A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies

• Trend surface analysis (TSA) and spectrum–area (S–A) multifractal are compared.
• Both TSA and S–A are powerful tools for decomposing mixed geochemical patterns into background and anomalous components.
• The S–A is better than TSA in revealing local geochemical anomalies.

The trend surface analysis (TSA) and the spectrum–area (S–A) multifractal model are two popular methods to identify geochemical anomalies. In this study, the robust principal component analysis (RPCA) was applied to integrate multi-geochemical variables in a regional stream sediment dataset related to major ore-forming elements in southwestern Fujian (China). We applied the TSA and S–A model to decompose the integrated geochemical pattern obtained from the RPCA and compared the results obtained from both methods. The obtained anomaly maps were similar, with the high anomaly areas showing a strong spatial relationship with intrusions that are related to Fe polymetallic mineralization, indicating that both the TSA and the S–A are useful tools to identify geochemical anomalies. The S–A model, based on distinct anisotropic scaling properties, was better in revealing local anomalies because it considered the spatial characteristics of the geochemical variables.