dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017). All these surfaces are placed within the standard European Ammonite Zones (Fig. 2 ,  Gradstein et al. , 2012). Petrophysical data from outcrops of the Massangis quarry were acquired during the last decade. Three types of porosity and permeability data from this quarry were compiled for the present study. (Casteleyn et al. , 2010 (Casteleyn et al. , , 2011;; Makhloufi et al. , 2013)  presented a total of 36 porosity and 10 permeability measurements. 18 total porosities come from plug samples taken from five large oriented blocks using water saturation within about 20 cubic decimeters each. On the same blocks, the 10 permeabilities were measured using N2 as fluid. 9 macroporosities (corresponding to pores exceeding 10 µm) were measured on thin-sections using a threshold surface and 9 microporosities were deduced.
Material and method Facies analysis was conducted including a description of lithology, texture, bedding pattern, sedimentary structures, and faunal content (Fig. 1 ). Field observations included sample collection, facies description of all accessible cliffs, logging at seven separate locations in the quarry (sampling points A, B, C, F, G, H and J, Fig. 1C ), and drone photogrammetry. Paleodepths were estimated for each facies by examining their sedimentary structures, textures, and allochems. Macrofacies analysis was supplemented by detailed observation of pore types and diagenetic features on 35 blue resin-impregnated thin sections using the JMicrovision® software (JMicrovision®,  Roduit, 2008). Petrophysical data from the studies of  Casteleyn et al. (2010 Casteleyn et al. ( , 2011) )  and  Makhloufi et al.
(2013)  were assigned to specific facies based on the depositional environment described by the authors (total porosity, macroporosity, microporosity, and permeability). Measures that could not be associated with confidence were excluded from the dataset. As pointed out by  Catuneanu et al. (2011) , surfaces selected as sequence boundaries vary from one sequence stratigraphy approach to another and the choice typically depends on which surfaces are best expressed in each situation. The surfaces described in this study underline a transition between different depositional environments and correspond to maximum regressive surfaces. The surfaces Bt4 and Bt3 (Fig. 2 ) were therefore used to characterize the evolution of facies in the geological model. Moreover, we did not differentiate between Highstand Systems Tract and Lowstand Systems Tract and they are grouped here within a single Regressive Systems Tract  (Embry, 2009; Catuneanu et al.
, 2011). The photogrammetric model was created from 7900 photos taken with a Mavic Pro 2 (Dji™) drone equipped with a 20 Mega Pixel resolution camera. Image resolution was about 2. 3 cm/pixel. Planar overflights were necessary to cover most of the quarry. Vertical flights were used to cover the vertical quarry walls more accurately, allowing closer study of the sedimentary formations. The study area covers 0. 4 km 2 (40. 04 ha). All pictures were georeferenced with an on-board GPS, gyroscope, and accelerometer. The photogrammetric model was created with Pix4D® software. The software outputs are a point cloud file (. xyz) composed of 67 million points, and a file containing mesh triangles (. obj) at high resolution (752 020 nodes, 1 510 551 triangles). Because all points are georeferenced, we obtain of the pictures in the dataset oriented and located on the triangles of the mesh (covered area of point with RGB color code: 0.
94 cm² in Pix4D, size of the texture file computed: 1,01 GPixels, size of the texture file used in VRGS® : 268 MPixels, excluding some parts of the photogrammetric model of the quarry) allowing us to reconstruct a 3D Digital Outcrop Model (DOM). The 3D object (. obj), with the developed by Hodgetts (2010) (Fig. 3 ) to interpret the sedimentary structures. The sedimentary and stratigraphic surfaces observed in the DOM were manually digitalized to create polylines exported in polygon (. ply) format (Fig. 3 ). Petrel® software (Schlumberger) was used to build the geomodel. The georeferenced point cloud extracted from Pix4D® (. xyz) was imported in Petrel® as a pointset to recreate a DOM, adding the offset used by the software as a transition in Petrel® to precisely locate the point cloud of the quarry.
