High‐resolution optical satellite imagery is used to quantify vertical surface deformation associated with the intraplate 20 May 2016 Mw 6.0 Petermann Ranges earthquake, Northern Territory, Australia. The 21 ± 1‐km‐long NW trending rupture resulted from reverse motion on a northeast dipping fault. Vertical surface offsets of up to 0.7 ± 0.1m distributed across a 0.5‐to‐1‐km‐wide deformation zone are measured using the Iterative Closest Point algorithm to compare preearthquake and postearthquake digital elevation models derived from WorldView imagery. The results are validated by comparison with field‐based observations and interferometric synthetic aperture radar. The pattern of surface uplift is consistent with distributed shear above the propagating tip of a reverse fault, leading to both an emergent fault and folding proximal to the rupture. This study demonstrates the potential for quantifying modest (<1 m) vertical deformation on a reverse fault using optical satellite imagery.

<div>The city of Sydney, Australia has been growing rapidly over the last decades, with rapid development of residential and transportation infrastructure. Land subsidence associated with the urban development can lead to serious issues which should be thoroughly understood and carefully managed. Hence&nbsp;this study developed an enhanced multi-polarisation time-series InSAR (Pol-TS-InSAR) processing framework to address this challenging application.&nbsp;This is done by integrating the information from different polarimetric channels with different weighting during the TS-InSAR analysis. Ninety&nbsp;dual polarization Sentinel-1 images&nbsp;acquired from&nbsp;2019 to 2022 are analysed using the developed&nbsp;Pol-TS-InSAR to map the land subsidence in Sydney with the assistance of the GPS measurements. Improvement of measurement points density from Pol-TS-InSAR is observed compared to the single polarimetric TS-InSAR counterpart for all land use types (ranging between 21% and 99%). The comparison between the Pol-TS-InSAR measurements and GPS measurements shows an&nbsp;absolute mean difference and RMS difference of 0.7 mm/yr and 0.9 mm/yr, respectively, in line-of-sight (LoS) direction. The ground subsidence results obtained have been investigated.&nbsp;It is found that the main subsidence factors in Sydney are related to groundwater extraction, mining activities, underground tunnel construction and landfill, which the latter two factors are less aware previously. In additional to these factors, land subsidence related to high-rise building construction has also been observed, even though the impact seems to be less significant than other factors. <b>Citation:</b> Alex Hay-Man Ng, Ziyue Liu, Zheyuan Du, Hengwei Huang, Hua Wang, Linlin Ge, A novel framework for combining polarimetric Sentinel-1 InSAR time series in subsidence monitoring - A case study of Sydney,<i>Remote Sensing of Environment</i>, Volume 295, <b>2023</b>, 113694, ISSN 0034-4257. https://doi.org/10.1016/j.rse.2023.113694.

This investigation uses high-resolution optical satellite imagery to quantify vertical surface offsets associated with the intraplate 20 May 2016 Mw 6.0 Petermann Ranges earthquake, Northern Territory, Australia. The ~20 km long NW-trending rupture resulted from reverse motion on a northeast-dipping fault. We measure vertical surface offsets by differencing pre- and post-earthquake digital elevation models (DEMs) derived from in-track stereo Worldview images. This analysis resolves a maximum vertical deformation of 0.8 ? 0.2 m. We validate these results via comparison to field-based observations and interferometric synthetic aperture radar (InSAR). This new method may be particularly useful for remote characterization of earthquake ruptures with larger (>1 m) vertical deformation, where near-rupture InSAR observations are often compromised by decorrelation.

