Structure of the Northern Los Angeles Basins Revealed in Teleseismic

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○E Structure of the Northern Los Angeles Basins Revealed in Teleseismic Receiver Functions from Short-Term Nodal Seismic Arrays by Guibao Liu, Patricia Persaud, and Robert W. Clayton

ABSTRACT

We use teleseismic receiver functions computed from an new low-cost autonomous sensors (nodes), short-term deploy- ∼35day nodal dataset recorded along three profiles in the ments in urban settings such as ours present an innovative way northern basins of Los Angeles, California, to map the depth to image Earth structure. and shape of the sediment–basement interface and to identify Nodal instrumentation is characterized by self-contained, possible deep fault offsets. The results show the Moho discon- short-period, single-component or three-component geo- tinuity, the bottom of the basement, and intermediary sedi- phones that can be deployed for ∼35 days. Their ease of mentary layers. There are also indications of midcrustal deployment and relatively low cost allow dense deployments. offsets along strike of the Red Hill and Raymond faults. The For example, Lin et al. (2013) used three-week-long ambient results are compared with receiver functions from nearby per- noise recordings from a 5200-node deployment in Long Beach, manent broadband stations and the 1993 Los Angeles Region California, to investigate the upper 1 km crustal structure. Seismic Experiment (LARSE) profile. The images show that Schmandt and Clayton (2013) analyzed teleseismic P waves dense deployments of node-type sensors can be used to char- from earthquakes recorded by the same array to investigate acterize basin structure in a noisy urban environment. lithospheric structure and Inbal et al. (2016) applied an earthquake-detection method to the full six-month dataset to study the deep seismicity. Earthquake hazard studies have also employed the use of Electronic Supplement: Example of the data recorded by the nodal nodal datasets, for example, to image the shallow seismic struc- array in the San Gabriel basin for the Bolivia event. ture of the San Jacinto fault zone in southern California (e.g., Ben-Zion et al., 2015; Roux et al., 2016). A 3D structural rep- INTRODUCTION resentation of a target area can be constructed over time through redeployments of the nodes in multiple profiles, each With a population of ∼18:7 million (U.S. Census Bureau, with a dense station spacing. As such, we have deployed three 2016), the Los Angeles metropolitan area is a zone of high pilot nodal lines in the greater Los Angeles area near the SAF seismic risk because it is located close to the southern segment (Fig. 1) composed of a total of 202 nodes in a first effort to of the San Andreas fault (SAF) where an M w 7.8 earthquake is image the basement of the northern basins using teleseismic expected in the near future (e.g., Jones et al., 2008; Porter et al., receiver functions. 2011). In addition, this region sits on top of sedimentary basins The receiver function technique is a well-established that amplify earthquake ground motions by focusing and trap- method that utilizes teleseismic Ps-converted phases to map ping seismic energy. Generally, the seismic amplification within subsurface discontinuities, for example, the crustal and upper- a basin is determined by the soft sediments and the basin’s mantle interface beneath a single seismic station. This provides shape. In the Los Angeles area, the northern sedimentary basins a good measurement of crustal thickness (H) and V P=V S ratio (San Gabriel and San Bernardino basins; Fig. 1) may act as a (κ) beneath a single station (e.g., Langston, 1977, 1979; waveguide channeling energy from a rupture on the southern Vinnik, 1977; Zhu and Kanamori, 2000; Beck and Zandt, SAF into the downtown Los Angeles area (e.g., Day et al., 2002) and has the potential to reveal shallow crustal structure 2012; Denolle et al., 2014). To determine the seismic ampli- (< 5km depth) with a dense (250 m) station spacing (Leahy fication due to the northern basins, it is important to use ac- et al., 2012). Sequential H‐κ stacking of receiver functions has curate information on basin structure in ground-motion been used to constrain basin properties and isolate sediment predictions. Active source studies with dense receiver arrays and basement V P=V S ratios and sediment thickness (Yeck would normally provide the highest resolution. However, with et al., 2013). Furthermore, the basement beneath the

