GNSS-Based Models of Displacement, Stress, and Strain in the SHETPENANT Region: Impact of Geodynamic Activity from the ORCA Submarine Volcano

Abstract

The South Shetland Islands and Antarctic Peninsula (SHETPENANT region) constitute a geodynamically active area shaped by the interaction of major tectonic plates and active magmatic systems. This study analyzes GNSS time series spanning from 2017 to 2024 to investigate surface deformation associated with the 2020–2021 seismic swarm near the Orca submarine volcano. Horizontal and vertical displacement velocities were estimated for the preseismic, coseismic, and postseismic phases using the CATS method. Results reveal significant coseismic displacements exceeding 20 mm in the horizontal components near Orca, associated with rapid magmatic pressure release and dike intrusion. Postseismic velocities indicate continued, though slower, deformation attributed to crustal relaxation. Stations located near the Orca exhibit nonlinear, transient behavior, whereas more distant stations display stable, linear trends, highlighting the spatial heterogeneity of crustal deformation. Stress and strain fields derived from the velocity models identify zones of extensional dilatation in the central Bransfield Basin and localized compression near magmatic intrusions. Maximum strain rates during the coseismic phase exceeded 200 $\mathrm{\nu }$strain/year, supporting a scenario of crustal thinning and fault reactivation. These patterns align with the known structural framework of the region. The integration of GNSS-based displacement and strain modeling proves essential for resolving active volcano-tectonic interactions. The findings enhance our understanding of back-arc deformation processes in polar regions and support the development of more effective geohazard monitoring strategies.

1. Introduction

Over the past three decades, continuous technological advancements have established Global Navigation Satellite Systems (GNSSs) as fundamental geodetic tools for the precise observation of dynamic Earth processes. These systems enable high-resolution monitoring of crustal deformation across terrestrial, marine, cryospheric, and atmospheric environments. Among them, the Global Positioning System (GPS) has become an integral component of geophysical research, supporting both real-time analyses and long-term assessments of natural hazards such as earthquakes, volcanic eruptions, and tsunamis.

Volcanic regions are governed by dynamic and complex processes that pose significant threats to human life and infrastructure. Understanding these processes is essential for hazard mitigation and risk preparedness. GNSS technology has proven to be a powerful tool in volcano monitoring, providing critical insights into deformation, uplift, and seismicity associated with volcanic systems [1,2,3,4,5,6,7,8,9,10].

A primary application of GNSS in volcanic environments is the detection of ground deformation. Volcanic eruptions are frequently preceded by uplift or subsidence due to magma movement or changes in internal pressure [5,6,7,9,10,11,12,13,14,15,16,17]. GNSS is also capable of detecting dike and sill intrusions [17,18,19], slip along tectonic or volcanic faults [15,20,21], and deformation signals associated with hydrothermal activity [22,23,24]. These measurements allow precise tracking of spatiotemporal deformation patterns and contribute to early warning systems for volcanic unrest and potential eruptions [5,6,7,8,10,11,15,16,25,26,27,28,29].

GNSS technology has been successfully applied at various volcanic systems worldwide. At Kīlauea (Hawai’i), real-time GNSS data during the 2018 eruption revealed significant caldera subsidence and radial deformation consistent with magma withdrawal and collapse-related faulting [15]. During Mauna Loa’s 2022 eruption, modern GNSS observations indicated summit deflation caused by dike intrusion aligned with surface fissures [17]. In the Lesser Antilles, low-cost GNSS units on Saba demonstrated monitoring capabilities comparable to conventional networks, enabling effective volcanic surveillance under financial constraints [10]. GNSS data from the 2022 unrest at São Jorge (Azores) detected vertical uplift prior to seismicity, interpreted as lateral sill emplacement due to magma migration [19].

At Vulcano (Italy), a real-time mobile GNSS network captured short-term surface deformation during the 2021–2022 unrest, associated with disruptions in the hydrothermal system likely driven by subsurface magmatic activity [24]. In Costa Rica, continuous GNSS data at Turrialba, integrated with seismic and gas monitoring, have improved forecasting of eruptive activity [5]. At Piton de la Fournaise (Réunion), the combination of GNSS and InSAR has provided complementary spatial and temporal resolution of magma intrusion processes [30]. In Japan, zenith tropospheric delay (ZTD) derived from GNSS measurements successfully detected volcanic cloud activity at Sakurajima during the 2014 eruptive phase [31]. Long-term GNSS monitoring at Timanfaya (Lanzarote) revealed southeastward displacements of 3 mm/year and localized subsidence of up to 6 mm/year in areas with geothermal anomalies [12].

