Deformation Monitoring and Prediction of Open-pit Mine Slope Based on GBInSAR Technology and LSTM Neural Network

Abstract. High-precision and real-time deformation monitoring of open-pit mine slopes provides reliable data support for slope safety early warning and governance, which is directly related to the production efficiency of open-pit mines and the safety of personnel and property. Compared with traditional measurement technologies, Ground-Based Synthetic Aperture Radar Interferometry (GBInSAR), as a novel deformation monitoring technology developed in the past two decades, possesses significant advantages including allweather operation, all-time coverage, large-scale monitoring, non-contact measurement, high precision, and real-time observation. It has become one of the core technical equipment for monitoring dangerous slopes in open-pit mines and is widely applied in open-pit mine slope safety monitoring scenarios. Against this background, how to effectively integrate ground-based InSAR data with advanced prediction models to enhance the early prediction capability of slope deformation has become a crucial research direction in this field. To address this issue, this paper proposes a GBInSAR time-series data processing method based on the Long Short-Term Memory (LSTM) model. Firstly, the initial deformation information of a slope is extracted from the pre-monitoring data of the IBIS system, and then an LSTM-based slope deformation prediction model is constructed to achieve short-term accurate prediction of future deformation trends. By organically combining the LSTM model with ground-based InSAR data, this paper deeply explores the temporal evolution characteristics of slope deformation and establishes a slope deformation prediction model. This study aims to explore the application of ground-based InSAR in slope deformation monitoring based on the LSTM model; by constructing a slope deformation prediction model and a risk early warning mechanism, it provides effective technical support and decision-making basis for the safety management of open-pit mine slopes.

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Publication Details

Journal
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
Published
2026-01-27
DOI
https://doi.org/10.5194/isprs-annals-x-3-w3-2025-173-2026
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
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Deformation Monitoring and Prediction of Open-pit Mine Slope Based on GBInSAR Technology and LSTM Neural Network

Lei Pang, Yanhui Qu, Keliang Ding, Ya Shu et al.
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Deformation Monitoring and Prediction of Open-pit Mine Slope Based on GBInSAR Technology and LSTM Neural Network

Lei Pang, Yanhui Qu, Keliang Ding, Ya Shu, Miao Li
article en

Abstract

Abstract. High-precision and real-time deformation monitoring of open-pit mine slopes provides reliable data support for slope safety early warning and governance, which is directly related to the production efficiency of open-pit mines and the safety of personnel and property. Compared with traditional measurement technologies, Ground-Based Synthetic Aperture Radar Interferometry (GBInSAR), as a novel deformation monitoring technology developed in the past two decades, possesses significant advantages including allweather operation, all-time coverage, large-scale monitoring, non-contact measurement, high precision, and real-time observation. It has become one of the core technical equipment for monitoring dangerous slopes in open-pit mines and is widely applied in open-pit mine slope safety monitoring scenarios. Against this background, how to effectively integrate ground-based InSAR data with advanced prediction models to enhance the early prediction capability of slope deformation has become a crucial research direction in this field. To address this issue, this paper proposes a GBInSAR time-series data processing method based on the Long Short-Term Memory (LSTM) model. Firstly, the initial deformation information of a slope is extracted from the pre-monitoring data of the IBIS system, and then an LSTM-based slope deformation prediction model is constructed to achieve short-term accurate prediction of future deformation trends. By organically combining the LSTM model with ground-based InSAR data, this paper deeply explores the temporal evolution characteristics of slope deformation and establishes a slope deformation prediction model. This study aims to explore the application of ground-based InSAR in slope deformation monitoring based on the LSTM model; by constructing a slope deformation prediction model and a risk early warning mechanism, it provides effective technical support and decision-making basis for the safety management of open-pit mine slopes.

ISPRS annals of the photogrammetry, remote sensing and spatial information sciencesVol. X-3/W3-2025
Beijing University of Civil Engineering and Architecture (CN)
Openalex Percentile: Top 80%
Synthetic Aperture Radar (SAR) Applications and Techniques
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