Application of fractal content-gradient method for delineating geochemical anomalies associated with copper occurrences in the Yangla ore field,China

Fractal and multi-fractal content area method finds application in a wide variety of geological,geochemical and geophysical fields.In this study,the fractal content-gradient method was used on1：10,000 scale to delineate geochemical anomalies associated with copper mineralization.Analysis of geochemical data from the Yangla super large Cu-Pb-Zn polymetallic ore district using the fractal content-gradient method,combined with other geological data from this area,indicates that oreprospecting in the ore district should focus on Cu as the main metal and Pb-Zn and Au as the auxiliary metals.The types of deposits include（in chronological order） re-formed sedimentary exhalative（SEDEX）,skarns,porphyries,and hydrothermal vein-type deposits.Three ore-prospecting targets are divided on a S-N basis：（1） the Qulong exploration area,in which the targets are porphyry-type Cu deposits;（2） the Zongya exploration area,where the targets are porphyry-type Cu and hydrothermal vein-type Cu-Pb polymetallic deposits;and（3） the Zarelongma exploration area,characterized mainly skarn-type ＂Yangla-style＂ massive sulfide Cu-Pb deposits.Our study demonstrates that the fractal content-gradient method is convenient,simple,rapid,and direct for delineating geochemical anomalies and for outlining potential exploration targets.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

A Study of the Driving Force Model Revealing Changes in Land Utilization Level Based on 3S Technologies ——The Example of Yuanmou, Yunnan, China

This paper introduced the theory and approaches of building driving forcemodels revealing the changes in land utilization level by integrating RS, GPS, and GIS technologiesbased on the example of Yuanmou County of Yunnan Province. We first created the land utilizationtype database, natural driving forces for land utilization database, and human driving forces forland utilization database. Then we obtained the dependent and the independent variables of changesin land utilization level by exploring various data. Lastly we screened major factors affectingchanges in land utilization level by using the powerful spatial correlation analysis and maincomponent analysis module of GIS and obtained a multivariable linear regression model of thechangesin land utilization level by using GIS spatial regression analysis module.

ClustNails:Visual Analysis of Subspace Clusters

Subspace clustering addresses an important problem in clustering multi-dimensional data. In sparse multi-dimensional data, many dimensions are irrelevant and obscure the cluster boundaries. Subspace clustering helps by mining the clusters present in only locally relevant subsets of dimensions. However, understanding the result of subspace clustering by analysts is not trivial. In addition to the grouping information, relevant sets of dimensions and overlaps between groups, both in terms of dimensions and records, need to be analyzed. We introduce a visual subspace cluster analysis system called ClustNails. It integrates several novel visualization techniques with various user interaction facilities to support navigating and interpreting the result of subspace clustering. We demonstrate the effectiveness of the proposed system by applying it to the analysis of real world data and comparing it with existing visual subspace cluster analysis systems.

Virtual geographic environments for water pollution control

Due to extensive water pollution in Chinese rivers and lakes,large efforts have to be made to improve the quality of drinking water and manage the sewage water treatment process.We propose a general workflow for integrating a large number of heterogeneous data sets relating to various hydrological compartments into a Virtual Geographic Environment(VGE).This allows both researchers and stakeholders to easily access complex data collections in a unified context,find interrelations or inconsistencies between data sets and evaluate simulation results with respect to other observations or simulations in the same region.A prototype of such a VGE has been set up for the region around Chao Lake,containing more than 20 spatial data sets and collections as well as first simulation result.The prototype has been successfully presented to researchers and stakeholders from China and Germany.