Current crustal deformation at the junction of collision to subduction around the Hualien area, Taiwan

Tectonophysics 617 (2014) 58–78

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Current crustal deformation at the junction of collision to subduction around the Hualien area, Taiwan Sean Kuanhsiang Chen a,b, Yu-Chang Chan b,⁎, Jyr-Ching Hu a, Long-Chen Kuo b a b

Department of Geosciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan Institute of Earth Sciences, Academia Sinica, No. 128, Sec. 2, Academia Road, Taipei 115, Taiwan

a r t i c l e

i n f o

Article history: Received 15 February 2013 Received in revised form 29 November 2013 Accepted 13 January 2014 Available online 22 January 2014 Keywords: Crustal deformation Continuous GPS Areal strain Seismicity Taiwan

a b s t r a c t We analyzed continuous GPS (CGPS) data recorded on 15 stations from 2002 to 2009 and examined the CGPSderived strain along with local seismicity to characterize the current crustal deformation at the plate junction around the Hualien area in Taiwan. By examining the CGPS time series in detail, we discovered abnormal variations in the CGPS horizontal displacements and an annual cycle with a peak-to-peak difference of more than 20 mm. Most stations move in the ESE direction during May to October, and move in the opposite direction during November to April every year. We found that the average semi-annual velocity of each CGPS station is generally parallel to the direction of convergence between the Eurasian and Philippine Sea plates, and that the CGPS temporal areal strain is strongly related to the occurrence of larger local earthquakes, while the strain reverses from contraction to extension. The CGPS displacement is well known to have been influenced by seasonal changes or loadings from several environmental factors. We tested these perceptions with the newly acquired CGPS data and seismicity, and found that the environmental factors are unlikely to explain the patterns of surface motion in the study area. We also compared our results with previously reported cases and found distinctive patterns in the temporal and spatial distributions of the CGPS data and seismic behavior. The geodetic and seismic observations should provide motion constraints for further studies of the plate junction kinematics from collision to subduction around the Hualien area in Taiwan. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Taiwan is a very young orogen which is located along the plate boundary between the Eurasian and Philippine Sea plates and is adjacent to two subduction zones (Barrier and Angelier, 1986). To the east of Taiwan, the Philippine Sea plate subducts northward beneath the Eurasian plate along the Ryukyu trench; by contrast, to the south of Taiwan, the Eurasian plate and the South China Sea oceanic crust subduct eastward along the Manila trench beneath the north Luzon arc of the Philippine Sea plate (Fig. 1). Relative to the Eurasian plate, the Philippine Sea plate is converging northwestward at a rate of about 82 mm/yr (Yu et al., 1997). Taiwan can be divided into six NNE-trending geological regions which are separated by major boundary faults. From west to east, these regions are the Coastal Plain, the Western Foothills, the Hsuehshan Range, the Slate belt of the Central Range, the Tananao complex of the Central Range and the Coastal Range (Fig. 1). The Coastal Plain is characterized by Quaternary alluvial deposits derived from the Western Foothills and the Central Range. The Western Foothills consists of a thick sequence of sediments from the late Oligocene to early Pleistocene. ⁎ Corresponding author at: Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan. Tel.: +886 2 27839910x411; fax: +886 2 27839871. E-mail address: [email protected] (Y.-C. Chan). 0040-1951/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.tecto.2014.01.014

The Hsuehshan Range is mostly composed of Eocene to Oligocene sediments. The Slate belt and Tananao complex of the Central Range are characterized mostly by Tertiary metamorphism during the Taiwan orogeny. The Coastal Range is composed of Neogene andesitic volcanic rocks of the north Luzon arc: a product of the subduction of the South China Sea oceanic crust beneath the Philippine Sea plate. A significant boundary between the Central and Coastal Ranges is the Coastal Range fault (or Longitudinal Valley fault), which is generally considered as the surface suture zone of the Eurasian plate and the Philippine Sea plate (Barrier and Angelier, 1986; Shyu et al., 2005). The Eurasian and Philippine Sea plates converge along the Taiwan orogen (e.g., Suppe, 1984), which can be separated into three kinematic regions: the northward subduction of the Philippine Sea plate, the arc– continent collision of the Eurasian plate and the northern Luzon arc, and the eastward subduction of the South China Sea oceanic crust beneath the Philippine Sea plate (Fig. 1). Previous studies suggested that the northern Taiwan orogen is now under post-orogenic collapse due to the westward propagation of the Okinawa extensional trough into the orogen (Teng, 1996). Contrary to this view, seismicity shows that the orogenic process is still active in the northern Taiwan orogen, particularly at greater depths between the interface of the Eurasian and Philippine Sea plates (Wu et al., 2009). Both views basically indicate that the location around Hualien is situated at the junction from collision to subduction between the Eurasian and Philippine Sea plates

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Fig. 1. Map of CGPS velocity field, tectonic surroundings and geodynamic processes around the Taiwan island. Subduction along the Ryukyu and Manila trenches is shown as a solid/dash thick line with triangles on the overriding plate, where the dash line is inferred. The CGPS velocity vectors derived from the GPS Lab, Institute of Earth Sciences, Academia Sinica, Taiwan since 1994 to 2008, are displayed as thin purple arrows with respect to the Paisha station (S01R) at Penghu. The thin black lines represent the inferred major geological provinces on land. These provinces (a–f) are the Coastal Plain, the Western Foothills, the Hsuehshan Range, the Slate belt of the Central Range, the Tananao complex of the Central Range and the Coastal Range. The black square indicates our study area.

