Application of a Portable XRF Spectrometer for <i>In-Situ</i>and Nondestructive Investigation of Pigments in Two 15th Century Icons

A simple portable X-Ray Fluorescence (XRF) spectrometer was successfully used for in-situ and nondestructive identification of the painting materials in two 15th century icons from the Onufri Museum in Beart, Albania. The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. A small number of pigments were clearly identified by X-Ray Fluorescence (XRF) measurements in both icons. This includes Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by the use of additional analytical techniques. The results show that in both icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.

基于物联网的嵌入式技术在区域地质调查中的应用

随着野外测试设备的小型化,集成了嵌入式系统的便携式智能野外测试设备应用也越来越广,但这些设备测试数据的采集途径、方法却各不相同,这就影响到这些设备与计算机的互联、互通,限制了它的使用。借助物联网技术,通过PDA可以将这些集成了嵌入式系统的野外测试设备无缝连接起来,简化用户的测试流程,测试数据自动采集、传输、处理,并由GIS软件自动生成各种专题地质图。基于物联网技术,笔者以便携式X荧光测试仪为例,设计了一套以集成了GPS的Windows Mobile智能手机为核心的野外测试数据无缝采集、处理系统。该系统改进了区域地质调查流程,提高了工作效率。

A New Discovery of Ag-Pb-Zn Mineralization via Modern Portable Analytical Technology and Stream Sediment Data Processing Methods in Dajiacuo Area, Western Tibet(China)

Tibet, which is rich in mineral resources, is a treasure trove for geological explorers. However, prospecting work has been slow, especially in the western part, due to the precipitous terrain, changeable climate and low access. Hence, modern advanced field analytical technology and effective data processing methods play significant roles in rapid and efficient exploration in Tibet. In this paper, spectrum-area fractal modeling and portable X-ray fluorescence analysis(pXRFA) were used to identify and verify geochemical anomalies associated with Ag-Pb-Zn mineralization based on a stream-sediment dataset of 39 elements in the Dajiacuo-Xurucuo region of western Tibet. First, staged factor analysis(SFA) was used to obtain the Ag-Pb-Zn-Cd geochemical assemblage. Second, the first-factor pattern obtained using SFA was dissociated by a spectrum-area(S-A) fractal model and a digital elevation model(DEM)-based geochemical model(DGM) was constructed. Finally, the sections of Ag, Cd, Pb, and Zn were obtained using pXRFA. The results show that Ag-Pb-Zn-Cd enrichment zones were mostly located around the contact belt of volcanic rocks and intrusions, or along SE-NW trending faults. Considering the variable terrain and catchment basin, the extension of long axes of Ag-Pb-Zn-Cd anomalies into higher elevation areas that are favorable for Ag-Pb-Zn mineralization should be investigated. Anomaly maps created with the aid of a DGM show promising potential for mineralization in the Dajiacuo-Xurucuo region, and abundant Ag-Pb-Zn mineralization was identified with the assistance of pXRFA in the source areas for the geochemical anomalies in the Dajiacuo. We conclude that SFA and the S-A fractal model constitute a valid tool to identify or verify geochemical anomalies in areas of low-density stream-sediment sampling. The pXRFA can accurately determine the source of geochemical anomalies and improve anomaly verification efficiency.

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.