Crustal structure beneath the High Lava Plains ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, B02313, doi:10.1029/2010JB007795, 2011

Crustal structure beneath the High Lava Plains of eastern Oregon and surrounding regions from receiver function analysis Kevin C. Eagar,1 Matthew J. Fouch,1 David E. James,2 and Richard W. Carlson2 Received 21 June 2010; revised 29 October 2010; accepted 23 November 2010; published 25 February 2011.

[1] We analyze teleseismic P‐to‐S receiver functions to image crustal structure beneath

the High Lava Plains (HLP) of eastern Oregon and surrounding regions. Coverage from 206 broadband seismic stations provides the first opportunity to resolve variations in crustal composition, thickness, and heterogeneity on scales of a few km in depth and tens of km laterally across the HLP region. We utilize both H − stacking and a new Gaussian‐weighted common conversion point stacking technique. We find crust that is ≥40 km thick beneath the Cascades, Idaho Batholith, and Owyhee Plateau and thinner (∼31 km) crust beneath the HLP and northern Great Basin. Low Poisson’s ratios of ∼0.240 characterize the granitic crust beneath the Idaho Batholith, while the Owyhee Plateau exhibits values of ∼0.270, typical of average continental crust. The Owyhee Plateau is a thick simple crustal block with distinct edges at depth. The western HLP exhibits high average values of 0.304, typical for regions of widespread basaltic volcanism. Combined with other geological and geophysical observations, the areas of abnormally high Poisson’s ratios (∼0.320) and low‐velocity zones in the crust beneath north‐central and southern Oregon are consistent with the presence of partial melt on either side of the HLP trend, suggesting a central zone where crustal melts have drained to the surface, perhaps enabled by the Brothers Fault Zone. Thicker crust and an anomalous N‐S band of low Poisson’s ratios (∼0.252) skirting the Steens Mountain escarpment is consistent with residuum from a midcrustal magma source of the massive flood basalts, supporting the view of extensive mafic underplating and intraplating of the crust from Cenozoic volcanism. Citation: Eagar, K. C., M. J. Fouch, D. E. James, and R. W. Carlson (2011), Crustal structure beneath the High Lava Plains of eastern Oregon and surrounding regions from receiver function analysis, J. Geophys. Res., 116, B02313, doi:10.1029/2010JB007795.

1. Introduction [2] The extent and volume of the back‐arc volcanism in the Pacific Northwest, United States, as well as its occurrence within the continent, makes the Cascadia subduction system unique. Subduction along the western margin of North America has been active since the Triassic [Dickinson, 2004]. Accretion of Mesozoic and early Cenozoic allochthonous terranes onto the western margin of Precambrian North America formed the foundation of the crust in the Pacific Northwest [Coney et al., 1980]. This boundary is often delineated by sharp gradients in 87Sr/ 86Sr isotope initial ratios (isopleths of 0.704 and 0.706 are hereafter referred to as the “0.704” and “0.706” lines) [Armstrong et al., 1977]. The terranes have varying composition, ranging from oceanic fore‐ arc basins [Dickinson, 1979] and island arcs [Gray and 1 School of Earth and Space Exploration, Arizona State University, Tempe, Arizona, USA. 2 Department of Terrestrial Magnetism, Carnegie Institution of Washington, Washington, D. C., USA.

Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2010JB007795

Oldow, 2005] to fragments of continental crust [Coney et al., 1980]. Large‐scale extension of the western United States beginning in the mid‐Tertiary was accompanied by the onset of widespread magmatism typified by rhyolitic ignimbrites, ash flow tuffs, and mostly rhyolitic lava flows [Lipman et al., 1972; Noble, 1972; Armstrong, 1978]. Magmatism in the Pacific Northwest includes not only Cascade arc volcanism, but also widespread and voluminous volcanism east of the arc, including the Columbia River and Steens flood basalts (Figure 1). The origin of the back‐arc magmatism remains a contentious subject of debate [e.g., Carlson and Hart, 1987; Camp and Ross, 2004; Jordan et al., 2004; Hales et al., 2005]. [3] The High Lava Plains (HLP) region of eastern Oregon represents one of the largest, yet least understood, intraplate magmatic centers on Earth. It is characterized by voluminous mid‐Miocene outpourings of the Steens and Columbia River flood basalts that erupted near the western edge of the craton [e.g., Carlson and Hart, 1987]. A period of extensive coeval rhyolitic and basaltic volcanism across central Oregon and Idaho followed the flood basalt event (Figure 1). Postflood basalt activity is marked by two semilinear time

