Decelerating Uplift at Lazufre Volcanic Center, Central Andes, from A.D. 2010 to 2016, and Implications for Geodetic Models GEOSPHERE; V

Research Paper THEMED ISSUE: PLUTONS: Investigating the Relationship between Pluton Growth and Volcanism in the Central Andes

GEOSPHERE Decelerating uplift at Lazufre volcanic center, Central Andes, from A.D. 2010 to 2016, and implications for geodetic models GEOSPHERE; v. 13, no. 5 Scott T. Henderson1,2, Francisco Delgado2, Julie Elliott3, Matthew E. Pritchard2, and Paul R. Lundgren4 1Departamento de Geociencias, Universidad de los Andes, Cr 1 #18A-12, Bogotá, Colombia doi:10.1130/GES01441.1 2Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York 14853, USA 3Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, Indiana 47907, USA 12 figures; 5 tables; 1 supplemental file 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA

CORRESPONDENCE: sth54@cornell.edu ABSTRACT vicinity of uplift since the late Pliocene (Naranjo, 2010). Although the majority CITATION: Henderson, S.T., Delgado, F., Elliott, J., of dated eruptive products have Pleistocene ages, the most recent lava flow Pritchard, M.E., and Lundgren, P.R., 2017, Deceler ating uplift at Lazufre volcanic center, Central Andes, Interferometric synthetic aperture radar (InSAR) and GPS measurements at Lastarria has been dated at ~2500 yr old (Naranjo, 2010), and at Cordon from A.D. 2010 to 2016, and implications for geo beyond 2010 are presented for the first time for the Lazufre volcanic center del Azufre flows have been dated to 0.3 ± 0.3 Ma (Wilder, 2015). The strike detic models: Geosphere, v. 13, no. 5, p. 1489–1505, in the Central Andes. Vertical uplift at Lazufre was known to affect an area axis of current uplift is NNE-SSW and aligned with structural lineaments, and doi:10.1130/GES01441.1. >50 km in diameter at rates exceeding 3 cm/yr between 1997 and 2010. Analy the spatial footprint contains a high concentration of volcanic vents (Ruch and sis of new InSAR data through August 2016 indicates that the spatial pattern Walter, 2010). Furthermore, topographical analysis combined with dated flows Received 7 October 2016 Revision received 16 May 2017 of uplift is relatively unchanged but the amplitude of uplift has significantly de suggests persistent tumescence at the location of uplift since at least 400 ka Accepted 5 July 2017 creased to <1.5 cm/yr since at least December 2011. We present a time-series (Perkins et al., 2016). Published online 9 August 2017 inversion for InSAR data between 1996 and 2016 that is well fit by adouble A striking characteristic of the Lazufre system is extremely vigorous degas- exponential model, with an inflection point occurring in 2006. For two con sing from the Lastarria edifice. In fact, localized uplift of the edifice of Lastarria tinuous GPS stations installed within the deformation footprint in November volcano has prompted the hypothesis that Lastarria may act as a “pressure 2010, we have determined vertical velocities through 2014 or 2015 (depending valve” for a deeper magmatic plumbing system (Froger et al., 2007; Ruch on the station) that agree with contemporaneous InSAR-derived velocities. et al., 2009). Furthermore, recent in situ studies of gas composition at Lastarria Velocities from campaign GPS benchmarks established in November 2011 and have suggested a possible transition from a hydrothermal character between reoccupied in March 2014 are also presented. We use a previously proposed A.D. 2006 and 2009 (Aguilera et al., 2012) to a shallow magmatic source in 2012

model of an inflating sill at 10 km depth to explain geodetically observed dis (Tamburello et al., 2014). Remote sensing measurements of SO2 emissions at placements. Opening rates are halved (6.8 ± 1.25 × 106 m3/yr) compared to in Lastarria from 2004 onwards also suggest a possible time dependence with ferred values using data prior to 2010. Subsurface heterogeneity is accounted a peak in 2006 (Carn et al., 2013). The recent temporal changes in gas emis- for by assigning elastic parameters based on local seismic tomography in a sions at Lastarria provide evidence for changes to the subsurface magmatic finite-element model. Surface displacements (or inferred volume change esti system, which motivates examining recent geodetic measurements for tran- mates) for heterogeneous models compared to homogeneous models are sient signals. amplified by up to 7% within a 10 km radius of the center of uplift. Uplift began at Lazufre between late 1997 and 2000, accelerating to a maxi mum line-of-sight (LOS) velocity of 3.5 cm/yr and affecting a broad region >50 km in diameter (e.g., Henderson and Pritchard, 2013). A second, smaller INTRODUCTION region (~2 km across) of uplift of about ~0.5 cm/yr starting in 2002 has been observed on top of Lastarria volcano (e.g., Froger et al., 2007; Ruch et al., 2009) The Lazufre volcanic uplift signal is centered between the summits of but is not discussed in this paper because it is not well resolved by the data Lastarria volcano (25.168°S, 68.507°W, 5706 m elevation) and Cordon del sets used in this study. Owing to the remote location of Lazufre, deformation Azufre volcano (25.336°S, 68.521°W, 5481 m elevation) in the Central Andes measurements have mostly been made with interferometric synthetic aperture Volcanic Zone (CVZ) of northern Chile and Argentina (Fig. 1). These volcanoes radar (InSAR); however, due to the end of Envisat (European Space Agency were identified as having had Holocene activity based on remotely sensed [ESA]) satellite observations in 2010, there was a gap in observations. For morphological indicators such as post-glacial eruptive features (Francis and de the first time, we present post-2010 InSAR observations along with continu- For permission to copy, contact Copyright Silva, 1989). Subsequently, extensive regional geological mapping has been ous GPS observations at Lazufre to address the question of whether previous Permissions, GSA, or [email protected]. conducted, which indicated ~120 km3 of erupted material from vents in the spatiotemporal surface uplift trends have continued in the past 6 yr.

Figure 1. Overview map of Lazufre defor mation and GPS network, Central Andes. Circles are continuous GPS sites installed in November 2010; squares are campaign GPS benchmarks installed and surveyed in November 2011. East-west (Ux) and vertical (Uz) components of displacement (in cm/yr) are derived from InSAR data from between May 2006 and November 2008 (Remy et al., 2014). Black star marks the center of vertical uplift (25.259°S, 68.483°W). Black triangles are active vol canoes from the Smithsonian Global Vol canism Catalog. Curving black line is the international border between Chile and Argentina. Black triangles are active vol canoes from the Smithsonian Global Vol canism (http://volcano.si.edu).

To date, many source models of deformation at Lazufre have been con- All geodetic models to date for Lazufre have assumed that the crustal struc- strained by a single LOS viewing geometry, utilizing data from European ture is homogeneous and elastic. In recent years however, seismic tomog Remote Sensing [ERS] Satellites 1 and 2 from ESA and Envisat tracks 282 raphy and conductivity models have been published, which help constrain spanning July 1995 to May 2010 (Pritchard and Simons, 2002, 2004; Froger subsurface heterogeneity under the Lazufre region. In particular, magneto et al., 2007; Ruch et al., 2008). Owing to the nonunique nature of geodetic telluric data revealed a strong conductor under the deformation anomaly dip- models, there has been some disagreement as to whether the source was a ping eastward to the base of the crust. Modeling suggests that the feature laterally propagating sill (e.g., Ruch et al., 2008; Anderssohn et al., 2009) or could be due to 5–8 vol% partial melt which is feeding current sill inflation had a fixed geometry with variable opening rate. These models were based in the upper crust (Budach et al., 2013). A follow-up study with additional lo- on a rectangular elastic dislocation model (Okada, 1985). All available as- cal instrumentation defined several distinct low-conductivity anomalies—one cending and descending data through 2010 were used recently by Pearse and directly under Lastarria volcano (1–10 W·m) to 1 km depth, another south of Lundgren (2013) to address the question of lateral growth of the sill, and the the edifice (5–10 W·m) at 7–8 km depth, and finally an anomaly (0.1–1 W·m) best-fitting source was concluded to have a fixed geometry with a depth of at 5–15 km depth under the deformation centroid (Diaz et al., 2015). These 8 km, strike of 10° east of north, dip of 10°, length of 30 km, width of 20 km, results match quite well with local seismic tomography in which roughly co- and maximum opening rate of 5 cm/yr (implying volume change on the order incident low S-wave velocities were observed: 1.2–1.8 km/s, 1.5–2 km/s, and of 0.01 km3/yr). Note that the depth in this case describes the middle of the 2.3 km/s, respectively (Spica et al., 2015). The prevailing interpretation is that sill below the local average elevation of 4.8 km. Remy et al. (2014) modeled, magmatic fluids migrate upward to ~6 km from a reservoir at 10 km depth, with different methodologies, the same InSAR data set, adding campaign thereby providing heat to the extensive and shallow hydrothermal system GPS observations spanning 2006–2008, and pointed out that either a trun- (Spica et al., 2015). cated cone-shaped reservoir or a sill with a geometry similar to that found by In the following sections we present up-to-date InSAR data analysis, and Pearse and Lundgren (2013) can explain the observations. Models generally measurements from recent continuous and campaign GPS surveys, extend- agree that the source must be ellipsoidal or rectangular at ~10 km depth with ing geodetic measurements through August 2016. We validate measurements a northerly strike (0°–25°) and slight dip to the east (0°–10°E). Both recent by comparing against past and contemporaneous data sets and constrain studies agreed that there was no evidence for a propagating sill and that the time dependence of uplift at Lazufre using InSAR time-series analysis. a fixed geometry was consistent with the data (e.g., Pearse and Lundgren, InSAR measurements after 2010 are then jointly inverted for sill opening rates 2013; Remy et al., 2014). in order to assess implications for source dynamics and volume changes.