(Fig. 2). Around the junction area on land, at least two active faults have been documented, i.e., the Coastal Range fault that separates the Coastal Range and the Longitudinal Valley alluvial plain, and the Milun fault that was ruptured during the 1951 Magnitude 7.3 Hualien earthquake (Hsu, 1976) (Fig. 2).

GPS is one of the most important geodetic tools for studying crustal deformation. An island-wide GPS network composed of more than 425 continuous stations and a large number of campaign-mode sites has been well established by the Institute of Earth Sciences, Academia Sinica, Ministry of the Interior, and other institutions since 1989. The

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Fig. 2. Geological map and active faults in the study area. The two active faults, the Milun fault and Coastal Range fault on land, shown as black lines, divide the late Paleozoic to Mesozoic Central Range from the Miocene to Pleistocene Coastal Range. Blue lines on land are the stream systems. Small white triangles show the locations of the CGPS stations.

GPS velocity field for the whole of the Taiwan area was first calculated from the 1990–1995 GPS observations by Yu et al. (1997). After this work, several updated GPS studies have provided better characterization of the crustal deformation in Taiwan (Ching et al., 2011a,b; Hsu et al., 2009; Lin et al., 2010; Rau et al., 2008; Yu and Kuo, 2001). The GPS displacements along the region of the arc–continent collision comprise one third of the total NW–SE convergence displacement across the Taiwan orogen (Hsu et al., 2009; Yu and Kuo, 2001). In contrast, along the region of northward subduction of the Philippine Sea plate, the GPS displacements indicate a clear extensional field which is associated with the Ryukyu subduction system (Hu et al., 1996, 2002; Rau et al., 2008). The extensional field is considered to be related to tectonic extrusion (Angelier et al., 2009), the back-arc opening of the Okinawa trough and the Ryukyu trench rollback (Hou et al., 2009; Rau et al., 2008). However, details of the transitional deformation between collision and subduction within the Hualien area remain unclear. Because more CGPS stations were established in the Hualien area in recent years, they provide a good opportunity to study the characteristics of the transitional deformation in the Taiwan orogen.

In this paper, we analyzed CGPS data from 2002 to 2009 around the Hualien area to better illustrate the deformation within the transitional junction between collision and subduction. Our analysis of the CGPS data emphasizes the annual variation of the displacement and areal strain, and their relation to local seismicity. In the following, we begin by introducing the CGPS data processing steps and show how we calculate the areal strain from CGPS baseline changes. We then present our results as temporal CGPS 3-component displacement, areal strain and seismicity variations. Finally, the observed variations in temporal and spatial deformation are attributed to annual motion caused by either environmental or tectonic factors, and the possible implications for the transitional deformation at the plate junction are discussed. 2. CGPS data analysis 2.1. Data collection and calculation We used CGPS data collected between January 2002 and September 2009, including an array of 15 continuously operating stations maintained

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by the Institute of Earth Sciences, Academia Sinica and the Central Weather Bureau. We processed the CGPS data using the Bernese v5.0 software (Dach et al., 2007). Station coordinates were determined by double-difference observations. We used the ephemerides provided by the International Global Navigation Satellite Systems Service (IGS). Using the values from Bulletin B of International Earth Rotation Service (IERS), the coordinate differences between UTC and UT1 were corrected. The CGPS data with elevation cutoff angle lower than 15° were excluded to reduce multipath effects and noise. We chose the double-difference ionosphere-free carrier-phase observations (L3) as the basic observables. The residual tropospheric zenith delay was calculated simultaneously every 2 h for all stations by least-squares adjustment. It is the difference between the actual zenith delay and the calculated zenith delay from a standard atmosphere model (Saastamoinen, 1973).

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The model used parameters including temperature (18 °C), relative humidity (50%) and atmospheric pressure (1013.25 mbar), which were similar to the local conditions in Taiwan. We also considered the effects of the Earth tides using GEMT3 (Goddard Earth Model T3) in Bernese. Daily solutions for station positions and corresponding matrices of the covariance among the three position components were determined within the International Terrestrial Reference Frame 2008 (ITRF2008) (Altamimi et al., 2011). We used daily frame data products provided by the available sites, and minimized common mode errors from a multiple regression model and linear velocities. There were 13 IGS sites (TSKB, USUD, TAEJ, SHAO, IRKT, XIAN, WUHN, COCO, YARI, PERT, TIDB, GUAM and KWJ1) (Fig. 3a), and 17 local continuous stations (S101, BANC, TWTF, SFON, TNML, PLAN, LUKN, PKGM, HOKN, CK01, HENC,

Fig. 3. (a) Thirteen global IGS standard stations around Taiwan on the International Terrestrial Reference Frame (ITRF) (2008) are used to determine the positions and velocities of the 17 local CGPS stations in Taiwan from 1994 to 2009 by minimizing common mode errors from linear velocities. (b) Seventeen fixed local CGPS stations used in this study. The fault traces of active faults (black curves) and inferred faults (dash curves) are from the Central Geological Survey of Taiwan. Background shaded map is the surface topography on land. (c) The CGPS time series recorded on the reference station S01R.

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Fig. 3 (continued).

