Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

2016 ◽  
Vol 87 ◽  
pp. 56-66 ◽  
Author(s):  
Guoxiong Chen ◽  
Qiuming Cheng
2007 ◽  
Vol 14 (3) ◽  
pp. 317-324 ◽  
Author(s):  
◽  
◽  
◽  

Abstract. There are many phenomena in nature, such as earthquakes, landslides, floods, and large-scale mineralization that are characterized by singular functions exhibiting scale invariant properties. A local singularity analysis based on multifractal modeling was developed for detection of local anomalies for mineral exploration. An iterative approach is proposed in the current paper for improvement of parameter estimations involved in the local singularity analysis. The advantage of this new approach is demonstrated with de Wijs's zinc data from a sphalerite-quartz vein near Pulacayo in Bolivia. The semivariogram method was used to illustrate the differences between the raw data and the estimated data by the new algorithm. It has been shown that the outcome of the local singularity analysis consists of two components: singularity component characterized by local singularity index and the non-singular component by prefractal parameter.


2019 ◽  
Vol 130 ◽  
pp. 43-56 ◽  
Author(s):  
Shuai Zhang ◽  
Keyan Xiao ◽  
Emmanuel John M. Carranza ◽  
Fan Yang ◽  
Zhicheng Zhao

Author(s):  
Qiang Miao ◽  
Hong-Zhong Huang ◽  
Xianfeng Fan

Damage detection of machinery is always a research field that attracts attentions of many people. In this paper, a novel technique for gearbox damage detection is investigated, which is a wavelet-based singularity analysis method. Wavelet transform is applied to vibration signal, modulus maxima lines are extracted, and conclusions are made based on these lines. Simulated and real vibration data have been analyzed using this approach. The results are presented to show the effectiveness of the method.


2018 ◽  
Vol 28 (1) ◽  
pp. 199-212 ◽  
Author(s):  
Yue Liu ◽  
Qiuming Cheng ◽  
Emmanuel John M. Carranza ◽  
Kefa Zhou

2021 ◽  
pp. 1-10
Author(s):  
Mengxue Cao ◽  
Laijun Lu ◽  
Yu Zhong

How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α-values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting.


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