Mining Subsidence Area Identification: An Image Segmentation-Driven Approach

Based on Patent Research | CN-115331096-A (2022)

Accurately identifying mining subsidence areas is crucial for safety and land management. Current methods using Differential Interferometric Synthetic Aperture Radar (DInSAR) data lack precision. This is especially true in areas with varying scales. Image segmentation, a computer vision task, offers a solution. Specifically, a U-Net model classifies each pixel in a phase diagram to identify these areas. This improves accuracy and reduces risks in mining operations by precisely locating ground deformation.

SOLUTION OVERVIEW

Manual Analysis Evolution: AI Segmentation

For Mining and Quarrying professionals, image segmentation offers a precise solution for identifying mining subsidence areas. This technology uses a U-Net model to analyze Differential Interferometric Synthetic Aperture Radar (DInSAR) data. The model classifies each pixel in a phase diagram, pinpointing ground deformation with accuracy. This detailed analysis allows for informed decision-making regarding safety and land management.

This technology integrates with existing monitoring systems, automating the detection process. Like a geologist using a detailed map to pinpoint valuable mineral deposits, image segmentation provides a similar level of detail for identifying subsidence areas. The improved accuracy enhances safety for workers and communities near mining operations. This leads to optimized resource allocation for ground stabilization efforts and supports proactive risk management in mining operations.

HOW IT WORKS

Subsidence Discovery in DInSAR Imagery

Capturing Initial Ground Data

Beginning with DInSAR data, the system analyzes satellite imagery to identify subtle changes in ground elevation. These changes are visualized as a phase diagram, highlighting potential areas of ground deformation. This initial step provides a comprehensive overview of the terrain, focusing on areas that require further investigation for mining subsidence.

Analyzing Phase Diagram with AI

The phase diagram is then fed into a pre-trained U-Net model, a type of neural network designed for image segmentation. This model intelligently classifies each pixel in the diagram, distinguishing between stable ground and areas indicating subsidence. This classification process transforms the raw data into a clear visual representation of potential risk zones.

Identifying Subsidence Areas Precisely

Finally, the segmented image is processed to delineate and highlight mining subsidence areas. This step transforms the pixel classifications into actionable information, marking the boundaries of ground deformation. This allows mining and quarrying professionals to precisely locate and assess the risk associated with these areas, enabling proactive safety measures and resource allocation.

Potential Benefits

Improved Accuracy and Safety

The AI system provides more precise identification of subsidence areas, reducing the risk of errors and improving the reliability of monitoring data, a critical advantage for safety in mining operations.

Increased Efficiency and Automation

By automating the detection of ground deformation, the AI reduces the need for manual inspections. This saves time and resources, allowing mining professionals to focus on other critical tasks.

Enhanced Data for Decision-Making

The detailed analysis of DInSAR data offers a more comprehensive understanding of ground stability. This enables better-informed decisions regarding resource allocation and risk management strategies.

Optimized Resource Allocation

Pinpointing unstable areas with greater accuracy allows for targeted stabilization efforts. This minimizes unnecessary interventions and optimizes the use of resources for ground stabilization, ultimately reducing costs.

Implementation

1 Data Acquisition Setup. Acquire DInSAR data covering the mining area. Ensure sufficient resolution for accurate analysis.

2 Software Installation. Establish a processing environment with U-Net capabilities. Install necessary software and libraries.

3 Model Configuration. Configure the U-Net model with DInSAR data inputs. Optimize parameters for subsidence area detection.

4 Run Segmentation Process. Process DInSAR data using the configured U-Net model. Generate segmented images highlighting subsidence areas.

5 System Integration. Integrate segmented data into monitoring systems. Visualize subsidence areas on geographical information systems (GIS).

6 Ongoing Monitoring. Regularly update DInSAR data for continuous monitoring. Retrain the model with new data to improve accuracy.

Source: Analysis based on Patent CN-115331096-A "Mining subsidence area identification method, system, storage medium and electronic equipment" (Filed: November 2022).