These points were interpolated to generate a topographic surface using the "make surface" tool built-in Petrel®. The geocellular model is 917 m long, 380 m wide, and 49 m-thick. The top and the base were defined by the highest and lowest topographic points, which also correspond to the top and bottom of the sedimentary section. Polylines were imported from VRGS® software as guidelines for creating surfaces and clinoforms in Petrel®. To generate the surfaces, the polylines were converted to point clouds and replicated in a 2D window, perpendicular to the direction of the accretion of the clinoforms (Fig. 3 , Supplementary Material Table  S1 ). Surfaces were then modeled using the "Make surface" tool of Petrel® and using the point clouds generated previously as input. This process has been repeated for the 16 original polylines transformed to surfaces.
Readers will need to bear these points in mind for a better understanding of the workflow in the following sections. (Casteleyn et al. , 2010 (Casteleyn et al. , , 2011;; Makhloufi et al. , 2013). Images were processed using Pix4D® and the resulting Digital Outcrop Model interpreted using Virtual Reality Geological Studio (VRGS®). Facies, porosity, and permeability were modeled using Petrel® software. Note the stereonet (upper right) showing the dips and azimuths observed on the beddings of clinoforms (data in the Supplementary material Table  S1 ).
A total of 52 surfaces (45 clinoform surfaces and 7 structural modeling surfaces) were thus integrated into the model. Note that there is no difference between clinoform surfaces and structural modelling surfaces from the software point of view. We used these surfaces to distinguish three zones (Petrel zones) in the geomodel, representing the sequences, to populate the facies properties. The first zone (zone 1) groups Sequence I and the transgressive systems tract of Sequence II, both displaying a horizontal sedimentary pattern. The top of zone 1 corresponds to the maximum flooding surface MFS Concinna (Fig. 5 ). The second zone (zone 2) corresponds to the clinoforms, which belong to the regressive systems tract of Sequence II and are bounded by the MFS Concinna at the base and the maximum regressive surface Bt4 at the top (Fig.
Some microporosity is present even within the facies with the highest macroporosity. Four petrophysical properties are thus included in the model: (1) total porosity, (2) macroporosity, (3) microporosity, and (4) permeability. They populate the grid according to their statistical distribution in each facies (Fig. 11 ). In the model and accordingly to the petrophysical data, total porosity ranges from 11. 3 to 20. 6%, macroporosity from 0 to 15%, microporosity from 4. 8 to 20. 6%, and permeability from 0. 1 to 5 mD (Table  2  and Supplementary Table  S2 ). Macroporosity is variable in facies association FA1. No significant macroporosity is attributed to marls (facies F1a). The permeability data were not previously measured and a low range of total porosity (0. 1 to 5%) and permeability (0. 1 to 0.
2 mD) is assigned from literature references (wackestone and mudstone zone of  Nurmi, 1986). Measurements made on equivalent marl deposits in the Middle Jurassic of the Paris Basin  (Brigaud et al. , 2010 ) support these values. In facies association FA1, the macroporosity ranges from 0. 2 to 9% in F1b, from 8. 5% to 15% in F1c, and from 0 to 3. 3% in F1d. In this facies association, macropores are dominated by moldic pores forming euhedral rhombs or approximate rhombs. They are clearly related to the dissolution of dolomite crystals. It appears in thin-sections that facies F1c is the most porous and has been modeled this way in the static model. Facies F1b, which has also some of the highest macroporosity values of the dataset, is present at the base of the clinoforms. It thus appears that facies F1b and F1c are connected and both present good macroporosity, enlarging the area that could be qualified as fair reservoir, if we consider high permeability values.
Value of coupling drone acquisition with reservoir modeling Drone photogrammetry allows detailed observation of the facies architecture along vertical and inaccessible faces of the quarry and from new angles. Compared to LiDAR technologies commonly used in carbonate studies  (Enge et al. , 2007; Buckley et al. , 2008; Rotevatn et al. , 2009; Marques et al. , 2020) , drone photogrammetry allows to take pictures from unreachable places. Drones are easier to use and move around in remote places than the other technologies actually used in the field (e. g. LiDAR). Quarries producing building stone and having vertical fronts cut at right angles are particularly suitable for 3D modeling. Kilometer scale drone acquisition is easy, relatively rapid (2 days of acquisition in this quarry) and the photogrammetric models are rather precise (mean ground sample of the photography dataset = 1.