At Deception Island (Antarctica), GNSS-derived deformation and seismicity patterns revealed magmatic intrusion into the conduit system at estimated depths between 6 and 10 km [32,33]. Similarly, the Orca volcano—an active submarine edifice located 20 km south of King George Island in the Bransfield Strait—is situated in a seismically active extensional rift between the Antarctic Peninsula and the South Shetland Islands [34]. This tectonic setting offers ideal conditions for evaluating GNSS-based monitoring of geodynamic processes. In 2020, a substantial increase in seismicity and uplift was detected in the vicinity of Orca, with GNSS data indicating magma intrusion beneath the volcano and providing insights into pre-eruptive processes [35,36].

Ref. [36] reported 3186 seismic events between 26 August and 11 September, 2020, with a daily average of 187 earthquakes and a peak of 359 on August 30. Using a single-station approach, 217 events were located with magnitudes up to 4. A seismic swarm was detected 25 km from the station, near the Orca seamount and associated ENE–WSW-trending listric normal faults, consistent with a volcanic origin. Concurrently, earthquakes linked to the NW–SE-trending Artigas Fault were also recorded, suggesting fault reactivation. The swarm exhibited a relatively high b-value (1.22), indicating heterogeneous stress conditions within the Bransfield Rift.

Between August 2020 and June 2021, 36,000 seismic events were detected using data from station JUBA and regional USGS stations, including 128 with magnitudes above 4 [35]. By applying template-matching and single-station methods using 114 VT templates, a total of 36,241 VT events were identified. The sequence began on 7 August 2020, with the first detected event dated 29 July. Seismicity peaked on 29 August with over 12,000 events in a single day, then declined following a temporal pattern similar to Omori’s law. A sharp decrease occurred on 6 November 2020. The b-value of 1.6 (±0.6) further supports a volcanic source for the crisis, along with the lack of a triggering mainshock, the spatiotemporal distribution of seismicity, and associated GNSS-detected deformation. These observations suggest that, in addition to the background deformation (7 cm/year) at the Bransfield Ridge, transient localized deformation events occur along its axial volcanic structures.

Ref. [37] identified 85,000 earthquakes over a six-month period in late 2020, including 128 with magnitudes above 4, based on continuous data from stations JUBA and R4DE4. Their analysis revealed that strike-slip events were linked to magmatic intrusion, while shallower events indicated lateral dike propagation over 20 km. A significant reduction in seismicity following the Mw 6.0 event on 6 November 2020 was interpreted as resulting from depressurization of the magmatic system.

Ref. [38] reported the deployment of five ocean-bottom seismometers (OBSs) by the Korea Polar Research Institute between 2020 and 2021. By cross-correlating OBS and land-based seismic data using template-matching techniques, they identified 67,952 events and manually relocated 2313 epicenters, revealing a seismicity cluster within the central basin. These findings improve the understanding of subduction-related tectonic processes in the region.

In this study, we assess the use of GNSS data for investigating volcanic deformation, with a specific focus on the Orca volcano. We analyze time series from geodetic stations located around the volcano to identify uplift, deformation patterns, and associated seismicity. To better understand the underlying geodynamic mechanisms, we compute preseismic, coseismic, and postseismic velocities. Additional stations from the Nevada Geodetic Laboratory (NGL) are included to expand spatial coverage and enhance the interpretation of regional deformation processes.

2. Geodynamic Frame and Seismic Activity

The South Shetland Islands, the Bransfield Strait, and the Antarctic Peninsula (SHETPENANT region) constitute a geodynamically complex area characterized by active tectonism, volcanism, and crustal deformation. This complexity results from the convergence of the South American and Antarctic Plates and their interaction with four minor plates—the Scotia, South Sandwich, Phoenix, and South Shetland plates—as well as two major fracture zones: Shackleton and Hero (Figure 1) [32]. The region is particularly affected by the rollback of the subducted Phoenix Plate and the extensional processes within the Bransfield Basin, with an estimated opening rate of 0.9 cm/year [32]. Continuous and campaign-based GNSS measurements over the past three decades have provided valuable insights into the regional tectonic framework, showing that Livingston and Deception Islands share a similar geodynamic behavior associated with the NW–SE extension of the Bransfield Basin and NE–SW compressive forces [32]. These observations confirm the presence of multiple tectonic blocks and reveal transient deformation patterns linked to both tectonic and magmatic processes. The maps in Figure 1a,c were produced using data from the QAntarctica project [39].

The region is seismically active, with more than 200 earthquakes of magnitude greater than 4 recorded over the past 50 years (USGS catalogue). Most of these were shallow (10 km depth), although some occurred at intermediate depths northwest of the South Shetland Islands, indicating ongoing subduction of the Drake Plate beneath the South Shetland continental block [40]. Temporary seismic deployments have also recorded hundreds of low-magnitude events (M 0.5–5) in the Bransfield Strait [34,40,41], typically occurring as swarms [42,43]. These events are often located near volcanic centers and are believed to have volcanic or volcano-tectonic origins, likely associated with magmatic intrusions or hydrothermal activity [40,41,44].

