###### Abstract  
The Qinghai-Tibet Plateau, abundant in mineral resources, is a treasure trove for geological explorers. However, exploration has been hindered by the presence of dense vegetation, weathering layers, and desert cover, particularly in the North Qaidam region. As a result, there is an urgent need to develop efficient geochemical composition methods. In a study of stream sediment geochemical data from the Luliangshan area of the North Qaidam, the log-ratio transformation was used for data processing, Robust Factor Analysis (RFA) was used for model construction, and the spectrum-area (S-A) model was used for anomaly separation. The outcomes identified two distinct groups of element combinations associated with mineralization. The first group consisted of Au + Ag + Pb + Sb + Hg linked to tectonic-altered rock-type Au (antimony) deposits, while the second group consisted of Cu + Zn + Co and was closely associated with Cu-Ni sulfide deposits. The S-A fractal filtering technique amplified weak anomalies and minimized the area of anomalies against strong backgrounds. The study successfully detected substantial Cu mineralization in the source areas of geochemical anomalies in the Luliangshan region. Consequently, the log-ratio-RFA-S-A fractal model has been proven to be an effective combination of methods for identifying and extracting geochemical anomalies from stream sediment samples and for mineral exploration in covered areas.  
geochemical anomaly; Robust Factor Analysis; multifractal filtering technology; mineral exploration; North Qaidam 2024142597. 14.  
effectiveness [3; 10; 13; 14; 15; 16]. In this regard, the singularity mapping technique, which involves data within a small singularity around a specific spatial location, can be an excellent multifractal tool for identifying weak anomalies. Additionally, it facilitates local neighborhood statistical analysis, reduces the effects of the regional background and provides useful statistical information [5; 10; 11; 14; 17; 18; 19; 20; 21; 22]. However, the above methods do not account for the compositional nature of geochemical data [4; 6; 23]. Two issues must be addressed to accurately obtain mineralization-related anomalies of multivariate geochemical footprints from the data: (1) the presence of outliers in geochemical data sets, which can lead to biased extraction of multivariate associations using classical statistical estimators [24; 25; 26], and (2) the closure problem of compositional data [27; 28; 29].  
Therefore, this study selected Robust Factor Analysis (RFA) to first extract combinations of metallogenic elements. Subsequently, singularity mapping was employed to investigate the spatial patterns of geochemical data and extract weak anomalies. Finally, we compared the spatial associations between Cu anomalies identified by different methods and known deposits in the North Qaidam (NQ) region. This comparison provided an excellent opportunity to (1) identify primary element patterns related to mineralization, (2) evaluate the advantages of using multifractal singularity mapping in covered areas, and (3) guide the verification of weak anomalies and identification of potential deposits in the Luliangshan area.