Intelligent Early Warning and Decision Platform for Long-Term Ground Subsidence in High-Density Areas for Sustainable Urban Development

By inergency On Mar 25, 2024

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The development of the intelligent early warning and decision platform mostly occurred on a computer loaded with the Windows 7 operating system. The released Android Package (APK) file version is generated through Android Studio and runs on the Android platform. The system version is higher than Android 4.4.

2.1. Software System Functions and Modules

2.1.1. Software System Functions

The functions of the intelligent early warning and decision platform system primarily include the early warning indicator input of LTGS, early warning indicator update of LTGS, early warning indicator weight setting of LTGS, and police judgment plan, as shown in Figure 1.

2.1.2. Software System Modules

The entire system of the intelligent early warning and decision platform consists of the database, data storage service, data interface, alarm–judgment–plan, early warning indicator weight setting, and the core modules of LTGS, as shown in Figure 2.

The primary functions of each module in the software system are described as follows.

1.: Database module of LTGS

The database module of LTGS is implemented using a lightweight SQLite database. This database primarily stores monitoring data related to regional ground subsidence, subway operation, and engineering disturbance.

2.: Data storage service module of LTGS

The data storage service module of LTGS is achieved by defining a service category. It exhibits the functions of adding and querying LTGS data for the data interface, alarm–judgment–plan, and early warning indicator weight setting modules of LTGS. This module stores LTGS data in the database module through SQL statements.

3.: Data interface module of LTGS

The data interface module of LTGS is achieved via DataInterfaceActivity, which includes the adding and updating of modules for LTGS monitoring locations. The updating module for LTGS can be accessed by clicking on the monitoring location number, allowing users to modify or delete the data associated with that location. In addition, data for the adding module of the LTGS monitoring point can be entered through the button “Add Subsidence data.”

4.: Alarm–judgment–plan module of LTGS

The alarm–judgment–plan module of LTGS is achieved via DataInterfaceActivity. The system interface displays three parameters: the monitoring location number, early warning value, and early warning levels of LTGS. This interface allows users to promptly assess the comprehensive early warning information for each location.

5.: Early warning indicator weight setting module of LTGS

The early warning indicator weight setting module of LTGS is achieved via the weight setting of early warning indicators of LTGS, including maximum ground subsidence, maximum long-term subsidence rate, geological conditions, maximum tunnel subsidence, tunnel leakage degree, surface building intensity, and tunnel construction disturbance level.

6.: Core module of LTGS

The entire system of the intelligent early warning and decision platform consists of the database module of LTGS. Four listeners are bound to each controller to transfer network data to the data storage service, data interface, and alarm–judgment–plan modules of LTGS. Users also have access to the data interface and early warning indicator weight setting modules of LTGS.

2.2. Overall Framework Structure of Software

Seven interfaces are designed to realize communication between users and the system, mainly including core interface, long-term subsidence early warning interface, early warning index weight setting interface, monitoring number list interface, adding subsidence data interface, updating subsidence data interface, and long-term subsidence help interface.

1.: Core interface

The core interface includes four button controls, namely “early warning of long-term ground Subsidence during metro operation”, “long-term ground Subsidence data during metro operation”, “long-term ground Subsidence setting during metro operation”, and “long-term ground Subsidence helps during metro operation”.

2.: Long-term ground subsidence warning interface

The list of long-term subsidence monitoring numbers, early warning values, and grades are displayed at the top of the interface. After the user clicks the “Start Alert” button at the bottom of the interface, the system will automatically alert each long-term subsidence monitoring point.

3.: Alert indicator weight setting interface

The early warning indicator weight setting interface is mainly used to set the indicator weight. A total of seven early warning indicators are set, including the maximum ground subsidence, the maximum long-term subsidence rate, geological conditions, the maximum tunnel subsidence, the degree of tunnel leakage, the degree of surface building density, and the degree of tunnel construction disturbance. Each early warning indicator corresponds to a text box. At the bottom of the interface, four buttons are available, namely “Update”, “Return”, “Default”, and “Alert”.

4.: Monitoring number list interface

This interface mainly displays the list number of long-term ground subsidence monitoring points. Users can access it by clicking the “Long-term ground Subsidence data during metro operation” button in the core interface and then selecting the “Add Subsidence data” button at the bottom of the interface to enter the list interface for adding monitoring points. This list interface displays the data of multiple monitoring points. To delete a monitoring point in the monitoring number list interface, users can press and hold the monitoring point number to be deleted, and the “Delete” button will appear. Clicking on this button will delete the selected monitoring point number.