Volcanic and tectonic features in the region include Deception Island, Penguin Island, Bridgeman Island, and several submarine volcanoes such as Orca, Three Sisters, and Humpback. These align along the Bransfield Rift, an active back-arc extensional system that accommodates the region’s transtensional deformation.

To investigate these dynamic processes, this study analyzes continuous GNSS data from a network of geodetic stations distributed throughout the SHETPENANT region. Although a detailed description of the GNSS networks and processing is provided in Section 3, it is important to note here that the observations serve as a critical tool for quantifying surface deformation and strain accumulation associated with tectonic and volcanic activity.

Two recent seismic sequences underscore the relevance of such geodetic observations. The first occurred between 2014 and 2015 southeast of Livingston Island and consisted of approximately 9000 earthquakes, with magnitudes reaching up to 4.6 [43]. This sequence may be related to submarine volcanic activity and could have contributed to stress changes in nearby volcanic systems, such as Deception Island (Figure 2).

The second and more significant sequence took place between August 2020 and November 2021 in the vicinity of the Orca submarine volcano. This swarm included more than 80,000 earthquakes, with focal depths reaching approximately 10 km [37]. Epicenters were concentrated northeast of the volcano (Figure 2). The activity began on 28 August 2020 and lasted about a year. Most of the seismicity was concentrated in the early months of the sequence [36]. Two distinct peaks in cumulative seismic moment release were identified: the first in November 2020, marked by numerous M > 4 events and a 5.9 Mw earthquake near Orca; and the second in January 2021, when the largest earthquake of the sequence (6.9 Mw) occurred farther east (Figure 3). Moment tensor solutions are consistent with strike-slip and normal faulting, with a predominant NW–SE-oriented T-axis, in line with previous studies and the regional transtensional regime [37].

3. Methodology

3.1. GNSS Stations

The data used in this work come from geodetic stations belonging to different GNSS networks, whose locations are shown in Figure 4 and detailed in

Table 1. Location and information of GNSS stations used in this study.

Stations	Latitude	Longitude	Network	Analysis Center	Location
ARMO	−62,294	−59,177	RGAE	LAGC	Harmony Point, Nelson Island
BEGC	−62,979	−60,674	RGAE	LAGC	Gabriel de Castilla Base, Deception Island
BEJ1	−62,663	−60,389	RGAE	LAGC	Juan Carlos I Base, Livingston Island
BYER	−62,666	−61,099	RGAE	LAGC	Byers Peninsula, Livingston Island
CACI	−64,156	−60,957	RGAE	LAGC	Caleta Cierva, AP
CPER	−62,397	−59,671	RGAE	LAGC	Coppermine Peninsula, Robert Island
DAL5	−62,241	−58,678	DNA-IAA	NGL	Carlini Base, King George Island
DUPT	−64,805	−62,817	ANET	NGL	Duthiers Point, AP
GRW1	−62,216	−58,962	RGAE	LAGC	Greenwich Island
HUGO	−64,963	−65,668	ANET	NGL	Hugo Island
ILOW	−63,271	−62,009	RGAE	LAGC	Low Island
MBIO	−64,240	−56,623	RAMSAC	NGL	Base Marambio, AP
MELU	−62,596	−59,903	RGAE	LAGC	Half Moon Island
OHI2	−63,321	−57,901	IGS	NGL	O’Higgins Antarctic Base, AP
OHI3	−63,321	−57,901	IGS	NGL	O’Higgins Antarctic Base, AP
PAL2	−64,775	−64,051	IGS	NGL	Palmer Antarctic Base, Anvers Island
PING	−62,099	−57,937	RGAE	LAGC	Penguin Island
PRPT	−66,007	−65,339	ANET	NGL	Prospect Point
ROBN	−65,247	−59,445	ANET	NGL	Robertson Island
SGP1	−65,557	−61,722	ANET	NGL	Cape Disappointment
SNOW	−62,728	−61,291	RGAE	LAGC	Snow Island
SPGT	−64,295	−61,052	ANET	NGL	Spring Point
SPRZ	−63,395	−56,996	RAMSAC	NGL	Esperanza Base, Esperanza Bay
UYBA	−62,184	−58,902	REGNA-ROU	NGL	Artigas Base, King George Island

: RGAE network (Red Geodésica Antártica Española), ANET (Antarctic Network), DNA (Dirección Nacional del Antártico), RAMSAC (Red Argentina de Monitoreo Satelital Continuo), IGS (International GNSS Service), and REGNA-ROU (Red Geodésica Nacional Activa de la República Oriental del Uruguay).

Stations PING, ARMO, GRW1, CPER, MELU, BEJ2, BYER, SNOW, BEGC, ILOW, and CACI belong to the RGAE network. The RGAE network is composed of benchmarks distributed across the South Shetland Islands and the Antarctic Peninsula, and began to be established in 1989. The absolute coordinates of these stations are precisely determined with respect to the WGS84 ellipsoid in the ITRF2008 reference frame [47].