1680 Seismological Research Letters Volume 89, Number 5 September/October 2018 doi: 10.1785/0220180071 Los Angeles basin has been clearly imaged in the common conversion-point migration using the basement PpPs phase (Ma and Clayton, 2016), suggesting that receiver function array investiga- tions can enhance lateral resolution and help recover features important for basin scale studies. Isolated single-station receiver functions are often uninterpretable for shallow structure because of scattering (Ma and Clayton, 2016). Recently, Ward and Lin (2017) demon- strated that nodal receiver functions show clear Moho Ps conversions that are nearly identical to those obtained from collocated broadband data, and Wei et al. (2018) utilized teleseismic receiver functions from a dense geophone array to image Moho topography. Here, we go a step further to illustrate the usefulness of receiver functions calculated from nodal data for investigating basin scale structure, which has potential value for seismic hazard studies and hydrocarbon exploration. We first focus on the analysis of the waveforms and spectral characteristics of broadband-versus- nodal data to illustrate that nodal datasets provide robust receiver functions. We then show that the structure of the San Gabriel and San Bernardino basins is revealed in high-frequency receiver functions.

DATA AND METHOD

Our nodal dataset was recorded along three lines: SG1, SG2, and SB4 (Fig. 1). The north-trending SG1 crossed the San Gabriel basin and was deployed with 60 nodes from the Incorporated Research Institutions for Seismology (IRIS) Portable Array Seismic Studies of the Continental Lithosphere (PASSCAL) Instru- ▴ Figure 1. (a) Map of the greater Los Angeles area showing the locations of the ment Center. The northwest-trending SG2 line nodal deployments in the San Gabriel and San Bernardino basins (small triangles) was located close to the Rose Bowl in Pasadena, and the Southern California Seismic Network broadband stations (large triangles California, and was deployed with 50 nodes from with labels) used in this study. The broadband station ADO and node 424 marked Louisiana State University. The north-trending with stars are compared in the Comparison of Nodal and Broadband Spectra and SB4 line through the San Bernardino basin had Nodal and Broadband Receiver Functions sections. The dashed line indicates the 92 nodes from the University of Utah. The in- portion of the Los Angeles Region Seismic Experiment (LARSE-93) dataset close to struments were all FairfieldNodal Zland 3C no- our study area with receiver functions along the profile analyzed by Zhu (2000) and des with a 5-Hz corner frequency that were shown in Figure 4. This profile extends to the north outside our map limits. (b) An placed ∼300 m apart and recorded continuously expanded map of the study area outlined with the white rectangle in (a). SG1, SG2, for ∼35 days at a 4 ms sampling interval (for and SB4 are the three nodal lines that were deployed in early 2017, with beginning examples of the waveforms, see Ⓔ Fig. S1, avail- and ending node numbers shown. Thin lines are the profiles shown in Figures 4a, 5, able in the electronic supplement to this article). and 6. Black dots are the pierce points for our arrays at 10-km depth for the Fiji and Because the study area is an urban environment, Bolivia events, based on the IASP91 model (Kennett, 1991). Active faults from the the nodes were placed with homeowners, in 2010 Fault Activity Map of California (Jennings and Bryant, 2010)areshown. parks, in schools, on a golf course, or on the sides LA basin, Los Angeles basin; RF, Raymond fault; RH, Red Hill–Etiwanda Avenue fault; of streets and buried in the near surface beneath a SB basin, San Bernardino basin; SG basin, San Gabriel basin; SJ, San Jose fault; SM, couple of inches of soil. The deployment of the Sierra Madre fault; WC, Walnut Creek fault; WF, Whittier fault. The color version of SB4 line took ∼4 hrs using five 2-person crews. this figure is available only in the electronic edition. The deployment teams were made up mainly of

