Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

Abstract

The smoothing effect of data interpolation could cause useful information loss in geochemical mapping, and the uncertainty assessment of geochemical anomaly could help to extract reasonable anomalies. In this paper, multiple-point geostatistical simulation and local singularity analysis (LSA) are proposed to identify regional geochemical anomalies and potential mineral resources areas. Taking Cu geochemical data in the Mila Mountain Region, southern Tibet, as an example, several conclusions were obtained: (1) geochemical mapping based on the direct sampling (DS) algorithm of multiple-point geostatistics can avoid the smoothing effect through geochemical pattern simulation; (2) 200 realizations generated by the direct sampling simulation reflect the uncertainty of an unsampled value, and the geochemical anomaly of each realization can be extracted by local singularity analysis, which shows geochemical anomaly uncertainty; (3) the singularity-quantile (S-Q) analysis method was used to determine the separation thresholds of E-type α, and uncertainty analysis was carried out on the copper anomaly to obtain the anomaly probability map, which should be more reasonable than the interpolation-based geochemical map for geochemical anomaly identification. According to the anomaly probability and favorable geological conditions in the study area, several potential mineral resource targets were preliminarily delineated to provide direction for subsequent mineral exploration.

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

“…Numerous studies have used fractal and correlation analysis and machine learning methods to identify potential mineralization zones separately [5,9,[18][19][20][21][22][23][24][25]. However, a few studies have used integrated and hybrid methods for the geochemical modeling of ore mineralization [26][27][28][29].…”

Geochemical Quantitative Assessment of Mineral Resource Potential in the Da Hinggan Mountains in Inner Mongolia, China

“…However, a great many of the previous studies focused on the screening and verification of geochemical anomalies with high concentration, multiple elements, and large size [18][19][20]. With the deepening of mineral prospecting, it can be generally found that the geochemical anomalies in mineralized bodies with large buried depth are very weak with low concentration and small-sized; hence, they are always ignored [21][22][23][24][25]. Therefore, it is of great significance to (a) summarize previous research and regional geochemical data of China, (b) identify the missed geochemical anomalies mentioned above, (c) establish geochemical models of metallogenic indicator elements for typical deposits, (d) explore the geochemical distribution of elements and its relationship with regional metallogeny and metallogenic potential, and (e) evaluate the potential for mineral resources.…”

Spatial Modelling of Kaolin Deposit Demonstrated on the Jimlíkov-East Deposit, Karlovy Vary, Czech Republic

“…The mineral resource deposits are modelled and categorized by N. Battalgazy and N. Madani [9], who employed the projection pursuit multivariate transformation method, and then, the outputs are compared with conventional (co)-simulation methods. Li et al [10] proposed multiple-point geostatistical simulation and local singularity analysis to identify regional geochemical anomalies and potential mineral resource areas. Liu et al [11] constructed 3D geometric models for evaluating the Dawangding gold deposit in south China using the FLAC3D (fast Lagrangian analysis of continua in three dimensions) modelling.…”