Finally, we examine the effect of subsurface elastic heterogeneity on surface TSX, and RS2 processing we utilized the recently released 30 m native-reso uplift and source models by incorporating newly available seismic tomogra- lution topographic data. All interferograms were masked (pixels with co phy into a finite-element model. herence < 0.3), spatially filtered (Goldstein and Werner, 1998), and finally unwrapped with the SNAPHU program (Chen and Zebker, 2001), before being converted to surface displacements. Upon geocoding, images have a 90 m DATA and METHODS posting; however, spatial filtering in the processing chain creates an effective ground resolution of ~200 m. InSAR Measurements

To densify InSAR observations prior to 2010, we processed Advanced C Band Data: Envisat ScanSAR, RADARSAT-2, and Sentinel-1 Land Observing Satellite (ALOS; operated by the Japan Space Agency [JAXA]) and Envisat scanning synthetic aperture radar (ScanSAR) data sets Envisat ScanSAR data have been largely overlooked, as only Anders- with acquisitions between October 2005 and February 2011. In addition, we sohn et al. (2009) were able to calculate a single coherent interferogram. processed recent InSAR data sets spanning May 2010 through August 2016 We processed the ScanSAR subswath containing Lazufre for scenes with (Table 1). Strip-map data from the ALOS-1 and TerraSAR-X (operated by the a burst overlap of at least 10%. This included 29 interferograms from 19 German Space Agency [DLR]) (hereafter TSX) satellites were processed with dates in Wide Swath track 361 (October 2005–November 2009) and eight ROI_PAC software (Rosen et al., 2004). Envisat ScanSAR data were processed interferograms from nine dates in WS89 (December 2005–October 2009). with a modified ROI_PAC workflow (Liang et al., 2013). Data from COSMO We stacked these interferograms to generate maps of average ground ve- (Constellation of Small Satellites for Mediterranean basin Observation, oper- locity (see Henderson and Pritchard [2013] and Supplemental Material1). ated by the Italian Space Agency [ASI])-SkyMed and RADARSAT-2 (Canadian The spatial footprint and signal amplitude in stacks agree with those from Space Agency [CSA]) (hereafter CSK and RS2) were processed with ISCE 2.0 Envisat strip-map data (Fig. 2). We did not reprocess the Envisat strip-map

Figure 1: Comparison of components of deformation derived from (Remy et al., 2014) (May 2006 11/2008) to independent stacks of Envisat ScanSAR and ALOS. Left column is InSAR software (Rosen et al., 2012). Data from Sentinel-1 were processed with ISCE interferograms, but used the results from Remy et al. (2014) and Henderson Stack, Middle column is Remy components projected into line-of-sight, Right column is the residual. The consistent residual in panel c could be related to a change in the deformation rate release 201604, which included a workflow specific to the unique Terrain Ob- and Pritchard (2013). between 2006-2008 (used to make the prediction in panel b) and the time series of ALOS in panel a that spans 2007-2011 (see main text for discussion and Table 1 for exact dates for each data set) servation with Progressive Sans (TOPS) observation mode of the Sentinel-1 We calculated an RS2 stack with 15 descending interferograms from six sensor. We removed the topographic signal with the Shuttle Radar Topog- acquisitions covering 2.25 yr between April 2014 and August 2016 (Fig. 2G). 1 Supplemental Material. InSAR methodology detail raphy Mission (SRTM; NASA) digital elevation model (Farr et al., 2007). For Due to the persistence of topography-correlated phase delays in half of the and additional figures; GPS tables and time series. Please visit http://doi .org /10 .1130/GES01441 .S1 or ALOS and Envisat we used topography with a 90 m posting, and for CSK, interferograms, we used an empirical correction in which the linear trend the full-text article on www.gsapubs.org to view the Supplemental Material. TABLE 1. RECENT InSAR DATASETS COVERING LAZUFRE, CENTRAL ANDES Mean incidence angle No. of No. of Bperp max. SatelliteTrack Orbit (°) dates IFGs Start End (m) Envisat WS361 Ascending 33.0 19 29 15 October 2005 28 November 2009 300 Envisat WS89Ascending 28.6 9805 December 2005 05 October 2009 300 ALOS 102 Ascending 34.3 16 19 09 February 2007 20 February 2011 1000 TSX 134 Ascending 29.5 8626 April 2008 27 July 2016 200 TSX 20 Descending 18.4 10 19 22 December 2011 22 August 2016 200 TSX 111 Descending 39.1 915 28 December 2011 06 August 2016 200 CSK_1a –Descending 36.0 10 17 29 April 2011 19 February 2012 150 CSK_1b –Descending 36.0 24 32 23 July 2013 31 October 2014 150 CSK_2a –Ascending 45.5 5525 March 2011 12 April 2012 150 CSK_2b –Ascending 44.9 36 64 01 August 2013 24 July 2016 150 RS2–Descending 25.0 615 18 April 2014 05 August 2016 300 Note: For each track, we list the orbit direction, mean incidence angle, the number of archived SAR acquisitions (No. of dates), the number interferograms used in stacks described in this paper (No. of IFGs), the oldest (Start) and most recent (End) dates of acquisition, and the maximum perpendicular baseline (Bperp max.) used to select which dates to use to make interferograms. The Envisat data are from wide swath (WS) ScanSAR mode. ALOS—“Advanced Land Observing Satellite” operated by the Japanese Space Agency (JAXA). TSX—TerraSAR-X is operated by the German Space Agency (DLR). CSK—COSMO-SkyMed, “Constellation of Small Satellites for Mediterranean basin Observation” operated by the Italian Space Agency (ASI). CSK_1a/1b and CSK_2a/2b represent subsets of data for the same track due to changes in acquisition parameters. RS2—RADARSAT2, Canadian Space Agency (CSA). Note that the CSK and RS2 archives are not organized by track numbers and therefore “–” is a placeholder.

