Multi-Stage Spatial Sampling for DNAPL Mass Discharge Uncertainty
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Solution Overview
Problem
Current methods for estimating mass discharge uncertainty in DNAPL source zones face challenges due to high sampling density requirements and limitations in geostatistical approaches, which are inefficient and unreliable, especially when dealing with spatially distributed processes and non-representative samples.
Innovation Solution
A multi-stage spatial sampling strategy is implemented, using Multiple Criteria Decision Making (MCDM) theory to select optimal sampling locations and determine minimal sampling density, incorporating criteria for hot spot delineation and domain coverage, allowing for adaptive weighting and real-time data-driven decision making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geostatistical approach with one-stage sampling design is used, then mass discharge uncertainty can be quantified, but sampling density must be as high as 6-7% to achieve accurate model
Solution Approach 1:
The sampling process is divided into multiple stages where samples are collected iteratively. In each stage, new samples are selected based on the uncertainty analysis from previous stages, allowing the sampling density to be reduced while maintaining model accuracy through adaptive, targeted sampling.
Solution Approach 2:
The sampling design transitions from a static one-stage approach to a dynamic multi-stage process. The sampling locations and density are adjusted adaptively in each stage based on emerging data and uncertainty patterns, enabling efficient sampling with lower overall density while capturing critical spatial variability.
2Ease of manufacture
If regular sampling pattern (rectangular) is used, then sampling design is simple, but sampling density must be very high to capture scattered small hot spots
Solution Approach 1:
Initial sampling is performed to establish a baseline dataset and preliminary uncertainty model. This preliminary action provides the foundation for subsequent stages where sampling is optimized based on identified hot spots and uncertainty patterns, avoiding the need for uniformly high sampling density from the start.
Solution Approach 2:
Each sampling stage provides feedback to the next stage through updated uncertainty analysis. The uncertainty model from previous stages guides the selection of new sampling locations, creating a feedback loop that concentrates sampling effort where most needed while reducing density in well-characterized areas.
3Measurement precision
If high sampling density (6-7%) is implemented, then accurate mass discharge model is achieved, but cost and time requirements increase significantly
Solution Approach 1:
Sampling is conducted in periodic stages rather than all at once. Each stage collects a subset of samples, analyzes uncertainty, and plans the next sampling round. This periodic approach spreads the time and cost burden while achieving the same ultimate accuracy through cumulative learning and adaptive optimization.
Data Source
AI summary
The present invention provides a method for implementing a multi-stage spatial sampling strategy to select optimal sampling locations and determine an optimal sampling density for a quantification of mass discharge uncertainty in a field. The present invention also provides systems and methods for estimating probability of a mass discharge in a control plane.