Stations DUPT, HUGO, PRPT, ROBN, SGP1, and SGPT are part of the ANET network, coordinated by Ohio State University. ANET comprises a combination of GPS and seismic stations distributed across West Antarctica and the Transantarctic Mountains, which serve as the main geological boundary between East and West Antarctica. A key aspect of ANET is its integrated design, which combines GPS and seismic instrumentation, enabling a more comprehensive analysis of the interactions between the ice sheets and the underlying lithosphere. The GPS stations specifically monitor bedrock motion in response to variations in ice mass.

The data used in this study include geodetic stations MBIO and SPRZ, which belong to the RAMSAC network, managed by the Instituto Geográfico Nacional (IGN) of Argentina. RAMSAC is a network of continuous GNSS stations distributed across various regions, including Antarctica. These Antarctic stations are strategically located to monitor geodetic parameters, providing precise positioning data referenced to the WGS84 ellipsoid in the ITRF2014 reference frame [48]. By continuously recording GNSS signals, the RAMSAC network enables the analysis of tectonic movements and interactions between the ice sheets and the underlying bedrock, contributing to international scientific initiatives and collaborative networks such as IGS, thereby enhancing our understanding of geophysical processes in polar regions.

In addition, data from the UYBA and DAL5 stations were also used in this study. The UYBA station is operated by the Instituto Geográfico Militar de Uruguay (IGM-UY) and forms part of the REGNA-ROU network. It contributes to the continental geodetic infrastructure within the SIRGAS (Sistema de Referencia Geocéntrico para las Américas) framework. The DAL5 station is managed by the Instituto Antártico Argentino (IAA) and is part of Argentina’s geodetic observation program in Antarctica under DNA. The stations OHI2, OHI3, and PAL2 belong to the IGS network.

Finally, the ELE1 and NOT1 stations, both taken from [49], have been included. ELE1 is located on Elephant Island and is integrated into the ITRF framework, while NOT1 is situated on the Antarctic Peninsula and is part of the UNAVCO network.

3.2. Data Processing

GNSS data from all stations used in this study were processed either directly, using the Bernese GNSS Software, or indirectly, through data products obtained from the Nevada Geodetic Laboratory (NGL), depending on the network of origin.

Time series for all non-RGAE stations were sourced from the NGL, which routinely collects and processes geodetic-quality GPS observations from over 17,000 stations worldwide. These include stations from various regional and commercial networks, as well as the IGS network [50]. NGL provides publicly available products such as daily position coordinates (latitude, longitude, and height) referenced to different realizations of the ITRF and available at various temporal resolutions [51]. Observations are processed using GIPSY-X version 1.0 [52], developed by NASA’s Jet Propulsion Laboratory (JPL), which implements precise point positioning (PPP) techniques [53], incorporating JPL’s Repro3 final GPS orbit and clock products. More details on the processing strategy can be found at http://geodesy.unr.edu/gps/ngl.acn.txt (accessed on 7 July 2024).

GIPSY-X enables the independent estimation of station coordinates using precise satellite orbit and clock information, achieving sub-centimeter positioning accuracy [52]. It applies advanced models to correct for solid Earth tides, ocean tidal loading, and atmospheric pressure variations, ensuring high-fidelity geophysical modeling. Its ability to process individual stations efficiently makes it especially suitable for remote or geodynamically active regions, such as Antarctica. NGL also routinely estimates station velocities in the global reference frame using the MIDAS method (Median Interannual Difference Adjusted for Skewness) [54], providing robust long-term deformation rates.

In contrast, GNSS data from the RGAE network were processed in-house using version 5.0 of the Bernese GNSS Software [55], developed by the Astronomical Institute of the University of Bern. Bernese is a high-precision geodetic software package that employs double-differencing techniques to mitigate satellite and receiver clock errors. It includes sophisticated modeling of tropospheric and ionospheric delays, Earth tides, and other geophysical effects, allowing accurate estimation of station positions, velocities, and atmospheric parameters.

The NGL solutions used in this study are referenced to the ITRF2014, whereas the RGAE data were processed using ITRF2008. No transformation was applied to homogenize the reference frames. However, differences in velocity estimates between ITRF2008 and ITRF2014 are typically below 0.5 mm/yr in most tectonically stable regions [48], which is negligible compared to the deformation signals analyzed in this study. Therefore, the combination of datasets referenced to different ITRF realizations does not compromise the integrity of the results.

Both processing strategies yield time series of topocentric coordinates (east, north, and elevation), derived by transforming geocentric positions (X, Y, Z) into a local reference frame based on each station’s latitude and longitude. This transformation, following the method described by Berrocoso et al. [56], facilitates a more intuitive interpretation of displacement patterns and aids in the identification of deformation trends [57].