A) Envisat IS2 track 282, B) Envisat WS track 89, C) Envisat WS track 361, D) ALOS path 102, N 09 March 2003 - 16 May 2010 05 December 2005 - 05 September 2009 15 October 2005 - 28 November 2009 09 February 2007 - 20 February 2011

23 29 33 34

E) TerraSAR−X orbit 134, F) TerraSAR−X orbit 111, G) TerraSAR − X orbit 20, H) RADARSAT−2, 10 July 2014 - 27 July 2016 20 July 2014 - 06 August 2016 20 May 2014 - 22 August 2016 18 April 2014 - 29 August 2016

cm/yr 25.2°S 3

−1

39 20 25 25.4°S 29

68.6°W 68.4°W 15 km

Figure 2. Stacks of InSAR data on the same color scale for different time periods, Lazufre volcanic center, Central Andes. Black circles represent continuous GPS stations (and are shown only in frames that span the time when the stations existed). Red triangles represent Holocene volcanoes. Large colored circles (gray, green, red) show the locations of the data used in the time series in Figure 3. Inset black arrow represents satellite heading and grey arrow represents the satellite look direction with the mean incidence angle (in degrees) for the scene. Top row includes stacks spanning October 2005 through February 2011 showing a rate of uplift >3 cm/yr. Bottom row contains TerraSAR-X and RADARSAT-2 stacks for the period May 2014–August 2016. Note the general consistency of the spatial footprint of deformation and general deceleration with time. Section A is Envisat data is from Image Swath 2 (IS2), while section B and C show Envisat Wide Swath (WS) data. The curving black line is the international border between Chile and Argentina. ALOS—Advanced Land Observing Satellite.

of unwrapped phase versus elevation in non-deforming far-field areas was L Band Data: ALOS and UAVSAR subtracted before stacking (e.g., Froger et al., 2007). Deforming pixels were masked by defining a region within a 50 km radius of the center of maximum There are observations of Lazufre from U.S. National Space and Aero uplift. This normalization resulted in a stacked result with a mean velocity cen- nautics Administration’s (NASA) airborne Uninhabited Aerial Vehicle Synthetic tered on zero and standard deviation of 1.5 mm/yr (Fig. S6 [footnote 1]). Aperture Radar (UAVSAR) sensor spanning 2013–2015 (24 March 2013, 29 April Sentinel-1 is a constellation of two C-band SAR satellites launched by ESA in 2014, 01 April 2015). Interferograms are processed by the Jet Propulsion Lab- April 2014 and April 2016. Although we don’t discuss the Sentinel-1 data set in oratory (JPL; Pasadena, California) and made available via the website uavsar this paper, we include a single interferogram spanning December 2014–August .jpl.nasa .gov. For a single L-band interferogram, 16.8 cm of cumulative defor- 2016 (Fig. S7 [footnote 1]) to demonstrate the promise of measurements from mation would be represented by a single cycle of phase, but at most we expect this system going forward. 3 cm of relative deformation between 2013 and 2015 at Lazufre. Consequently,

the uplift signal does not stand out from background noise in currently avail- and polarization in 2012, we are unable to create stacks from a single set of able interferograms, but we mention the UAVSAR data set here because future interferograms. Instead, we divided acquisitions into two sets before and after acquisitions could prove useful. May 2012, creating four stacks in total (Supplemental Fig. S4 [footnote 1]). There are 16 ALOS L-band (23.6 cm wavelength) acquisitions between 09 February 2007 and 20 February 2011, providing additional observations to- ward the end of the Envisat mission. Unfortunately, many of these acquisitions InSAR Time Series show strong ionospheric signals, which have been noted in other regional studies (e.g., Fournier et al., 2010). We stacked 19 successful interferograms Stacks represent the average velocity over the elapsed time of observa- and found that the spatial and temporal patterns of deformation agree with tions, but more detailed temporal evolution can be determined by a least- those of previous data sets (Fig. 2). squares time-series inversion using the set of overlapping interferograms (e.g., Berardino et al., 2002; Schmidt, 2003). In brief, we assumed constant velocity between consecutive dates and solved for cumulative displacement X Band Data: TerraSAR-X and COSMO-SkyMed relative to the first date for each pixel (see Supplemental Material [see footnote 1] for further detail). We calculated an InSAR time series for RS2 and TSX o20 TSX provides X-band (3.1 cm wavelength) observations of Lazufre from data sets only, because they have very similar viewing geometry compared to three different orbits (o134, o20, o111). Unfortunately, the smaller footprint of the long-term 1995–2010 ERS–ENVISAT track 282 time series (Table 1). Each TSX acquisitions results in only partial coverage of the complete uplift foot- data set was inverted independently, and there is a gap with no connected print. Observations with TSX began in April 2008 (o134), but most acquisitions observations between May 2010 and December 2011. To create a continuous are after 2011 with the most recent in August 2016. We created stacks from the time series between 1996 and 2016, we fit linear rates before and after the gap time span 2014–2016 to directly compare with C-band measurements (Fig. 2). in observations (specifically before and after February 2011). These rates are in- CSK is also an X-band platform, and we generated data stacks using two terpolated across the gap in observations in order to obtain the final 1995–2016 ascending and descending satellite passes. Due to a change in incidence angle times series shown in Figure 3.

45 ERS/Envisat track 282, θ = 23° 40 TerraSAR−X orbit 20, θ = 20° RADARSAT−2, θ = 25° Figure 3. InSAR time series for the maxi mum uplift rate at Lazufre (locations are 35 colored points in Fig. 2). Note that each time-series inversion was performed 30 separately for each data set (ERS and Envisat Image Swath 2, TerraSAR-X, and RADARSAT-2 satellites), however the simi 25 lar orbits and incidence angles (θ = 20°– 25°) allows for direct comparison on this plot. Data sets are aligned along the y-axis 20 given the assumption of a constant uplift rate until the middle date between the 15 last Envisat observation (May 2010) and the first TerraSAR-X observation (Decem LOS displacement (cm ) ber 2011). Blue line represents the model 10 fit using a double exponential model in which reservoir overpressure increases lin early until 2006 (dotted line), after which 5 Onset point the overpressure remains constant (Le Mével et al., 2016). LOS—satellite line of sight displacement; ΔP—reservoir over 0 pressure. Linear ΔP Constant ΔP −5 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 Year (A.D.)

GPS Observations ucts from JPL and transformed the daily solutions into the International Terres- trial Reference Frame 2008 (ITRF2008). These daily solutions were combined Three continuous GPS stations were installed in the deforming region in in a linear least-squares inversion to estimate velocities at each GPS site. The November 2010 and November 2011. These stations could only be installed velocities were then referenced to the GEODVEL (a model of major tectonic in the country of Chile, and consequently measurements are limited to the plate velocities from space observations from GPS, SLR, VLBI, and DORIS over western side of the deformation footprint (Fig. 1). Station LCEN was placed 25 years) estimate of South America plate motion (Argus et al., 2010). Further as close as possible to the center of deformation given the existing network details on the processing scheme can be found in Fu and Freymueller (2012). of roads, but is ~12 km southwest of the centroid of vertical surface displace- Our two campaign surveys occurred in March and November, which in- ment (25.259°S, 68.483°W) based on the analysis of Remy et al. (2014). Station troduces the possibility of a bias due to seasonal effects. Water loading in the LLST was installed on the edifice of Lastarria volcano and is one of the high- Amazon Basin generates a strong vertical seasonal undulation in the GPS data est-elevation continuously operating GPS stations in the world (5272 m), and throughout northern South America (Davis, 2004; Bevis, 2005) as well as radial station SOCM was intended as a far-field reference station at approximately horizontal motion from the center of the loading (Fu et al., 2013). Estimates of equal longitude compared to the other stations (~300 km east of the Nazca seasonal deformation from the GRACE (Gravity and Recovery Climate Experi trench). We supplemented continuous measurements with campaign surveys, ment by NASA) satellite capture the broad-scale regional signal but may not installing a total of 10 benchmarks in 2011 (Fig. 4 and Supplemental Table S1 be able to detect more localized seasonal effects, and our sparse continuous [footnote 1]). Two campaigns were conducted in November 2011 and March GPS (cGPS) network makes it difficult to estimate seasonal deformation from 2014. All of the benchmarks were surveyed in 2011 and four benchmarks were a collection of regional stations. In fact, stations LCEN and LLST are nearly resurveyed in 2014, with each survey consisting of a minimum of 48 consecu- 300 km from the nearest cGPS station to the north (station CJNT) on the border tive hours of data collection. Table 2 lists details of the occupation histories of with Bolivia, and 300 km from the nearest cGPS station to the south (station the sites used in this study. COPO) in Copiapo, Chile. We used the GIPSY/OASIS II software developed by the JPL to produce Instead of attempting to remove an estimated seasonal signal from the data, precise point positioning (PPP) solutions for the GPS data used in this study we focused on gauging how much uncertainty campaigns in different seasons (Zumberge et al., 1997). We used reprocessed satellite orbits and clock prod- added to our velocity estimates. To accomplish this, we ran three velocity solu-