KDNM, ILAN, HNSN, CHEN, PANG and S01R) (Fig. 3b). The averaging of multiple points for reference to study the velocity and strain fields should reduce the errors resulting from using a single reference point. However, previous studies in Taiwan all chose a single station (S01R), which lies on the passive continental margin of the Eurasian plate and far from the active Taiwan orogen, as a reference to study the velocity fields (Ching et al., 2011a,b; Hsu et al., 2009; Lin et al., 2010; Yu et al., 1997, 2003). We used station S01R as a reference point because its time series was stable and without obvious background noise (Fig. 3c). In addition, it is easier to compare our results with those from previous studies. The time series of the CGPS signals were analyzed mainly by methods of multiple regression and estimation of noise characteristics. First, the components of the CGPS raw time series were defined using

the least-squares method described in Nikolaidis (2002) according to the equation: yðt i Þ ¼ a þ bt i þ c sinð2πt i Þ þ d cosð2πt i Þ þ e sinð4πt i Þ þ f cosð4πt i Þþ ng nh nk   X   X       X g j H t i −T gj þ h j H t i −T hj t i þ k j exp − t i −T kj =τ j H t i −T kj þ vi j¼1

j¼1

j¼1

ð1Þ where ti are the time variables, n is the daily solution epochs in years, and H is the Heaviside step function. The first two terms contain the site position, a, and the linear rate, b. The third to sixth terms represent the sinusoidal annual and semi-annual signals. The seventh term corrects for any number (ng) of the coseismic offsets and GPS antenna replacements with magnitudes g and epochs Tgj. The eighth and ninth

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Fig. 3 (continued).

terms denote the postseismic motion which is introduced as a rate change hj and a logarithmic decay with magnitude kj at the selected earthquake epochs Thj and Tkj. vi is the measurement error. Here, a rate

change hj of postseismic motion is added because Taiwan has abundant seismicity and larger earthquake events often occur. A change of velocity unusually occurs after a main shock, especially for Mw greater than

Fig. 4. Power spectrums calculated by stacking 3-component CGPS records for data from the study area. Each diagram shows EW (blue), NS (red), and vertical (green) components. All of them are consistent with a model of combined white noise (κ = 0) and flicker noise (κ = −1) and within the standard deviation. The volume κ, means the stacking slope of each red line using least-squares fitting. The best fitting straight lines are estimated in the log–log domain.

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Table 1 Noise amplitudes of white plus flicker type in the model. Station

BLOW CHNT FLNM HUAL HUAP NDHU PEPU SCHN SHUL SICH SLIN SOFN SPAO TUNM YENL

East

North

Up

aw

b−1

aw

b−1

aw

b−1

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

4 7 6 6 5 5 7 5 6 6 4 3 5 6 6

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

5 4 3 5 4 4 5 4 5 4 7 7 4 4 4

18 10 8 11 12 7 20 8 10 6 13 6 8 13 8

25 12 11 22 16 11 28 12 15 13 24 12 15 21 14

Note: East, North and Up are the three components of CGPS time series; aw (mm) and b−1 (mm/yr0.25) are the magnitudes of white noise and flicker noise, respectively.

6.5, e.g., the 1999 Chi-Chi Mw 7.6 and 2003 Chengkung Mw 6.8 earthquakes. Thus, we included the term hj for analysis in all CGPS stations. Second, to accurately estimate model parameters and their uncertainties, temporal noises have to be evaluated using a more realistic model. The CGPS measurement noise can be approximated by a power-law process or a temporal distribution that has a power spectrum of the form (Agnew, 1992; Hackl et al., 2011; Williams, 2003; Yu et al., 2003) Pð f Þ ¼ P0

 κ f f0

ð2Þ

where f is the temporal frequency, P 0 and f0 are the normalizing constants, and κ is the spectral index, which is the slope of the spectrum in log–log space. The errors in CGPS data are best described as a combination of white (κ = 0) and flicker noises (κ = − 1) or as a fractional white noise (− 1 b κ b 1) (Hackl et al., 2011; Mao et al., 1999; Nikolaidis, 2002; Williams, 2003; Zhang et al., 1997). The noise characteristics of the GPS measurements following the 1999 Mw 7.6 Chi-Chi earthquake in Taiwan were considered as a combination of white and flicker noises (Yu et al., 2003). We modeled the time series using Eq. (1) to obtain postfit residuals that can be used for the noise analysis. We used linear least-squares fitting to obtain the best-fitting line of spectral indices from each stacked components. The main reason

for stacking the data was to enhance the signal/noise ratio and to understand the local noise pattern, following the steps proposed by previous studies (e.g., Yu et al., 2003). Fig. 4 shows stacked power spectra of the postfit residuals in the East, North and Up directions for all CGPS stations used in this study. The slopes of the spectra (κ) are − 0.61 ± 0.0077, − 0.62 ± 0.0074 and − 0.55 ± 0.0081, respectively for the East, North and Up directions. These slopes of spectral indices are higher because the high-frequency portions of the spectrum beyond a noise peak at 1/(14 days) were not used in our best-fit straight lines. If we considered the entire data, including the high-frequency portions, the estimated slopes become as low as − 0.36 ± 0.01, − 0.45 ± 0.01 and −0.40 ± 0.01, respectively for the East, North and Up directions. In addition, we also checked the individual spectral index from each station, and these indices range between − 0.35 and − 0.86, which limits the scatter to the range between 0 and −1. Thus, the noise characteristics were again confirmed as a combination of white and flicker noises. We applied the maximum likelihood estimator (MLE) to estimate amplitudes of white and flicker noises present in each CGPS time series of the postfit residuals (Langbien and Johnson, 1997; Williams, 2003; Zhang et al., 1997). The full covariance matrix is a sum of the temporally uncorrelated (white) and correlated (flicker) noise covariance matrices. The estimated amplitudes of white and flicker noises from our stations are listed in Table 1. After modeling the time series again through full data covariance matrices, we obtained the clean time series to create the final regressions in Fig. 6. The secular velocities of three components at each CGPS station were calculated using least squares and assuming a linear trend in the position with time.

