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Article

Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data

by
Felipe Orellana
1,*,
Joaquín Hormazábal
2,
Gonzalo Montalva
3 and
Marcos Moreno
4
1
Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, 00184 Rome, Italy
2
Department of Geophysics, FCFM, University of Chile, Santiago 8370449, Chile
3
Department of Civil Engineering, University of Concepcion, Concepcion 4070409, Chile
4
Department of Geophysics, University of Concepcion, Concepcion 4091124, Chile
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(7), 1611; https://doi.org/10.3390/rs14071611
Submission received: 7 February 2022 / Revised: 14 March 2022 / Accepted: 14 March 2022 / Published: 28 March 2022
(This article belongs to the Special Issue Measuring Surface Deformation of Coastal Areas with SAR Interferometry)
Browse Figures
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Abstract

:
Coastal areas concentrate a large portion of the country’s population around urban areas, which in subduction zones commonly are affected by drastic tectonic processes, such as the damage earthquakes have registered in recent decades. The seismic cycle of large earthquakes primarily controls changes in the coastal surface level in these zones. Therefore, quantifying temporal and spatial variations in land level after recent earthquakes is essential to understand shoreline variations better, and to assess their impacts on coastal urban areas. Here, we measure the coastal subsidence in central Chile using a multi-temporal differential interferometric synthetic aperture radar (MT-InSAR). This geographic zone corresponds to the northern limit of the 2010 Maule earthquake (Mw 8.8) rupture, an area affected by an aftershock of magnitude Mw 6.8 in 2019. The study is based on the exploitation of big data from SAR images of Sentinel-1 for comparison with data from continuous GNSS stations. We analyzed a coastline of ~300 km by SAR interferometry that provided high-resolution ground motion rates from between 2018 and 2021. Our results showed a wide range of subsidence rates at different scales, of analyses on a regional scale, and identified the area of subsidence on an urban scale. We identified an anomalous zone of subsidence of ~50 km, with a displacement <−20 mm/year. We discuss these results in the context of the impact of recent earthquakes and analyze the consequences of coastal subsidence. Our results allow us to identify stability in urban areas and quantify the vertical movement of the coast along the entire seismic cycle, in addition to the vertical movement of coast lands. Our results have implications for the planning of coastal infrastructure along subduction coasts in Chile.

