Multidimensional scaling and inverse distance weighting transform for image processing of hydrogeological structure in rock mass

Abstract

A new imaging method based on the multidimensional scaling (MDS) and inverse distance weighting (IDW) transform is proposed in this study. This method aims to identify, characterize and process an image of the preferential flow path in a rock mass, which strongly governs the hydraulic behavior of this rock mass. This methodology uses pair-wise hydraulic diffusivity data from cross-hole hydraulic testing as the input data. The input data are then processed by MDS and IDW to generate a spatial distribution map of the hydraulic properties, which can be used to 2 infer the preferential flow path in the rock mass. The reliability of this novel method was validated through numerical experiments using several continuum models with different hydrogeological structures, and the applicability of the developed method to the actual field was verified through in-situ experiments.

Characteristics of climate change and its relationship with land use/cover change in Yunnan Province, China

“…Following common practice (e.g. Goovaerts, 2000;Mito et al, 2011;Shi et al, 2016aShi et al, , 2017b, this study sets the value to be 2, and the IDW method turns into the inverse distance squared method.…”

Spatial distribution of monthly potential evaporation over mountainous regions: case of the Lhasa River basin, China

“…The power β The power β in equation (10) is a key parameter in computing spatially-distributed meteorological variables, and is normally set to be 1 or 2 (e.g. Gotway et al 1996, Nalder and Wein 1998, Mito et al 2011. However, Husar and Falke (1996) indicated that a smoother result for integration can be obtained with a higher power value and Gotway et al (1996) proved that the accuracy of the IDW method tended to increase along with the power increase.…”

Evaluation of the gridded CRU TS precipitation dataset with the point raingauge records over the Three-River Headwaters Region

“…where N is the number of used meteorological stations, P p is the interpolated value at the place of interest, P i is the value at the i-th given station, D i is the distance from the i-th given station to the place of interest, and β is the power of D i . Following common practice (e.g., Goovaerts, 2000;Mito et al, 2011;Shi et al, 2016aShi et al, , 2017, this study adopted the value β to be 2, and the IDW method turns into the so-called inverse distance squared method.…”