## 1 Introduction  
On 4 October 2023, a catastrophic glacial lake dam failure occurred at South Lhonak Lake in Sikkim, triggering a flash flood in the Testa Valley and causing extensive damage in northern Sikkim. The flood resulted in significant casualties, destruction of downstream infrastructure including bridges and roads, and widespread damage to residential properties. This disaster had a profound impact on various towns and infrastructure across Sikkim, with Chungtang Town being the worst affected, where over 80% of the area suffered damage. Glacial lakes, formed by depressions carved out by glaciers (i.e., cirques) or moraines blocking water in ice valleys, are typically surrounded by unstable glacial moraines consisting of loose glacial till. The moraine dams of these lakes are inherently unstable, and dam break events occur under specific conditions, resulting in the sudden discharge of water and debris known as glacial lake outburst floods (GLOFs). Glacial lakes serve as sensitive indicators of climate change in alpine and cold regions and represent a potential source of disasters triggering mountain outburst floods or debris flows. Most glacial lakes are formed during glacier retreat [1, 2]. Due to the serious consequences of GLOFs, there is an urgent need to gain a deeper understanding of the spatiotemporal evolution characteristics and triggering factors of GLOFs. In recent years, analysis of triggering factors and discussion of the impact of moraine collapse on GLOF disasters have been on the rise [3; 4]. Interferometric Synthetic Aperture Radar (InSAR) possesses all-day and all-weather monitoring capabilities, enabling the rapid acquisition of regional deformation information and facilitating regular and continuous observations. Consequently, it has emerged as a vital tool for monitoring the stability of surrounding slopes of glacial lakes. Numerous scholars have utilized D-InSAR, Permanent Scatter Interferometry (PSI) [5], Small Baseline Subsets InSAR (SBAS-InSAR) [6; 7], offset tracking methods, and pixel offset (PO) tracking for small baseline subsets (PO-SBAS) to acquire surface deformation results around glacial lakes and assess their stability [8; 9; 10; 11; 12]. [PERSON] et al. (2022) provides a systematic analysis of the contributing factors that lead to GLOF disasters through the utilization of meteorological data, SAR, and optical images [10]. Currently, the rapid advancement of SAR platforms has resulted in explosive data growth [13], and the field of InSAR image processing is shifting towards big data analysis. The influx of remote sensing big data presents both opportunities and challenges in data processing, management, and analysis [14]. Processing massive InSAR data within a limited time constraint poses a significant challenge [15]. The emergence and evolution of high-performance computing (HPC) presents a novel approach to tackling the challenge of large-scale InSAR processing. As a quintessential technology in HPC, graphics processing units (GPUs) have attracted increasing attention from researchers owing to their potent parallel processing capabilities, high memory bandwidth, low power consumption, and excellent performance in handling computationally intensive problems [13]. Cloud computing can more effectively tackle these challenges by offering on-demand access to a shared pool of computing resources, allowing users to leverage the capabilities of distributed computing and storage without requiring substantial upfront investments in hardware and software infrastructure [16; 17; 18]. We employed the AI Earth cloud platform to develop an InSAR processing system, which integrated the platform's massive SAR remote sensing data and the advantages of cloud computing with our automated, high-performance InSAR processing algorithms [19], aiming at measuring surface deformation around glacial lakes efficiently. This paper is organized as follows: Section 2 introduces the regional overview and disaster situation of the glacial lake studied in this paper.