1. Introduction

Chile is one of the most seismic countries in the world [1], where Chile’s seismicity and tectonics are characterized by the subduction of the oceanic Nazca Plate below the continental lithosphere of South America (Figure 1) [2]. As a result of the influence of an active subduction margin, Chile has a high and varied level of natural threats resulting from a variety of geomorphological and geological processes. Therefore, the Chilean coast is a geographical area of dynamic terrestrial processes, by definition a highly dynamic area which is constantly in a condition of extreme fragility from high seismic activity. Along the coast of central Chile, the 2010 Maule (Mw 8.8), its aftershocks, and postseismic deformation have dramatically impacted the coastal relief [3,4,5] over more than 500 km. The relief changes generated by the coseismic coastal uplift, such as the emersion of the marine abrasion platform, widening of beaches, and drying of wetlands, had an important impact on the intertidal biota and anthropic activities [6]. The exposure and risk are also considerable since many cities, some of them densely populated, are located in high-risk areas [7]. Along with surface level changes, coasts are being affected by climate change, which may increase sea level in the coming decades. Therefore, quantifying with high resolution the impact of recent earthquakes on the coast is of vital importance to understand the processes that control the stability of the coastline. In this context, coastal cities constitute highly vulnerable areas due to the occurrence of threats of marine and continental origin, originating from endogenous and exogenous processes.
The main natural phenomena responsible for the land level changes along coastal areas arise from deeper-lying processes caused by tectonic movements [8,9], such as near-instantaneous coseismic displacements, or slow rates of vertical motions in the interseismic and postseismic periods. These processes produce large-scale deformations involving regional scale effects. Intersismic and postseismic deformations induce low subsidence or uplift rates, thus they do not involve situations of immediate risk, and the effects are observed after several years; however, over a period of several years their effects can change the coastal morphology. On the other hand, subsidence in many cases is caused by the exploitation of groundwater [10,11,12] and of hydrocarbons [13,14,15] which imply subsidence at a regional level. Furthermore, much faster anthropogenic causes may be related with urbanization, typically on unconsolidated alluvial deposits, and are usually superficial processes [16,17].
Therefore, the coastal land level is affected by these types of phenomena; that is, land deformation and subsidence or uplift arising from both deep and superficial processes. The quantitative evaluation of subsidence and soil displacement that affects the coast can be carried out based on terrestrial instrumentation and traditional measurement techniques (related to surveying/geodesy), e.g., levelling and GPS, provided that reliable measurements to monitor subsidence are possible in various places [18]; however, these are limited in terms of producing high spatial resolution maps of surface displacement over wide areas. MT-InSAR technology is an alternative solution that can be fully assimilated to terrestrial monitoring [19]. Subsidence monitoring with MT-InSAR takes advantage of the amount of available data that is acquired more frequently and accurately at low cost, and such characteristics make it an attractive source of information [20].
The technology used in this research is based on advanced MT-InSAR approaches [21,22,23,24], which consist of exploiting SAR acquisition sequences collected over long periods of time, acquired in the same geometry; we can use these data to provide useful information on spatial and temporal patterns or displacements detected by generating time series with centimeter-to-millimeter precision [25,26]. The MT-InSAR technique is an extension of the traditional InSAR technique involving pairs of images, and allows us to measure subcentimeter ground displacements with millimeter precision, using the phase difference between a series of multiple SAR images that it acquires at different times in the same scene [27]. The MT-InSAR deformation time series have been largely exploited in a wide variety of geophysical contexts, such as seismic, volcanic, and mass movement scenarios, with a dual objective: to map and monitor detected displacements over large areas [28,29,30,31,32]. One of the main attractions of satellite-based MT-InSAR is its ability to cover very large areas remotely at a systematic and continuous rate, making it suitable for regional scale monitoring, as a control tool [33,34,35], or even as infrastructure networks [19].
In the last decade, many studies around the world have addressed coastal subsidence using differential interferometry to explain and analyze coastal dynamics. Some researchers have integrated MT-InSAR techniques with sea level rise [36,37,38,39], mainly addressing environmental processes and climate change. Other studies address subsidence as a result of coastal urbanization, mainly due to the extraction of groundwater [40,41,42].
In this study, multi-temporal differential interferometry MT-InSAR is used, in addition to the big data of Sentinel-1 in C band coming from the ESA (European Space Agency). We were able to process multiple SAR images using the P-SBAS (Parallel Small BAseline Subset) algorithm [43,44,45,46], an evolution of the traditional SBAS (Small BAseline Subset) method [47] developed by the CNR–IREA (Italian Institute of remote sensing for the environment). The approach used is based on the detection of persistent scatterers (PS) targets that allow the production of projected mean strain rate line-of-sight (LOS) maps and corresponding displacement time series by exploiting interferograms characterized by a small temporal and/or spatial separation (baseline) between the acquisition orbits.
The methodology of this study aims to show the coastal subsidence of central Chile, using the SAR interferometry technique and processing open big data from Sentinel-1, through the iCloud platform (GEP) [48,49,50,51]. The method focuses on the presentation of MT-InSAR results, the time series, the comparison with GNSS data, and the geodynamic interpretation of the coastal subsidence phenomenon in Chile. We also compare the MT-InSAR and GNSS-derived displacements of stations mainly from the coast, which is our study area. The displacements along the LOS, in addition to the vertical and EW displacements of the subsidence area, mainly the urban area, were analyzed.
Remotesensing 14 01611 g001 550
Figure 1. Tectonic setting of the study area. Blue contours indicate the slip of earthquakes that occurred in 2010 (Mw 8.8) and 2015 (Mw 8.2). Gray dots show background seismicity (earthquakes smaller than Mw 6.5) recorded in the study area between 2014 and 2018 [52]. Two earthquakes of magnitudes greater than Mw 6.5 have been recorded in recent years (red dots) [53].
Figure 1. Tectonic setting of the study area. Blue contours indicate the slip of earthquakes that occurred in 2010 (Mw 8.8) and 2015 (Mw 8.2). Gray dots show background seismicity (earthquakes smaller than Mw 6.5) recorded in the study area between 2014 and 2018 [52]. Two earthquakes of magnitudes greater than Mw 6.5 have been recorded in recent years (red dots) [53].
Remotesensing 14 01611 g001

2. Study Area

The study area extending between 33.75° S and 34.75° S is located on the area’s northern boundary affected by the 2010 earthquake. This zone is characterized by the existence of active crustal faults (Figure 1). This is demonstrated by the pair of earthquakes (magnitude Mw 6.9 and 7.0) that occurred one week after the 2010 mainshock [54]. During the observation period analyzed in this paper, an earthquake of magnitude Mw 6.8 occurred in August 2019. This event is a reverse earthquake, a typical quake occurring at the interface between tectonic plates (Figure 1).
The study area is along the coast between Valparaiso and Pichilemu. The landscape in this region is defined by the coastal range and the deltas of the rivers that are born in the Andes range. Its main urban center is Pichilemu; other small coastal towns are Navidad, La Estrella, Marchigue, Litueche, and Paredones (Figure 2). As a result of the tectonic configuration of central Chile and the orogenesis of the Andes, the study area presents three well-differentiated morphostructural zones, which from west to east are the Coast Range or Cordillera de la Costa, the Central Depression, and the Main Range or Cordillera de los Andes.