72 cm; note that only the most accurate pictures were used to get the value of the final model down to 0. 94 cm). The complex geometries of clinoforms from hundred meters to kilometer scale within a carbonate platform can be easily observed, analysed, and interpreted after fieldwork with the photogrammetric model. The combination of direct outcrop observations and numerical models allows us to observe the complex geometries of the progradation within the stratigraphic framework in real 3D (Fig. 13 ). Coupled with a detailed and complete facies description, the photogrammetric model provides a continuous representation of architectures along the cliffs. Using VRGS®, it is then possible to manually draw surfaces on outcrops. All the datasets are easily imported into a geomodeler software package such as Petrel® (e.
Acknowledgements We thank Polycor for allowing us access to the quarry and for authorizing drone photogrammetry. This work is supported in part by the "Geothermal water circulation in heterogeneous aquifers" project funded by the " Paris-Saclay Initiative d'Excellence", a state aid program  managed by the  Agence Nationale de la Recherche  ( ANR-11-IDEX-0003 ). This work is also supported by the program UPGEO "UPscaling and heat simulations for improving the efficiency of deep GEOthermal energy" funded by the  Agence Nationale de la Recherche  ( ANR-19-CE05-0032-01 ). University Paris-Saclay benefits from the  Schlumberger Software Donation Program. We are grateful to Schlumberger for the academic license for Petrel software and for very valuable support throughout its utilization. We also thank  Virtual Reality Geological Studio  (special thanks to  David Hodgetts ) and  Pix4D  for providing annual academic licenses for VRGS® and Pix4D®.
Data Availability Datasets of the 3D model of Massangis quarry containing the Digital Outcrop Model (DOM), construction surfaces, facies and petrophysical data are included in the rar file, which can be found at https://data. mendeley. com/datasets/fcdg3jvjdp/1, an open-source online data repository hosted at Mendeley Data. This file can be read only with Schlumberger software Petrel®, version 2019. 2 and later. The 3D photogrammetric model of Massangis quarry can be found on Sketchfab at https://skfb. ly/6RYGF with highlights.
The Tehran basin has been increasingly affected by subsidence during the last few decades due to groundwater withdrawal. Hence, the study of the strength of the power towers (PTs) of transmission lines, as vital structures, is an important subject. In this paper, the persistent scatterer interferometry (PSI) method was applied on data stacks from two satellites (i. e. , X-band COSMO-SkyMed (CSK) and C-band Sentinel-1A (S-1A)) obtained between 2014 and 2016 to investigate the deformation and the exact amount of displacement in each PT of the area of interest. Based on the results, during the same time interval (between October 2014 and February 2016), the vertical velocities calculated using CSK and S-1A were about -86 and -79 mm/y, respectively. Although the CSK data analysis resulted in a better displacement interpretation of PTs, due to its high resolution and shorter wavelength, the S-1 data analysis also demonstrated sufficient persistent scatterer (PS) points.
Other studies also indicated that satellite images (e. g. , TSX and CSK) with a high resolution have the ability to allow the extraction of three-dimensional (3D) information of PTs  [23, 29]. Although many studies have reported estimating and predicting the rate of subsidence in Tehran, none of them are related to the control and analysis of PTs. To the best of our knowledge, this is the first attempt to investigate the displacement of PTs, as vital infrastructure, based on the results of PS-InSAR time series analysis. The paper most importantly aimed to monitor the deformation rate of PTs based on the PSI analysis of two different datasets, X and C bands, to help in understanding how much they are affected by land subsidence and at risk of destruction. For this, both the spatial and temporal evolutions of the towers' deformation were analyzed.
The second objective of the research was to investigate the effectiveness of C-band data, with a lower spatial resolution, compared to X-band data, in the monitoring of such point-like targets. Since commercial high-resolution X-band data are not available in many cases, a comparison between the efficiencies of both kind of datasets in land subsidence monitoring could be worthwhile in such point-wise applications. As proved, CSK high-resolution data provide a high potential for detecting PTs and monitoring their deformation patterns. Hence, we applied the PSI technique to the Tehran basin area using CSK time series data stacks to monitor the land subsidence. On the other hand, Sentinel-1A (S-1A) free C-band data provide an excellent opportunity to monitor the total rate of subsidence over wide areas.