Seismological Research Letters Volume 89, Number 5 September/October 2018 1681 Table 1 Teleseismic Events Used in This Study Time (UTC) Latitude Longitude Depth Geographic Back Azimuth Distance (yyyy/mm/dd hh:mm:ss) (°) (°) (km) Magnitude Location (°)* (°)* 2017/02/24 17:28:44 −23.26 −178.803 414.89 6.9 South of Fiji 234.253 81.1105 Islands 2017/02/21 14:09:04 −19.281 −63.9047 595.98 6.5 Southern Bolivia 127.149 74.0354 2017/03/04 02:58:20 −7.3277 155.748 17 6.1 Solomon Islands 261.805 90.8667 2017/02/18 12:10:17 −23.861 −66.6592 222 6.3 Argentina 132.32 75.5975 *Referenced to SG2. nonexperts with no previous training. Because of the densely volved in the deconvolution. Then, by comparing the calculated populated urban setting and our weekend deployment dates, we and synthetic receiver functions, the actual noise PSD that were able to get permission and install the nodes all in the same contributed to the uncertainty in the receiver functions was visit, and no advanced site visits were needed. The SB4 deploy- estimated and used in the final receiver-function calculation. ment was from 26 January 2017 to 3 March 2017, whereas the The receiver functions were finally convolved with a low-pass SG1 and SG2 deployments were from 11 February 2017 to 18 Gaussian filter, which has the form in frequency domain of March 2017. − ω2 G ωe 4a2 ; 1 The broadband data were chosen from 15 permanent EQ-TARGET;temp:intralink-;df1;311;519 stations of the Southern California Seismic Network mostly located on the northern periphery of the Los Angeles area in which ω is the angular frequency and a provides the width of (Fig. 1). The broadband stations were selected based on data the Gaussian. availability and their proximity to our deployment. In addition, The Gaussian filter is commonly used to remove high- different geologic settings (bedrock and sediments) were also frequency noise in receiver functions, with a controlling the considered to facilitate the comparison between nodal and frequency content. As a increases, more noise tends to be in- broadband data. cluded in the receiver-function calculation. Hence, there is a The criteria for selecting the teleseismic earthquakes used trade-off between vertical resolution in imaging subsurface in this study are M w > 6:0 and an epicentral distance between structures and potential artifacts generated from noisy signals 30° and 90°. Based on these criteria, we found four suitable (Ward and Lin, 2017). In this study, a was set to 2 or 6 with a events (Table 1) for the receiver function analysis. Because this maximum frequency of ∼1 and ∼3Hz, respectively. study, in part, aims to demonstrate the suitability of nodal data for producing Ps receiver functions, we used two events (Fiji RESULTS AND DISCUSSION and Bolivia) to calculate receiver functions for both the nodal and broadband data. The Solomon Islands event was also an- We first compare the raw and filtered waveforms, frequency alyzed for SG1, and the Argentina event was analyzed for SG2. – spectra, and receiver functions from the nodal and broadband Spectral and time frequency analyses were carried out to data to show that useful receiver functions can be obtained compare the frequency content of the broadband and nodal from nodal data. We then show representative profiles of nodal data. For the receiver function computation, the preprocessing receiver functions across the San Gabriel and San Bernardino procedures were common to the two data types. First, the basins to illustrate the continuity of the Moho Ps conversion. waveforms were windowed around the predicted teleseismic Finally, we examine Ps conversions that occur at the sediment– −60 P arrival ( to 60 s). Then, the windowed data were deci- basement interface and compare our results with a recent to- ≤ 50 samples=s mated to after an antialiasing filter was ap- mographic study. Because the H‐κ stacking for basement depth plied, and the mean and trend were removed. Finally, the traditionally requires multiple events and assumptions of horizontal components were rotated into the radial and tan- V P=V S ratios, and the common conversion point migration gential coordinate system. is typically limited by the assumed velocity model, we show Before deconvolution, estimates of the power spectral den- our receiver functions and interpretations in time rather than sity (PSD) of the pre-event noise were calculated on all compo- in depth, to avoid introducing any misinterpretations or arti- nents by dividing the noise records into segments of equal facts related to the assumed velocity model or V P=V S ratios. lengths and averaging the PSD estimate over these segments. In All interpretations are based on qualitatively consistent arrivals the spectral estimations, the PSD values were evaluated over fre- in the receiver functions. Furthermore, changes in the shallow- quency bands one-quarter of an octave wide (Di Bona, 1998). crustal velocity structure between the closely spaced stations in The receiver functions were computed through a frequency- our array are expected to be small, and therefore changes in the domain deconvolution (Oldenburg, 1981), in which the seismic arrival time of basement-converted phases from one station to noise PSD was used as a preliminary estimate of the noise in- the next can be interpreted as due to changes in the basement

