Inverse Radius Weighting (IRW) interpolation model: A new interpolation considering morphology

Geochemical sampling has limitations due to budget constraints and restricted access to certain areas. This results in some regions being unsampled, making it difficult to generate comprehensive geochemical anomaly maps and distinguish between background values and anomalies. To address this, different interpolation models have been developed, such as inverse distance weighting (IDW) and kriging techniques. However, both IDW and kriging only consider the horizontal distance between samples and ignore elevation changes, which is important in real-world terrains. This effect is most pronounced in mountainous terrains. To address this, we propose a new interpolation technique called Inverse Radius Weighting (IRW). IRW factors in both horizontal distance and elevation changes between sample pairs, resulting in more accurate predictions. Detailed elevation data is available for the entire globe in the form of digital elevation maps from the Shuttle Radar Topography Mission (SRTM) and other sources. In this research, both IDW and IRW models were applied to soil samples in Cyprus. A comparative analysis between IDW and IRW models showed that IRW gives more accurate predictions, especially in terrains with complex morphologies. IRW's ability to account for topographical influences makes it the preferred choice in such scenarios.