3.3. Time Series Analysis

The developed methodology has been applied to all the analyzed stations. However, to clearly illustrate the process, this section presents the time series data for the OHI3 station, located at the Chilean O’Higgins base (Figure 5). This station has been chosen as a representative example to explain the procedure, detailing the steps and techniques used in the analysis. In this way, the aim is to comprehensively understand how the methodology was applied across the different stations.

This study analyzes time series data and incorporates earthquakes with a magnitude greater than 4 that occurred in the region from 2017 to 2024. The time series are represented in blue, while earthquakes with a magnitude between 4 and 5 are shown in yellow, between 5 and 6 in orange, and earthquakes greater than 6 in red. This color coding helps to visually differentiate the intensity of the seismic events alongside the temporal data, providing a clearer understanding of their occurrence in the time series (Figure 5).

Based on the recorded earthquakes, the phases of the seismic cycle have been identified: preseismic, coseismic, and postseismic. In Figure 5, these phases are delineated by dashed magenta lines, clearly marking the boundaries between phases of the seismic cycle. These phases represent distinct stages in the build-up and release of tectonic stress within the Earth’s crust. The preseismic phase is characterized by subtle changes in seismicity and potential precursory signals, which might provide insight into earthquake prediction. The coseismic phase corresponds to the main shock, involving the rapid release of accumulated stress along a fault. Finally, the postseismic phase encompasses the aftershocks and slower deformation processes that occur as the crust adjusts to the new stress state [58].

For each phase of the seismic cycle, the displacement velocities of the east, north, and elevation components are calculated using the CATS adjustment method (Create and Analyze Time Series) [59], represented by a black line. This approach provides a precise estimation of ground motion by modeling the time series data with a comprehensive adjustment algorithm [57].

The CATS adjustment is based on a stochastic analysis of GNSS time series using Maximum Likelihood Estimation (MLE). This method allows the simultaneous estimation of noise amplitudes, linear trends, seasonal signals, and the magnitude of any discontinuities (e.g., coseismic offsets), as well as the uncertainty of these parameters [59]. It enables the separation of the linear and nonlinear components of the series, where the linear part includes trends, outliers, jumps, and harmonic terms, while the nonlinear part accounts for temporally correlated noise. To estimate the noise parameters using MLE, a combined noise model is assumed, typically white noise (WN) and power-law noise (PLN), and the likelihood function is iteratively maximized. This joint estimation of stochastic and deterministic parameters leads to more realistic uncertainty estimates and improves the reliability of GNSS-derived velocities [60].

The functional model used to analyze the GNSS coordinate time series is:

$$x\left(t\right)=a+bt+\sum _{j=1}^2\left(A_{j}sin\left({\omega }_{j}t\right)+B_{j}cos\left({\omega }_{j}t\right)\right)+\sum _{j=1}^n{C}_{j}H(t-T_j)$$

(1)

where $x\left(t\right)$ is the coordinate at time t; a is the initial position; b is the linear velocity; $A_j$ and $B_j$ are the amplitudes of the annual (${\omega }_1$) and semi-annual (${\omega }_2$) harmonic components; $C_j$ are the amplitudes of the discontinuities occurring at epochs $T_j$; and $H(t-T_j)$ is the Heaviside step function:

$${\displaystyle H\left(\tau \right)=\left\{\begin{array}{cc}0\hfill & \mathrm{if}\tau <0\hfill \\ 1\hfill & \mathrm{if}\tau \ge 0\hfill \end{array}\right.}$$

3.4. Stress and Strain Parameters

At present, many investigations into geodynamic deformation rely on displacement fields obtained from GNSS time series [61,62]. The deformation of a solid body, treated as a continuous medium, is examined by analyzing how the position of each material point changes spatially over time [63,64,65]. Estimating stress–strain rate fields from GNSS geodetic data is a critical step in understanding crustal deformation, tectonic processes, and seismic hazard analysis. This process involves converting the velocity field derived from the GNSS data into a strain rate field using mathematical techniques.

The velocity field, representing the horizontal movements of the crust, is derived from the temporal derivatives of the GNSS position time series. To estimate continuous deformation models from discrete GNSS station data, various interpolation techniques can be applied, including Inverse Distance Weighting (IDW), Delaunay triangulation, and exponential weighting methods.

In this study, we use IDW to generate smooth maps of GNSS-derived horizontal velocities, allowing reliable interpolation in areas with irregular station distribution. For the estimation of continuous fields of stress–strain parameters, we adopt the Delaunay triangulation method, which constructs a triangular mesh connecting GNSS stations such that no station lies inside the circumcircle of any triangle. Within each triangle, the velocity field is linearly interpolated, ensuring smooth spatial transitions and preserving the relative geometry among stations [66]. Generating continuous maps of the horizontal strain rate tensor components offers valuable insights into the tectonic regime and deformation style of the study area.