2.2. Velocity field around Hualien Based on the island-wide 425 CGPS observations with respect to S01R, Paisha station at Penghu as reference (Fig. 1), an obvious clockwise rotation of the velocity field in northern Taiwan is identified. To better illustrate transitional crustal deformation around Hualien, analyses of CGPS data were carried out for the 15 sites in the study area. The velocity field with respect to the S01R station was established in Table 2. Our estimates of the surface velocity indicate that the study area moves in the direction N30°W–N85°W, with rates of 6–43 mm/yr decreasing from south to north (Fig. 5a). The velocity on the eastern side of the Coastal Range fault is 20–25 mm/yr in directions between N55°W and N60°W, but decreases to 6–15 mm/yr in directions between N40°W and N50°W to the west of the fault. With respect to the Coastal Range fault, a convergence rate of 5–19 mm/yr with a left-lateral component is consistent with different periods of GPS observations (Hsu et al., 2009; Lin et al., 2010; Yu and Kuo, 2001). In contrast, the HUAP station

Table 2 Station velocity components in the study area relative to Penghu (S01R). Station

Lon.

Lat.

VE

EE

VN

EN

VV

EV

Data period

BLOW CHNT FLNM HUAL HUAP NDHU PEPU SCHN SHUL SICH SLIN SOFN SPAO TUNM YENL

121.5712 121.6619 121.4534 121.6135 121.7494 121.5508 121.6103 121.6516 121.5627 121.6544 121.4414 121.5982 121.4849 121.4936 121.6019

24.1718 24.1492 23.7463 23.9754 24.3090 23.8972 24.0179 24.1278 23.7876 24.1257 23.8119 23.8702 24.2050 23.9652 23.9035

−7.9 −10.4 −42.9 −24.8 3.2 −27.5 −20 −14.7 −28.3 −12.1 −27.6 −27.2 −7.8 −24.2 −27.5

0.1 0.1 0.8 0.6 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

8.0 1.6 29.9 18.6 −11.6 19.4 8.4 3.3 32.1 4.9 19.4 30.1 12.0 14.2 26.8

0.1 0.1 0.6 0.2 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1

4.9 −5.9 −5.0 −6.6 −5.7 −13.6 0.2 −23.8 −26.0 −6.1 5.8 −6.2 7.2 5.4 −18.9

0.4 0.3 0.7 0.6 0.4 0.5 0.6 0.4 0.5 0.4 0.3 0.4 0.5 0.4 0.7

2006.03–2009.09 2003.01–2009.09 2002.01–2009.09 2002.01–2009.09 2004.05–2009.09 2005.04–2009.09 2002.01–2009.09 2008.10–2009.09 2005.06–2009.09 2005.12–2009.09 2002.01–2009.09 2005.12–2009.09 2004.04–2009.09 2002.01–2009.09 2003.01–2009.09

Note: VE, VN, and VV are the east, north and vertical components of interseismic velocities, respectively; EE, EN, and EV are errors in the east, north and vertical components, respectively.

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located in the northeast part of study area moves in the N170°E direction at a rate of about 8 mm/yr. This direction is remarkably different from other CGPS stations in the study area, and the movement is reversed about 20 km to the south (Fig. 5a). A clear transition zone of

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horizontal velocity between the stations CHNT and HUAP, namely the Chingshui area, is precisely defined by our results. Previous studies mainly discuss a larger and more general velocity transition due to plate corner clockwise rotation in eastern Taiwan (Hu et al., 1996,

Fig. 5. (a) CGPS horizontal velocity field and (b) vertical velocity field around the Hualien area with respect to station S01R, Penghu, at the passive continental margin. The blue arrows show horizontal velocities resulting from the convergence between the Philippine Sea and Eurasian Plates. A 95% confidence error ellipse is shown at the tip of each velocity vector. Small white triangles represent the locations of the CGPS stations. Black dash lines indicate the known active faults.

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Fig. 5 (continued).

2002; Rau et al., 2008). They suggested that a combination of mechanisms including oblique collision, through opening and trench rollback, has resulted in the plate corner rotation as manifested by the velocity transition. The pattern of the current vertical velocity field is generally tilted, and shows subsidence along the eastern coast and small uplift in the Tananao complex of the Central Range (Fig. 5b). The coastal

subsidence rates range from 5 to 26 mm/yr, while three stations exceed 20 mm/yr (SCNH, YENL and SHUL). This regional subsidence agrees with the results of previous studies from leveling and GPS measurements (Ching et al., 2011a,b; Liu and Yu, 1990). The small uplift rates in the Tananao complex range from 4 to 7 mm/yr, and they seem to increase from east to west.

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2.3. CGPS time series around Hualien The CGPS time series provide better resolution and opportunity for characterizing the regional deformation around the Hualien area. Each CGPS time series contains several important signals to study crustal

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deformation. To identify the signals contained in the whole time series, we estimated both the distributions of white and flicker noises in the CGPS raw time series, using the processing procedures described in Section 2.1. Our detrended regression model fits the displacements well, including some shifting effects resulting from local coseismic

Fig. 6. Modeled and detrended time series of stations (a) HUAL, (b) PEPU, (c) YENL and (d) CHNT with coordinate changes relative to Penghu (S01R) at the passive continental margin. The components are north (red), east (green) and vertical (blue). A regional sustained EW annual variation is apparent. The black curves represent the predicted final model after considering the noises. The offsets on the CGPS records and model curves refer to the nearby earthquakes of Taiwan. i.e., the model includes one of coseismic offsets in the north component shown in (a), (b) and (d), which is related to a main shock that occurred on 31 March 2002.