This assists in evaluating S-1A SAR data in PT deformation monitoring applications as well. Meanwhile, we aimed to present a brief analysis of water extraction observations as the main cause of the land subsidence in the study area. In order to validate the results, GPS observations were also used, which were highly compatible with the PSI estimates. The remainder of the paper is organized as follows: Section 2 provides a brief description of the used methodology. The region of interest and datasets are presented in Section 3. Section 4 is dedicated to the results, followed by a discussion. The validation of the results based on in situ measurements is explained in Section 5. The concluding remarks are finally presented in Section 6.
The PSI method presented in this research tries to find a single master configuration that minimizes the noise level in the dataset (spatiotemporal decorrelation). The master image is chosen in the middle of the 2D spatiotemporal space in a way that minimizes the decorrelations and guarantees high coherence for all the formed interferograms  [9, 17]. Based on the single master configuration, all single look complex (SLC) images (slave images) are interferometrically connected to the unique image (master image). The interferometric phase for a single pixel is composed of some distinct contributions, i. e. , orbital errors, deformation, and topographic and other noise effects. To extract the deformation phase component, the removal of all the other, unwanted, effects from the interferometric phase is necessary.
The Datasets and Area of Interest The Tehran basin is located in the north of Iran. It is surrounded by the Alborz mountain to the north and Arad and Fashapouyeh to the south. Based on previous studies, this area suffers from a high rate of subsidence caused by the withdrawal of groundwater  [5, 34, 35]. This can cause many problems for PTs. To apply the PSI technique, a region with a center of 35. 6281 • latitude and 51. 2915 • longitude and a radius of 10 km in the south west of the Tehran metropolitan area, which consists of both urban and rural areas, was considered (see Figure  1 ). surface. The estimated unknowns for each connection are then integrated using the selected reference point to obtain the absolute values at each PS candidate. The atmospheric phase ambiguities are resolved based on the spatial and temporal phase differences.
The Datasets and Area of Interest The Tehran basin is located in the north of Iran. It is surrounded by the Alborz mountain to the north and Arad and Fashapouyeh to the south. Based on previous studies, this area suffers from a high rate of subsidence caused by the withdrawal of groundwater  [5, 34, 35]. This can cause many problems for PTs. To apply the PSI technique, a region with a center of 35. 6281° latitude and 51. 2915° longitude and a radius of 10 km in the south west of the Tehran metropolitan area, which consists of both urban and rural areas, was considered (see Figure  1 ). In order to remove the topographic phase contribution, a TanDEM-X (TDM) Digital Elevation Model (DEM) tile with a resolution of 12 m (pixel spacing, 0. 4 arcsec) was used as an external DEM. This DEM was processed by the German Aerospace Center (DLR) using images from 2010 to 2016  [36].
The use of a more accurate elevation model helps to accurately calculate the topographic phase contribution, and a better estimation of the atmospheric effects can be made. Moreover, daily GPS observations at only one GPS station in the study area were used to validate the PSI analyses. The measurements were acquired between 3 April 2014 and 20 December 2016, which matches our data stacks. The station is located at 35°38′45. 38′N latitude and 51°17′53. 53′′E longitude (see Figure  3 ). The raw data were provided in meters as absolute height values at the station. The last two stacks were scenes from CSK satellites in StripMap (HIMAGE) imaging mode with just HH polarization and with a resolution of 3 meters and average interval of 16 days. One stack consisted of twenty-one images along the ascending orbit from 6 April 2014 to 27 February 2016, and the other one consisted of twenty-two images along the descending orbit from 3 April 2014 to 5 February 2016 (see Figure  2 ).