Geological Setting

The geology of the study area is characterized by the presence of plutonic bodies associated with the Coastal Batholith; their ages vary from the Upper Paleozoic to the Upper Jurassic. These plutonic bodies are aligned in NW-SE direction stripes. On the other hand, volcanic sequences and to a lesser extent sediment with ages ranging from the Jurassic to the Lower Cretaceous are recognized. In most of the Paleozoic units, foliation has been registered in a NW-SE direction and keeping towards the SW [55]. As for the eastern section of the study area, it is characterized by the presence of a series of stratified sequences of NS orientation and with ages that vary from the Upper Jurassic to the Lower Cretaceous. These units are intruded by Jurassic and Cretaceous granitoids (see Figure 3).
The Paleozoic units correspond to large plutonic bodies that are distributed along the western slope of the Cordillera de la Costa. Its lithology consists mainly of tonalites, granodiorites and monzogranites from amphibole and biotite. In addition, metamorphic rocks are found, of plutonic protolith with tectonic foliation [55]. Triassic intrusive rocks occur in the SW zone of the study area. The lithology consists mainly of gneissic diorites, amphibolites, quartz diorites, and partially metamorphosed gabros; they present ductile gneissic foliation, with a NW orientation keeping towards the south. The most voluminous intrusive units correspond to the Jurassic granitoids. The lithology varies between granite, tonalite, granodiorite, and gabros to a lesser extent; towards the westernmost sectors there is a ductile NW-SE foliation; towards this, foliation decreases progressively. Intrusive Cretaceous outcrops are on the eastern slopes of the Cordillera de la Costa. They are plutonic granitoids that intrude both Jurassic intrusives and volcanic and sedimentary rocks of Jurassic and Cretaceous ages [57].
The Cretaceous units correspond to the Lo Prado, Veta Negra, and Las Chilcas formations. The first is arranged in a concordant way on the Horqueta Formation, and consists largely of siliceous volcanic rocks (rhyolites and ignimbrites) [58,59], with volcanic rocks of a maphic character towards the roof of the sequence. The Lo Prado Formation is covered by the Veta Negra Formation, which is made up of a large volume of basaltic to andesitic–basaltic volcanic rocks. The Veta Negra Formation underlies the Las Chilcas Formation in angular unconformity, which corresponds to a volcanic and continental sedimentary sequence, approximately 3000 m thick [60,61]. The Las Chilcas Formation underlies the Lo Valle Formation in unconformity of erosion [57], which corresponds to a pyroclastic sequence of andesitic to rhyolitic composition, with intercalations of lavas and continental sedimentary rocks reaching 1800 m thick [60].

3. Materials and Methods

3.1. Data Set: SAR Imagery

A total of 320 SAR Sentinel-1 images operating in C band were used, covering the period of January 2018–May 2021, using ascending and descending orbits. We limit our analysis to Sentinel-1 data only (12-day repeat cycle) (Table 1), which is considered sufficient given the expected magnitude of ground displacements and the availability of a large file on the study area. The advantages of Sentinel-1 come from its wide range of coverage (250 km swath in wide interferometric mode) and sufficient spatial resolution for large areas (90 m × 90 m range vs. azimuth, for this case). The wide interferometric fringe (IW) acquisition mode is based on the ScanSAR terrain observation mode with progressive scans (TOPS) [62]. The width of the IW 1–3 swath was 250 km, and the angle of incidence of the line of sight (LOS) ranged between 31 and 46 degrees from the near to far range (i.e., ~39 degrees in the center of the scene). Using GEP, SAR images are retrieved, such as Single Look Complex (SLC) data, in addition to SAR images available in the ESA Open Access Hub, ONDA-DIAS, and CREODIAS repositories.