5.: Add subsidence data interface

This interface displays the data of a certain long-term subsidence monitoring point. On the left side of the interface, the long-term subsidence early warning indicator names are listed, including the maximum ground subsidence, the maximum long-term subsidence rate, geological conditions, the maximum tunnel subsidence, the degree of tunnel leakage, the density of surface buildings, and the degree of tunnel construction disturbance. On the right side of the interface, corresponding text boxes display the values of the long-term subsidence early warning indicators. Users are required to enter the indicator values in the text boxes based on the actual measurements of the long-term land subsidence warning indicators.

6.: Update subsidence data interface

In the monitoring number list interface, click on the existing monitoring point number to enter the subsidence update interface. The subsidence update interface closely resembles the subsidence addition interface, with the key distinction being that the long-term subsidence data stored in the database are now displayed in the text box on the right side of the update interface. Users can modify any data of long-term land subsidence in the interface according to their own needs.

7.: Long-term ground subsidence help interface

The long-term ground subsidence help interface displays the input and modification of data within the intelligent early warning decision platform. It provides information on the early warning functionalities of the intelligent early warning decision platform and details the setting and principles governing its operation.

2.3. Data Processing

The atmospheric effect is identified as one of the most significant sources of error in this work. To mitigate these errors, a combination of GPS technology for precise coordinates and multi-scene image data is employed. In addition, azimuth filtering processing is applied to filter out non-overlapping parts of the main and auxiliary image spectra, while retaining their common Doppler spectral parts to enhance image coherence. The use of satellite precision orbit data and precise orbit ephemerides data is also implemented to eliminate systemic errors induced by orbital inaccuracies. These various data processing techniques collectively contribute to ensuring the integrity and purity of the collected data.

In this work, the initial phase of data processing involves a meticulous focus on the processing of satellite image data. At the master–slave image file polarization mode selection stage, master and slave images with the appropriate polarization are selectively chosen to form GIS Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data pairs. During this selection process, it is imperative to input the reference Digital Elevation Model (DEM) file and the reference ellipsoid for subsequent alignment and terrain correction.

The interferogram is derived by computing the phase difference between the master and slave images, with the stripe variations in the interferogram serving as indicators of surface deformation. Parameters such as distance-oriented view number, azimuth-oriented view number, and mapping resolution must be meticulously configured during interferogram generation, as these settings significantly impact the interferogram’s quality and the accuracy of deformation extraction.

Post-interferogram generation, filtering and coherence calculation become imperative. The primary objective of filtering is the removal of noise and interference signals from the interferogram, enhancing the precision of deformation extraction. Coherence calculation is employed to assess the interferogram’s quality and ascertain the reliability of deformation extraction. Various filtering methods and coherence calculation approaches can be selectively applied during these stages.

Subsequent to filtering and coherence calculation, essential processes including phase untangling, orbit refinement, and re-flattening are executed to optimize the sharpness of the interferogram and derive the deformation values within the study area. The outcomes of deformation extraction are typically presented in the form of a deformation map, providing a visual depiction of surface deformation distribution and magnitude. Additionally, the deformation results can be subjected to in-depth analysis and interpretation through integration with other geographic information data.

2.4. Implementation of DInSAR-GPS-GIS Technology

The data used is from Sentinel-series satellite data (https://scihub.copernicus.eu/ accessed on 10 May 2017). Based on the ENVI-SARscape platform, the process commenced with the utilization of multi-view data, which formed the foundational dataset for subsequent deformation analysis. This analysis was facilitated through an environmental satellite imagery platform and digital elevation model data. Subsequently, the study area underwent deformation monitoring using radar interferometric data. This data was calibrated and localized with the support of high-precision orbital data and GPS control points.

The selection of appropriate Ground Control Points (GCPs) emerged as a crucial step in the processing pipeline, ensuring the accuracy and reliability of the data. The GCPs played a pivotal role in the calibration and localization process. Coupled with the SAR SLC data pairs post-GIS precise position processing and radar interferometric processing, deformation maps with coordinated color schemes were generated in the DInSAR processing platform. These maps provided a visual representation of surface deformation within the study area.

In the final stages, utilizing tools such as ArcMap and other GIS software (pro2.0), subsidence deformation values were extracted and processed. This comprehensive approach offers essential decision support for various applications, including geological disaster early warning and urban planning. The entire processing workflow highlights the integrated application of DInSAR-GPS-GIS technology, delivering an efficient and accurate technical framework for surface deformation monitoring.

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