Analysis of ground subsidence in coal mining area using SAR interferometry

Multi-Temporal Interferometric SAR (MT-InSAR) analysis technique can provide spatially continuous measurements of the ground deformation and monitoring caused by the movement of four segments in the Kendeng Fault, East Java. Most ground deformation in these segments is indicated by the dominance of local thrust movements, folds, and the seismic density of earthquakes. In this study, ground deformation detection was carried out using the Small Baseline Subset Algorithm in the MT-InSAR process with the tropospheric correction from GACOS to determine ground deformation during 2017-2020. The percentage of change in the deformation velocity value resulting from applying the GACOS correction is up to 33.8% compared to without the tropospheric correction. The most significant troposphere effect correction is the area around the Cepu segments, which are dominated by high elevation thrust and folds. The results of the mean LOS velocity around the southern segments of Cepu, Waru, and Surabaya based on the Sentinel 1A SAR represent the occurrence of land subsidence in the south part of the area around segments with values ranging from 3.0 mm/yr to 6.0 mm/yr. Thus, the deformation effect causes land subsidence in the area around the delineation of the northern fault segment up to -3.0 mm/yr.

Ground subsidence is a common geological phenomenon occurring in mining areas. As an important Chinese gold mine, Sanshandao Gold Mine has a mining history of 25 years, with remarkable ground subsidence deformation. Mining development, life security, property security and ecological protection all require comprehension of the ground subsidence characteristics and evolution in the mining area. In this study, the mining subsidence phenomenon of the Sanshandao Gold Mine was investigated and analyzed based on Persistent Scatterer Interferometry (PSI) and small baseline subset (SBAS). The SAR (synthetic aperture radar) images covering the study area were acquired by the Sentinel-1A satellite between 2018 and 2021; 54 images (between 22 February 2018 and 25 May 2021) were processed using the PSI technique and 24 images (between 11 April 2018 and 12 July 2021) were processed using the SBAS technique. In addition, GACOS (generic atmospheric correction online service) data were adopted to eliminate the atmospheric error in both kinds of data processing. The interferometric synthetic aperture radar (InSAR) results showed a basically consistent subsidence area and a similar subsidence pattern. Both InSAR results indicated that the maximum LOS (line of sight) subsidence velocity is about 49 mm/year. The main subsidence zone is situated in the main mining area, extending in the northwest and southeast directions. According to the subsidence displacement of several representative sites in the mining area, we found that the PSI result has a higher subsidence displacement value compared to the SBAS result. Mining activities were accompanied by ground subsidence in the mining area: the ground subsidence phenomenon is exacerbated by the increasing mining quantity. Temporally, the mining subsidence lags behind the increase in mining quantity by about three months. In summary, the mining area has varying degrees of ground subsidence, monitored by two reliable time-series InSAR techniques. Further study of the subsidence mechanism is necessary to forecast ground subsidence and instruct mining activities.

Abstract. Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.

Ili Lewotolo Volcano, a stratovolcano in East Nusa Tenggara, Indonesia, erupted on November 27, 2020, following eight years of dormancy, releasing significant ash and sulfur dioxide (SO₂) emissions. This study is the first to combine multi-temporal Interferometric Synthetic Aperture Radar (InSAR) and SO₂ imaging for monitoring volcanic activity, offering a comprehensive pre- and post-eruption analysis approach. The New Small Baseline Subset (NSBAS)-InSAR method, using Sentinel-1 SAR data (from November 2019 to November 2020), detected notable ground deformation, while the Tropospheric Monitoring Instrument (TROPOMI) sensor on Sentinel-5P (from January 2020 to December 2021) captured fluctuations in SO₂ emissions. InSAR time-series analysis revealed significant ground uplift, indicating a magma source around 2000 m depth, confirmed through parametric optimization using the Mogi model. Furthermore, SO₂ levels surged by 512 % during the eruption compared to pre- and post-eruption periods, demonstrating a clear correlation between ground deformation and gas emissions. This combined approach provides deeper insights into volcanic behaviour and underscores the value of combining remote sensing techniques to enhance hazard mitigation strategies, paving the way for an effective early warning system for monitoring volcanic activity. • First study combining NSBAS InSAR & Sentinel-5P for Ili Lewotolo volcano dynamics, showing potential for global application. • NSBAS InSAR shows a 35 mm/ year ground deformation produced by over-pressurized magma at around 2000 m depth. • SO2 emissions peaked at 1.4 DU post-eruption, declining below 0.4 DU by 2021's end.