10 mm/yr ± 1 mm/yr SOCM SOCM 24°30′S 24°30′S Figure 4. GPS velocities for the Lazufre re 10 mm/yr ± 1 mm/yr gion. Left panel shows the horizontal ve locities while the right panel shows verti cal velocities. Data were collected over the November 2011–April 2014 time period. Velocities are relative to stable South America (Argus et al., 2010). Error bars are drawn at the 95% confidence level. Black triangles denote campaign sites while black dots show locations of continuous 25°00′S 25°00′S sites (Table 3). Note that uncertainties are much less for continuous sites given daily solutions compared to yearly solutions for FOLD campaign surveys. Gray triangle marks FOLD LLST LLST the location of Lastarria volcano. Curving LAZC RDHL black line is international border between RDHL LAZC Chile and Argentina. SDHL LCEN SDHL LCEN

25°30′S 25°30′S 69°00′ W 68°30′ W 68°00′ W 69°00′W 68°30′W 68°00′W

TABLE 2. OCCUPATION HISTORIES OF GPS STATIONS INSTALLED AS PART OF THE PLUTONS PROJECT, LAZUFRE, CENTRAL ANDES No. of First Last Time span 2010 2011 2012 2013 2014 2015 Station obs. days obs. day obs. day (yr) (d) (d) (d) (d) (d) (d)

Continuous LCEN 1583 2010.85 2015.26 4.41 55 358365 366344 95 LLST 705 2010.85 2014.22 3.37 55 349298 040 SOCM 1212 2011.87 2015.26 3.39 042365 366344 95 Campaign LAZC 7 2011.86 2014.22 2.36 0 30040 FOLD 7 2011.87 2014.22 2.35 0 40040 RDHL 5 2011.87 2014.22 2.36 0 30040 SDHL 8 2011.87 2014.22 2.36 0 20030 Note: For the campaign survey, recording equipment was set up at each benchmark for at least 3 d during the weeks of 14 November 2011 and 20 March 2014. Date format used in First obs. day and Last obs. day columns is decimal years (e.g., January 1, 2010 is 2010.0). The columns with years as headings contain the days of valid measurements in that year. obs.—observation.

tions in which station LCEN only had a small number of days of data, turning rates apparently located southeast of Lastarria volcano. However, the highest it into the equivalent of a campaign site. In the first solution, LCEN had days velocities for campaign stations with sparse time series are large. For example, of data corresponding to the campaign surveys in November 2011 and March at station RDHL (Fig. 1) we estimate 20.8 ± 9.5 mm/yr, compared to continuous 2014. In the second and third, LCEN had days of data from March 2011 and 2014 station LLST where we estimate 7.9 ± 0.8 mm/yr. and November 2011 and 2014, respectively. The three estimates did display dif- ferences, with LCEN showing a maximum difference of 2 mm/yr between the velocity estimates in the vertical component, 1.3 mm/yr in the east horizontal RESULTS AND MODELING component, and 1.7 mm/yr in the north horizontal component. We added these values to our formal uncertainty estimates for the campaign site velocities. InSAR Stacks after 2011 Table 3 lists the horizontal and vertical GPS velocities in the Lazufre region. The dominant linear horizontal motion is due to interseismic strain accumu- Clearly, new InSAR data show that uplift at Lazufre has continued at least lation. Vertical velocities suggest a concentration of uplift around the Lazufre until August 2016. However, comparisons between InSAR uplift rates are com- region compared to the far-field site (station SOCM), with the highest uplift plicated by different viewing geometries associated with different satellites. In

TABLE 3. GPS VELOCITIES AND UNCERTAINTIES, LAZUFRE, CENTRAL ANDES

Latitude Longitude Elevation VE VN VZ SigmaE SigmaN SigmaZ Station (°S) (°W) (m) (cm/yr) (cm/yr) (cm/yr) (cm/yr) (cm/yr) (cm/yr)

Continuous LCEN 25.326 68.603 4271 1.92 0.42 0.83 0.04 0.04 0.03 LLST 25.165 68.520 5272 1.68 0.54 0.79 0.06 0.04 0.08 SOCM 24.455 68.295 3969 1.90 0.51 0.01 0.05 0.04 0.04 Campaign LAZC 25.268 68.481 4646 2.44 –0.461.170.380.290.63 FOLD 25.178 68.626 4275 2.81 0.82 0.75 0.36 0.28 0.59 RDHL 25.257 68.380 4343 1.68 0.25 2.08 0.54 0.35 0.95 SDHL 25.324 68.683 3916 2.49 0.62 0.76 0.37 0.28 0.60

Note: Velocities are relative to South America (Argus et al., 2010). Uncertainties for campaign sites are augmented to account for seasonal effects as discussed in text. VE, VN, and VZ are the east-west, north-south, and vertical components of velocity respectively. SigmaE, SigmaN, and SigmaZ are the corresponding standard errors for velocity component estimates.

order to assess a possible change in the uplift rate we used the east-west (Ux) InSAR and GPS Comparison and vertical (Uz) displacements calculated by Remy et al. (2014) from a sub- set of Envisat strip-map data spanning May 2006–November 2008 (Fig. 1) and The continuous GPS station LCEN was installed in November 2010, then projected them into the CSK, TSX, and RS2 LOSs. We benchmarked the and data indicate a constant vertical velocity of 8.3 ± 0.3 mm/yr through Ux and Uz displacement maps by comparing with our independent ALOS and April 2015 (Fig. 5). We have assumed that the vertical rate is purely due Envisat ScanSAR stacks spanning 2003–2010 (Supplemental Fig. S1 [footnote to volcanic inflation and does not contain interseismic signal. We believe 1]), which were not used to estimate the Ux and Uz displacements by Remy this is reasonable given the prediction of <2 mm/yr uplift of interseismic et al. (2014). Agreement is remarkable between the Envisat data sets even coupling models (Béjar-Pizarro et al., 2013) and the measurement of neg- though the time period of the data sets and their LOSs are different (~20° and ligible uplift at the reference station SOCM far from Lazufre. Importantly, 40° incidence for strip map compared to 29° and 33° incidence for ScanSAR), there is no apparent residual trend after removing this linear vertical ve- suggesting a relatively constant rate of uplift between 2003 and 2010. How- locity from the LCEN time series aside from seasonal oscillations. LCEN is ever, the mean rate from ALOS data is ~3 mm/yr less than that observed with 12 km southwest of the inferred center of deformation, where the compo- Envisat. Therefore, it could be argued that the rate decreased slightly between nents of deforma tion from May 2006–November 2008 predict uplift of 12 May 2010 (the end of the Envisat time series) and February 2011 (the end of the mm/yr and westward motion of 9 mm/yr (Remy et al., 2014). The predicted ALOS time series), but too few data points are available during that short time magnitude of westward motion should clearly reduce interseismic motion period to assess this robustly. at LCEN compared to regional stations (because eastward regional rates Projecting the 2006–2008 Ux and Uz components derived from Envisat into 300 km from the trench are 15–20 mm/yr in the International GNSS Service the CSK, RS2, and TSX LOSs results in rates that are 1.5 cm/yr higher than the 2008 reference frame). However, this is not observed—in fact, the eastward observations made between 2014 and 2016. To assess the significance of motion of stations LLST and LCEN, 16.8 ± 0.6 and 19.2 ± 0.4 mm/yr respec- the overestimate with respect to data noise, we used the approach of Finnegan tively, are comparable to that of far-field reference station SOCM, 19.0 ± et al. (2008) to estimate a minimum bound of InSAR stack uncertainties. This 0.5 mm/yr, far from the deformation field (Table 3). The predicted vertical method compares InSAR stacks with different incidence angles covering the rate at LCEN of 12 ± 1 mm/yr using Envisat data from 2006 to 2008 is much same geographic region. We used the RS2 and TSX o111 stacks and converted higher than the observed rate of 8.3 ± 0.3 mm/yr between November 2010 the mean LOS velocities into pseudo-vertical rates by dividing them by the co- and April 2015. sine of the incidence angle. The co-registered stacks are then compared in a Both InSAR and GPS measurements since 2010 show evidence for a de- correlation plot. Gaussian noise of variable amplitude is added to a 1:1 correla- crease in uplift compared to pre-2010 rates. The question is, when did the tion until the spread matches the data. We find that noise with a magnitude of deceleration occur? Given the variable spacing of consecutive SAR acquisi- 1.2 mm/yr matches the scatter between the rate maps, thus we use this figure tions, it is difficult to determine a precise inflection point in the uplift rate at as a proxy for the uncertainties on the LOS velocity in our interferogram stacks Lazufre. Indeed, it can be seen in Figure 3 that for any 5 yr period it is difficult (Supplemental Fig. S5 [footnote 1]). This figure is about one order of magnitude to distinguish an accelerating rate from a constant rate of uplift with InSAR smaller than the residuals of the 2014–2016 stacks minus the synthetic interfero data alone. Only considering the full data set does a clear trend emerge: Ap- grams calculated with the 2006–2008 data (i.e., 1.5 cm/yr), hence we conclude plying a double exponential model to the time series results in an inflection that although the spatial footprint of the inflation signal has remained relatively point in the time series in 2006 (see the Double Exponential Pressure Evolu- unchanged, the LOS uplift rate has decreased from >3 cm/yr to 1.5 cm/yr. tion section). Unfortunately, this inflection predates the installation of GPS, We also decomposed our recent InSAR data into Ux and Uz components to but the lack of evidence for any significant residual trend in the GPS time constrain the ratio of maximum horizontal to maximum vertical displacement series since November 2010 suggests that a large deceleration took place which can be diagnostic of source geometry. RS2 descending (18 April 2014– over 5 yr between 2006 and 2011. 29 August 2016), TSX descending o111 (20 July 2014–06 August 2016), and CSK ascending tracks (01 August 2013–06 August 2016) were used. Maximum verti-