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Fig. 6 (continued).

events, postseismic decay and sinusoidal annual and semi-annual motion (Fig. 6). By evaluating the displacement residuals between raw time series and regression models, we confirmed that all the residuals are white random distribution. This means that the noise can be fully represented by white and flicker types. However, we found interesting phenomena of annual terms in the time series. Traditionally, the annual and semiannual terms were removed to obtain a linear trend in the GPS displacement. In this study, these terms were preserved for comparing our results with examples of periodic deformation elsewhere in the

world. Specifically, we found a particular periodic type in the CGPS time series of the study area. This periodic type shows a sinusoidaltype annual cycle mainly in the EW component (Fig. 6). The annual cycle also appears in the NS and vertical components of some CGPS stations, but is not as obvious as that in the EW component. In addition, large amplitudes of the annual cycle in the EW component were detected simultaneously in the stations. The amplitudes have an average value of about 20 mm peak-to-peak in the time series and occasionally they are larger than 30 mm (Fig. 6). We examined the sinusoidal-like annual

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Fig. 7. CGPS semi-annual velocity field around the Hualien area relative to Penghu (S01R). It demonstrates a coherent vector that parallels the direction of plate convergence. Blue arrows show half-year velocities from which the long-term trend was removed between May and October. Red arrows show another half-year reversal motion between November and April.

cycle in detail and discovered that CGPS stations move eastward during about May to October, and then westward from about November to April every year. Fig. 6 shows a series of CGPS raw time series with regression curves at four stations (HUAL, PEPU, YENL and CHNT), in

which common mode errors and temporal noises were considered. The processed time series indicate that the annual movement is more pronounced on the EW component than on the vertical component for most CGPS stations in the study area. We also calculated and plotted

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Fig. 9. Correlation between annual variation of four CGPS horizontal detrended displacements and seismicity observed around the Hualien area. A highly periodic modulation of seismicity is visible, especially for the events larger than Mw 5.0. The upper four diagrams show the modeled time series in the east (green) component at the stations HUAL, PEPU, YENL and CHNT. The lower diagram shows the total number of earthquakes larger than Mw 3.0 each month in the study area. The white rectangular areas indicate periods of northwestward motion, while the blue rectangular areas show peroids of southeastward motion.

the average peak-to-peak velocity from each detrended time series by least-squares fitting (Fig. 7). The large annual horizontal displacements are nearly parallel to the direction of convergence between the Eurasian and Philippine Sea plates. All the CGPS stations are time correlated and the large periodic amplitudes, which are not related to a site effect, are at a regional scale around the Hualien area. 2.4. Comparison of CGPS data in Taiwan and local seismicity An annual or semi-annual signal within CGPS time series is a common phenomenon, especially in the vertical component. However, we discovered that the sinusoidal-like annual signals mainly appeared in the EW component instead of the vertical component in the study area. To investigate and clarify this phenomenon from our CGPS results, we collected all the available CGPS data in Taiwan between 1994 and 2007, which were processed by the GPS Lab at Academia Sinica. We then analyzed the properties of the time series using the procedures described in Sections 2.1–2.3. We calculated the average peak-to-peak

amplitudes of the horizontal annual signals from individual stations, and then plotted the amplitudes along their linear vectors relative to the S01R station (Fig. 8). We found that an unusually large horizontal periodic motion is a significant regional pattern in the Hualien area. In contrast, most of the peak-to-peak amplitudes in other areas of Taiwan range only between 2 and 5 mm. We checked that all processing parameters for the Taiwan region used as input in the calculations were correct. Because we have not seen a systematic periodic anomaly in the other 410 stations, the large periodic motion observed in the 15 stations near Hualien should reflect significant local effects. In addition, the time series processed by other groups in Taiwan also contain a similar horizontal periodic amplitude at Hualien (Hung, 2013; Shin, 2009). We also collected information on all nearby earthquakes between 2002 and 2009 to compare and correlate the observations of large horizontal displacements with earthquakes in the study area. More than 300 events with moment magnitudes (Mw) larger than 3 were obtained from the catalogs of the Broadband Array in Taiwan for Seismology (BATS). The locations and focal depths of earthquakes

Fig. 8. Cyclic peak-to-peak amplitudes of CGPS horizontal components relative to Penghu (S01R). An obviously large-amplitude pattern is visible around the Hualien area. Blue arrows show average amplitudes with the stacking vector from both north and east components of each CGPS station. A 95% confidence error ellipse is shown at the tip of each velocity vector. The black lines show the active faults on land with the inferred portions denoted by dash lines.

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Fig. 10. The temporal areal strain diagrams associated with the distribution of CGPS stations in this study. The northern and southern networks are named Taroko and Soufong, respectively. Both networks consist of five CGPS stations and six baselines.

are included in the supplemental material. We established a temporal correlation between observed surface deformation and seismicity using the CGPS horizontal displacements and three separate ranges of earthquake magnitudes: M w 3 to 4, Mw 4 to 5, and M w 5 to 7 (Fig. 9). We discovered that a high ratio of about 0.8 between the

eastward moving periods (blue windows) and earthquakes larger than Mw 5 existed in the study area. The magnitudes ranging from Mw 3 to 5 also have a ratio of about 0.65 compared with eastward moving periods, especially the three local earthquake swarms that occurred during the eastward motion in the years of 2002, 2005 and 2009 (Fig. 9).

