Mine Water Inrush Point Detection with AI and Simulated Annealing
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Solution Overview
Problem
Existing methods for identifying water inrush points in mines lack accuracy and reliability, as they rely on regional reports or water chemistry analysis, and cannot directly determine the coordinates or key model parameters for simulation and prediction, leading to ineffective disaster prevention and control.
Innovation Solution
An artificial intelligence-based method using a numerical groundwater-flow model, deep learning, and simulated annealing algorithm to identify water inrush points and model parameters by constructing a nonlinear optimization model, integrating deep convolutional neural networks and Latin hypercube sampling for efficient parameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If water chemistry analysis and cluster analysis algorithms are used to identify water inrush sources, then water source identification can be achieved, but the coordinates of water inrush points cannot be located
Solution Approach 1:
The patent introduces an intermediary optimization model that connects water chemistry analysis results with spatial location determination. The optimization model uses water quality characteristics as input parameters and iteratively searches for the spatial coordinates that best match the observed water chemistry data, thereby bridging the gap between qualitative water source identification and quantitative location positioning.
Solution Approach 2:
The patent transforms the water inrush identification problem from static water chemistry analysis to a dynamic parameter optimization process. By changing the parameter space to include both water quality parameters and spatial coordinates, and using iterative optimization algorithms, the system simultaneously determines both the water source characteristics and the precise location coordinates.
2Ease of manufacture
If numerical model parameters are obtained through existing measurement means, then some parameters can be measured, but many parameters cannot be directly obtained
Solution Approach 1:
The patent applies inverse modeling techniques where instead of directly measuring model parameters through field measurements, the system inverts the problem by using observable data (water levels, flow rates) to deduce the unobservable parameters (permeability, storage coefficients). This inversion approach allows obtaining parameters that cannot be directly measured while maintaining simulation reliability.
Solution Approach 2:
The patent transforms difficult-to-measure physical parameters into observable hydrological responses through the numerical model. By changing the measurement approach from direct parameter measurement to indirect inference through model simulation and data matching, the system can obtain reliable values for parameters like hydraulic conductivity and storativity that are otherwise inaccessible.
3Measurement precision
If traditional optimization methods are used for inverse simulation, then model parameters can be identified, but computational efficiency is insufficient
Solution Approach 1:
The patent replaces traditional mechanical optimization algorithms with deep learning neural networks. The neural network is trained on synthetic data generated from the numerical model and then used to rapidly predict model parameters from field observations, substituting the slow iterative optimization process with a fast inference process that maintains accuracy while dramatically improving computational efficiency.
Solution Approach 2:
The patent performs preliminary training of the deep learning model using synthetic data generated from comprehensive numerical simulations. This preliminary action creates a pre-trained model that has already learned the complex relationships between hydrological responses and model parameters, enabling rapid parameter identification in field applications without requiring time-consuming iterative optimization during actual rescue operations.
4Productivity
If deep learning and simulated annealing algorithm are combined for parameter optimization, then optimization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent merges deep learning and simulated annealing algorithms into a unified hybrid optimization framework. The deep learning component provides rapid initial parameter estimates and the simulated annealing component performs local refinement, combining the advantages of both methods to achieve superior optimization efficiency while managing system complexity through modular integration.
Data Source
AI summary
The present disclosure provides an artificial intelligence-based method for enhancing mine safety by identifying and predicting locations of water inrush points in a mine, including the following steps: S1: constructing a numerical model to determine priori information of parameters to be recognized based on observation data, including coordinates of locations of water inrush points; S2: generating a training sample dataset and a test sample dataset of an alternative model based on the numerical model and the priori information of the parameters; S3: constructing and training a neural network of the alternative model; S4: testing an accuracy of the alternative model; and S5: performing a simulated annealing algorithm to identify the locations of water inrush points and simulation model parameters.