Improved inverse distance weighting method application considering spatial autocorrelation in 3D geological modeling

Wang

et al. 2022

Sci Rep

The development and use of intelligent drilling rigs make it available to obtain accurate lithology data of blast drilling. In order to make full use of drilling data to improve blasting efficiency, the following research was carried out. First, a database is established to manage and store the blast hole data recognized by the intelligent drill. Secondly, the blast hole lithology data is taken as a sample, and the inverse distance square method is used to interpolate the blasting range's solid elements to generate a three-dimensional solid model of the blasting rock mass. Afterward, the blasting range polygon and stope triangle grid are used successively in the solid model to obtain the cut 3D solid model of the blasting rock mass; finally, the blast hole charge is calculated based on the cut 3D solid model of the blasting rock. The C++ programming language is used to realize all the blast hole charge amount processes based on the three-dimensional solid model of the blasting rock mass. With the application example of No. 918 bench blasting of Shengli Open-pit Coal Mine in Xilinhot, Inner Mongolia, the blast hole charge amount in the blasting area is calculated and compared with the results of single hole rock property calculation, the results show that the blast hole charge calculated by three-dimensional rock mass model can be effectively reduced.

Section: Introductionmentioning

confidence: 99%

Calculation of blast hole charge amount based on three-dimensional solid model of blasting rock mass

Wang

et al. 2022

Sci Rep

“…Modified IDW versions have also been proposed in [14][15][16], some of them based on artificial intelligence methods [7,8], while other authors used remote sensing and GIS-based modeling for interpolating hydro-meteorological series [17,18]. It was shown that there is no best method for all the studied problems since that the modeling quality also depends on the series characteristics [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Precipitation Amount at Regional Scale Using a New Tool, Climate Analyzer

2021

Different methods are known for interpolating spatial data. Introduced a few years ago, the initial version of the Most Probable Precipitation Method (MPPM) proved to be a valuable competitor against the Thiessen Polygons Method, Inverse Distance Weighting and kriging for estimating the regional trend of precipitation series. Climate Analyzer, introduced here, is a user-friendly toolkit written in Matlab, which implements the initial and modified version of MPPM and new selection criteria of the series that participate in estimating the regional precipitation series. The software provides the graphical output of the estimated regional series, the modeling errors and the comparisons of the results for different segmentations of the time interval used in modeling. This article contains the description of Climate Analyzer, accompanied by a case study to exemplify its capabilities.

“…Nevertheless, no matter what kinds of contexts are being faced, enhancing the estimation accuracy and reliability is a common goal that most SI methods pursue, and so does the typical SI method—inverse distance weighting (IDW) 1 , 5 , 15 – 21 . In general, the interpolation accuracy of the conventional IDW or its variants could be improved by choosing a set of appropriate parameters such as the search model of local samples or observed data 3 , 22 – 24 , the type of distance metric 19 , 25 , 26 , and the exponent imposed on the distance 7 , 22 , 23 , 27 , 28 . 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.…”

Section: Introductionmentioning

confidence: 99%

An enhanced dual IDW method for high-quality geospatial interpolation

2021

Sci Rep

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.