Noise sensitivity analysis of mineral prospectivity models based on root mean square, geometric average, and isolation forest integration methods

• Root mean square (RMS) prospectivity modeling approach is proposed. • RMS is compared with isolation forest (IForest) and geometric average (GA). • The comparison focuses on the noise analysis. • The RMS method mitigates the noise issue in terms of weighting strategy. • RMS outperforms the IForest and GA unsupervised anomaly detection techniques. There are a variety of multicriteria decision-making approaches to pinpoint the areas with significant mineral potential. Since exploration data need to be weighted or transformed into the same range before integration, and because there are multiple approaches to weighting, the chosen weighting strategy has a strong influence on the resulting prospectivity model. Consequently, a major challenge in mineral prospectivity mapping is the sensitivity of the integration methods to input data and the methods applied to generate weighted exploration evidence layers, which results in inconsistency in the recognized targets. In this paper, we perform a noise analysis to assess the sensitivity of integration and modeling results to the variation of weighted layers of the same input exploration data. For this, we use a dataset of porphyry copper mineralization in the Chahargonbad district within the Urumieh-Dokhtar Volcanic Belt of Iran, and we employ the root mean square (RMS) function as an alternative to the existing geometric average (GA) integration approach for mineral exploration targeting. Furthermore, the isolation forest (IForest) algorithm, an unsupervised anomaly detection method, is employed for the identification of anomalous patterns for comparison purposes. All three methods were evaluated using the prediction–area plot and the normalized density index, demonstrating the superiority of the RMS method over other approaches in identifying Cu mineralization. The proposed integration framework exhibits reduced sensitivity to noise and weighting variability in exploration datasets. The outcomes demonstrate that the RMS method outperforms both the GA and IForest approaches in recognizing exploration targets.