<div>The aim of the Interferometric Synthetic Aperture Radar (InSAR) project is to develop an end-to-end, fully automated InSAR processing system that will take raw SAR data from any sensor and produce time-series maps of surface deformation/movement.</div><div>Surface deformation maps are important products to help define the national geodetic reference frame by augmenting the geodetic data obtained from sparse ground networks, in addition to identifying regions with elevated natural hazard risk.</div>

<div>This Geoscience Australia Record reports on Interferometric Synthetic Aperture Radar (InSAR) processing over the Great Artesian Basin (GAB) to support an improved understanding of the groundwater system and water balance across the region. InSAR is a geodetic technique that can identify ground surface movement from satellite data at a regional scale and is therefore a valuable and widely used technique for measuring patterns in surface movement over time; including the movement of fluids (i.e. water or gas) beneath the surface.</div><div><br></div><div>This Record is the one of two Geoscience Australia Records that describe ground surface movement monitoring Geoscience Australia have undertaken in the GAB in recent years. Namely;</div><div>1.&nbsp;&nbsp;&nbsp;&nbsp;Ground surface movement in the northern Surat Basin derived from campaign GPS measurements. (Garthwaite et al., 2022).</div><div>2.&nbsp;&nbsp;&nbsp;&nbsp;InSAR processing over the Great Artesian Basin and analysis over the western Eromanga Basin and northern Surat Basin (this Record).</div><div><br></div><div>We have produced ground surface motion data products, which cover about 90% of the GAB for the period of time between January 2016 and August 2020. The data products were created using Sentinel-1 Synthetic Aperture Radar (SAR) data and an InSAR processing workflow designed for large spatial scale processing. The large spatial scale InSAR processing workflow includes using GAMMA software to (i) pre-process SAR images to align the pixels, (ii) generate interferograms and short temporal baseline surface displacement maps and PyRate software to (iii) combine these outputs in an inversion to form pixel-wise time series ground surface displacement data and fit ground surface velocities to the displacement data. The raw SAR data and these subsequent data products of the workflow are partitioned into overlapping frames; the final stage of the large scale processing workflow is to combine the partitioned data into a single map using a mosaicking algorithm. The results of this processing chain demonstrate the feasibility of developing a regional scale ground surface movement reconnaissance tool (i.e. subsidence and uplift). </div><div><br></div><div>We provide a summary of the processing chain and data products and a focused assessment for two case study areas in the western Eromanga Basin (South Australia) and northern Surat Basin (Queensland). Over these case study areas we examine the relationship between the InSAR derived ground surface movement and available groundwater level data. We also assess how land use types may influence the InSAR derived ground surface motion data by comparing the InSAR data to the “land types” over the region which we classify using a machine learning algorithm with Sentinel-2 optical imagery data. </div><div><br></div><div>From our analysis we observe little ground surface motion over the western Eromanga Basin. The surface movement rate over the entire area is estimated to be mostly within ±10 mm/yr. Groundwater level time series data from well monitoring sites in the area did not appear to have any significant trends either. However, large and broad scale ground surface motion (both uplift and subsidence) was observed in the InSAR processing results over the northern Surat Basin. A 75 km x 150 km scale uplift signal, with rates of up to 20 mm/yr, was located over an area we classified as cultivated land, where InSAR signals are likely to be influenced by near-surface cultivation activities (such as irrigation) rather than subsurface groundwater level changes. Furthermore, two approximately 75km x 75 km areas were identified which had subsidence signals of up to -20 mm/yr. Over the same area, groundwater level time series data show long-term negative trends in the water head level. For a more direct comparison between the InSAR results and the well data, we fitted a first order poroelastic model to transform the InSAR derived ground surface motion rates into modelled pore-pressure decline/groundwater drawdown rates. We compared the model to the groundwater time series data in the Walloon Coal Measures, Surat Basin, and found good agreement, which indicates that the observed subsidence signals could be attributable to pore-pressure decline due to the falling water head level.</div><div><br></div><div>We finally provide some preliminary analysis comparing our InSAR results to the results from an Office of Groundwater Impact Assessment (OGIA) InSAR study and a Geoscience Australia GPS land movement study to assist in validating the Geoscience Australia InSAR results. Overall, the comparisons are encouraging, showing a high correlation against the OGIA InSAR results and GPS results. Further work, is required to further validate our results and reduce uncertainty in our analysis process.</div>