3.2. GNSS Data

From this data set we selected the GNSS along the coast of central Chile (Figure 4) from an open access resource using daily solutions processed in the Nevada Geodetic Laboratory (NGL) of the University of Nevada, NV, USA [63], with the time series processed in the IGS14 reference frame [64]. NGL also routinely updates station speeds, the speed, and the path that stations are moving in the global reference frame, which can be used to image displacement rates of the surface deformations.
NGL provides GPS data from more than 17,000 stations around the world and manufactures data products that are multipurpose; for example, the same data can be used to study geodynamics and tectonic deformations. In our case we selected the GNSS stations that are along the coast and we concentrated analysis on the subsidence area using stations RCSD, NAVI, PCMU, and ILOC.
With the continuous improvements of GNSS technology, signals of deformation of the earth’s crust, such as deformation of plate boundaries, coseismic static offsets, postseismic deformations lasting for years to decades, and glacial isostatic adjustments have been observed with very accurate results. The description of the temporal evolution of the position of a GNSS station must take into account all these effects, and it has been described using an extended trajectory model (ETM) [65].

3.3. P-SBAS Processing

We adopted the SAR processing that was executed on the ESA iCloud GEP platform, in the service “CNR-IREA P-SBAS Sentinel-1 on-demand processing” v.1.0.0, implemented in the operating environment based on ESA GRID computing [66]. The processing approach is based on the SBAS technique [47], applied along the ascending and descending orbits of the Sentinel-1 satellites (C-Band SAR sensor wavelength = 5.6 cm); the P-SBAS has been adapted to run efficiently on high-performance distributed computing and configured for Sentinel-1 IW TOPS data processing [45].
The main processing steps of the SBAS approach consist of the generation of differential interferograms from the pairs of SAR images formed with a small orbital separation (spatial baseline) to reduce spatial decorrelation and topographic effects. The Shuttle Radar Topography Mission (SRTM) [67] with NASA’s 1 arc-second DEM (~30 m pixel size) and precise orbits from the European Space Agency (ESA) was used for co-registration and for elimination of the topographic phase from the interferometric phase in each of the computational interferograms.
Processing begins with the retrieval of SLC input data from image files, which are primarily based on the ESA Open Access Hub, ONDA DIAS, and CREODIAS repositories. Each stack of SLC data was jointly recorded at the single burst level, ensuring very high joint recording precision (on the order of 1/1000 of the azimuth pixel size), as required for TOPS data due to the large Doppler centroid along the path variations [68]. The next step is the calculation of temporal coherence and the threshold of the minimum temporal coherence, allowed to select coherent targets set at 0.85. Atmospheric phase components were identified and removed. The output data sets that include geolocation (i.e., latitude and longitude in the WGS84 system, and elevation above the reference ellipsoid), annual LOS velocity, and date-by-date offset histories (time series) for each coherent objective values were exported, separated by commas in (.csv) ASCII format, in accordance with the specifications of the European Plate Observing System—Implementation Phase (EPOS—IP) project, where the metadata is standardized to that which corresponds to a set of output data, LOS speed in raster (.png), and Google Earth (.kmz).
The control point for the processing of P-SBAS was established in the same location in the city of Santiago −70,654, −33,443, and in the City of Rancagua −71,648, −33,023, where the annual LOS speed values and the time series were referenced accordingly. The use of a common reference point allowed internal calibration of the two output data sets.

3.4. Post-Processing

Vertical and East-West Deformation Component Estimation

The ascending and descending data sets were combined to obtain the vertical displacement V u and the V E east-west velocity field. A 90-meter square element network was used to resample the point data sets in a regular grid and to link the output data sets into a single layer. Both P-SBAS outputs (ascending and descending) were available at the same location i. The combination was achieved under the assumption of negligible north-south velocity, V N = 0. This assumption is typically used in MT-InSAR studies to take into account the relatively poor visibility of north-south horizontal movements that the LOS sensor is limited to [69].
Given the known values of the deformation velocity LOS in the ascending ( V a s c ), descending ( V d s c ), and the unit vectors ( E D i   ,   U D i ) modes at each location i, the V U and V E were estimated as follows:
V u i = E D i ∗   V A i − E A i ∗   V D i E D i ∗   U A i − E A i ∗   U D i
V E i = U D i ∗   V D i − U D i ∗   V A i E D i ∗   U A i − E A i ∗   U D i

4. Results and Discussion

The results were analyzed using GIS-based cartography (WGS84—UTM zone 19S), with MAXAR images from ESRI source for the background maps. In the first instance we obtained an overview of surface deformation velocity (mm/year); later, we concentrated our analysis on showing the subsidence area, and on analyzing urban centers located along the coast using spatial interpolation techniques and cumulative displacements of MT-InSAR.
Our results show a high agreement between the estimates obtained from InSAR and GNSS. In the observed period, the coast subsided at peak ratios of ~25.00 mm/year. This maximum coincides with the area affected by the aftershock of 1 August 2019 (Mw 6.9). This earthquake demonstrates that a local effect (~20 km area) can produce moderate magnitude earthquakes.
The field of vertical and horizontal displacement of the subsidence area between February 2018 and May 2021 unequivocally highlights that the promontory is affected by land subsidence. The vertical velocity increases from the agricultural zone towards the coast. Despite the lower density of coherent targets throughout the rural landscape, the east-west horizontal field makes it clear that the movements of the earth’s crust converged towards the coastal zone; this is the place around which the subsidence seemed to center. Our SAR processing results are available as Supplementary Materials, in .kmz format, they indicate the target-PSI of each frame used.