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EAGAR ET AL.: CRUSTAL STRUCTURE BENEATH THE HIGH LAVA PLAINS, OREGON

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Figure 1. Geologic and tectonic map of the Pacific Northwest, United States. The Juan de Fuca plate (JdF), North American plate (NA), McDermitt caldera (MC), Jordan Craters (JC), Diamond Craters (DC), Newberry Volcano (NB), Steens Mountain escarpment (SM), Harney Basin (HB), Brothers Fault Zone (BFZ), Oregon‐Idaho graben (OIG), Monument (MDS) and Chief Joseph (CJDS) dike swarms and other geologic provinces are labeled. Mid‐Miocene Steens and Columbia River flood basalt are shown as light brown shaded regions with overlain associated dike swarms [after Camp and Ross, 2004]. Areas of Quaternary basalt are shown in red. Locations of Holocene volcanism are denoted by triangles [Siebert and Simkin, 2002]. Isochrons of rhyolitic volcanism, as well as eruptive centers (gray dotted outlines), are shown across the High Lava Plains and Snake River Plain [Jordan et al., 2004; Pierce and Morgan, 1992]. Gray dashed line marks the boundary of the Precambrian Belt Basin [Harrison, 1972; Ross and Villeneuve, 2003]. Approximate locations of 87Sr/86Sr isopleths of 0.704 and 0.706 (“0.704” and “0.706” lines) are denoted by black dashed lines [after Armstrong et al., 1977; Kistler and Peterman, 1978; Leeman et al., 1992].

progressive tracks that both started in the area surrounding the Owyhee Plateau. One, propagating to the northeast with the direction and speed of North American plate motion, formed the Snake River Plain to the present‐day actively

volcanic Yellowstone area [Pierce and Morgan, 1992]. The other propagated northwest to Newberry Volcano, forming the High Lava Plains [MacLeod et al., 1976; Jordan et al., 2004]. In contrast to the rhyolites, the basalts along both

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Figure 2. Broadband seismic stations used for crustal receiver function analysis. White symbols denote station locations (squares, High Lava Plains array; triangles, USArray Transportable Array; circles, regional networks); solid black lines denote active source experiments used to obtain a priori crustal Vp for H − stacking. Dashed lines denote regions for which each average velocity profile (R1–R6b) was applied.

tracks appear to have no spatial correlation with age [e.g., Draper, 1991]. The effects of regional late Cenozoic extension are evident in southern and central Oregon via well developed Basin and Range normal faulting. Basin and Range structure terminates at the southern edge of the Brothers Fault Zone, a series of NW striking, small displacement en echelon faults [e.g., Lawrence, 1976; Pezzopane and Weldon, 1993; Meigs et al., 2009] and extension across Oregon has been estimated to be ∼17% [Wells and Heller, 1988]. Results from paleomagnetic data analyses suggest the E‐W trending Blue Mountains terrane has seen significant lateral translation and rotation since post‐Cretaceous time [e.g., Riddihough et al., 1986; Housen and Dorsey, 2005]. Measurements of present‐day deformation from GPS data indicate a pole of rotation on the Washington‐Oregon border that is consistent with the extension in southern Oregon [McCaffrey et al., 2000]. [4] In this paper, we analyze receiver functions from teleseismic earthquakes recorded at broadband seismic stations in the region of central and eastern Oregon, southern Washington, western Idaho, and northern Nevada. The focus

of our analysis is the first‐order determination of variations in thickness and seismic properties of the crust beneath the region. These new constraints on crustal properties enable a better understanding of the consequences of extension and volcanism in modifying the crust of the HLP, and how those processes link to an improved understanding of regional tectonic evolution.

2. High Lava Plains Passive Seismic Experiment and Data Collection [5] Our data set for this study consists of teleseismic data from 206 broadband seismic stations within the High Lava Plains and surrounding regions that operated between 2004 and 2009 (Figure 2). The bulk of the recording stations were part of the High Lava Plains project [Carlson et al., 2005] in which 118 broadband stations, spaced 15–20 km apart, operated between January 2006 and September 2009 over a ∼220,000 km2 area across eastern Oregon, northern Nevada, and western Idaho. Sensors included nearly all Guralp 3T and Streckeisen STS‐2 broadband seismometers, as well as

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were obtained using the Standing Order for Data (SOD) software package [Owens et al., 2004]. The initial number of station‐event pairs based on these criteria was 114,897. As a result of our data culling process, described below, we retained 14,356 seismograms from 849 events for our receiver function analysis (Table S1).1 The final data set provides excellent coverage from the NW and SE directions, including mostly distant events from the SW, and more scattered, sparser coverage from the NE (Figure 3).

