An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali ZnPb (±Au) prospect,Northwest Iran     

Zeinab Soltani  Ali Imamalipour 《Chemie der Erde / Geochemistry》2022,82(1):125850

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.  相似文献   

1.26米红外望远镜电控系统软件设计与实现     

葛亮  王金虎  卢晓猛 《天文研究与技术》2015,(3)

2011年,国家天文台兴隆基地1.26 m红外望远镜进行了全面升级改造。主要讲述望远镜电控系统软件的设计与实现,用于实现望远镜各种观测策略和运动方式的自动化操作。为了提高稳定性和可靠性,软件基于有限状态机原理设计,定义了望远镜的状态集和动作集,以及各个状态间的状态转换图;同时给出了望远镜常见异常及其处理方式,并在本地控制的基础上提供远程控制接口,使得望远镜可方便纳入兴隆基地望远镜集中控制系统。该软件及其设计思想可推广至我国其他中小口径望远镜。  相似文献   

基于LSTM网络的中国夏季降水预测研究     

沈皓俊  罗勇  赵宗慈  王汉杰 《气候变化研究进展》2020,16(3):263-275

基于BCC-CSM季节气候预测模式系统历史回报数据和国家气象信息中心提供的中国地面降水月值数据,通过多方法对比并讨论了影响预测结果的因素,利用长短期记忆（Long Short-Term Memory,LSTM）网络预测2014年和2015年中国夏季降水。结果表明:LSTM网络的预测效果较逐步回归、BP神经网络及模式输出结果有一定优势。参数调优对于LSTM网络预测效果影响较大,重要参数有隐含层节点数、训练次数和学习率。选择合适的起报月份数据有助于提升季节预测的准确性,利用4月起报的数据预测夏季降水效果较好。海冰分量因子对降水季节预测有正贡献。在2014年、2015年夏季降水回报试验中,LSTM网络对降水整体形势有一定的预测能力,Ps评分分别为74分、71分,距平符号一致率分别为55.63%、55.25%,Ps评分的均值高于同期全国会商及业务模式。  相似文献   

Flooding and its relationship with land cover change,population growth,and road density     

Mahfuzur Rahman  Chen Ningsheng  Golam Iftekhar Mahmud  Md Monirul Islam  Hamid Reza Pourghasemi  Hilal Ahmad  Jules Maurice Habumugisha  Rana Muhammad Ali Washakh  Mehtab Alam  Enlong Liu  Zheng Han  Huayong Ni  Tian Shufeng  Ashraf Dewan 《地学前缘(英文版)》2021,12(6):16-35

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.  相似文献   

基于PCA-GASVM的晋陕甘宁地区生态环境评价   总被引：1，自引：0，他引：1  

陈莉 《干旱区地理》2015,38(6):1262-1269

我国新型城镇化进程中产生较严重的生态环境问题。选取晋陕甘宁地区为研究对象,从城镇生态压力、状态、响应3个方面构建了生态环境的评价指标体系,并通过2013年中国统计年鉴整理到相关数据,利用PCA-GA-SVM对晋陕甘宁地区生态环境进行评价。结果表明:PCA-GASVM生态环境评价比GASVM评价具有更高的准确率,而且PCA-GASVM比GASVM评价收敛速度更快。甘肃、宁夏的自然保护区占辖区面积比重指标排名比山西、陕西两个省名次靠前,从森林覆盖率分析只有陕西位于第11位,山西、甘肃、宁夏森林覆盖率均位于后20名;从SO₂排放量、烟尘(粉尘)排放量、工业废弃物产生量、废水排放量上分析,在晋陕甘宁地区中,宁夏最优,甘肃次之,陕西、山西排放很严重。晋陕甘宁地区生态环境保护应在"新常态"下要有"新状态",将市场机制与政府的补贴、税收等政策宏观调控相结合,严守生态红线,发展生态经济绿色科技、清洁生产,减少工业废弃物排放,建立环保交易市场,推行污染第三方治理,完善生态补偿制度,走集约、智能、绿色、低碳的新型城镇化道路。  相似文献   

Future changes in rainfall,temperature and reference evapotranspiration in the central India by least square support vector machine     

