Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm

Due to the combined influences such as ore-forming temperature, fluid and metal sources, sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc (Pb-Zn) deposits. Therefore, trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types. However, previous discriminant diagrams usually contain two or three dimensions, which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits. In this study, we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can discriminate Pb-Zn deposit types using machine learning algorithms. A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications, containing 12 elements (Mn, Fe, Co, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, and Pb) from 5 types, including Sedimentary Exhalative (SEDEX), Mississippi Valley Type (MVT), Volcanic Massive Sulfide (VMS), skarn, and epithermal deposits. Random Forests (RF) is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits, most of which are falsely distinguished as skarn and epithermal types. To further discriminate VMS deposits, future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when constructing the classification model. RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification. Besides, a visualized tool, t-distributed stochastic neighbor embedding (t-SNE), was used to verify the results of both classification and evaluation. The results presented here show that Mn, Co, and Ge display significant impacts on classification of Pb-Zn deposits and In, Ga, Sn, Cd, and Fe also have relatively important effects compared to the rest elements, confirming that Pb-Zn deposits discrimination is mainly controlled by multi-elements in sphalerite. Our study hence shows that machine learning algorithm can provide new insights into conventional geochemical analyses, inspiring future research on constructing classification models of mineral deposits using mineral geochemistry data.     

Spatial variability of soil water erosion: Comparing empirical and intelligent techniques

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of soil erosion susceptibility is a first and vital step in land management and soil conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately map SWE susceptibility. These optimized algorithms include Convolutional Neural Networks (CNN and CNN-GWO), Support Vector Machine (SVM and SVM-GWO), and Group Method of Data Handling (GMDH and GMDH-GWO). Results obtained using these algorithms can be compared with the well-known Revised Universal Soil Loss Equation (RUSLE) empirical model and Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply these methods together with the frequency ratio (FR) model and the Information Gain Ratio (IGR) to determine the relationship between historical SWE data and controlling geo-environmental factors at 116 sites in the Noor-Rood watershed in northern Iran. Fourteen SWE geo-environmental factors are classified in topographical, hydro-climatic, land cover, and geological groups. We next divided the SWE sites into two datasets, one for model training (70% of the samples = 81 locations) and the other for model validation (30% of the samples = 35 locations). Finally the model-generated maps were evaluated using the Area under the Receiver Operating Characteristic (AU-ROC) curve. Our results show that elevation and rainfall erosivity have the greatest influence on SWE, while soil texture and hydrology are less important. The CNN-GWO model (AU-ROC = 0.85) outperformed other models, specifically, and in order, SVR-GWO = GMDH-GWO (AUC = 0.82), CNN = GMDH (AUC = 0.81), SVR = XGBoost (AUC = 0.80), and RULSE. Based on the RUSLE model, soil loss in the Noor-Rood watershed ranges from 0 to 2644 t ha^–1yr^?1.     

石英Ti/Ge-P:基于机器学习的矿床类型判别新图解SCIEI北大核心CSCD

石英的微量元素记录了石英生长的物理化学条件。通过微量元素对石英原岩进行分类的研究历史已久,经典工作是在以微量元素为坐标轴的图解上绘制各类型石英的分布范围,以区分石英类型。经典图解包括Rusk(2012)提出用于区分三种矿床类型石英的Al-Ti二元图解,和Schr9n et al.(1988)提出的用于判别不同岩浆岩类型石英的Ti-Al-Ge三元图解。越来越多的研究表明,上述图解不能满足对更多石英类型进行分类的需求,同时也出现与部分已知产状类型的石英微量元素判别相矛盾的情况。随着石英原位微区测试方法的成熟,高精度石英微量元素数据逐渐丰富为系统开展机器学习提供了大数据基础,为石英微量元素研究提供了新的角度和可能性。本研究运用机器学习分类方法对石英微量元素进行精确数学分析,提出Ti/Ge-P图解为石英成因研究提出新的地球化学指标。本文同时测试了六种经典机器学习分类算法,提高Ti/Ge-P图解在石英成因分类研究上的精度。此Ti/Ge-P图解适用于多种矿床研究,包括但不局限于斑岩型矿床、矽卡岩型矿床、浅成低温热液型矿床、卡林型矿床以及造山型矿床中的石英。这项工作是大数据技术与机器学习技术在地球化学研究中的积极探索。     

The Open Geospatial Consortium Sensor Web PivotViewer: an innovative tool for worldwide sensor web resource discovery

In recent years, large-scale sensor arrays and the vast data-sets they produce worldwide are being utilized, shared, and published by a rising number of researchers on an ever-increasing frequency. An increasing number of sensor web services are deployed to host and share the large volume of sensor data online. How to efficiently discover the sensor web resources and visualize different types of sensor data in a coherent environment becomes an important research question that is still not fully resolved. In this paper, we propose the Sensor Web PivotViewer system. By integrating the Geospatial Cyberinfrastructure for Environmental Sensing (GeoCENS) cyber infrastructure, the Microsoft PivotViewer, and the Microsoft BingMaps, the proposed system fills in the missing software components for users to easily and intuitively discover and utilize the worldwide sensor web resources.     

