Groundwater NAPL Image Assessment for Early Contamination Warning
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Solution Overview
Problem
Existing methods struggle to provide precise and timely assessment of non-aqueous phase liquid (NAPL) contamination levels in groundwater due to factors like hydrophobicity, differential migration, and subsurface heterogeneity, complicating the monitoring and remediation of groundwater contamination.
Innovation Solution
A method and system utilizing image learning and deep learning to analyze water image data from monitoring wells, constructing a groundwater NAPL contamination database, and establishing a contamination level assessment model to determine NAPL types and concentrations, enabling early warning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to assess NAPL contamination in groundwater, then the assessment process is simple to implement, but the measurement precision and reliability are insufficient due to NAPLs' hydrophobicity, differential migration behaviors, and subsurface heterogeneity
Solution Approach 1:
The patent introduces image sensors as intermediary devices that capture visual information of groundwater and NAPL interfaces. These images serve as mediators between the complex subsurface environment and the assessment system, enabling indirect observation and analysis of contamination levels without direct contact with NAPLs, thus improving measurement precision while managing system complexity.
Solution Approach 2:
The patent replaces traditional mechanical sampling and laboratory analysis methods with an optical-based image sensing system. By substituting physical sampling mechanisms with optical field-based detection, the system achieves higher precision in assessing contamination levels while reducing the complexity of physical sampling operations and laboratory procedures.
2Reliability
If comprehensive monitoring data collection is performed to account for subsurface heterogeneity and differential migration, then the assessment reliability improves, but the loss of time and productivity decrease due to extensive data collection and processing requirements
Solution Approach 1:
The patent implements preliminary actions by pre-processing images through denoising, enhancement, and partitioning before analysis. The system also performs preliminary feature extraction and database construction with hydrogeological information, preparing the data structure in advance to reduce processing time during actual contamination assessment while maintaining comprehensive monitoring for reliability.
Solution Approach 2:
The patent employs dynamic adaptive processing where the system adjusts its data collection and processing intensity based on contamination risk levels and subsurface conditions. The deep learning model dynamically adapts to different contamination scenarios, optimizing the balance between comprehensive monitoring for reliability and processing speed to minimize time loss.
3Measurement precision
If image processing and deep learning models are implemented to achieve precise contamination assessment, then measurement precision and reliability improve, but the device complexity and manufacturing complexity increase
Solution Approach 1:
The patent designs a multi-functional integrated system where image sensors serve multiple purposes: capturing groundwater clarity, detecting NAPL interfaces, and providing visual data for deep learning analysis. The deep learning model performs multiple functions including feature extraction, contamination classification, and concentration estimation. This universality improves measurement precision while consolidating multiple functions into a single system, partially offsetting manufacturing complexity.
Solution Approach 2:
The patent uses image copies of the actual groundwater environment as proxies for direct measurement. By creating digital replicas through photography and processing these copies through deep learning models, the system achieves precise contamination assessment without requiring complex physical measurement devices, thereby improving precision while managing manufacturing ease through software-based solutions.
Data Source
AI summary
A method and system for assessing contamination level of non-aqueous phase liquids (NAPL) in groundwater based on image learning are provided herein. A water image data of groundwater monitoring wells is acquired, processed, and extracted to give image features. Types and concentrations of the NAPLs in groundwater are obtained based on monitoring data of groundwater monitoring wells collected within a preset period, and matched with the image features of the water image data to construct a feature dataset. A groundwater NAPL contamination database is established based on the feature dataset in combination with local hydrogeological information. A groundwater NAPL contamination level assessment model is constructed based on deep learning, and trained by utilizing the groundwater NAPL contamination database. The NAPL contamination level of water in the groundwater monitoring wells is determined to issue the contamination early warning.