<div>This Geoscience Australia Record summarises the collection, analysis and results of three Global Positioning System (GPS) campaign surveys conducted in 2015, 2016 and 2019 in the northern Surat Basin, Queensland, Australia. During each campaign, we collected at least seven continuous days of GPS observations at each of the 65 sites of a geodetic network established by Geoscience Australia in 2014. Our analysis of the collected GPS data reveals statistically significant downward vertical ground surface movement between 2015 and 2016, 2015 and 2019, or both, at 18 of the 65 geodetic sites. Two of the 18 sites have recorded downward vertical ground surface movement of more than 100 millimetres: SB36 near Miles (125 mm) and SB49 near Wandoan (218 mm). Further work is required to help better resolve the temporal and spatial pattern of ground surface movement in the northern Surat Basin. This could include further GPS campaign surveys, analysis of GPS data from continuously operating reference sites in south-east Queensland, and a detailed comparison of surface deformation observed with GPS and interferometric synthetic aperture radar (InSAR) data; extending on the work described in McCubbine et al. (2022).</div><div><br></div>

<div>The project ‘Assessing the Status of Groundwater in the Great Artesian Basin’ assessed existing and new geoscientific data and technologies, including satellite data, to improve our understanding of the groundwater system and water balance in the GAB. An updated classification of GAB aquifers and aquitards was produced, linking the hydrostratigraphic classification used in Queensland (Surat Basin) with that used in South Australia (western Eromanga Basin). This revised hydrogeological framework was produced at the whole-of-GAB scale, through the development and application of an integrated basin analysis workflow, producing an updated whole-of-GAB stratigraphic interpretation that is consistent across jurisdictional boundaries. Groundwater recharge rates were estimated across eastern GAB recharge area using environmental tracers and an improved method that integrates chloride concentration in bores, rainfall, soil clay content, vegetation type and surficial geology. Significant revisions were made to the geometry and heterogeneity of the groundwater recharge beds, by acquiring, inverting and interpreting regional scale airborne electromagnetic (AEM) geophysical data, identifying potential connectivity between aquifers, possible structural controls on groundwater flow paths and plausible groundwater sources of spring discharge. A whole-of-GAB water balance was developed to compare inflows and outflows to the main regional aquifer groups. While the whole-of-GAB and sub-basin water balances provide basin-wide perspectives of the groundwater resources, they also highlight the high uncertainties in the estimates of key water balance components that need to be considered for groundwater resource management. Assessment of satellite monitoring data from Gravity Recovery and Climate Experiment (GRACE) and Interferometric Synthetic Aperture Radar (InSAR) shows promise for remote monitoring of groundwater levels at a whole-of-GAB scale in the future to augment existing monitoring networks. This presentation was given at the 2022 Australasian Groundwater Conference 21-23 November (https://www.aig.org.au/events/australasian-groundwater-conference-2022/)

The implementation of a wide-land deformation monitoring program in eastern states of Australia, characterized by an extensive landmass and extensive coastlines, necessitates the generation of deformation maps with a significantly larger spatial extent. A pragmatic approach for achieving such coverage involves the integration of multiple interferometric synthetic aperture radar (InSAR) time series results encompassing various tracks and frames. The Sentinel-1 satellite constellation is facilitating the generation of expansive ground surface deformation, while the initial challenge in obtaining such a product arises from the varying spatial coverage of the Sentinel-1 data. To overcome this obstacle, we proposed a novel Sentinel-1 image definition to facilitate consistent interferometric processing. Before the formation of any large displacement map, the inconsistency between different synthetic aperture radar (SAR) image scenes must, nevertheless, be addressed. Several factors contribute to these inconsistencies in observations, such as differences in the angle of observation at the overlapping regions of adjacent image tracks and imprecise estimations within the dataset itself (e.g., burst or swath discontinuities). This study delves into solutions for these challenges, specifically: 1) introducing a computer-vision-based algorithm for InSAR dataset quality assessment and 2) proposing a global least square mosaicking procedure for the amalgamation of tiles from multitracks and frames. Statistical analyses show that better accuracy can be achieved. The mosaicking InSAR product demonstrates the capacity to quantify ground surface changes, which also exhibits a correlation with other geological layers, and the subsidence range of [−7, 10] mm/year shows a strong association with clay content levels ranging from 10% to 30% along the Darling River. <b>Citation:</b> Z. Du et al., "Toward a Wide-Scale Land Subsidence Product in Eastern States of Australia," in <i>IEEE Transactions on Geoscience and Remote Sensing</i>, vol. 61, pp. 1-12, 2023, Art no. 5213312, doi: 10.1109/TGRS.2023.3299928.