4. Results

The results are presented through the time series from the DAL5, UYBA, and PAL2 stations, which include the analyses described in Section 3.3. The displacement models for the preseismic, coseismic, and postseismic phases refer to the horizontal component, and the corresponding calculated velocities are also shown. Finally, the stress–strain models are displayed as contour maps of horizontal dilatation, maximum shear deformation modulus, and maximum geodetic deformation values.

4.1. Time Series

Valuable information about ground deformation associated with volcanic activity and tectonic movements can be gleaned from time series data collected at GNSS stations. These time series can display characteristics of both linear and non-linear trends. The analysis of topocentric time series facilitates the identification of pre-seismic, co-seismic, and post-seismic phases that occur within a seismic cycle.

Data from the stations DAL5, UYBA, and PAL2, spanning the period from 2017 to 2024, were analyzed in accordance with the method outlined earlier. The results of the analysis indicate that the stations nearest to the Orca Volcano (DAL5 and UYBA) exhibit non-linear behaviors, whereas the station situated on the peninsula displays a linear behavior across all components (PAL2).

The DAL5 station, illustrated in Figure 6, is situated at the Carlini Antarctic Base in Argentina. Conversely, the UYBA station, depicted in Figure 7, is located at the Artigas Antarctic Base in Uruguay. The distinct stages depicted in Figure 6 and Figure 7 are delineated by dashed magenta lines, providing a clear separation between each phase of the seismic cycle.

On the left side of the figure, the pre-seismic phase shows minimal fluctuations in seismic activity, including roughly 10 earthquakes of magnitude over 4 and a single one with a magnitude over 5. A linear pattern is apparent in the time series data, directly linked to ongoing tectonic activity.

The central section of the figure comprises the majority of the seismic crisis, featuring earthquakes with magnitudes exceeding 4 and 5, including a notable earthquake of magnitude 6. These is the coseismic stage is marked by a rapid unloading of pre-existing stress along a fault line. The variation in jump range among nearby stations is due to their distinct locations. The station DAL5 experienced a more significant rise of approximately −0.21 cm compared to UYBA, which rose by −0.11 cm, due to DAL5’s proximity to the Orca volcano.

The right-hand side of the figure depicts the post-earthquake phase, characterized by aftershocks resulting from around eight earthquakes with a magnitude greater than four, and two larger than five, which display linear behaviour associated with a gradual deformation process evolving over time. The affected stations suffered consequences from a geodynamic event that took place at the ORCA volcano in 2020.

The PALM station (Figure 8) is situated at the U.S. Palmer Antarctic Base on Anvers Island. Its time series are not affected by geodynamic activity associated with the ORCA volcano and, as a result, exhibit a linear trend across all components.

4.2. Preseismic, Coseismic, and Postseismic Displacement Models

The preseismic, coseismic, and postseismic phases were determined for each component of the time series. The velocities corresponding to each phase, along with their associated error ellipses, are summarized in

Table 2. Preseismic, coseismic, and postseismic velocities (mm/yr) estimated using the CATS method. Standard deviations of each velocity component are also reported (mm). Velocities are referenced to January 2021. RGAE stations are expressed in ITRF2008, and NGL stations in ITRF2014.