cal uplift is determined to be ~1.5 cm/yr with a Uxmax/Uzmax ratio of 0.20, some- Elastic Okada Sill Model what lower than 0.27 obtained using data from 2006–2008 (Remy et al., 2014) and in the range that is commonly cited as diagnostic of a flat-topped source Motivated by the observed change in deformation rate, we tested (e.g., Dieterich and Decker, 1975). We caution, however, that using this ratio whether the sill model of Pearse and Lundgren (2013)—which uses distrib- as diagnostic of source geometry is only valid for a homogeneous crust (e.g., uted sill opening constrained by data from before 2010—is consistent with Geyer and Gottsmann, 2010) – in the Finite-Element Model section we explore the InSAR data sets after 2010. Volume change for the sill is found via a the impact of a heterogeneous crust, determined by seismic studies, upon the linear inversion for opening on 2 × 2 km patches within a predefined sill ground deformation. geometry (Table 4). If descending data only are used, this model fits most

150

100

East (mm) 0 2011 2011.5 2012 2012.5 2013 2013.5 2014 2014.5 2015 150

100

50 Figure 5. Time series of positions for the

continuous GPS station LCEN relative to ) North (mm North 0 the first point in each series. Rows show 2011 2011.5 2012 2012.5 2013 2013.5 2014 2014.5 2015 east displacements (in mm) (top), north displacements (in mm) (second), vertical

) 50 displacements (in mm) with 8.3 mm/yr up

m lift as thin red line (third), and de-trended

( vertical displacements (in mm) (bottom).

l See also Table 3 for associated rates and a 0

i uncertainties. X-axis shows decimal year

e (A.D.). V −50 2011 2011.5 2012 2012.5 2013 2013.5 2014 2014.5 2015

) 50

(

l d

a 0

e V D −50 2011 2011.5 2012 2012.5 2013 2013.5 2014 2014.5 2015 Decimal Year

data sets well with a volumetric change of 0.006 km3/yr, but residual ampli- were added to the model results and re-inverted. The amplitude of noise tudes of ~4 mm/yr are obtained near the maximum uplift in the RS2 stack. was estimated from the standard deviation of values in non-deforming re- Including ascending TSX data changes this estimate to a volume change gions in the RS2 stack, 1.5 mm/yr (Supplemental Fig. S6 [footnote 1]). We of 0.0068 km3/yr (6.8 × 106 m3/yr), which is approximately half of the previ- obtained a final volume change estimate of 6.8 ± 1.25 × 106 m3/yr (Fig. 6). ously estimated rate between 2003 and 2010 (Remy et al., 2014). To estimate Maximum sill opening values are 2.5 cm/yr, and contours of opening are uncertainties, we ran a Monte Carlo test in which 100 realizations of noise shown in Figure 7.

TABLE 4. REVIEW OF LAZUFRE (CENTRAL ANDES) GEODETIC MODELS Depth Volume Semi-major axis length Semi-minor axis length Strike Dip Geometry (km) (km3/yr) (km) (km) (°E of N) (°E)Reference Horizontal square sill 10 –7 700 Pritchard and Simons (2004) Horizontal ellipsoid 10 0.010 10 1259Froger et al. (2007) Expanding horizontal sill 13 –20728 0 Ruch et al. (2009) Expanding horizontal sill 10 –2413300Anderssohn et al. (2009) Dipping rectangular sill 8 0.030 30 20 10 10 Pearse and Lundgren (2013) Dipping ellipsoidal sill 11 0.015 30 12 34 3 Remy et al. (2014) Dipping rectangular sill 8 0.006 30 20 10 10 This study Note: All values are rounded to the nearest kilometer; depths are relative to the surface (4.8 km above sea level). Semi-major axis is aligned to strike direction and semi-minor axis is perpendicular to strike. All models assume Poissonian crust. Note that sill opening may be variable within the prescribed geometry.

GEOSPHERE | Volume 13 | Number 5 Henderson et al. | Decelerating uplift at Lazufre volcanic center and implications for geodetic models Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/13/5/1489/3995723/1489.pdf 1497 by guest on 28 September 2021 on 28 September 2021 by guest Downloaded from http://pubs.geoscienceworld.org/gsa/geosphere/article-pdf/13/5/1489/3995723/1489.pdf Research Paper Red triangles are active Holocene volcanoes. All colors in first and middle sections are with respect to scale in section A. test in which the inversion is run 100 times with randomly generated noise (see main text for details). Curving black line is international border between Chile and Argentina. patches are the same as in Pearse and Lundgren (2013) (Table 4). Opening results in a volume 1.25 × estimate of 6.8 ± 10 Figure 6. Elastic half - space inversion results using InSAR data sets from A.D. 2014–2016 for sill opening, Lazufre volcanic center, Central Andes. Sill geometry and opening 25.4°S 25.2°S cm/yr J) RADARSAT−2 G) TerraSAR−X orbit 20 D) TerraSAR−X orbit 111 A) TerraSAR−X orbit 134 6 8 . 6 ° −0. 5 W 0. 0 0. 5 1. 0 1. 5 6 8 . 4 ° W K) Synthetic H) Synthetic E) Synthetic B) Synthetic 15 km 15 km 15 km 15 km cm/y r 6 L) Residual I) Residual F) Residual C) Residual m 68.6°W 68.6°W 3 /yr. Uncertainty is estimated from a Monte Carlo −0.3 −0.2 −0.1 0. 0 0. 1 0. 2 0. 3 68.5°W 68.5°W 68.4°W 68.4°W N

GEOSPHERE | Volume 13 | Number 5 Henderson et al. | Decelerating uplift at Lazufre volcanic center and implications for geodetic models 1498 Research Paper

N Finite element mesh

Sill Opening (10 km depth) 100 km

cm/yr 2.5 2.0 25.2°S 1.5 1.0 0.5 0.0 200 km

200 km

25.4°S Figure 8. Isometric schematic of finite element mesh (200 × 200 × 100 km) with “point” sill open ing at 10 km depth. Half of the ground surface is shown with element edges to illustrate the hexahedral mesh. Calculations were implemented with PyLith 2.0 software (Aagaard et al., 2013).