1682 Seismological Research Letters Volume 89, Number 5 September/October 2018 component waveforms and spectral characteristics of ADO and node 424. Because of very different corner frequencies of the broadband instruments and nodes, their raw waveforms (Fig. 2a,b) and frequency spectra (Fig. 2c,d) are distinct. With a 5-Hz corner frequency, the nodal data are dominated by higher frequencies (> ∼2Hz), whereas the broadband signals are mainly of lower frequency, below ∼5Hz (Fig. 2c,d). The P arrivals in the nodal data are hidden in the high-frequency spikes and noise. For broadband data, the P arrivals are hard to recognize, but the P-wave coda is clearly dis- tinguishable from the noise. The conventional bandwidth used for robust teleseismic receiver- function calculation is between 0.01 and ∼1Hz(e.g., Zhu, 2000). This rule of thumb is adequate for broadband signals that have sig- nificant energy below 1 Hz, but for nodal data the availability of higher-frequency components allows the use of higher frequencies in receiver function studies. The spectral analysis of the raw data provides useful information but presents an overall spectral distribution instead of separating the contribu- tions from noise and useful signal. We therefore examined the spectrogram (time-varying frequency spectrum) of the broadband (Fig. 2e,g) and nodal data (Fig. 2f,h) in a 120-s window ▴ centered around the P arrival. The spectra of the Figure 2. (a) The unfiltered waveform (vertical component) for the Bolivia event broadband data (Fig. 2e,g)showthatthesignal from the broadband station ADO, (c) the corresponding frequency spectra, (e) its consists mainly of low frequencies below 1 Hz, – spectrogram, and (g) an expanded plot of the 0 2 Hz frequency band. Most of the which is consistent with the spectral analysis ∼5Hz energy is focused below . (b) The unfiltered nodal waveform (vertical com- above (Fig. 2c). From the spectrogram of node ponent) for the Bolivia event from node 424 in the SB4 line, (d) the corresponding 424 (Fig. 2f,h), the high-frequency content is still – frequency spectra, (f) the spectrogram, and (h) an expanded plot of the 0 2Hz more prominent than the lower frequencies; ∼2Hz frequency band. Most of the energy in the nodal data is found above . however, the expanded plot (Fig. 2h) shows P P The letter indicates the predicted -wave arrival calculated for the IASP91 clearly that the lower frequency (< ∼1Hz)en- model. See Figure 1a for the locations of Station ADO and node 424. The color ergy is, in fact, relatively strong and can be recov- version of this figure is available only in the electronic edition. ered with filtering. To isolate the teleseismic signals from the high-frequency depth and possibly related to its dip, offset along a fault or noise and local events, a band-pass filter (0.05–1.2 Hz) was ap- thickening of the overlying sedimentary layer. plied to both data types (Fig. 3). After filtering, the P arrivals in both signals are more evident (Fig. 3a,b). The waveforms and spectra have some differences, due to the different site properties Comparison of Nodal and Broadband Spectra at the stations (Fig. 1a). According to our spectrogram analysis, Considering that the nodes were deployed in basins, broadband the useful signals for receiver functions (P arrival and coda) in stations within the basins would be the best candidates for a ∼1:5Hz ∼1‐Hz thenodaldatacanbeashighas , which is higher than direct comparison (Fig. 1a). However, robust receiver that of broadband data at ∼1:0Hz(Fig. 3e,f ). Although these functions could not be calculated for any of the basin stations are only two representative stations for comparison, the results for the two selected events, due to the low signal-to-noise ratio still provide a basis for obtaining robust receiver functions from resulting from the high cultural noise level in the city and in- nodal datasets. coherent scattering. Therefore, we chose Station ADO, which is located in the Mojave Desert. We selected node 424 from the SB4 line for comparison to ADO (stars in Fig. 1a). Instrument Nodal and Broadband Receiver Functions responses of ADO and the nodes are provided in Ⓔ Figure S2. For the Bolivia event, the radial ∼1‐Hz receiver functions In Figure 2, we show the differences in the unfiltered vertical- along our SB4 line are projected onto a linear profile (Fig. 4a),

Seismological Research Letters Volume 89, Number 5 September/October 2018 1683 broadband results is clear. Ignoring the Moho conversion, the nodal receiver functions have stronger conversions and multiples with larger relative amplitudes after the direct P arrival compared with the broadband data, suggesting that nodes may provide a more equalized representation of intracrustal conversions and multiples that are useful in the stacking and interpretation of receiver functions and may thus serve as a complement to broadband datasets. Furthermore, closely spaced nodes make the interpretation of throughgoing structures that change depth, strike, or dip over small lateral distances possible because the corresponding conversions can be more easily traced from one station to the next.

