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.
Highlights
The weak anomaly extraction of geological and geochemical information has played an important role in discovering potential mineral deposits [1]
In regional geochemical data processing, the complex geological and geographical background may result in difficulties associated with geochemical anomaly extraction, where high element content values may not be associated with orebody, but some low content values are
A key problem is that it is difficult for a simple mathematical function or a deterministic model to accurately describe the spatial variability of an element to infer the value at the unsampled location from limited or sparse geochemical exploration data [5,7,8,9,10,11,12], which will result in uncertainty in the spatial prediction
Summary
The weak anomaly extraction of geological and geochemical information has played an important role in discovering potential mineral deposits [1]. Algorithm [28]; the filter-based pattern simulation (FILTERSIM) algorithm [29], and the cross-correlation-based simulation (CCSIM) algorithm [30] These methods have been effectively applied in reservoir simulation, hydrogeological modeling, porous media reconstruction, and other geoscience fields [31,32,33], but there are few applications related to geochemical exploration [17,34,35]. A hybrid method combining the direct sampling (DS) algorithm of multiple-point geostatistical simulation and local singularity analysis (LSA) is proposed to estimate Cu anomalies associated with copper mineralization based on stream sediment geochemical data in the so-called ”Mila Mountain Integrated Exploration Region” in Tibet, China
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