can simply be rotated. The model domain of 200 × 200 × 100 km is suffi- ciently large to utilize zero-displacement boundary conditions along edges 68.6°W 68.4°W and the bottom. We use hexahedral elements with 2 km sides, resulting in 500,000 elements in total. A map view depicting the extent of the finite-ele- Figure 7. Contours of opening (every 2.5 mm/yr) for distributed opening sill model in an elastic half-space using data between 2014 and 2016, Lazufre volcanic center, Central Andes. Model ment model in relation to seismic tomography used to assign material prop- parameters are given in Table 4. Black outline shows the extent of the sill with distributed open erties is shown in Figure 9. ing patches. Compare with InSAR data in Figure 6. Curving black line is international border To validate the implementation of our finite-element model, we compared between Chile and Argentina. Red triangles are active Holocene volcanoes. solutions for a point sill opening with the solution for a rectangular dislocation in an elastic half-space (Okada, 1985, 1992). The analytical solution is expected Finite-Element Model to have some deviation from the finite element model solution due to finite element model nodal displacements tapering to zero. For example, for the Numerical studies have shown that upper crustal heterogeneities can simplest initial condition of opening at a single node, the dislocation takes on significantly influence parameter estimates and modeled surface displace- a pyramidal shape that has a base length of twice the element size. Opening ments from homogeneous half-space crustal deformation models (e.g., more closely approximates the analytical rectangular dislocation geometry as Masterlark, 2007). In order to evaluate the role of crustal heterogeneity on the mesh is refined and more nodes within the sill area are prescribed displace- surface uplift at Lazufre, we constructed a finite-element model using Pylith ments. Performing a convergence test, we find that a cell size of 2 km agrees 2.0 software (Aagaard et al., 2013). We used a rectangular mesh with a hori with the analytical solution to within <5% (Supplemental Fig. S10 [footnote 1]). zontal (dip = 0°) sill-like reservoir located at 10 km depth, which is the ap- We emphasize that a “point sill” is defined by the sill horizontal dimensions proximate depth found in previous work (Table 4). An isometric view of the being much less than depth (e.g., ratio of <1/5). In general, finite sources with mesh with sill opening and surface displacements is shown in Figure 8. All distributed opening patches in a plane result in shallower source depths (e.g., nodes along the 10-km-depth slice can be assigned an initial displacement Fialko et al., 2001a, 2001b). or traction boundary condition, so that we can test rectangular sill geome- To add realistic heterogeneous elastic properties to the finite-element tries of various lengths and widths. Although most studies suggest a strike model, local seismic tomography is required. Recently, a regional (12°S–42°S) of >20°, we keep a strike of zero for mesh simplicity and note that solutions ambient noise tomography (ANT) data set became available that covers the

FEM Extent

Ward et. al. 2013 Tomography

Spica et. al. 2015 Tomography

1 cm/yr uplift contours

Figure 10. Depth-averaged tomography velocity model for Lazufre, Central Andes, for a 50 × 50 km square around the uplift centroid extracted from Ward et al. (2013). Solid black line represents mean velocities with depth; gray hori zontal bars represent one standard deviation. Dashed vertical line represents the assumption of homogeneous material used in modeling (Del Negro et al., 2009). Vp—P-wave velocity, Vs—S-wave velocity, and Rho—density.

Figure 9. Overview map of Lazufre, Central Andes, with seismic tomography and finite element model extents. Finite element model areal extent is represented by a white dashed rectangle, in addition to a densified local network consisting of 18 stations deployed be- tomography extracted from Ward et al. (2013) as a black solid rectangle, and tomography from tween February and March 2008 (Spica et al., 2015). The tomographic model Spica et al. (2015) as a blue solid rectangle. Thin black line is the international border between Chile and Argentina. Red triangles are active Holocene volcanoes. Green inverted triangles produced from this analysis has a smaller spatial extent compared to that are seismic stations from the GFZ array, and yellow inverted triangles are stations from the of Ward et al. (2013) but higher resolution (100-m-sided cells). We present a PLUTONS seismic array—different collections of these stations were used in the two different depth-averaged profile of this model to give a first-order comparison to others seismic tomography studies. Thin black curves are the contours of deformation every 1.0 cm/yr from InSAR data before 2010. discussed so far (Fig. 11). The final step in converting tomographic models into elastic material parameters requires assuming a relationship between Vp, Vs, and density. Lazufre region (Ward et al., 2013). This analysis utilized 330 broadband stations Often, a constant Vp/Vs ratio is used, because the assumption of Poissonian throughout South America operating at different times over the period May material requires that Vp/Vs = 1.732. Alternatively, empirical relations 1994 to August 2012 and provides a model of seismic shear wave velocities derived from field studies and laboratory experiments can be used to (Vs) to a depth of 50 km (at 10 km horizontal and 1 km vertical grid postings). obtain both Vp and density, for example at Mount Etna (Italy; Currenti We present depth-averaged profiles of P-wave velocity (Vp) and S-wave veloc- et al., 2007). We use the empirical equations of Brocher (2005) to obtain ity (Vs) for comparison with homogeneous half-space values (Fig. 10). Impor- the elastic material moduli that represent the crust in our finite-element tantly, the ANT technique is insensitive to sharp impedance contrasts, which model (Table 5). are better detected with receiver function analysis. A joint inversion for a Vs Depth-averaged velocity models, though a simplification of the true 3-D velocity model that combined ANT and receiver functions at Uturuncu volcano variation, provide a simple assessment of the effect of material property varia- (300 km north) led to a significant changes in the three-dimensional (3-D) sub- tion with depth. Indeed, the tomographic model of Ward et al. (2013) contains surface velocity field (Ward et al., 2014). Similarly, we suspect that the inclusion predominantly 1-D variation with depth. For the same 10 cm of opening on of local receiver functions at Lazufre would reduce velocities in the mid-crust a 10 × 10 km sill at 10 km depth, this heterogeneous configuration causes a and reduce the vertical extent of low-velocity zones, but this work has not yet subtle 1.7% increase in maximum vertical displacement and 3.5% increase in been done. maximum radial displacement (Fig. 12). We also utilized the 3-D tomographic We also utilized a local Vs velocity tomographic model centered on Lastarria data set from Spica et al. (2015) in order to quantify the effect of this model on volcano using seismic data from eight stations from January to March 2012, computed surface displacements. We again utilized the finite element model

DISCUSSION

Double Exponential Pressure Evolution

Studies to date have considered uplift at Lazufre to be due to an elastic sill-opening process, and consequently an overpressure time function identi- cal to the functional form of the uplift time series could be invoked to explain observations. The magma recharge model presented by Le Mével et al. (2016) provides a straightforward explanation of how a double exponential trend in ground deformation time series (Fig. 3) can arise. The theory is developed for spheroidal reservoirs; however, we assume a sill can be considered a limiting geometry of a very thin ellipsoid. Essentially, the theory predicts that for a reservoir fed by a conduit with a ramping inlet pressure, the reservoir’s pressure

evolution DP0(t) is given by:

  t τ −t /τ  ∆Pi  +−(et10) if ≤≤t* Figure 11. Depth-averaged tomography velocity model for Lazufre, Central Andes,  tt**  after Spica et al. (2015). Blue line represents the mean velocity for an ~10 × 10 km ∆Pt0()=  , (1)  ττ()  square around the uplift at Lastarria volcano. Solid black line represents the mean  P e −−t /τ e tt− */τ 1if tt* ∆ i  − +  > velocities with depth from Ward et al. (2013). Blue horizontal bars represent one  t**t  standard deviation in depth-averaged variation. Dashed vertical line represents the assumption of homogeneous material used in modeling (Del Negro et al., 2009). Vp—P-wave velocity, Vs—S-wave velocity, and Rho—density. where DPi is the maximum reservoir overpressure achieved at time t* and t is an exponential time constant that depends on magma supply dynamics. We

assume that DPi is directly proportional to maximum ground uplift, which is at domain with a 10 × 10 km sill at 10 km depth and an opening of 10 cm. We least true for the point source approximation for a penny-shaped crack (Fialko find that vertical displacements are amplified by up to 6.6% and radial dis- et al., 2001a) in an elastic half-space. In other words, we assume that the displacements are increased by up to 4.9% (Fig. 12). Assuming that the seismic placement time series in Figure 3 is directly proportional to overpressure so tomography does not have significant temporal variation during the period that we can invert the time series for the temporal terms t* and t. We also as- of geodetic observations (1998–2016), existing elastic models of sill opening sume that inflation began in October 1997, although there is a large uncertainty likely overestimate opening values by <10%. on the uplift onset due to the lack of data between this date and March 2000.