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Fig. 11. (a) Areal strain time series of the Soufong network. (b) Areal strain time series of the Taroko network. Blue curves indicate the amount of areal strain with one standard deviation. The Soufong network appears to reflect the stable tectonic convergence (negative strain) of about 3.5 μstrain/yr, and accompanies the variation of periodic strain, which is associated with larger local earthquakes. The Taroko network reflects periodic strain which is apparently related to larger local earthquakes. The red and green straight lines mean local and far earthquakes larger than Mw 5.0 around Taiwan. The missing portions of time series result from deficient or poor quality CGPS data.

3. Surface areal strain analysis 3.1. Method to estimate areal strain The strain-rate field implicates local strain accumulation rate and a possible connection to potential seismic hazards (Shen et al., 1996;

Ward, 1998). The establishment of more CGPS stations in the Hualien area in recent years provides an opportunity to supplement the wellstudied spatial strain variations in Taiwan with more detailed temporal strain variations (Chang et al., 2003; Ching et al., 2011a,b; Hsu et al., 2009; Lin et al., 2010). To obtain a compatible local areal strain, daily solutions for CGPS station positions should be re-calculated for baseline-

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length changes between each two sites. We used engineering strain for calculating areal strain and shearing components in spherical coordinates. At each location, a uniform strain-rate field is assumed, and a leastsquares inversion was performed over the deviations of baseline length and their covariance for three unknowns (εa, γ1, γ2). We thus estimated the areal strain distribution according to the deviations of the CGPS baseline length based on: 2

1 62 e1 6 1 4 e2 5 ¼ 6 6 62 e3 41 2

3

1 cos2θ1 2 1 cos2θ2 2 1 cos2θ3 2 2

3 1 sin2θ1 7 2 3 2 7 εa 1 7 sin2θ2 7:4 γ1 5 7 2 5 γ2 1 sin2θ3 2

ð3Þ

where the areal strain, pure NE and NW shearing components (εa, γ1, γ2) and each azimuth of baselines (θ1, θ2, θ3) are related to the observed three horizontal components of baseline change (e1, e2, e3). It considered at least three different azimuths of baseline change for uniform spatial baseline distribution. If all the baselines are almost aligned, the strain transformation will be unstable and have large biases. We also applied more baselines in a small network to avoid unreasonable strain variations without data constraint at the vacant azimuth range. Two small networks were established in the Hualien area to detect the CGPS temporal strain variation (Fig. 10). One is on the NW side of the Milun fault within the Tananao complex of the Central Range. This network includes 6 CGPS stations (CHNT, SICH, SCHN, SPAO, BLOW and TUMN). Another network crosses the northern Coastal Range fault, which includes 5 CGPS stations (YENL, SOFN, SHUL, NDHU and SLIN).

3.2. CGPS areal strain around Hualien Complete CGPS displacement records in the time domain provide an opportunity to investigate the relationships between large-amplitude periodic signals, strain rates and earthquakes using individual CGPS stations. First, we established two networks and analyzed areal strain variations from the baseline changes between each pair of CGPS station in the Taroko and Soufong regions (Fig. 10) to check their relation with larger earthquakes in both the local and far fields around Taiwan. The two CGPS networks were transformed into temporal areal strains. The surface areal strains and larger local earthquakes (Mw N 5.0) in the time domain were synthesized (see Fig. 11a and b) to examine spatial and particularly temporal relations between the strain variations and earthquake occurrence. In the Soufong network, the surface areal strain rates indicate that the network region is experiencing high NW to SE contraction (negative strain) with an average rate of about 2.5 μstrain/ yr and a maximum rate of up to 3.5 μstrain/yr from 2004 to 2009. Another noticeable variation of temporal areal strain shows a periodic contraction and extension (positive strain) in the time series (Fig. 11a). This periodic strain variation contains a peak-to-peak amplitude commonly between 2 and 3 μstrain, and the amplitude can reach up to 5 μstrain when local earthquakes occur. Moreover, we find that the temporal areal strain is surprisingly consistent with the occurrence of larger local earthquakes (Fig. 11a). Specifically, these earthquakes mostly occur during the time periods of transition between contraction and extension. Similar phenomena are also found in the Taroko network, but without long-term tectonic contraction (Fig. 11b). The Taroko network shows lower peak-to-peak amplitudes than the Soufong network with a

Fig. 12. A synchronous comparison between CGPS displacement and local environmental parameters around the Hualien area. The four diagrams from top to bottom are daily averages of CGPS east component, temperature, barometric pressure and groundwater level. The last diagram is monthly averages of rainfall records. The groundwater level was measured daily from a borehole located in the Hualien City (121.6189°, 24.0069°).

