Predictive geochemical mapping using environmental correlation

The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time.Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km²), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations.Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments.     

A note on “Origin of components in Chilean thermal waters” by

Risacher et al. (2011) have presented voluminous data on thermal waters of Chile that is quite appreciable; however, their work still falls short on several counts. The most notable shortcoming of the work is the presentation and treatment of data. The interpretations are based on questionable premises (viz., extent of seawater intrusion) and considerations (viz., using average chemical composition of rock types for geochemical modeling, considering volcanic rocks as the only rock type in contact with the geothermal fluids) and assumptions not always substantiated by facts (with proper references) could have been corroborated. Use of Cl/Br ratio for discrimination purpose is unconvincing, considering the uncertainty in the measurement of low (<1 mg/L) Br concentration.     

月球东海盆地综合解析与撞击初始条件的研究

东海是月球上最年轻的多环撞击盆地,关于其形成机制的研究很多,但成果大都基于正撞击的机制提出的,虽然有部分学者提出东海是斜撞击的,但缺乏具体撞击参数。本文通过多源数据融合,综合分析LRO影像数据、LOLA地形数据、M~3高光谱数据和IIM高光谱数据,对东海地区的地貌特征、物质成分进行了较为系统的解译,发现在东海中央熔融区存在一条与东海撞击方向垂直的中央隆起区域(中央隆起线),其也是中央熔融区粗糙部分与光滑部分的分界线,结合撞击坑成坑理论,认为其可能是撞击过程冲击波作用引起的堆叠作用形成的。同时利用GRAIL数据及对该地区的重力异常的成因进行了分析,认为异常是由于压强、温度及岩石粘度的改变引起局部莫霍面抬升和中央熔融物的形成而出现的,进而估算出熔融物占盆地内物质的25%,约为1.1×10~6km~3。同时,对GRAIL数据的剖面分析结果也支持了本文的斜撞击理论。最后,综合多方面的信息和撞击理论获取东海盆地构造分布图,并根据中央隆起线、溅射物及线性构造的分布特征等,提出东海盆地理论上是由一直径在50~100km的撞击体以10~30km/s的速度自东偏北约20°~30°方向以20°~30°的角度斜撞击月表而形成的。这可为研究更早期的月球撞击坑提供理论参考。     

Identification of Uranyl Minerals Using Oxygen K‐Edge X‐Ray Absorption Spectroscopy

Although most of the world's uranium exists as pitchblende or uraninite, this mineral can be weathered to a great variety of secondary uranium minerals, most containing the uranyl cation. Anthropogenic uranium compounds can also react in the environment, leading to spatial–chemical alterations that could be useful for nuclear forensics analyses. Soft X‐ray absorption spectroscopy (XAS) has the advantages of being non‐destructive, element‐specific and sensitive to electronic and physical structure. The soft X‐ray probe can also be focused to a spot size on the order of tens of nanometres, providing chemical information with high spatial resolution. However, before XAS can be applied at high spatial resolution, it is necessary to find spectroscopic signatures for a variety of uranium compounds in the soft X‐ray spectral region. To that end, we collected the near edge X‐ray absorption fine structure (NEXAFS) spectra of a variety of common uranyl‐bearing minerals, including uranyl carbonates, oxyhydroxides, phosphates and silicates. We find that uranyl compounds can be distinguished by class (carbonate, oxyhydroxide, phosphate or silicate) based on their oxygen K‐edge absorption spectra. This work establishes a database of reference spectra for future spatially resolved analyses. We proceed to show scanning X‐ray transmission microscopy (STXM) data from a schoepite particle in the presence of an unknown contaminant.     

地震勘探技术在煤炭勘查中的应用与展望

地震勘探作技术为一种重要的勘探方法,可以经济、有效的发现资源和解决资源开采过程中遇到的构造、地层、岩性等问题。煤炭地震勘探发展过程中地震野外数据采集、地震数据处理和地震解释都取得了重大成就,目前煤炭地震勘探正进一步向高信噪比、高分辨率、高保真度、高清晰度、高精度、定量分析的方向发展,未来也必将在煤炭资源发现和矿井隐蔽致灾因素的探测中发挥重要作用。     

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.