Moho beneath the Northern Basins We show representative ∼1Hzradial receiver functions along our three lines (SB4, SG1, and SG2) in Figure 5. Benefiting from the denser station spacing, our profiles show a more con- ▴ Figure 3. (a) The band-pass-filtered (0.05–1.2 Hz) vertical component of the tinuous Moho and upper–lower crustal inter- broadband data from Station ADO that is also shown in Figure 2a, (c) the corre- face than Zhu (2000) and therefore reveal sponding frequency spectra, and (e) its spectrogram. (b) The band-pass-filtered subtler details with higher lateral resolution. We (0.05–1.2 Hz) vertical component from node 424 in the SB4 line, which is also shown can easily trace the Moho relief at ∼4–5sin all in Figure 2b, (d) the corresponding frequency spectra, and (f) its spectrogram. Ac- profiles. The Ps conversions at the upper–lower cording to (e,f) the time–frequency analysis, the nodal data appear to have energy crustal interface are interpreted along most of at higher frequencies than the broadband data. The letter P indicates the predicted the SB4 and SG2 profiles (Fig. 5a,c), but for P-wave arrival calculated for the IASP91 model. See Figure 1a for the locations of SG1 (Fig. 5b), no obvious upper–lower crustal Station ADO and node 424. The color version of this figure is available only in the interface is observed. electronic edition. Obvious time shifts in the direct P arrivals are likely due to interference from multiples and the broadband radial ∼1‐Hz receiver functions from the trapped in the very shallow sedimentary layers. This in turn Fiji event are plotted with an equal spacing from the north to can result in interference between the time-shifted direct P south (Fig. 4c). It is noteworthy that the Fiji event provided and the Ps conversion occurring at the sediment–basement in- useful receiver functions for 9 out of 15 broadband stations. terface if the basin is shallow near the station. Numerous studies The Bolivia event produced only five receiver functions from examined how the presence of sediments will affect the accuracy the broadband data. In contrast, receiver functions were recov- of the calculated Moho depth, and many approaches have been ered for most of the nodes for all events analyzed (Table 2), proposed (e.g., Zelt and Ellis, 1999; Thorwart and Dahm, 2005; with 68% being the lowest percentage of receiver functions re- Langston, 2011; Bostock and Trehu, 2012; Tao et al., 2014), covered (for SB4 and the Fiji event), which is otherwise > 84% including the sequential H‐κ stacking by Yeck et al. (2013) for the three profiles. Though not directly comparable because and resonance removal filtering by Yu et al. (2015). In our case, results for basin stations were not presented by Zhu (2000),we the most obvious time shifts occur near the southern end of also include their receiver functions (Fig. 4b) computed from SG2; however, the time shift varies depending on the back the Los Angeles Region Seismic Experiment (LARSE-93), an azimuth of the event. We interpret the time shifts in the direct array with instrument responses having cutoff frequencies of P arrivals as a limitation in vertical resolution that cannot be ∼1Hz ∼1Hzdeployed from the northern Los Angeles basins to resolved with low-frequency ( ) receiver functions, sug- the Mojave Desert (black dashed line in Fig. 1a). gesting that higher-frequency receiver functions should be used H‐κ The receiver functions from the three data types all reveal in the stacking for Moho depths in such settings. strong conversions at the Moho just before 5 s (indicated by white arrows in Fig. 4a–c). A possible upper–lower crustal in- Inferred Deep Fault Structures terface interpreted by Zhu (2000) is also recognizable in the The northern basins are located in a complex stress field. First, broadband and nodal results (black arrows in Fig. 4), but is strike-slip faulting is expected at a regional scale, due to the trans- not as easily identified in the profile of Zhu (2000). The dis- form nature of the Pacific–North America plate boundary. At tinct difference in the overall character between the nodal and the same time, a north–south compressive stress field results

1684 Seismological Research Letters Volume 89, Number 5 September/October 2018 the surface trace of the Red Hill–Etiwanda Avenue fault located in the northern part of the San Bernardino basin (Cramer and Harring- ton, 1980) and is thus interpreted as associated with such fault (see Fig. 1b for locations). In the SG2 profile (Fig. 5c), the surface projection of the inferred fault is close to the Raymond fault (Fig. 1). Along the SG1 profile (Fig. 5b), the inferred deep offset does not correspond to a documented fault. For all three profiles, the inferred faults seem to be south-dipping, but this should be interpreted with caution because the profiles are in time not depth, and our study would only allow for a determination of the apparent dip of these structures.

