. When the mining stress exceeded a critical value, the permeability of the coal sample increased with the increase in the number of loading and unloading cycles, but the reverse was true when the mining stress was lower than the critical value. The effective sensitivity of seepage to the applied stress decreased with an increase in the number of stress cycles. With a decrease in the deviation stress, that is, with lower severity of mining influence, the effective sensitivity of coal seepage to stress gradually decreased.

     

3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling,Anhui, China

Natural Resources Research - In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random...     

Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression

Natural Resources Research - A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck...     

Waveform Characteristics of Earthquakes Induced by Hydraulic Fracturing and Mining Activities: Comparison with Those of Natural Earthquakes

Natural Resources Research - Some industrial activities, such as underground mining, hydraulic fracturing (HF), can cause microearthquakes and even damaging earthquakes. In recent years, with the...     

无人机在重大地质灾害应急调查中的应用

传统的地质灾害应急调查受限于地形、天气等外界条件,不能快速全面地获取灾害的详细信息,而无人机具有灵活性强、时效性高和不受复杂地形影响等特点,在地质灾害应急调查中有独特的优势。本文以“6·24”新磨村滑坡和“10·11”白格滑坡为例,阐述了无人机数据获取及处理流程,重点介绍了无人机获取的数字地形产品在地质灾害精确描述、定性及定量分析中的应用。结果表明：无人机摄影测量技术为重大地质灾害应急调查提供了更加科学高效的现场影像采集和遥感成果处理及应用方案,为应急救灾工作的顺利实施及分析研判提供了重要数据支撑,科学有效地保证了现场施工救援人员的安全。     

Tile‐based polygon overlay of vector data in a virtual globe

The demand for analysis of large‐scale data has increased with increased access to spatial vector data. Polygon overlay of vector data in a virtual globe requires proprietary data structures and proprietary analysis algorithms. A vector data structure is designed for rapid polygon overlay in a virtual globe by recording the metadata of the triangles (TriMeta) that constitute polygons. A polygon overlay algorithm is proposed based on this data structure. The overlay of two complex GIS polygons is transformed into the intersection computation of their 3D triangular presentations. The intersection computation of two sets of triangles is reduced considerably by utilizing TriMeta to filter out the disjoint triangle pairs and rapidly identify the contained triangle. The new method improves the overlay efficiency in a virtual globe because the amount of computation required to calculate the intersections of two large polygons and drape the intersections onto a terrain surface is reduced.     

Spatio‐temporal prediction of land surface temperature using semantic kriging

Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemK_NSep) and separable ST‐SemK (ST‐SemK_Sep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.     

近30年长江口海域生态系统健康状况及变化趋势研究

为评估长江口海域生态系统健康状况,本文以鱼类浮游生物为指示生物,选择1986年、1999年、2007年和2016年4个年份的数据,选取鱼类浮游生物总种类数、底栖鱼种类数、水层鱼种类数、低耐污鱼种类数、高耐污鱼种类数百分比、杂食性鱼种类数百分比、虫食性鱼种类数百分比、肉食性鱼种类数百分比、鱼类取样个体数、天然杂交种种类数百分比等10个评价指标,采用1、3、5赋值法计算4个不同年份的河口生物完整性指数(Estuarine Biotic Integrity Index,EBI),并以此评价近30年长江口海域生态系统健康状况变化趋势。结果显示,1986年、1999年、2007年和2016年4个不同年份的EBI值分别是52、40、36、34,对应的EBI等级分别为“好”、“一般”、“一般?差”、“差”。通过分析EBI年际变化的结果,发现近30年来长江口海域生态系统健康状况呈现先下降,而后稳定在较低水平的趋势,说明长江口海域生态系统亟需及时进行保护和修复工作。