3. Methods

Figure 3. Teleseismic earthquakes used for crustal receiver function analysis. Earthquakes with mb greater than 5.5 at distances 30°–95° from our stations are shown as gray circles; black star denotes center of seismic station coverage.

three Guralp ESP and one 40T intermediate band seismometers from the IRIS PASSCAL, Carnegie Institution of Washington, and Arizona State University instrument pools. Stations continuously recorded data at 40 samples per second per channel using either a Reftek RT130 or Quanterra Q330 data logger. GPS timing was used throughout the network. The array was configured into two swaths, one NW‐SE following the age progressive trend of the HLP eruptive centers, and one N‐S from the northern Great Basin to the Blue Mountains (Figure 2). Station deployment occurred in stages, with the bulk of the array installed by the end of summer 2007, at which point 106 stations were simultaneously collecting data. Twelve of the seismometers were moved to new sites during summer 2008 to enhance regional coverage during the last year of the deployment. [6] During the operating phase of the HLP array, broadband stations from several other networks throughout the Pacific Northwest, notably the EarthScope/USArray Transportable Array (USArray TA) and other regional networks, also recorded data. The majority of USArray TA stations in the vicinity of the HLP array operated between summer 2006 and fall 2008. The nominal spacing for USArray TA stations was ∼70 km and provided a coarse grid of stations around and within the denser HLP array to provide regional context for results from HLP data analyses. [7] We used the National Earthquake Information Center (NEIC) monthly hypocenter catalog to select 1960 candidate earthquakes for analysis. We searched the catalog based on origin times between January 2004 and September 2009, epicentral distances between 30° and 95°, and mb ≥ 5.5. The list of earthquakes fitting these criteria was compiled and waveform data from the IRIS Data Management Center

3.1. Receiver Function Estimation [8] The receiver function method has become a commonly employed imaging technique used to study discontinuities in seismic wave speeds at crustal, lithospheric, and upper mantle scales. The technique relies on forward scattered P‐to‐S converted waves from seismic impedance boundaries beneath a single station. The receiver function itself is a time series that represents the approximate wavefield separation of SV energy from P energy on the radial component, where time is relative to the direct P wave arrival. The computation of a receiver function is accomplished through a deconvolution operation of the vertical component from the radial component [Langston, 1979]. Both vertical and radial seismograms are assumed to contain the same information related to the source time function, instrument response, and path effects through the lower mantle. Hence, this process isolates the radial Earth impulse response below the station and is often alternatively referred to as source equalization or normalization [Langston, 1979]. Deconvolution can be performed either in the frequency domain or the time domain. For this study, we chose to use the time domain iterative deconvolution method of Ligorria and Ammon [1999]. The advantage of this approach is that the signal produced is truly causal and is free from regularization methods such as water level stabilization [Clayton and Wiggins, 1976] in the frequency domain or damping parameters in time domain inversions [Sheehan et al., 1995]. [9] We follow the same general procedure for computing receiver functions as described by Eagar et al. [2010], with a few notable differences to enhance imaging of crustal structure. In this process, we cut the raw seismograms 40 s before and 135 s after the predicted P wave arrival based on the 1‐D IASP91 velocity model [Kennett and Engdahl, 1991] and removed the mean and the first‐order trend from the cut waveforms. The seismograms were filtered using a 0.02 Hz high pass and a 10% cosine taper. We then rotated the horizontal seismograms along the free surface to obtain radial and transverse components of motion. Receiver functions for each source‐station pair were generated using iterative deconvolution [Ligorria and Ammon, 1999], a forward modeling procedure that predicts a receiver function using a series of Gaussian pulses convolved with the vertical seismogram, resulting in a predicted radial or transverse seismogram. The pulse width of the receiver function is determined by the Gaussian parameter [Ligorria and Ammon, 1999], which we set at 2.5. This results in a 1

Auxiliary material data sets are available at ftp://ftp.agu.org/apend/jb/ 2010jb007795. Other auxiliary materials are in the HTML.