Sananda Kundu  Deepak Khare  Arun Mondal 《地学前缘(英文版)》2017,8(3):583-596

Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotranspiration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ET₀ is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ET₀ have been shown by downscaling the HadCM3 (Hadley Centre Coupled Model version 3) model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ET₀ and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine (LS-SVM) model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ET₀. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081–2091 and 2091–2099 in maximum temperature and 2091–2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ET₀ in some decades. Two peaks of the increase are observed in ET₀ in the April–May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.  相似文献   

基于最小二乘支持向量机的城市桥梁震害预测模型研究     

肖行辉  高惠瑛  孙海  王俊杰? 《震灾防御技术》2016,11(1):93-99

针对地震中城市桥梁震害状态具有较强的非线性、复杂性的特点,采用了具有RBF核函数的最小二乘支持向量机(LS-SVM)算法。在大量收集我国地震中城市桥梁震害资料的基础上,将此算法引入桥梁的震害预测中,选取了地震烈度、上部结构、地基失效程度、支座类型、墩台高度、桥梁跨数和场地类别等因素作为模型的特征输入向量,建立了最小二乘支持向量机的桥梁震害预测模型。通过反复地样本训练及模型参数设置,仿真结果表明,该方法具有一定的准确度和可行性。基于最小二乘支持向量机的桥梁震害预测方法是一种可以用于地震中桥梁震害预测的良好方法。  相似文献   

基于三种机器学习方法的降水相态高分辨率格点预报模型的构建及对比分析     

杨璐  南刚强  陈明轩  宋林烨  刘瑞婷  程丛兰  曹伟华 《气象学报》2021,79(6):1022-1034

冬季降水相态及其转变时间的精细化客观预报对提高气象预报和服务质量具有重要的现实意义。利用京津冀地区国家级自动气象站观测资料及网格化快速更新精细集成产品,统计分析了京津冀地区复杂地形下各类降水相态温度和湿球温度平均气候概率的分布差异及不同降水相态时网格化快速更新精细集成产品中可能影响降水相态判断的特征信息。然后将地面观测天气现象资料、复杂地形下降水相态气候特征及高分辨率模式输出产品作为特征向量,分别基于梯度提升（XGBoost）、支持向量机（SVM）、深度神经网络（DNN）3种机器学习方法建立了降水相态的高分辨率客观分类模型,并对同样条件下3种机器学习方法对雨、雨夹雪和雪3种京津冀主要降水相态的预报效果进行了对比检验,进一步提升了雨夹雪复杂降水相态的客观分类预报技巧。   相似文献   

A SEM-based method to determine the mineralogical composition and the particle size distribution of suspended sediment     

Sylvain Pinet  Bruno Lartiges  Jean-Michel Martinez  Sylvain Ouillon 《国际泥沙研究》2019,34(2):85-94

A robust method for characterizing the mineralogy of suspended sediment in continental rivers is introduced. It encompasses 3 steps: the filtration of a few milliliters of water, measurements of X-ray energy dispersive spectra using Scanning Electron Microscopy (SEM), and robust machine learning tools of classification. The method is applied to suspended particles collected from various Amazonian rivers. A total of more than 204,000 particles were analyzed by SEM-EDXS (Energy Dispersive X-ray Spectroscopy), i.e. about 15,700 particles per sampling station, which lead to the identification of 15 distinct groups of mineralogical phases. The size distribution of particles collected on the filters was derived from the SEM micrographs taken in the backscattered electron imaging mode and analyzed with ImageJ freeware. The determination of the main mineralogical groups composing the bulk sediment associated with physical parameters such as particle size distribution or aspect ratio allows a precise characterization of the load of the terrigenous particles in rivers or lakes. In the case of the Amazonian rivers investigated, the results show that the identified mineralogies are consistent with previous studies as well as between the different samples collected. The method enabled the evolution of grain size distribution from fine to coarse material to be described in the water column. Implications about hydrodynamic sorting of mineral particles in the water column are also briefly discussed. The proposed method appears well suited for intensive routine monitoring of suspended sediment in river systems.  相似文献   

Is it possible to discern Striga weed (Striga hermonthica) infestation levels in maize agro-ecological systems using in-situ spectroscopy?     

《International Journal of Applied Earth Observation and Geoinformation》2020

The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.  相似文献