4.1. Regional Displacement Overview

In this section, we show the regional deformation phenomenon detected by the MT-InSAR analysis in the coast of central Chile, with a length of 300 km effectively covered by SAR data. In order to represent a regional displacement overview, we use big data based on PSI (Persistent Scatterers Interferometry), obtained from interferograms, using total coverage of the Sentinel-1 images, which is 250 km in S-1 IW mode. This allowed us to cover very large areas over the central region of Chile.
The regional displacement is presented in Figure 5. The large data used for the construction of these maps are all from PSI with a coherence >0.85. We obtained maps of more than 5 mls of points for the ascending orbit and 2 mls of points for the descending orbit. It should be noted that to generate this amount of points (PSI), at least 48 h of processing were necessary for each frame. For the classification of the PSI displacement speed we used a range of −20 to 20 (mm/year), for which we detected the area of subsidence in the SW of the analyzed region, indicated in red.

4.2. Subsidence Area

The coastal subsidence area covers ~2000 km2. The area includes urban districts, such as Pichilemu, and small rural towns such as Navidad, Litueche, La Estrella, Marchigue, and Paredones. The coastline represents a length of 50 km. It is affected mainly by the effects of tectonic subsidence.
To show the subsidence we used both ascending and descending orbits, where a similar spatial density of ~100 PSI/km2 was detected; however, it can be noted that there is a foreshortening effect, since the descending orbit is less than the slope of the angle out of nadir and tends to be perpendicular to the line of sight (LOS).
The velocities of LOS during January 2018–May 2021 ranged between −57 and +26 mm/year for the upstream data set, and between −40 and +23 mm/year for the downstream data set. Negative velocity values indicate movement away from the satellite sensor (dark red dots), while positive values indicate movement toward the sensor (yellow dots). Figure 6 shows the sink area detected by the ascending and descending orbits.

4.2.1. Time Series Verification

The deformation time series represent the most advanced MT-InSAR product. They provide the history of deformations during the observed period, which is fundamental for many applications to study the kinematics of a given phenomenon (failures, activation, acceleration, etc.) and its correlation with inducing factors. In order to properly use, interpret, and exploit the deformation time series, it is important to consider that they are a zero product redundancy. In fact, they contain an estimate of deformation for each SAR acquisition, that is, for each observation. For this reason, they are particularly sensitive to phase noise [70].
The time series of the P-SBAS algorithm have been successfully compared and validated in previous works [45]. For our case and following the results in the subsidence area, we focused on an extended quantitative analysis that assessed the quality of the P-SBAS and GNSS measurements. We present graphs in Figure 7 that show the comparisons and validation between the displacement time series recovered from Sentinel-1 interferometric data developed by the P-SBAS processing chain (red dots), and the corresponding GNSS projected by LOS (black lines) for stations labeled RSCD, NAVI, PCMU, and ILOC. Consistent with the above results, there is very good agreement between the P-SBAS and GNSS measurements projected by LOS.
There are certain types of surface deformation phenomena that InSAR processing chains could not detect as a result of atmospheric noise factors. However, the results obtained by both signals in the LOS show similar displacement trends; for example, for the R 2 of P-SBAS and GPS, NAVI, are 0.75 and 0.73, respectively, also in the PCMU DSC time series. A very similar lag has been detected for both signals, which can be associated with the tectonic process. The closest GNSS station (PCMU) to the earthquake indicated a subsidence of ~30 mm (Figure 7). InSAR spatially complements the surface level change record, providing a record similar to GNSS but precisely determining the area that subsides by this earthquake.

4.2.2. Vertical Displacement Estimation

The vertical velocity components (Vup) were calculated after combining the two upstream and downstream data sets, where a 90-square-meter network was used to resample the point data sets on a regular grid and link the output data sets into a single digital layer in SHP format.
The coastal subsidence shows a generalized presence of negative velocities that indicate subsidence, with observed rates of up to −25.00 mm/year (Figure 8). Along the oceanic coast, a subsidence pattern that increases from north to south can be recognized, involving towns such as Navidad and Pichilemu; in addition, the subsidence pattern that increases from east to west can be recognized (Figure 9), which is due to the occurrence of the subduction phenomenon of the Chilean coast. The area of greatest coastal subsidence is between the districts of Navidad and Pichilemu, where the rates reached −25.00 mm/year.