TALE 5. ELASTIC PROPERTIES FROM TOMOGRAPH AT LAFRE, CENTRAL ANDES Save velocity Pave velocity Density Poisson oungs Shear s p rho Ratio Modulus E Modulus G Reference m/s m/s g/cm3 nu GPa GPa

Del Negro et al. 2009 Homogeneous 3.3 5.72.7 0.25 75 30 ard et al. 2013 Minimum 35.1 2.60.235824 Mean 3.3 5.72.7 0.23 75 47 Maimum 46.9 2.90.25116 77 Spica et al. 2015 Minimum 1.3 2.72.2 0.23 9.53.5 Mean 2.9 52.6 0.24 57 23 Maimum 3.6 6.12.8 0.37 89 36 Notes: Data from ard et al. 2013 go to 54 m belo the surface in an area 50 50 m. Data from Spica et al. 2015 etend to 7. 3 m belo the surface in an area approimately 10 10 m. Typical halfspace parameters for volcano modeling are taen from Del Negro et al. 2009.

receiver function studies is estimated to be 60 km deep in this region; Wölbern homogeneous et al., 2009). We note that the inflection in pressure history in late 2006 could

heterogeneous (Ward et al. 2013) be associated with an independent observation that SO2 emissions at Lastarria

t heterogeneous (Spica et al. 2015) volcano appeared to have peaked in 2006 (Carn et al., 2013). However, this gas analysis only considered measurements between 2004 and 2008. The absolute value of overpressure cannot be determined independently without knowing the original reservoir size and crustal elastic moduli. The following formula relating overpressure to surface displacements for a thin sill-like reservoir holds when sill radius is much less than depth, and therefore serves as only an approximation of the absolute overpressure evolution at Lazufre:

πµd 2 ∆Pt()= Uz ()t . (2) 0 41− ν a3 max

Normalized displacemen Considering values of shear modulus m = 30 GPa, sill radius a = 5 km, d = 10 km, and Poissonian crust (n = 0.25), we find that overpressure scales as 0.025 MPa/mm. Therefore, 400 mm of observed cumulative uplift can be explained by a pressure function in which overpressure grew by 1 MPa/yr between 1997 and 2006, after which point overpressure has been holding at a relatively constant value of 10 MPa/yr. This calculation illustrates the dynamics of the hypothesized pressure evolution at Lazufre; however, the stated pres- Figure 12. Comparison of surface displacements for models assuming a crust with homo sure values are crudely approximated and should be considered upper bounds geneous or heterogeneous distribution of elastic properties. Solid lines represent vertical dis because for a given volumetric change, overpressure is greatly reduced for placements, and dashed lines are radial displacements. Black corresponds to the homogeneous larger chambers (e.g., Fialko et al., 2001a, 2001b). domain; red corresponds to one-dimensional heterogeneity based on Ward et al. (2013); blue corresponds to three-dimensional heterogeneous domain based on Spica et al. (2015). All dis Importantly, the transition to constant overpressure in late 2006 predicts placements are normalized to maximum vertical displacement for the homogeneous case of that uplift at Lazufre will decelerate until it asymptotically reaches a total cumu 10 cm opening of a 10 × 10 km sill at 10 km depth. lative displacement of 50 cm by 2040. Furthermore, if excess pressure is relieved, we expect a transition to subsidence. A recent geomorphic study identified the existence of a long-wavelength topographic dome at Lazufre (500 m We used the nonlinear least-squares Levenberg-Marquardt algorithm to fit height across 70 km distance) which has existed for at least 400 ka (Perkins

Equation 1 to our displacement time series, resulting in DPi = 48.2 ± 1.2 (scaled et al., 2016). The development this topography can be explained by an average pressure units, discussed subsequently), t = 9.8 ± 0.8 yr, and t* = 8.7 ± 0.4 yr. relative uplift rate of 1 mm/yr, an order of magnitude less than geodetically The uncertainties for these parameters were estimated by inverting synthetic determined rates over the last two decades. Therefore, the active uplift episode data predicted by the best-fit model with synthetic noise created from a diag is likely one of many occurring in the same spatial location, but separated by onal covariance matrix (Lohman and Simons, 2005) under the assumption long periods of quiescence and possibly even periods of subsidence as seen that the data sets are spatially and temporally uncorrelated. We repeated this at nearby volcanic centers such as Cerro Overo and Cerro Blanco, Chile (e.g., process for 100 synthetic noisy data sets, such that the model parameter un- Henderson and Pritchard, 2013). certainties are the standard deviation of the ensemble of inversion results. We consider the stated uncertainties to be underestimates given the assumption of spatially and temporally uncorrelated noise. Comparison to Other Volcanic Systems The best-fit model (Fig. 3) requires linearly increasing overpressure over 8.7 yr (from October 1997 until August 2006) due to magma input, after which Similar patterns of ground uplift rate increase followed by decreasing rates overpressure is held constant. The characteristic time scale, t = 9.8 yr, is related have been observed for analogous deformation episodes at Three Sisters (Ore to the dynamics of magma ascent assuming the shallow chamber is connected gon; 1995–2010), Yellowstone (Wyoming, USA; 2004–2014), and Laguna del to a deeper source via a direct conduit. The characteristic time scale of as- Maule (Chile; 2007–2014) in which one time constant describes initial uplift and cent is related to conduit length and radius, and magma properties such as another prolonged deceleration (Le Mével et al., 2015). This “double exponen- viscosity and compressibility. However, most of these parameters are poorly tial” trend requires an initially increasing pressurization (Le Mével et al., 2016), constrained (e.g., the conduit could extend as far as the Moho, which from and eventual deceleration can be explained either with a constant inlet pressure

into a compliant elastic reservoir (e.g., Le Mével et al., 2016) or time-dependent and Tape, 2016; Shen et al., 2016; Farrell et al., 2017), and a single permanent deformation in a viscoelastic region (e.g., Newman et al., 2006). seismometer from OVDAS (Chilean Volcano Observatory) was installed on Changes in caldera-scale inflation signals have been shown to correlate Lastarria volcano in December 2013. A detailed study of the shallow seismicity with the occurrence of seismic swarms: for example, at Yellowstone (e.g., at Lastarria has not yet been completed, but the limited available time series Waite and Smith, 2002; Chang et al., 2007; Tizzani et al., 2015) and Long Val- will make comparison with the deformation time series difficult. ley Caldera (California, USA; e.g., Newman et al., 2001; Hill et al., 2003; Mont Similarly, one might expect changes in the degassing Lastarria volcano gomery-Brown et al., 2015). In this conceptual model, caldera inflation is pro- system to be correlated with changes in deformation rate, especially given duced by basalt intrusion from a deep source into the brittle upper crust. When the hypothesis that Lastarria degassing is the “pressure valve” for the Lazufre a threshold pressure is reached, a sealed layer inferred to be located within the magmatic system (e.g., Froger et al., 2007). Gas output at Lastarria volcano brittle and ductile transition is breached, thus allowing fluids exsolved from is by far the greatest in the Central Andes Volcanic Zone, at times exceeding the magma to be injected into the brittle shallower parts of the volcanic sys- 13,000 tons/d (e.g., Tamburello et al., 2014). Temporal changes in gas composi- tem where the fluid pressures are much lower. As this processes happens, the tion between 2006 and 2012, specifically higher proportions of magma soluble

ground uplift is either reversed to subsidence or stops and is accompanied by gases H2O, SO2, and HCl and lower proportions of CO2, could reflect ascent

seismic swarms with fluid-like signatures includingb -values that deviate from of magma (Aguilera et al., 2012; Tamburello et al., 2014). SO2 flux atLastarria one and stress tensors that deviate from the background stresses (e.g., Waite volcano was measured at 884 tons/d with differential optical absorption spec- and Smith, 2002; Hurwitz and Lowenstern, 2014). troscopy on 27 November 2012 (Tamburello et al., 2014). On 17 March 2012,