	Preseismic						Coseismic						Postseismic
Stations	East	North	Up	${\mathit{\sigma }}_{\mathit{e}}$	${\mathit{\sigma }}_{\mathit{n}}$	${\mathit{\sigma }}_{\mathit{u}}$	East	North	Up	${\mathit{\sigma }}_{\mathit{e}}$	${\mathit{\sigma }}_{\mathit{n}}$	${\mathit{\sigma }}_{\mathit{u}}$	East	North	Up	${\mathit{\sigma }}_{\mathit{e}}$	${\mathit{\sigma }}_{\mathit{n}}$	${\mathit{\sigma }}_{\mathit{u}}$
BEGC	8.9	8.7	−5.2	0.18	0.20	0.14	9.8	27.6	−25.2	0.80	1.06	1.97	-	-	-	-	-	-
DAL5	8.8	16.4	−3.9	0.14	0.13	0.31	−175.7	101.4	53.7	2.02	1.14	2.09	7.3	19.7	1.8	0.11	0.11	0.28
DUPT	12.7	10.7	9.9	0.01	0.01	0.03	13.8	12.2	6.4	0.43	0.67	1.59	12.7	10.2	0.7	0.11	0.16	0.40
MBIO	16.6	9.8	3.3	0.02	0.02	0.05	16.1	11.1	14.2	0.47	0.65	1.80	16.9	7.8	5.8	0.17	0.20	0.45
OHI2	14.5	9.4	−1.7	0.25	0.60	1.10	19.4	4.6	17.5	0.78	1.93	4.04	17.1	11.1	−1.6	0.17	0.32	0.76
OHI3	14.9	10.2	0.2	0.08	0.14	0.39	20.2	9.8	16.4	0.70	1.61	4.23	15.0	12.6	0.4	0.26	0.42	0.88
PAL2	13.0	10.7	3.3	0.06	0.08	0.20	13.6	11.6	5.2	0.44	0.71	1.56	14.2	9.8	−0.4	0.09	0.12	0.31
PRPT	14.8	10.1	0.6	0.02	0.03	0.06	15.7	10.5	−6.8	0.47	0.67	1.60	17.4	9.6	−8.4	0.43	0.49	1.28
ROBN	16.6	8.6	5.8	0.02	0.01	0.03	20.8	10.7	10.2	0.52	0.67	1.35	15.8	9.7	8.1	0.14	0.15	0.35
SGP1	13.2	5.9	8.1	0.13	0.17	0.46	13.4	11.9	12.8	0.56	0.70	2.07	12.6	9.8	5.7	0.20	0.19	0.53
SPGT	15.3	12.2	8.8	0.02	0.02	0.05	14.3	14.8	8.5	0.41	0.60	1.49	14.2	11.8	5.6	0.14	0.17	0.43
SPRZ	15.9	10.2	7.3	0.03	0.03	0.34	19.2	8.9	8.0	1.02	0.92	2.58	15.9	10.6	6.2	0.25	0.22	0.54
UYBA	10.8	18.4	−4.5	0.11	0.15	0.47	−87.0	74.2	9.3	1.31	1.23	2.46	8.0	24.0	−2.7	0.60	0.80	2.50
HUGO	15.0	10.7	−1.3	0.01	0.01	0.04	-	-	-	-	-	-	16.0	10.0	2.6	0.09	0.09	2.45
ARMO	11.7	17.0	−6.4	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
BEJ1	11.7	17.0	−4.9	0.13	0.16	0.25	-	-	-	-	-	-	-	-	-	-	-	-
BYER	13.1	17.0	−3.2	0.16	0.08	0.30	-	-	-	-	-	-	-	-	-	-	-	-
CACI	16.3	14.8	25.6	0.10	0.05	0.56	-	-	-	-	-	-	-	-	-	-	-	-
CPER	9.3	14.8	−16.9	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
ELE1	4.2	14.4	2.8	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
GRW1	6.3	18.4	−4.4	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
ILOW	14.1	11.3	8.3	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
MELU	11.7	25.8	−6.1	0.01	0.01	0.01	-	-	-	-	-	-	-	-	-	-	-	-
NOT1	10.5	10.8	8.0	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
PING	3.8	26.5	−5.2	0.02	0.02	0.14	-	-	-	-	-	-	-	-	-	-	-	-
PRA1	4.9	16.2	0.0	0.00	0.00	0.00	-	-	-	-	-	-	-	-	-	-	-	-
SNOW	11.9	15.7	−1.6	0.01	0.01	0.01	-	-	-	-	-	-	-	-	-	-	-	-

. The displacement models for these phases are depicted in Figure 9, Figure 10 and Figure 11.

Figure 12 illustrates 2D horizontal displacement models (east and north components) for three distinct phases: preseismic, coseismic, and postseismic. These models were generated using IDW interpolation based on velocity data.

The color gradients in the maps represent the magnitude of displacement, with blue tones indicating lower displacement values and red tones indicating higher values. Contour lines are also included to highlight variations in displacement magnitude and provide additional spatial context.

Notably, the preseismic and postseismic phases share the same color scale to facilitate direct comparison of their displacement magnitudes. In contrast, the coseismic phase is displayed with its own distinct scale, as it exhibits significantly higher displacement values compared to the other phases. This visualization emphasizes both the spatial distribution and relative intensity of horizontal displacement during each seismic phase.

4.3. Stress and Strain Models

Figure 13 presents contour maps of horizontal dilatation, maximum shear strain, and maximum geodetic deformation, expressed in $\nu$strain/year, and derived from the horizontal velocity fields of the preseismic (a), coseismic (b), and postseismic (c) phases. A consistent color scale is used across all phases to allow direct comparison, although the scale ranges were adjusted to accommodate the significantly higher deformation magnitudes observed during the coseismic phase.

The spatial distribution of deformation reveals that the most intense activity occurred during the coseismic phase, with peak dilatation and shear strain concentrated in the region where the earthquakes originated. This area, located in the central Bransfield Basin, corresponds to a known active extensional zone. The observed deformation pattern is therefore consistent with ongoing crustal thinning and back-arc spreading processes in the region encompassing the South Shetland Islands and the northern Antarctic Peninsula.