4.2.3. E-W Displacement Estimation

The horizontal component EW allowed us to identify common patterns of east-west displacement. The effect of the displacement of the earth’s crust towards the west is evidenced mainly in the area of subsidence, with a displacement rate of up to −10.00 mm/year (dark green); in contrast (Figure 9), the eastward shift increases as you move away from the subsistence zone, with shift rates of up to 5.00 mm/year (light color). This relationship of subsidence and velocities to the west can be associated with a postseismic period of the seismic cycle, where there is still a relaxation of stresses after an earthquake. Particularly in this zone a postseismic influence can be appreciated as a result of the Maule 2010 earthquake and possibly as a result of the Pichilemu 2019 earthquake. Even so, it should be noted that the horizontal displacements show that between Navidad and Pichilemu there is a greater postseismic influence, since in this zone the velocities are oriented towards the trench, while the vertical velocities show that the zone is a little wider, which may be related to existing rheological properties in the area.

4.3. Urban Areas

The area of subsidence includes different municipalities located along the coast. These urban districts are mainly the following: Pichilemu, which is the most important urban center; Paredones, La Estrella, Mirchigue, Litueche, and Navidad. The urban areas were affected by two large earthquakes and their coseismic effects 6.9 (Mw), some of which caused significant changes in the seafloor that could partially explain the changes in the coastline and its dynamics. These changes, coseismic uplift/subsidence obtained from post-tsunami studies, and source inversions, are detailed for these earthquakes and their effects on the coastal areas of Navidad and Pichilemu [71,72,73,74], where coseismic changes can be highly variable in space and determined by changes in the coastline. In addition, coseismic subsidence in combination with sea level rise enhances beach erosion, and coastal subsidence can induce accretion [75].
In order to show subsidence areas at urban scales, we use the IDW (Inverse Distance Weighting) interpolation method. The values assigned to the unknown points are calculated with a weighting of the average PSI values available at the known points. In this case we use the input of the vertical displacement value (Vup).
In Figure 10 we classify each of the maps according to the vertical displacement, with a value of up to −25.00 mm/year, where the greatest subsidence occurs on the coast (red color). Tectonic subsidence is identified within the current urban limits for each of the municipalities and is described in Table 2. The urban infrastructure network, road network, buildings, and primary infrastructure are all exposed to the phenomenon of subsidence.
The morphodynamic effects of the 2010 Mw 8.8 seismic event on the Pichilemu coastline were the extreme erosion of beaches and dunes, leaving as main geomorphological characteristics monosequential erosive beach profiles, with cliffs in previous dune cords on the order of meters high [76]. The coseismic deformation induced by the 2010 earthquake caused a general retreat and subsidence of the coast [3]. Currently the urban center of Pichilemu is the most exposed to the subsidence zone. This area comprises 158.75 km2 located along the coast; however, to confirm the level of risk to urban infrastructures, it is necessary to rectify with data and in situ surveys carried out at a local level. The resolution and extent of these maps allow detailed planning for a number of hazards.
For urban areas, the PSI-targets were sufficient to characterize the phenomenon of subsidence. We identified Pichilemu, Litueche, and Navidad as the areas most exposed to coastal subsidence, and observed large rates of vertical displacement (PSI < −20,00). In Table 2 we show the count of PSI-targets for each municipality from the study area, evidencing the negative values of the mean, in addition to a common trend of displacement R 2 for the subsistence areas (km2).

5. Conclusions

MT-InSAR satellite interferometry techniques have proven to be useful for observing displacements on different spatial scales. The large amount of data available from Sentinel-1 with high frequency has allowed us to create robust interferometric data stacks that cover very wide regions, obtaining very reliable multi-temporal results. It has also allowed us to investigate the large-scale evolution of a coastline; it has also allowed us to investigate displacement at urban scales, and create useful maps for spatial planning.
The results of both orbits of the satellite sensor were shown in a similar way from the point of view of spatial density; however, there is a foreshortening effect for some areas that can be improved by complementing the orbits. The available GNSS data have shown correlations with SAR data, considering that both complementary technologies are reliable, low-cost resources.
We have shown that the deformation of the coastal surface is imposed by the seismic activity of the subduction area on a large scale, and that there are different factors that can enhance the subsidence of urban areas, such as geological faults and soil typology. From the analysis of the data and the cartography, it is concluded that there are tectonic conditions that configure current scenarios and potential threats inherent to a tectonically active seismic coastal zone. We also conclude that in the medium term there may be an increase in risk resulting from greater exposure generated by the protrusion of the urban fabric on dune fields near the coast, susceptible to tsunami flooding or increases in sea level.
To verify the risks associated with the effects of earthquakes and coastal tectonic subsidence in urban centers, it is necessary to complement our information with that obtained from local surveys; however, the results obtained allow us to understand the phenomenon of tectonic subsidence of the Chilean coast in urban areas, detect potentially hazardous areas, and contribute to correct spatial planning.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14071611/s1. PSI data. kmz for ascending and descending frames + .png image output data.