Imaging the Basement beneath the Northern Basins Previous studies for obtaining estimates of the basement depths in or near our study area include modeling of gravity and aeromagnetic data ▴ Figure 4. (a) The radial receiver functions from the SB4 nodal deployment and to determine the structure of the San Bernardino the Bolivia event projected onto the thin line shown in Figure 1b, (b) the LARSE-93 basin (Anderson et al.,2004). Süss and Shaw deployment across San Andreas fault (SAF) from Zhu (2000), and (c) from the (2003) used the geostatistical analysis of sonic broadband stations used in this study and the Fiji event. Along the LARSE-93 profile well logs and reflection-stacking velocities to (dashed line in Fig. 1a), “0” is the SAF, and the receiver functions with negative construct a high-resolution 3D P-wave seismic- distances are located to the south in the San Gabriel Mountains, with the northern velocity model embedded in a lower-resolution end of the profile extending outside the map area in Figure 1a. Receiver functions model for the Los Angeles basin to the west for stations in the basin closer to our deployment were not shown by Zhu (2000). of our study area. Magistrale et al. (2000) pro- Gray arrows indicate the P arrival; black arrows mark the Ps conversion at the duced velocity models for the major southern upper–lower crustal interface; and white arrows mark the Moho Ps conversion. California basins by combining geotechnical data and Faust’s empirical equation for obtaining V P from the “Big Bend” of the SAF zone (Wentworth and Yerkes, from sediment age and depth. However, the spatial resolution of 1971; Savage et al.,1978; Thatcher, 1981). In our study area, the the latter two models is limited by the availability of oil wells and deformations present along the Moho discontinuity are thought geotechnical boreholes, which are sparse in our study area. Our to be associated with the north–south compressional stresses. results will therefore help better resolve these gaps in data We indicate the apparent offset or lateral change of the Moho coverage. and upper–lower crustal interfaces in Figure 5, based on an We show ∼3Hzradial receiver functions in Figure 6 that abrupt change in the lateral continuity of phases and/or a dis- correspond to the same events shown at ∼1Hzin Figure 5. tinct difference in the character of multiple receiver functions on Among the three profiles, the San Gabriel basin has the thick- the two sides of the interpreted offset. We therefore propose that est sediments at ∼1:5–2s, which is revealed in the SG1 profile the inferred faults may exist near these locations, although the (Fig. 6b), whereas the San Bernardino basin has a thinner sedi- specific geometry and extent of these inferred structures are un- mentary package at ∼1s(Fig. 6a). However, the SB4 line may determined. In the case of SB4, the interpreted offset is close to not be representative of the San Bernardino basin as a whole

Table 2 Number of Useful Receiver Functions for Each Event–Dataset Pair Dataset Total Stations Bolivia* Fiji* Solomon Islands* Argentina* SB4 92 81 63 No data No data SG1 60 58 58 57 No data SG2 50 42 44 No data 44 Broadband stations 15 5 9 Not computed Not computed Useful receiver functions are those with low presignal noise levels that are useful for revealing regional structures. *The number of useful receiver functions computed from the full dataset for each event.