The Digital Elevation Models (DEMs) are an important source of topographical data for many scientific and engineering applications. Where topographical data are unavailable, global coverage elevation data sets, typically DEMs from remotely sensed data, are the main sources of such information. Interferometric SAR (InSAR), is a useful method for low-cost, relatively precise and wide-coverage surface DEM generation. However, ground deformation should somehow be excluded from InSAR-based DEM generation. To identify surface deformation areas, the so-called Differential InSAR (DInSAR) is a commonly used method. In this paper, the authors propose a two-step DEM generation method: the ground deformation area detection using DInSAR technique and deformation area exclusion in InSAR DEM generation by detected mask.

Excessive groundwater exploitation in Sirjan plain, located in south of Iran, has caused severe land subsidence. The main purpose of this paper is to determine the hydrogeological parameters of the aquifer system in Sirjan plain. To this end, the groundwater flow was simulated in both steady and transient states using the United States Geological Survey Modular Three-Dimensional Finite-Difference Groundwater Flow Model, MODFLOW. The hydraulic conductivity parameter of the aquifer system was first estimated and calibrated in the steady state by simulating the hydraulic head at 63 piezometric wells. The specific yield and recharge parameters were then calibrated in the transient state by simulating the drawdown within a period of interest, i.e. 23 September 2004 to 22 September 2005. The main innovation of this paper, however, is to use the potential of Synthetic Aperture Radar (SAR) interferometry time-series analysis in the last step of the proposed algorithm. Small Baseline Subset (SBAS) time-series analysis was applied using Envisat Advanced SAR (ASAR) images acquired from 1 June 2004 to 28 September 2010. The merit of SBAS time series is that it enables us to calculate the amount of subsidence in each period of interest while minimizing the temporal decorrelation effect. In the last step, the SBAS time-series analysis results were obtained to extract the accumulated subsidence from 19 October 2004 to 30 August 2005. The aquifer compaction due to this period was calculated in the Interbed Storage Package (IBS1) for MODFLOW in order to calibrate storage coefficient in a trial-and-error manner so as the calculated subsidence and the observed one by SAR interferometry be in a good agreement. The Root Mean Square Error (RMSE) between the simulated subsidence and the observed one is calculated as 0.019 m which is an indication of the high performance of modelling.

Interferometric Synthetic Aperture Radar (InSAR) has become an essential tool for monitoring surface deformation with high precision and wide spatial coverage. Among various InSAR techniques, the Stacking InSAR approach is widely used for geological hazard assessments due to its computational efficiency and robustness against decorrelation noise. However, Digital Elevation Model (DEM) errors remain a significant challenge, introducing spurious deformation signals and degrading deformation rate estimates. We present here a rigorous analysis of the impact of DEM errors on Stacking InSAR-derived deformation rates and introduce an enhanced method that effectively mitigates these errors. Unlike conventional correction techniques that require explicit DEM error estimation, the proposed method leverages perpendicular baseline averaging, eliminating DEM-induced biases while maintaining computational simplicity. The method is validated using both simulated and real Sentinel-1A datasets from two tracks, with results compared against conventional Stacking and Small Baseline Subset (SBAS) approaches. The findings demonstrate that the proposed method significantly improves deformation rate estimation by suppressing DEM-induced artifacts, thereby enhancing the reliability of InSAR applications in geological hazard monitoring and deformation assessment.

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

Interferometric synthetic aperture radar (InSAR) processing, if lacking high quality ground control points (GCPs), may produce large errors in the final DEM. Some kind of alignment or registration can reduce these errors. This paper evaluates the accuracy of InSAR processed digital elevation models (DEM), against a high resolution DEM. InSAR DEMs were aligned and least-squares registered with USGS, 1/3 arc second NED. The accuracy evaluations after registration show InSAR DEMs alignment and registration can eliminate DEM errors caused by lack of accurate GCPs. In some cases, this process improves the accuracy of the InSAR DEM even more efficiently than including GCPs into InSAR processing.

