Boring Point Layout Using Kriging for Ground Model Uncertainty
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Solution Overview
Problem
Current digital ground modeling methods lack the ability to accurately determine uncertainty ratios in soil conditions, leading to potential risks, high costs, and uncertainties in infrastructure projects due to the use of simple mathematical functions and deterministic interpolation methods that fail to account for spatial correlations.
Innovation Solution
The method employs geostatistical interpolation using kriging to determine the interaction between geological exploration data and identify uncertainties in digital ground models, allowing for the determination of optimal boring points and minimizing risks and costs through virtual boring activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional geostatistical interpolation methods are used to determine boring points, then the method is simple and easy to implement, but the determination of boring points is not accurate enough and cannot fully reflect the spatial distribution characteristics of rock mass quality
Solution Approach 1:
The patent combines multiple interpolation methods (inverse distance weighting, kriging, and radial basis function) into a composite adaptive interpolation framework. This composite approach leverages the strengths of each individual method while compensating for their weaknesses, achieving higher accuracy in determining boring points without excessively increasing implementation complexity.
Solution Approach 2:
The patent dynamically adjusts interpolation parameters based on local data characteristics and spatial variability. By changing parameters adaptively rather than using fixed values, the method achieves higher precision in boring point determination while maintaining reasonable computational efficiency through localized parameter optimization.
2Measurement precision
If the number of boring points is increased to improve rock mass quality assessment accuracy, then the assessment precision is improved, but the drilling cost and time consumption increase
Solution Approach 1:
The patent performs preliminary adaptive interpolation analysis using existing borehole data to predict optimal boring point locations before actual drilling. This preliminary action identifies high-value areas where additional boring points will most improve assessment accuracy, avoiding unnecessary drilling in areas where data is already sufficient and thus maintaining productivity while improving precision.
Solution Approach 2:
The system uses the existing borehole data itself to guide the placement of new boring points through adaptive interpolation. The data automatically identifies gaps and areas of high variability, serving as its own guide for optimal sampling locations, thereby improving assessment accuracy without requiring external expert intervention or excessive drilling.
3Adaptability or versatility
If traditional interpolation methods are used, then the computational speed is fast, but the ability to reflect local spatial variability of rock mass quality is insufficient
Solution Approach 1:
The patent divides the study area into multiple zones based on local data characteristics and spatial variability patterns. Each zone is processed with appropriate interpolation parameters and methods tailored to its specific characteristics. This segmentation enables the system to capture local spatial variability accurately while managing computational time by focusing detailed analysis only where needed rather than uniformly across the entire area.
Data Source
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AI summary
The invention relates to the method of determining the boring points using the geostatistical interpolation method, which recognizes the interaction between geological exploration data (soil types such as sand, clay, stone, etc.) and also has the ability to determine the uncertainties in digitally generated ground models.