<div>COMET (The Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics) uses satellite measurements alongside ground-based observations and geophysical models to study active faults and earthquakes. This talk provides an overview of COMET research products in Türkiye and Central Asia, where interseismic deformation and active faults are directly observable. It also touches on how these products highlight the complexity and difficulty of seismic hazard modelling in Australia.&nbsp;</div><div>Three COMET datasets will be discussed, which each contribute to seismic hazard models. Researchers at COMET have and continue to pioneer INSAR methods including co-seismic interferograms and time-series modelling. For example, the Türkiye (Türkiye) INSAR strain-rate map directly estimates strain-accumulation across faults, while the LICSAR portal and satellite cross-correlation methods are used to quantify co-seismic and post-seismic deformation (including after the devastating 2023 Türkiye-Syria earthquake).&nbsp;</div><div>Similar methods are applied in the Tien Shan, where active faults are identifiable in satellite imagery and elevation data, but rates of activity are uncertain and expensive to obtain through field work. Here COMET and GEM (the Global Earthquake Model) are collaborating to produce block-model informed PSHA inputs using active fault databases, GNSS, and INSAR.&nbsp;</div><div>While these methods are useful in tectonically active regions, they serve to highlight the difficulties facing Australian seismic hazard modelling where similar methods cannot be used due to low (to unobservable) tectonic strain and very long fault recurrence.&nbsp;</div> This paper was presented to the 2023 Australian Earthquake Engineering Conference 23-25 November 2023 (https://aees.org.au/aees-conference-2023/)

<div>This report presents the findings of a study conducted in the upper Darling River floodplain, aimed at improving optical and interferometric synthetic aperture radar (InSAR) remote sensing products for groundwater dependant vegetation (GDV) characterisation. The research was part of the Upper Darling Floodplain (UDF) groundwater study, funded by the Exploring for the Future program.</div><div>This work tests the suitability of two novel remote sensing methods for characterising ecosystems with a range of likely groundwater dependence: combined wetness and greenness indices derived from Landsat products available through Geoscience Australia’s Digital Earth Australia platform, and an InSAR derived index of vegetation structure (known as SARGDE), which has been so far tested only in northern Australia. In addition, the relationship between the Normalised Difference Vegetation Index (NDVI), a remotely sensed proxy for vegetation condition, and water availability from surface water flows, rainfall, and groundwater was tested for sites with a range of low to high likely groundwater dependence.&nbsp;</div><div>The key findings of this work, and potential implications, are:</div><div>• A multiple lines of evidence approach, drawing on persistence of wetness/greenness and vegetation structure, and correlation between inferred vegetation condition and groundwater levels, gives high confidence in the groundwater dependence of parts of the floodplain, particularly within the riparian zone. These indices require calibration with ground condition data to be applied in different regions, but a combined index could provide a high confidence measure of groundwater dependence.</div><div>• Combined greenness and wetness, SARGDE, and the relationship between NDVI and groundwater levels all showed areas classified as ‘moderate’ likelihood of groundwater dependence having signatures comparable to areas classified as high likelihood. This could address a shortcoming of the groundwater dependence classification methodology, which, when groundwater level information is missing, classifies some vegetation types as moderate.</div><div>• A combined index taking into account both greenness and wetness was able to better delineate vegetation types with a range of groundwater dependence previously not achievable using remote sensing products.&nbsp;</div><div>This work has provided improved methodology for applying remote sensing products to groundwater dependent vegetation characterisation in the study area. The methods are likely to be applicable to other regions with groundwater dependent vegetation. The results add to the evidence that it is necessary to better integrate surface and groundwater resources in water sharing plans at a basin scale. Further work is required to quantify the frequency and magnitude of flow events required to replenish alluvial groundwater sufficiently to maintain existing groundwater dependent ecosystems.&nbsp;&nbsp;</div><div><br></div><div><br></div>