The uplift episode at Long Valley caldera between 1995 and 1999 also ex- 16 March 2013, and 22 November 2014, SO2 emissions were measured at ~800 hibited exponential growth coinciding with a large seismic swarm, followed by tons/d, but large uncertainties in these measurements do not permit the con- exponential decay. One possible mechanical model behind this deformation clusion of a decreasing emission rate between 2012 and 2014 (Taryn Lopez,

pattern is the intrusion of magma into a reservoir surrounded by a viscoelastic 2016, personal commun.). More recently, the SO2 flux spanning 21 and 22 No- shell (Newman et al., 2001, 2006). If the crust is hot enough, it is likely that vember 2014 was measured at 861 ± 250 tons/day, such that the infrequent the area that surrounds the inflation source behaves as a viscous medium on measurements and large uncertainties do not permit concluding any recent time scales of decades, creating a time-dependent uplift after the intrusion of trends. Magma ascent as evidenced by compositional gas changes could be a single batch of magma. accompanied by increasing uplift rates, which have been observed between Whether deceleration in uplift is due to a ramped pressurization history or 1998 and 2006. As the volume of gas emissions increases, reservoir pressures viscoelastic relaxation process cannot be determined with currently available may be relieved, causing the decrease in uplift observed since 2006. These data. Determining the correct model could be aided by repeated microgravity ideas assume an instantaneous connection between reservoir processes and studies to constrain evolving mass distributions, as has been proposed for the surface gas emissions and can be tested in the future with dense gas sampling deformation episode at Three Sisters volcano in Oregon (Dzurisin et al., 2009). and integrated modeling. Furthermore, continuous gas or seismic monitoring could help constrain the timing of potential magmatic additions that are below the detection threshold of deformation measurements. Finally, the viscoelasticity of rocks in volcanic CONCLUSION regions is poorly constrained, but heat flow measurements and seismic attenuation tomography could narrow in on appropriate values for the Lazufre We have demonstrated that deformation at Lazufre continued past 2010, system. In summary, with geodesy data alone it is not possible to disentangle but at a reduced rate. InSAR data through August 2016 show spatial uplift pat- subsurface processes responsible for the observed surface uplift rate changes terns like those observed with analysis through 2010, but maximum LOS uplift at Lazufre, and it could be that a combination of magma injection, volatile rates of up to 1.5 cm/yr. Continuous GPS time series for a point 12 km from exsolution, and viscoelastic rheology are responsible for the observed tran- the maximum deformation shows a clear linear vertical displacement rate of sient signal. 8.3 ± 0.3 mm/yr between November 2010 and April 2015, consistent with the reduced rate measured with InSAR. Assuming the same sill-opening model of previous studies, we determine that opening rates (and therefore minimum Correlation of Deformation with Other Data Sets volume changes) are approximately halved to match observations since 2010. Variations in modeled surface displacements due to subsurface heterogeneity Unfortunately, there has not been a continuously operating seismic net- known from current seismic tomography are <10% and mostly confined to work at Lazufre to compare seismic activity with deformation. Seismic stations within 10 km of the uplift source. have only been deployed at Lazufre during January–March 2008 (Spica et al., The deformation time series from InSAR between 1997 and 2016 is well 2015) and November 2011–March 2013 by the PLUTONS project (e.g., Alvizuri fit by a double exponential model. In this model, magmatic reservoir over

pressure increases at a constant rate over 8.7 years (from October 1997 until Bevis, M.G., 2005, Seasonal fluctuations in the mass of the Amazon River system and Earth’s August 2006), after which overpressure is held constant. Importantly, this elastic response: Geophysical Research Letters, v. 32, L16308, doi:10.1029/2005GL023491. Brocher, T.M., 2005, Empirical relations between elastic wavespeeds and density in the Earth’s model predicts decreasing uplift over the next several decades at which point crust: Bulletin of the Seismological Society of America, v. 95, p. 2081–2092, doi:10 .1785 uplift should stop. 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Continued GPS measurements, combined with Carn, S.A., Krotkov, N.A., Yang, K., and Krueger, A.J., 2013, Measuring global volcanic degassing more frequent InSAR observations with Sentinel-1 and other satellites, as well with the Ozone Monitoring Instrument (OMI), in Pyle, D.M., Mather, T.A., and Biggs, J., eds., Remote Sensing of Volcanoes and Volcanic Processes: Integrating Observation and Model- as contemporaneous analysis of gas emission and seismic activity will help de- ling: Geological Society of London Special Publication 380, p. 229–257, doi:10.1144/SP380.12. termine the physical mechanism behind the observed deceleration at Lazufre. Chang, W.-L., Smith, R.B., Wicks, C., Farrell, J.M., and Puskas, C.M., 2007, Accelerated uplift and magmatic intrusion of the Yellowstone caldera, 2004 to 2006: Science, v. 318, p. 952–956, doi:10.1126/science.1146842. ACKNOWLEDGMENTS Chen, C.W., and Zebker, H., 2001, Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization: Journal of the Optical Society of Amer- We thank members of the PLUTONS team and Jillian Pearse for critical discussions, and Domi ica, v. 18, p. 338–351, doi:10.1364/JOSAA.18.000338. nique Remy for sharing data files from his 2014 study. We also thank two anonymous review- Currenti, G., Del Negro, C., and Ganci, G., 2007, Modelling of ground deformation and gravity ers for their insightful feedback. This work was supported by NASA grants NNX08AT02G and fields using finite element method: An application to Etna volcano: Geophysical Journal NNX10AN57H issued through the Science Mission Directorate’s Earth Science Division, and Na- International, v. 169, p. 775–786, doi:10.1111/j.1365-246X.2007.03380.x. tional Science Foundation (NSF) grant EAR-0908281, which is part of the PLUTONS project. Some Davis, J.L., 2004, Climate-driven deformation of the solid Earth from GRACE and GPS: Geophysi of the data used are courtesy of the Committee on Earth Observation Satellites (CEOS) Volcano cal Research Letters, v. 31, L24605, doi:10.1029/2004GL021435. pilot project coordinated by Mike Poland (U.S. Geological Survey) and Simona Zoffoli (Agenzia Del Negro, C., Currenti, G., and Scandura, D., 2009, Temperature-dependent viscoelastic modeling Spaziale Italiana), and we thank the space agencies of Japan, Germany, Italy, and Canada and the of ground deformation: Application to Etna volcano during the 1993–1997 inflation period: European Space Agency for the data used in this paper along with the Alaska Satellite Facility for Physics of the Earth and Planetary Interiors, v. 172, p. 299–309, doi:10 .1016 /j .pepi .2008 .10 .019 . distributing some of it. COSMO-SkyMed data were provided courtesy of the Italian Space Agency Díaz, D., Heise, W., and Zamudio, F., 2015, Three-dimensional resistivity image of the magmatic (ASI), under CSK AO project ID 188. RSAT2 data were provided by the Canadian Space Agency system beneath Lastarria volcano and evidence for magmatic intrusion in the back arc (north- (CSA); MacDonald, Dettwiler and Associates Ltd.; and the Science and Operational Applications ern Chile): Geophysical Research Letters, v. 42, p. 5212–5218, doi:10.1002/2015GL064426. Research (SOAR) and CSA-SOAR-ASI programs. GPS equipment and field support were provided Dieterich, J.H., and Decker, R.W., 1975, Finite element modeling of surface deformation asso- by UNAVCO with support from the NSF and NASA under NSF Cooperative Agreement EAR- ciated with volcanism: Journal of Geophysical Research, v. 80, p. 4094–4102, doi:10 .1029 0735156. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute /JB080i029p04094. of Technology, under a contract with NASA. Finally, we thank Sarah Doelger, Jennifer Jay, Hector Dzurisin, D., Lisowski, M., and Wicks, C., 2009, Continuing inflation at Three Sisters volcanic Toro, Greco Ramirez, and Doug Christensen for fieldwork assistance. center, central Oregon Cascade Range, USA, from GPS, leveling, and InSAR observations: Bulletin of Volcanology, v. 71, p. 1091–1110, doi:10.1007/s00445-009-0296-4. 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