During the preseismic and postseismic phases, deformation patterns are significantly weaker, indicating relative tectonic quiescence or postseismic relaxation. The spatial distribution of geodetic deformation reveals extension near the Bransfield Basin and compression along the South Shetland Trench, in line with the regional tectonic regime characterized by subduction-driven compression and back-arc extension. Shear strain patterns also point to localized right-lateral strike-slip deformation along mapped faults, consistent with previous geophysical and geological studies.

These results demonstrate a strong correlation between strain accumulation and known tectonic structures, underscoring the role of active fault systems in accommodating present-day deformation. Incorporating stress and strain modeling provides a more comprehensive view of the ongoing geodynamic processes and supports interpretations derived from GNSS displacement time series. Together, these analyses contribute to a better understanding of the current tectonic activity in the Bransfield back-arc region.

5. Discussion

The SHETPENANT region exhibits complex geodynamic behavior, driven by the convergence of major tectonic plates and the interaction of several microplates. Within this tectonic framework, the 2020–2021 seismic swarm near the Orca submarine volcano provided a valuable opportunity to analyze deformation processes associated with magmatic activity, using GNSS-based geodetic data.

GNSS time series from stations DAL5 and UYBA (Figure 6 and Figure 7), located near the Orca volcano, show significant coseismic displacements and nonlinear postseismic trends. In particular, station DAL5 recorded coseismic velocities of approximately 175.7 mm/year in the east component and 101.4 mm/year in the north component (Table 2), corresponding to a total horizontal velocity of about 202 mm/year. This geodetic signal is interpreted as clear evidence of reactivation of the Orca volcanic system, coinciding with an active phase of rifting in the Bransfield Strait.

The spatial alignment between the coseismic displacement vectors (Figure 10), the orientations of regional fault systems (Figure 3), and the strike-slip focal mechanisms derived from moment tensor solutions suggests that magmatic intrusions disturbed the local stress field, facilitating the reactivation of pre-existing tectonic structures.

The dilation and shear strain fields derived from GNSS velocity models (Figure 13) show, during the coseismic phase, a concentrated distribution of high values in the central Bransfield Basin, consistent with an active extensional regime. Near the Orca volcano, areas of negative dilation and distinct orientations of the deformation vectors can be observed, differing from the patterns in the rest of the basin. In contrast, the models for the pre- and postseismic phases show a more homogeneous spatial distribution, although the postseismic phase reveals slight anomalies that may be related to the system’s ongoing recovery.

Postseismic deformation patterns (Figure 11) indicate a relatively slow recovery of velocities and strain, particularly near the volcanic center. This behavior is also evident in the time series from stations DAL5 and UYBA (Figure 6 and Figure 7), where nonlinear displacements persist beyond the main seismic events. Such a prolonged response is characteristic of volcanic environments, where viscoelastic relaxation and long-lasting magmatic adjustments often control the post-event evolution. In contrast, purely tectonic settings tend to reestablish deformation more rapidly, generally following more linear and stable trends.

The comparison with the more distant station PAL2 (Figure 8) reinforces this interpretation: while PAL2 shows a stable and linear displacement throughout all phases, DAL5 and UYBA display clear variations between the pre-, co-, and postseismic phases, confirming the localized and complex nature of volcano-tectonic deformation.

These results highlight the usefulness of integrating displacement models and stress–strain analysis with long-term GNSS time series to characterize active deformation in back-arc volcanic systems. Although spatial resolution is limited by station distribution, this approach captures both rapid and gradual deformation phases and provides a coherent framework for interpreting the interaction between magmatism and regional tectonics.

6. Conclusions

This study demonstrates the effectiveness of long-term GNSS time series in characterizing the spatial and temporal evolution of surface deformation associated with magmatic and tectonic processes in the SHETPENANT region. By analyzing the 2020–2021 seismic swarm near the Orca submarine volcano, we were able to resolve the preseismic, coseismic, and postseismic phases, capturing both rapid displacements during seismic events and slower, nonlinear postseismic trends.

The largest displacements occurred near the Orca volcano during the coseismic phase, where GNSS stations such as DAL5 and UYBA recorded transient motions markedly different from the stable, linear trends observed at more distant stations like PAL2. These spatial variations, supported by moment tensor solutions and stress–strain modeling, are consistent with magmatic intrusions interacting with regional fault systems.

Strain field analysis revealed a well-defined extensional regime in the central Bransfield Basin and zones of localized compression near magmatic centers. The correspondence between these patterns and known tectonic structures suggests that crustal deformation is strongly influenced by structural inheritance and the dynamics of back-arc spreading.

Overall, this integrated approach combining GNSS displacement data with stress and strain modeling provides a coherent framework to assess active deformation in complex geodynamic settings. The results underscore the critical importance of continuous geodetic and seismic monitoring in volcanic regions, particularly in remote and tectonically active areas such as Antarctica. These findings contribute to a better understanding of crustal processes in back-arc systems and support future efforts aimed at hazard assessment and early warning strategies in similar environments.