Abstract
Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.
Highlights
Many geoscience problems involve predicting attributes of interest at un-sampled locations
Our results demonstrate that the dual IDW (DIDW) with locally varying exponents (LVEs) stably produces more accurate estimates than Inverse distance weighting (IDW)-L and DIDW-GG; it yields more robust estimates than ordinary kriging (OK) in the face of varying variogram parameters
A new dual IDW framework (DIDW with LVEs) that can account for the D-D and D-U correlations flexibly is proposed
Summary
Many geoscience problems involve predicting attributes of interest at un-sampled locations. IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. It yields more robust estimates than ordinary kriging in the face of varying variogram parameters. One exception is that such parameters are not available for traditional IDW when an uneven sampling rule (which is commonly used in geosciences) is the dominant factor that leads to its low-accuracy estimates The reason caused this exception is that classical IDW omits the data-to-data relationship. Compared to globally constant exponents used in the traditional DIDW, LVEs are appropriately incorporated and optimized in the proposed method
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