Seismological Research Letters Volume 89, Number 5 September/October 2018 1685 ▴ Figure 5. Preliminary interpretations of three representative profiles of ∼1‐Hz radial-receiver functions from our nodal dataset along (a) the SB4, (b) the SG1, and (c) the SG2 lines. Profile locations are indicated by thin lines in Figure 1b. Inferred faults may exist though the depth and geometry cannot be determined. For clarity, only select node numbers are shown at the top of each panel. RF, Raymond fault; RH, Red Hill–Etiwanda Avenue fault; HWY, Highway. The color version of this figure is available only in the electronic edition. because it is located close to the basin’s edge. In general, for the v.4.26 (CVM-S4.26; Lee et al., 2014) shown in Figure 7 northern basins the sediments thicken southward, which is and the Unified Structural Representation of the southern more obvious for the San Gabriel basin profiles. In the case California crust (USR; Shaw et al., 2015), the SG1 and SG2 of SG2 (Fig. 6c), the overall geometries of the basement profiles have similar basin depths and shapes to the CVM- and the Moho are similar, which is not observed along the S4.26 and USR. We assume the sediment–basement interface other two profiles. In the deeper parts of the SG2 profile, occurs at 2:5km=s in the tomographic model based on esti- the upper–lower crustal interface present in the 1-Hz results mated San Bernardino basin basement depths from Anderson (Fig. 5c) is clearly visible. Noteworthy is an offset or step in the et al. (2004) and Graves (2008). However, based on our results, SB4 basement close to node 444 (profile distance 12.5 km, out- the San Bernardino basin has a more intricate shape than in the lined with a dashed box in Fig. 6a), which closely corresponds tomographic model and is deepest near an ∼10‐km profile to a gap in continuity of the upper–lower crustal interface distance, shallows southward, and deepens again near the (Fig. 5a). It should be noted that the lower crustal layer along southern end of the profile. SB4 thins as the Moho shallows and the overall crustal thickness decreases, but the upper crust shows no obvious change in CONCLUSIONS thickness (profile distance 10–15 km in Fig. 5a). Comparing our preliminary results to the Southern In this study, we compared the waveforms and spectral character- California Earthquake Center Community Velocity Model istics of nodal and broadband data in the greater Los Angeles

1686 Seismological Research Letters Volume 89, Number 5 September/October 2018 ▴ Figure 6. High-frequency (∼3Hz) radial receiver functions from our nodal dataset, with preliminary interpretations along (a) the SB4, (b) the SG1, and (c) the SG2 lines. The corresponding ∼1‐Hz radial-receiver functions are shown in Figure 5. Profile locations are shown with thin lines in Figure 1b. The dashed box outlines an offset or step in the sediment–basement interface described in the Imaging the Basement beneath the Northern Basins section. For clarity, only select node numbers are shown at the top of each panel. RF, Raymond fault; RH, Red Hill–Etiwanda Avenue fault; HWY, Highway. The color version of this figure is available only in the electronic edition. area and show that nodal teleseismic waveforms have sufficient Our results demonstrate that nodal experiments with bandwidth for receiver-function calculations. Similarly, the shorter deployment periods, lower costs, flexible schedules receiver functions calculated from nodal data share common and sites, and denser station spacing compared to traditional characteristics with those from broadband data. However, the broadband stations have the potential to fill a gap in the high- dense spacing and higher-frequency content (up to ∼3Hzin resolution imaging of crustal structure. Nodal studies not only our study) afforded by nodal receiver-function profiles show provide new acquisition methods and datasets but may open up more continuous crustal structures (e.g., the Moho discontinu- new avenues for the passive-source community to integrate the ity) and have the ability to reveal the sediment–basement inter- sophisticated processing and imaging techniques widely used in face and fault offsets because of their higher lateral resolution. active-source research (Ryberg and Weber, 2000). Based on the preliminary interpretation of three nodal profiles, we discuss the shapes of the northern basins in the Los Angeles DATA AND RESOURCES metropolitan area that are thought to present waveguides for seismic energy from an SAF rupture into the downtown Los The broadband seismograms used in this study were down- Angeles. We also identified three possible deep offsets in our loaded from the Incorporated Research Institutions for Seismol- profiles, two of which may be related to nearby documented ogy (IRIS) Data Management Center (DMC) at www.iris.edu faults. (last accessed October 2017) or from the Southern California

Seismological Research Letters Volume 89, Number 5 September/October 2018 1687 Seismology (IRIS) Portable Array Seismic Stud- ies of the Continental Lithosphere (PASSCAL) nodes that were deployed along SG1. The authors thank Carl Tape, an anonymous reviewer, Editor-in-Chief Zhigang Peng, and Guest Edi- tors Marianne Karplus and Brandon Schmandt for their thoughtful and constructive comments that helped improve the article. G. L. and P. P. thank the Geology and Geophysics Department at Louisiana State University for supporting this project. This research was partially supported by the U.S. Geological Survey (USGS) Award GS17AP00002.

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