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

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.

Artificial Detection of Lower-Frequency Periodicity in Climatic Studies by Wavelet Analysis Demonstrated on Synthetic Time Series

The continuous wavelet transform (CWT) is a frequently used tool to study periodicity in climate and other time series. Periodicity plays a significant role in climate reconstruction and prediction. In numerous studies, the use of CWT revealed dominant periodicity (DP) in climatic time series. Several studies suggested that these “natural oscillations” would even reverse global warming. It is shown here that the results of wavelet analysis for detecting DPs can be misinterpreted in the presence of local singularities that are manifested in lower frequencies. This may lead to false DP detection. CWT analysis of synthetic and real-data climatic time series, with local singularities, indicates a low-frequency DP even if there is no true periodicity in the time series. Therefore, it is argued that this is an inherent general property of CWT. Hence, applying CWT to climatic time series should be reevaluated, and more careful analysis of the entire wavelet power spectrum is required, with a focus on high frequencies as well. A conelike shape in the wavelet power spectrum most likely indicates the presence of a local singularity in the time series rather than a DP, even if the local singularity has an observational or a physical basis. It is shown that analyzing the derivatives of the time series may be helpful in interpreting the wavelet power spectrum. Nevertheless, these tests are only a partial remedy that does not completely neutralize the effects caused by the presence of local singularities.

Comparison of Methods for Determining the Thresholds of Geochemical Anomalies and the Prospecting Direction—A Case of Gold Deposits in the Gouli Exploration Area, Qinghai Province

Geochemical anomalies play an important role in mineral exploration, because they are closely related to the formation of ore deposits. In this paper, the Au in the geochemical samples of 1:50,000 stream sediments in the Gouli exploration area, Qinghai Province, is selected as an example. The cumulative frequency method, local singularity analysis, and exploration data analysis method are used to process the Au data and determine the abnormal thresholds. Robust principal component analysis is used to explore the elemental associations. Among them, the effect of local singularity analysis is the best and it delineates the weak geochemical anomalies not delineated by cumulative frequency method and exploration data analysis method, which shows its superiority. Robust principal component analysis shows that Sb, W, and As are closely related to Au mineralization. Three anomalous areas are delineated in the Gouli exploration area. Gold ore-body is found to occur within anomaly area I. The other anomalous areas are highly consistent with abnormal elemental composition of Sb, W, and As. Moreover, these two areas are extremely conformable with the high-value areas determined by the binary state method. The optimality of the three methods are compared comprehensively, it is pointed out that it is more reasonable to use the local singularity analysis to determine the abnormal threshold. The anomaly areas II and III of Au determined by local singularity analysis have shown great potential for prospecting.