In order to remove the topographic phase contribution, a TanDEM-X (TDM) Digital Elevation Model (DEM) tile with a resolution of 12 m (pixel spacing, 0. 4 arcsec) was used as an external DEM. This DEM was processed by the German Aerospace Center (DLR) using images from 2010 to 2016  [36]. The use of a more accurate elevation model helps to accurately calculate the topographic phase contribution, and a better estimation of the atmospheric effects can be made. Moreover, daily GPS observations at only one GPS station in the study area were used to validate the PSI analyses. The measurements were acquired between 3 April 2014 and 20 December 2016, which matches our data stacks. The station is located at 35 • 38 45. 38 N latitude and 51 • 17 53. 53 E longitude (see Figure  3 ). The raw data were provided in meters as absolute height values at the station.
Results and Discussion For the processing of each dataset, a single master based on minimizing the spatiotemporal decorrelation criterion was selected. Figure  2   The geocoded deformation rates of all the PS points for both the CSK and S-1A data stack analyses, which were estimated along the vertical direction, are presented in Figure  3. In our case, the E-W component had small values, so its impact was neglected. Moreover, according to the S-1A data stacks analysis, it can be concluded that the vertical velocity rates have a range between 0 and -220 mm/y with respect to the reference point, while the vertical rates according to the CSK analysis ranged from 0 to -260 mm/y. This difference is related to the different wavelengths of the data used. S-1A with C bands has fewer estimates of subsidence since it is less sensitive to deformation signals due to having a shorter wavelength.
Additionally, CSK with the higher-resolution cell can identify more PSs, which might have a higher deformation rate. It should be mentioned that during the same time interval (between October 2014 and February 2016), the vertical velocity shown by the CSK data is about -86 mm/y and that shown by the S-1A data is about -79 mm/y. According to the results, the land subsidence in the non-urban areas is obviously much higher than in the urban regions. The significant rates from both results are related to Firuz Bahram village in the middle-left side of the subset, where there is farmland with an over-extraction of groundwater. Hence, water level information for some piezometric wells was used to investigate this issue. There are two piezometric wells, which were spatially located in our selected subset (see Figure  3   The results of the bimestrial piezometric measurements according to Figure  4b  show that the water levels of the wells are constantly decreasing with seasonal changes.
Additionally, only the results of every second image relative to the first image were considered for plotting Figure  6a ,b, in which the trends show the different time series results and the colors indicate the strength levels of the occurred subsidence. As demonstrated in the figures, a complete dynamic development of one subsidence bowl can be observed. Clearly, among a total of 80 PTs, 25 and 17 PTs were detected as PS points, leaving PTs number 52 and 50 situated in the maximum range of the bowl, with maximum vertical deformation estimates of -357 and -370 mm from the CSK and S-1A analyses, respectively. This does not mean that the estimates of the CSK analysis provided lower values than the S-1A data analysis, but these values came from different locations in the bowl zone and also different time intervals.
As demonstrated in the figures, a complete dynamic development of one subsidence bowl can be observed. Clearly, among a total of 80 PTs, 25 and 17 PTs were detected as PS points, leaving PTs number 52 and 50 situated in the maximum range of the bowl, with maximum vertical deformation estimates of -357 and -370 mm from the CSK and S-1A analyses, respectively. This does not mean that the estimates of the CSK analysis provided lower values than the S-1A data analysis, but these values came from different locations in the bowl zone and also different time intervals. It should also be noted that these maximum estimates are not the maximum in the whole area because they were only estimated along the transmission line. Vertical cumulative time series estimates of these two PTs over their processing time periods are also shown in Figure  7.
The subsidence trends declined over time, leading the PTs into a dangerous situation. This behavior is the same for most PTs, especially those located in the subsidence areas. Therefore, subsidence has had a severe impact on PTs that are located in farmland with the overexploitation of groundwater, and it is necessary to strengthen the protection of them. Generally, it could be argued that X-band data are more sensitive to small rates of deformation as a result of their short wavelength. Here, higher deformation rates were determined from the CSK time series results. Considering the same time interval of the acquisitions, from October 2014 to February 2016, and also the same PT (e. g. , nr. 49), the vertical displacement based on the CSK analysis was -245 mm, while the same value from the S-1A analysis was -231 mm.