In this study we present modeling results derived from ground deformation observed in the Auckland Volcanic Field (AVF) by the C‐band Envisat Synthetic Aperture Radar. Auckland, the largest city in New Zealand with a current population of over one million, coincides with the AVF, which comprises about 50 individual, largely monogenetic, basaltic volcanoes distributed across a total area of 360 km2. The most recent and largest eruption there occurred 600 years ago. While it is anticipated that the chance of any existing volcano reawakening is very low, a new volcano could be created at any time in a new location within the field. The aim of this work is to evaluate the feasibility of interferometric synthetic aperture radar (InSAR) for mapping ground deformation associated with magma ascent, which would be a likely precursor to the onset of volcanic activity. For this study we acquired and processed 23 single look complex (SLC) images from the Envisat satellite (Track 151, Frame 6442, IS2, VV) spanning from July 2003 until November 2007. All possible combinations of differential interferograms were created. Stacking, Small Baseline Subset (SBAS) and Permanent Scatterers (PS) processing algorithms were used to determine spatial and temporal patterns of surface deformation as well as their average rates. A number of localized deformation regions were consistently observed by all three techniques. Due to a lack of evidence pointing toward a relationship with volcanic or tectonic sources it was assumed that they are produced by groundwater withdrawal and recharge. Three largest regions of subsidence (S1–S3) and also three largest regions of uplift (U1–U3) were modeled with the derivative‐free simplex algorithms for location, depth and source volume change using a Mogi point source approximation. The results show that InSAR is a viable technique capable of detecting the scale, rate and spatial distribution of precursory deformation that would likely be associated with resumption of volcanic activity in the Auckland urban area.

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.

In this work, the statistical fundaments of the recently proposed enhanced, multi-temporal interferometric synthetic aperture radar (InSAR) noise-filtering (E-MTInSAR) technique is addressed. The adopted noise-filtering algorithm is incorporated into the improved extended Minimum Cost Flow (EMCF) Small Baseline Subset (SBAS) differential interferometric SAR (InSAR) processing chain, which has extensively been used for the generation of Earth’s surface displacement time-series in several different contexts. Originally, the input of the InSAR EMCF-SBAS processing toolbox consisted of a sequence of multi-looked, small baseline interferograms, which were unwrapped using the space-time EMCF phase unwrapping algorithm. Subsequently, the unwrapped interferograms were inverted through the SBAS algorithm to retrieve the expected InSAR deformation products. The improved processing chain has complemented the original codes with two additional steps. In particular, a new multi-temporal noise-filtering algorithm for sequences of time-redundant multi-looked DInSAR interferograms, followed by a proper interferogram selection step, has been proposed. This research study is aimed at primarily assessing the performance of the E-MTInSAR noise-filtering algorithm from a theoretical perspective. To this aim, the principles of directional statistics and errors propagation are exploited. Experimental results, carried out by applying the E-MTInSAR algorithm to a sequence of SAR data collected over the Los Angeles bay area, have been used to corroborate the academic outcome of this research.

Present-day tectonic activity along the central section of the Altyn Tagh fault derived from time series InSAR

The development of satellite technology is rapidly increasing the evolution of remote sensing. Satellite images give extensive useful information about the land structure that is easily manageable in the process of generating true, high-speed information which allows the forecasting of future environmental and urban planning. Remote sensing comprises active and passive systems. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding area being observed. Active systems which produce their own electromagnetic energy and their main properties are their ability of collecting data in nearly all atmospheric conditions, day or night. These systems are frequently used to generate a digital elevation model (DEM) because they cover large areas. DEM supplies essential data for applications that are concerned with the Earth’s surface and DEMs derived from survey data are accurate but very expensive and time consuming to create. However, the use of satellite remote sensing to provide images to generate a DEM is considered to be an efficient method of obtaining data. Interferometric Synthetic Aperture Radar (InSAR) is a new geodetic technique for determining earth topography. InSAR measurements are highly dense and they only give information in Line of Sight of Radar. In the study, interferograms were produced from the InSAR images taken by ERS satellites in 1992 and 2007 and we developed the methods to generate a DEM using the InSAR technique and present the results relating to Kayseri Province in Turkey. The accuracy of the DEM derived from the InSAR technique is evaluated in comparison with a reference DEM generated from contours in a topographical map.

Comparison and Validation of Different DEM Data Derived from InSAR