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Figure 4. (left and middle) Seismograms and (right) computed receiver functions from a mb 6.1 Aleutian event on 16 April 2008. Approximate arrival times of Ps, PpPs, and PsPs phases are marked by arrows. Gray shaded waveforms in Figure 4 (right) denote positive arrivals.

pulse width of ∼1 s (or ∼2 s dominant period) and is an effective low‐pass filter typical of teleseismic crustal studies that results in ∼3.5 km vertical resolution. Receiver function preprocessing and computation were automated via batch processing on a 32 CPU computing cluster to significantly reduce computation time for the large data set presented here. While we computed both radial and transverse receiver functions, we focus on the results from the radial receiver functions in this study. We show example waveforms and resulting receiver functions from one station in Figure 4. 3.2. H − k Stacking [10] To determine average crustal properties, we analyzed receiver functions for each station using the stacking technique of Zhu and Kanamori [2000]. This method enables the determination of Moho depth (H) and the ratio of crustal P to S wave speeds (Vp/Vs, or ) by treating the crust as a

homogeneous, horizontal, isotropic layer over a half‐space. The differential traveltime between the direct P and the primary conversion from the Moho (Ps) can be used to estimate crustal thickness; however, the Ps − P traveltime has an inherent tradeoff between the layer thickness and crustal seismic wave speeds [e.g., Ammon et al., 1990]. Zandt and Ammon [1995] showed that traveltime information of reverberations within the crust (PpPs and PsPs + PpSs, which we refer to just as PsPs) can help reduce the nonuniqueness of the velocity/thickness tradeoff. Figure 4 shows an example of approximate arrival times of the Ps, PpPs, and PsPs on individual receiver functions and their associated seismograms. To better constrain H and , assuming a locally horizontal discontinuity, Zhu and Kanamori [2000] introduced a method that utilizes these multiple crustal reverberations along with the Ps primary phase information. We rearrange the terms of the original equations given by

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Table 1. Seismic Refraction Profiles Used for Regional Average Crustal Vpa Refraction Profile

Reference

R1 R2 R3 R4a

Leaver et al. [1984] Catchings and Mooney [1988a] Catchings and Mooney [1988b] Cox and Keller (manuscript in preparation, 2011) Cox and Keller (manuscript in preparation, 2011) Lerch et al. [2007] Lerch et al. [2007] Hill and Pakiser [1967] Hill and Pakiser [1967]

R4b R5a R5b R6a R6b

6.29 6.19 6.36 6.13 6.27

a Average Vp calculated from locations near center of regions defined in Figure 2.

Zhu and Kanamori [2000] to describe the relative arrival times for each of these phases as a function of both H and and also account for a spherical coordinate system by

tPs

2sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi3 2 2 H4 R R 2 ¼ p p2 5; R Vp= Vp

tPpPs

2sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi3 2 2 H4 R R 2 ¼ p þ p2 5; R Vp= Vp

tPpSsþPsPs

2H ¼ R

s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2 R 2 p ; Vp=

ð1Þ

ð2Þ

ð3Þ

where p is the ray parameter of the incident P wave in km/radian, R is the radius of Earth, and Vp is the average crustal P wave speed. [11] The method for finding the optimal H and values to describe the crust is a grid‐searching algorithm that maximizes the stacked amplitudes of Ps, PpPs, and PsPs. The parameters H and are varied between a range of values, and relative traveltimes for each phase are computed by equations (1)–(3). For this study, we explored H ranges from 20 to 65 km using a step size of 0.01 km and ranges from 1.50 to 2.20 using a step size of 0.005. The amplitudes at the computed traveltimes of all receiver functions recorded by a single station are then summed using the stacking function sð H; Þ ¼

determined where s(H, ) was a maximum. We converted the Vp/Vs ratio beneath a station to Poisson’s ratio (s) using

Average Crustal Vp (km/s) 6.55 6.41 6.44 6.10

n X w1 Ai ðtPs Þ þ w2 Ai tPpPs w3 Ai tPsPsþPpSs ; ð4Þ i¼1

where Ai is the amplitude of the ith receiver function as a function of predicted traveltime and w1, w2, and w3 are weighting terms given to each of the respective phases. We note that the third term in equation (4) has a negative value due to the opposite polarity of PsPs. For this study, we usually defined w1, w2, and w3 as 0.5, 0.3, and 0.2, respectively, although we adjusted these values at a few stations (Data Set S1) based on a qualitative assessment of the visibility of the phases, described below. The best solution was