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difference of about 0.6 μstrain. Also, this network shows more obvious coincidence between the larger local earthquakes and the strain transition. Our combined analysis of the variations of temporal surface deformation, strain and seismicity suggested that the surface periodic movement correlates relatively well with the occurrences of earthquakes in seismogenic depth, especially during the periods of the observed strain transition. Possible mechanisms for generating the periodic movement observed in CGPS records and the correlation between CGPS and seismicity observations may have important tectonic implications. 4. Discussion 4.1. Classical explanation for periodic CGPS signal Periodic displacement variations were commonly represented as a major effect of seasonal changes (Dong et al., 2002; Langbein et al., 1990; Mao et al., 1999; Nikolaidis, 2002; Zhang et al., 1997). They are controlled by complex processes with many parameters, which may include variations of temperature, water vapor in the troposphere, soil swelling and station stability (Mao et al., 1999; Zhang et al., 1997). Most seasonal effects of signal transmission between GPS satellites and ground stations can be largely removed during data processing. However, the surface deformation induced by seasonal changes contained in the CGPS time series cannot easily be distinguished and isolated by common data processing. It is not clear whether the signals are controlled by surface deformation due to seasonal changes or by crustal deformation. We plotted the observed periodic movements and local environmental parameters together (Fig. 12) to compare quantitatively the amplitude and phase with previous observations and model predictions. 4.1.1. Atmospheric pressure loading Earth deformation by atmospheric pressure loading is broadly detected on the vertical component of CGPS records (van Dam et al., 1994). Modeling of the loading showed a variance of 5 mm on the CGPS vertical component, which resulted from the annual peak-topeak amplitude of 1.2 mbar at high latitudes. The loading is dominated by periods of approximately 2 weeks and associated with the passage of atmospheric pressure systems of synoptic scale. Considering the case of the Hualien area, the annual peak-to-peak amplitude is only about 0.2 mbar (Fig. 12), which means that the predicted CGPS amplitudes may become much lower. Vertical rebound of the pressure loading may only lead to a small fraction of the horizontal CGPS component. Our observed peak-to-peak amplitude is more than 10 mm in the EW component (Fig. 6), which represents more than twice the observed amplitude found by van Dam et al. (1994). Thus, even if atmospheric pressure loading may have played a role, it is not a dominant process in the EW periodic deformation around Hualien. 4.1.2. Non-tidal oceanic loading A periodic signal on CGPS is often attributed to non-tidal oceanic loading for stations near oceans. This loading is considered to be the dominant driving mechanism for the periodic deformation along the coast. Based on the loading, a signal root mean square of about 5 mm peak-to-peak in the CGPS vertical component, and about one-third of that in the horizontal component was detected (van Dam et al., 1997). We reassess the role of the non-tidal oceanic loading and try to explain the observed periodic anomaly of the CGPS horizontal displacement at Hualien. Because the small local atmospheric pressure variations are about 0.2 mbar (Fig. 12), the non-tidal oceanic loading cannot account for the large amplitude of the CGPS displacement. The large-amplitude displacement will require significant amounts of pressure loading, about 6 mbar, along the coast of the Hualien area. Also, the large-amplitude displacement was not detected along most of the eastern coastline of Taiwan

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(Fig. 8). Thus, the non-tidal oceanic loading along the coast is too small to account for the observed large periodic EW motion alone. 4.1.3. Surface temperature Surface temperature variation is a significant contributor to the CGPS seasonal signals and may induce the strain changes in the elastic crust. Predicted thermoelastic strain fits well the CGPS horizontal displacements with peak-to-peak amplitudes of 5 mm, as well as a phase delay between the waveforms of temperature and CGPS time series (Prawirodirdjo et al., 2006). In the Hualien area, we discovered a similar phase delay between the temperature and CGPS records (Fig. 12). However, the thermoelastic strain model cannot produce significant deformation to fit the observed large amplitude variations in CGPS data, due to the limited variation of annual temperature of about 12 °C (Fig. 12). The observed phase delay between temperature and CGPS displacements appears in all CGPS stations deployed either on bedrock or on sediments. This characteristic is also not consistent with the thermoelastic strain model which requires stations situated on loose sediments. Therefore, the mechanism of thermoelastic strain does not explain the observed large CGPS amplitudes and the phase delays in the Hualien area. 4.1.4. Groundwater level Groundwater-level change is considered as an important factor affecting the vertical deformation from CGPS displacements (Bawden et al., 2001; Watson et al., 2002). It can periodically lead to significant vertical deformation and some horizontal deformation, especially in sedimentary basins. Simulation using an elastic half-space aquifer was consistent with the CGPS displacement inside and around the basin (Bawden et al., 2001). We have collected the groundwater levels, which are shown in Fig. 13 together with the CGPS time series in the study area except for station SCHN which provided limited data. The average correlation coefficients between phases and amplitudes of groundwater levels and the CPGS data are about 0.5663 (Fig. 13). We believe that the average correlation coefficient is not large enough to prove the dominant influence of groundwater-level change on our CGPS data. Moreover, the CGPS stations with higher correlation coefficients were mostly located both on bedrock and alluvium around Hualien; this is inconsistent with the explanation of an elastic halfspace aquifer. The aquifer model and the aforementioned models produce signals that are individually much smaller than the observed data, and it is unlikely that they act completely on phase. 4.2. CGPS periodic deformation and absence of SSE Periodic surface deformation induced by crustal fault slip and stress release may exist regionally, especially during a slow slip event (SSE), which occurs with surface displacement in the opposite direction to convergence. The motion is slow, taking days to weeks, or even months to be completed, depending on the released energy of the SSE. The pattern of strain release through several regular SSEs in an earthquake cycle is obviously recorded on CGPS data at several subduction zones. However, our observed periodic pattern is not similar to the wellknown cases, such as the Cascadia subduction zone and the Himalayas. 4.2.1. The Cascadia The first observation of a SSE in the world was made along the northern portion of the Cascadia margin from CGPS data (Dragert et al., 2001). These data indicated that in the fall of 1999 the convergence suddenly reversed its motion for a period of about 2 weeks at each station in southern Vancouver island and western Washington state. This network revealed the occurrence of six similar SSEs with an average recurrence interval of 13–16 months. The amplitude of each SSE was about 5 mm, accompanied by simultaneous high tremor activities (Rogers and Dragert, 2003). In contrast, our observations show a sinusoidal- instead of sawtooth-like curve in the CGPS time series, and