Author Contributions

Conceptualization, F.O., M.M. and G.M.; methodology, F.O. and M.M.; software, F.O. and J.H.; validation F.O. and J.H.; writing F.O.; supervision, M.M. and G.M. All authors have read and agreed to the published version of the manuscript.

Funding

We acknowledge financial support from the Millennium Nucleus CYCLO (The Seismic Cycle Along Subduction Zones) funded by the Millennium Scientific Initiative (ICM) of the Chilean Government grant NC160025. F.O. acknowledges financial software support from the ESA NoR project ID: 65514, M.M. acknowledges financial support from the FONDECYT Project ANID 1181479.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The geological maps are available on the public portal SERNAGEOMIN, Chilean National Geological and Mining Service. https://www.sernageomin.cl/. GNSS data is available on the platform of Nevada Geodetic Laboratory, of the University of Nevada, NV, USA. http://geodesy.unr.edu/index.php. The catalog of seismic faults is available in the Data Base Chilean active faults, of Millennium Nucleus CYCLO. https://fallasactivas.cl/.

Acknowledgments

F.O. acknowledges academic and technical support of PhD thesis advisor professor Maria Marsella and co-advisor professor Paola Di Mascio. F.O. also acknowledges the financial support from La Sapienza University of Rome (DICEA), Millennium Nucleus CYCLO, and the Department of Civil Engineering of University of Concepcion for doctoral stay. M.M. and J.H. acknowledge support from PRECURSOR ANILLO Project PIA ACT-192169 and FONDECYT Project ANID 1181479.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 2. Study area and location of urban areas: Navidad, Litueche, La Estrella, Marchigue, Paredones, and Pichilemu; Coast Range, Central Depression, and Main Range. Background DTM ALOS 30 mts.
Figure 2. Study area and location of urban areas: Navidad, Litueche, La Estrella, Marchigue, Paredones, and Pichilemu; Coast Range, Central Depression, and Main Range. Background DTM ALOS 30 mts.
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Figure 3. Simplified geological map of Chile central, with main geologic units [56].
Figure 3. Simplified geological map of Chile central, with main geologic units [56].
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Figure 4. (a) Data set used, tracks Sentinel-1 along both relative orbits for ascending (18ASC) and for descending (156DSC), and coastline GPS (red points); (b) GPS plot data, with black points representing GPS selected for subsidence areas RCSD, NAVI, PCMU, and ILOC.
Figure 4. (a) Data set used, tracks Sentinel-1 along both relative orbits for ascending (18ASC) and for descending (156DSC), and coastline GPS (red points); (b) GPS plot data, with black points representing GPS selected for subsidence areas RCSD, NAVI, PCMU, and ILOC.
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Figure 5. Regional displacement overview maps (mm/year), with (a) representing ascending orbit 18ASC and (b) descending orbit 156DSC, for the MT-InSAR observation period 2018–2021. The subsidence detected is red for the SW area.
Figure 5. Regional displacement overview maps (mm/year), with (a) representing ascending orbit 18ASC and (b) descending orbit 156DSC, for the MT-InSAR observation period 2018–2021. The subsidence detected is red for the SW area.
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Figure 6. Land displacement maps in subsidence area (mm/year) for (a) ascending orbit 18ASC and (b) descending orbit 156DSC. Small towns are evidenced by white points.
Figure 6. Land displacement maps in subsidence area (mm/year) for (a) ascending orbit 18ASC and (b) descending orbit 156DSC. Small towns are evidenced by white points.
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Figure 7. Comparison between the P-SBAS surface deformation time series (red points) and the projected GNSS LOS (black lines) relevant to the stations identified in the subsidence areas NAVI, PCMU, RSCD and ILOC.
Figure 7. Comparison between the P-SBAS surface deformation time series (red points) and the projected GNSS LOS (black lines) relevant to the stations identified in the subsidence areas NAVI, PCMU, RSCD and ILOC.
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Figure 8. Vertical displacement component estimation map (mm/year) for subsidence area.
Figure 8. Vertical displacement component estimation map (mm/year) for subsidence area.