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¼

1 1 1 2 ; 2 1

ð5Þ

for purposes of interpretation of rock material properties. [12] The traveltimes used for crustal receiver function analysis are much less sensitive to Vp than to Vs [Zhu and Kanamori, 2000], and it is common to assume an average Vp for the entire crust. Previously obtained active source models in this region provide us with a priori estimates for crustal Vp averages (Table 1). We assigned an average Vp to each station (Figure 2) based on the proximity of stations to refraction lines, regional geologic boundaries, and preliminary results using a constant Vp. A comparison of results using a constant Vp versus regional Vp variations (Data Set S1) yielded an average difference of only ±0.7 km in Moho depth and ±0.006 in Vp/Vs. These differences are small enough not to affect the geologic interpretations of our results. [13] There are several benefits of the H − grid‐searching analysis, as well as a few limitations to consider given the assumptions necessary to perform H − analysis. For instance, stacking receiver functions from many events from all azimuths and epicentral distances at a given seismic station enables the determination of an average crustal structure in the vicinity of each station. This provides for comparison of results at different stations that were not necessarily deployed in the same time period. Another benefit is that the H − analysis does not require manual picking of arrival times of converted phases, which can be difficult given the low amplitudes of the primary and multiple converted phases relative to the direct P on a single record. Removing the restrictions of manual picking also enables many components of the analysis to be readily automated [e.g., Crotwell and Owens, 2005]. We note that the largest limitation to this stacking method is that complex crustal structures, such as anisotropy, dipping or sharply varying Moho, or other strong velocity contrasts (i.e., near‐ surface sedimentary layers or midcrustal layers) can incorrectly bias the estimates of Moho depth and Vp/Vs ratio. [14] To estimate uncertainties in the final stacking solution, we constructed standard deviation contours in s(H, ). pffiffiffiffiffiffiffiffiffiffiffi These contours are defined by 1 − 2 =N , where s2 is the variance and N is the number of waveforms [Eaton et al., 2006]. There is a separate family of uncertainties that is more difficult to quantify. In particular, the initial choice of Vp shifts the final H − value; thus choosing Vp based on other local constraints where available is important. Finally, this method assumes a homogeneous, isotropic, horizontal crust, which is an oversimplification of real earth crustal structure. 3.3. Gaussian‐Weighted Common Conversion Point Stacking [15] In addition to the single station measurements obtained from H − stacking, we constructed subsurface images using a modified version of common conversion point (CCP) stacking [e.g., Dueker and Sheehan, 1997; Eagar et al., 2010]. In this method, we back project the receiver function amplitudes along rays toward each corresponding origin. We use the same 1‐D velocity model (Table 2) from Eagar et al. [2010], which combines the TNA S wave velocities

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Table 2. One‐Dimensional Velocity Model for Time to Depth Conversions in GCCP Stacking Depth (km)

Vpa (km/s)

Vsb (km/s)

0 25 38 50 75 100

6.25 7.20 8.10 8.00 7.95 7.89

3.47 4.00 4.40 4.35 4.32 4.29

a Average upper and lower crustal P velocities were derived from Catchings and Mooney [1988b]. Mantle P velocities were calculated using the TNA 1‐D S model of Grand and Helmberger [1984] and an assumed Vp/Vs of 1.84. b Average upper and lower crustal S velocities were calculated using the P velocities derived from Catchings and Mooney [1988b] and an assumed Vp/Vs of 1.84. Mantle S velocities are from the TNA 1‐D S model of Grand and Helmberger [1984].

[Grand and Helmberger, 1984] modified for crustal velocities from Catchings and Mooney [1988b]. We divide our volume into imaging points spaced laterally every 10 km and vertically every 1 km to a depth of 80 km. To compute piercing points of converted (Pds) waves at each depth, we used the spherical traveltime equation TPds

2sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3 n X R ðri Þ 2 Rðri Þ 2 Dr 4 ; ¼ p2Pds p2P 5 v v R ð r Þ ð r Þ ðri Þ S i P i i¼1 ð6Þ

where R(ri) is the Earth’s radius from each ith depth shell r in km, Dr is the depth interval, and p is the ray parameter expressed in sec/radian. From this point, our method deviates from CCP stacking in that we do not produce linear stacks based on location binning of piercing points. Because the imaging targets are shallow (