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Fig. 13. Correlation between the CGPS east components and groundwater-level change in our study area. The SCHN station is excluded because its data are insufficient to estimate the correlation coefficient. The correlation coefficients of amplitude and phase between groundwater records and each CGPS station are shown. The average value of the correlation coefficients is about 0.5663 from 14 stations. The blue curve in the bottom diagram indicates the daily records of groundwater level at Hualien.

the observed peak-to-peak amplitude is twice as large as that within the Cascadia subduction zone. The SSE phenomenon is not observed around the Hualien region, and the transitional strain changes are associated with larger magnitude earthquakes, higher seismic activity and episodic earthquake swarms. In addition, the recurrence intervals also differ between the two regions. There is no convincing evidence that the Cascadia and Hualien regions behave similarly, possibly because of the different tectonic conditions affecting the two regions, such as subduction geometry, frictional properties and earthquake nucleation. 4.2.2. The Himalayas A similar seasonal seismic activity is observed in the Himalayas. Seasonal strain variation revealed by CGPS data which were influenced by fluctuating hydrology in the Nepal Himalayas is consistent with the

seasonal modulation of local seismicity (Bettinelli et al., 2008; Bollinger et al., 2007). The CGPS peak-to-peak amplitude from their results can be up to 10 mm, which is accompanied by a periodic opposite direction and seismicity modulation in the winters. They proposed that the seasonal geodetic displacements reflected a lithospheric response through the seismic activity to the seasonal variation of hydrological surface loads (Bettinelli et al., 2008). However, this observation is contrary to what is observed around the Hualien area, where more earthquakes occur during the summer when rainfall is largest (Fig. 12). We found that most other reports of seasonal effects and loading contributing to the CGPS annual movements do not agree with our observations. Our observations should be related to the complex junction geometry where the plate collision gradually shifted to plate subduction around the Hualien area.

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4.2.3. The plate junction at Hualien Plate deformation on interface asperities through earthquake ruptures in the upper crust and aseismic slow slips at depth is commonly observed in large subduction zones. SSEs appear to have a significant influence on interrelated tectonic processes, such as pore-fluid pressurization and earthquake nucleation (Bettinelli et al., 2008). Similarly, the observed periodic modulation of seismicity may also affect the tectonic processes and future large earthquakes in the Hualien area. In the case of the plate junction at Hualien, we did not find evidence for SSEs in the CGPS records. Because SSEs and non-volcanic tremors are commonly found in subduction zones, the absence of the phenomenon may only be surmised to have resulted from the unusual properties of the plate junction. The periodic CGPS signals are not related to seasonal surface loadings or variations, as discussed previously. Our observations of high seasonal correlation with larger earthquakes, seismicity and strain rates imply that the seismicity in the region is sensitive to strain variations. In addition, ta, the characteristic time for seismicity to return to a steady state for earthquake nucleation (Dieterich, 1994), is shorter than a year in the study area. It is too early in this study to identify the real source generating the large horizontal sinusoidal-like CGPS signals and their behavior. However, classical theories, including atmospheric loading, surface temperature and non-tidal oceanic loading, are unlikely to generate large periodic variations in the CGPS signals in the area. Further studies of these phenomena are critical for a better understanding of deformation mechanisms in plate junction zones.

5. Conclusions We presented the displacement models and temporal strain variations using CGPS data between 2002 and 2009 to better demonstrate current deformation patterns around the Hualien plate junction area. A clear transitional motion in the Chingshui area can be identified through the observed CGPS velocities. We found a sinusoidal-like annual horizontal motion with large amplitude which is not observed in other areas of Taiwan from CGPS stations. We reported evidence for annual strain transition from contraction to extension based on the calculated CGPS displacements, which are closely related to the local seismicity, especially larger earthquakes (Mw N 5.0). We excluded possible causes on the CGPS large periodic signals around the Hualien area, such as atmospheric loading, non-tidal oceanic loading, temperature variations and groundwater-level changes. Although such causes may influence the triggering of periodic seismicity at seismogenic depths, the phase delays associated with depth-dependent triggering of seismicity are not observed in our study. A comparison of our results from Hualien with similar subduction cases, such as the Cascadia subduction zone and the Himalayas, did not assist our interpretation, because the patterns of CGPS movements and seismicity are obviously different. Nevertheless, geodetic and seismic networks spanning the Hualien area of Taiwan characterize well the timing and spatial distributions of current crustal deformation along the complex junction boundary. The data may eventually enable us to establish a better junction model in Taiwan.

Acknowledgments We are grateful to the Central Weather Bureau and Institute of Earth Sciences, Academia Sinica for providing the CGPS raw data. We thank the BATS team which maintains the stations and routinely processes the seismic data. The helpful and constructive comments by the reviewers are greatly appreciated. This study is partly supported by the National Science Council projects (Nos. NSC99-2116-M-001-014 and NSC1002116-M-001-012). We have benefited from helpful discussions with C.-C. Liu, J.-P. Avouac, Y.-J. Hsu, F. T. Wu and J.-C. Lee. This is Institute of Earth Sciences, Academia Sinica contribution number IESAS 1849.

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