Discovering relative motion patterns in groups of moving point objects

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been implemented and tested with two case studies: tracked soccer players and data points representing political entities moving in an abstract ideological space. In both case studies, a set of non‐trivial and meaningful motion patterns could be identified, for instance highlighting the characteristic ‘offside trap’ behaviour in the first case and identifying trendsetting districts anticipating a political transformation in the latter case.     

ST‐DMQL: A Semantic Trajectory Data Mining Query Language

Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.     

A method for discovery and analysis of temporal patterns in complex event data

Pattern analysis techniques currently common within geography tend to focus either on characterizing patterns of spatial and/or temporal recurrence of a single event type (e.g., incidence of flu cases) or on comparing sequences of a limited number of event types where relationships between events are already represented in the data (e.g., movement patterns). The availability of large amounts of multivariate spatiotemporal data, however, requires new methods for pattern analysis. Here, we present a technique for finding associations among many different event types where the associations among these varying event types are not explicitly represented in the data or known in advance. This pattern discovery method, known as T-pattern analysis, was first developed within the field of psychology for the purpose of finding patterns in personal interactions. We have adapted and extended the T-pattern method to take the unique characteristics of geographic data into account and implemented it within a geovisualization toolkit for an integrated computational-geovisual environment we call STempo. To demonstrate how T-pattern analysis can be employed in geographic research for discovering patterns in complex spatiotemporal data, we describe a case study featuring events from news reports about Yemen during the Arab Spring of 2011–2012. Using supplementary data from the Global Database of Events, Language, and Tone, we briefly summarize and reference a separate validation study, then evaluate the scalability of the T-pattern approach. We conclude with ideas for further extensions of the T-pattern technique to increase its utility for spatiotemporal analysis.     

贵州兴仁县落水洞金矿的发现及其研究意义

根据灰家堡背斜成矿规律分析研究,2006年11月在回龙镇落水洞布置ZK001钻孔,在其深部发现高品位矿体,这一发现为太平洞金矿提供了后备勘查基地,填补灰家堡背斜中部无工业矿体的空白。本文简要地论述其地质特征和研究意义。     

融合土地覆盖和土壤水分产品的近地表空气温度空间化方法

空气温度是评价人居环境的重要指标,与人类的生产生活息息相关;其观测对于水文、环境、生态和气候变化等方面的研究具有重要意义。传统的大范围空气温度观测数据一般通过气象站点获取,但由于气象观测站点空间分布离散稀疏的特点,所获取的数据不能精确描述空间连续的空气温度变化情况。因此,实现基于遥感数据的近地表空气温度精准估算具有重要的现实意义。本研究基于精细的地表覆盖类型、空间连续的土壤水分、地表温度（LST）数据,并结合其他辅助数据,构建了近地表空气温度空间化模型,并对近地表空气温度影响因子进行评估,发现地表覆盖类型对近地表空气温度的影响最大,土壤水分为最活跃的影响因素,经验证,模型精度较高,R²接近0.85,RMSE为0.5℃。本研究获取的精确空间连续的近地表空气温度信息,能够充分表达其空间异质性,为农业气象灾害灾变过程监测、农作物生长过程模拟、区域气候变化分析等研究提供良好的近地表空气温度数据支撑。     

冬奥会复杂山地百米尺度10m风速预报的机器学习订正对比试验

本文以传统机器学习算法XGBoost和深度学习算法CU-Net为基础,针对北京快速更新无缝隙融合与集成预报系统（RISE系统）预报的北京冬奥会延庆及张家口赛区100米分辨率的冬季近地面10 m风速数据,进行每日逐小时起报的未来逐6小时间隔的冬奥高山站点及其周边地区风速预报偏差订正方法研究和对比分析。对于站点订正,首先将RISE系统预测的10 m风速插值到对应的自动气象站站点,然后根据风速等级表归类,针对每个分类单独构建XGBoost模型,每个区间模型合并后形成L-XGBoost,使用均方根误差和预报准确率作为评分标准,结果表明风速归类的L-XGBoost算法订正效果比不归类的原始XGBoost模型有一定提升,说明在传统机器学习中加入归类方法有助于改善复杂山地站点风速预报技巧。对于站点及其周边地区风速订正,本文在CUNet模型基础上,通过引入不同深度的CU-Net子网络,构建了新的算法模型CU-Net++,并考虑了预报日变化误差和复杂地形对10 m风速的影响,以自动气象站为中心构建空间小区域样本数据,对RISE系统风速预报偏差进行订正。试验结果表明,CU-Net和CU-Net++均可以充...