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Figure 9. E-W displacement component estimation map (mm/year) for the subsidence area.
Figure 9. E-W displacement component estimation map (mm/year) for the subsidence area.
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Figure 10. Vertical displacement, Vup maps (mm/year), for each of the municipalities in the subsidence area (Navidad, Pichilemu, Paredones, Litueche, La Estrella, and Marchigue).
Figure 10. Vertical displacement, Vup maps (mm/year), for each of the municipalities in the subsidence area (Navidad, Pichilemu, Paredones, Litueche, La Estrella, and Marchigue).
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Figure 11. Geological map of the study area for Navidad and Pichilemu, with main lithology [56], black segmented lines for geological faults [53] denominated 1,2, and 3 for the areas of Navidad and Pichilemu, and blue lines denoting urban infrastructure for the study area.
Figure 11. Geological map of the study area for Navidad and Pichilemu, with main lithology [56], black segmented lines for geological faults [53] denominated 1,2, and 3 for the areas of Navidad and Pichilemu, and blue lines denoting urban infrastructure for the study area.
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Figure 12. Comparison LOS displacement for Navidad and Pichilemu areas (a) pre-earthquake: February 2018–June 2019, (b) earthquake: July 2019–September 2019, and (c) post-earthquake October 2019–May 2021.
Figure 12. Comparison LOS displacement for Navidad and Pichilemu areas (a) pre-earthquake: February 2018–June 2019, (b) earthquake: July 2019–September 2019, and (c) post-earthquake October 2019–May 2021.
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Figure 13. Cumulative displacement maps [mm] for Navidad and Pichilemu; white lines represent geologic faults crossing coastal zone of Navidad and urban area of Pichilemu; background DEM ALOS PalSAR 12.5 mts.
Figure 13. Cumulative displacement maps [mm] for Navidad and Pichilemu; white lines represent geologic faults crossing coastal zone of Navidad and urban area of Pichilemu; background DEM ALOS PalSAR 12.5 mts.
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Figure 14. Profile lines are perpendicular to Fault 1 and Fault 3. Figures (A) and (B) show the terrain profile (A-A’, B-B’), and (C) and (D) show the estimated ground deformation obtained with MT-InSAR.
Figure 14. Profile lines are perpendicular to Fault 1 and Fault 3. Figures (A) and (B) show the terrain profile (A-A’, B-B’), and (C) and (D) show the estimated ground deformation obtained with MT-InSAR.
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Table
Table 1. Sentinel-1 data set, including the main features of SLC products used.
Table 1. Sentinel-1 data set, including the main features of SLC products used.
OrbitAscending Descending
Sensor 1B1B
N° acquisitions22496
Date of measurement start5 February 201810 January 2018
Date of measurement end26 May 202130 May 2021
Relative orbit18156
Polarization VVVV
SwathIW 1–3IW 1–3
Table
Table 2. Basic statistics of PSI MT-InSAR for municipalities.
Table 2. Basic statistics of PSI MT-InSAR for municipalities.
MunicipalitiesCount
PSI 		MinMaxMeanStandard DeviationSubsidence Area km2 R 2
Navidad52,659−2.700.68−0.650.6244.130.89
Litueche 60,676−3.420.58−1.090.7258.350.85
Pichilemu50,250−3.28−0.23−2.000.50231.070.77
La Estrella45,827−1.920.56−0.330.390.000.00
Paredones48,077−2.840.18−0.830.4217.040.95
Marchigue 58,816−2.70−0.24−1.430.3716.930.90
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Orellana, F.; Hormazábal, J.; Montalva, G.; Moreno, M. Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data. Remote Sens. 2022, 14, 1611. https://doi.org/10.3390/rs14071611

AMA Style

Orellana F, Hormazábal J, Montalva G, Moreno M. Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data. Remote Sensing. 2022; 14(7):1611. https://doi.org/10.3390/rs14071611

Chicago/Turabian Style

Orellana, Felipe, Joaquín Hormazábal, Gonzalo Montalva, and Marcos Moreno. 2022. "Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data" Remote Sensing 14, no. 7: 1611. https://doi.org/10.3390/rs14071611

APA Style

Orellana, F., Hormazábal, J., Montalva, G., & Moreno, M. (2022). Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data. Remote Sensing, 14(7), 1611. https://doi.org/10.3390/rs14071611

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