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Recognition of weak geochemical anomalies from background is an overlooked issue in the interpretation of regional geochemical data used to define the exploration targets in the areas covered by deserts, regolith, or vegetation. In this study, the log-ratio transformation methods were used to account the compositional character of the geochemical data. A robust compositional factor analysis (CoDa-FA) was utilized to identify the ensembles of elements related to mineralization. Multifractal singularity analysis and the mean +/- 2 standard deviation (mean +/- 2STD) method were used to identify the Au mineralization-related geochemical anomalies in North Qaidam Gobi Desert landscape area. The results show that CoDa-FA for the centered logratio-transformed geochemical data can accurately recognize the geochemical compositional assemblages related to Au and Pb-Ag mineralization, representing the orogenic Au mineralization and hydrothermal-type Pb-Ag mineralization, respectively. The geochemical anomalies delineated by the mean +/- 2STD method showed weak signals in SE Shuangkoushan. However, singularity mapping is efficient for enhancing and outlining the weak anomalies in a low background relative to the mean +/- 2STD method. The anomaly maps determined by singularity mapping show good ore-potentiality in SE Shuangkoushan, and many Au and Pb-Ag ore bodies have been found in the anomalous areas. The results would not only be useful to guide anomaly verification and potential exploration targets in the North Qaidam metallogenic belt, but also raise the awareness of reanalyzing those weak anomalies in a covered area.