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Ground surface deformation field anomaly detection method based on neural network

A neural network algorithm and neural network technology are applied in the field of abnormal detection of surface deformation field based on neural network, to achieve the effect of reducing processing time, reducing detection processing time, and high detection accuracy

Pending Publication Date: 2022-08-05
CHONGQING INST OF GEOLOGY & MINERAL RESOURCES +1
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AI Technical Summary

Problems solved by technology

These human-intensive steps have become the bottleneck for fast and reliable utilization of geodetic data for long-term monitoring and SAR data event response

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  • Ground surface deformation field anomaly detection method based on neural network
  • Ground surface deformation field anomaly detection method based on neural network
  • Ground surface deformation field anomaly detection method based on neural network

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Embodiment Construction

[0031] The following is further described in detail by specific embodiments:

[0032] In the present invention, S1 represents step 1, S101 represents step 01 in S1, S102 represents step 02 in S1, and so on.

[0033] The specific implementation process is as follows:

[0034] S1: select algorithms and methods;

[0035] Using a state-of-the-art random forest algorithm, and an improved neural network approach, the updated landslide inventory map and CSK dataset generated by the Landslide Detection Integrated System (LADIS) were used as input to the analytical model, and the output dataset was labeled as "Signal / Anomaly or noisy / no anomaly" data composition for training random forests and neural networks. figure 1 Data processing workflows are provided.

[0036] S2: Select InSAR time series analysis;

[0037] SAR is a coherent active sensor operating in the microwave band that utilizes the relative motion between the antenna and the target to obtain finer spatial resolution i...

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Abstract

The invention relates to the field of landslide disaster prediction, and discloses an earth surface deformation field anomaly detection method based on a neural network. The method is based on a supervised classification method using a combination of sight multi-time and multi-geometric interferometric synthetic aperture radar (InSAR) time sequence displacement. A training data set is created that has been compared to independently validated data and currently most advanced classification techniques. According to the method, a full-automatic anomaly detection system using an InSAR surface deformation time sequence is created. Compared with InSAR time sequence processing, the method enables the processing time after abnormity identification to be negligible. By displaying how the proposed neural network system provides an accurate and temporarily feasible solution, compared with a radio frequency method and an analysis model, the abnormal time sequence detection processing time is greatly reduced.

Description

technical field [0001] The invention relates to the field of landslide disaster prediction, in particular to a method for abnormal detection of a surface deformation field based on a neural network. Background technique [0002] Synthetic Aperture Radar (SAR)-based geodetic imaging has revolutionized geoscientific research in disciplines such as the solid Earth, ecosystems, and the cryosphere. However, the ability to effectively utilize SAR data for research, long-term monitoring of extensive spatial areas of interest (AOIs), and rapid disaster response is limited due to the complexity of end-to-end process processing, data size, and latency. For example, barriers to emergency response include the lack of automated data triggers from forecasting, the need for specialized processing parameters that currently rely on expert intervention. Decision support is generally most useful when it is generated quickly and has simplified information (eg, damaged or not damaged or acceler...

Claims

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Application Information

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IPC IPC(8): G01S13/90G01S7/41G06K9/62G06N3/04G06N3/08
CPCG01S13/9023G01S7/417G06N3/08G06N3/045G06F18/214
Inventor 梁丹陈立川杨海清徐洪曾亮张毅戴雄辉周春来
Owner CHONGQING INST OF GEOLOGY & MINERAL RESOURCES
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