U.S. Geological Survey Award Number G17AP00022 Final
Total Page：16
File Type：pdf, Size：1020Kb
U.S. Geological Survey Award Number G17AP00022 Final Technical Report Site characterization in the Sacramento-San Joaquin Delta using seismic surface wave and reflection methods Project Period: January 1st, 2017 – December 31st, 2017 Mitchell Craig Department of Earth and Environmental Sciences California State University, East Bay 25800 Carlos Bee Blvd. Hayward, CA 94542 Phone: 510-885-3425, 510-885-3486 [email protected] Niket Kundariya Department of Earth and Environmental Sciences California State University, East Bay 25800 Carlos Bee Blvd. Hayward, CA 94542 [email protected] Maximilian Burnham Department of Earth and Environmental Sciences California State University, East Bay 25800 Carlos Bee Blvd. Hayward, CA 94542 [email protected] Koichi Hayashi Geometrics, Inc. 2190 Fortune Dr. San Jose, CA 95131 [email protected] Key Words: near surface geophysics, surface wave methods, shear wave velocity, site characterization, San Francisco-San Joaquin Delta, levees Site characterization in the Sacramento-San Joaquin Delta using seismic surface wave and reflection methods M. Craig, N. Kundariya, M. Burnham, and K. Hayashi Abstract Seismic surface wave surveys were performed at nine new sites on three islands in the Sacramento-San Joaquin Delta to measure shear wave velocity (VS). The surveys provide improved VS models of the near surface. Eight surveys used small linear arrays to estimate VS in the upper 30 m, and two surveys used large triangular arrays to estimate VS to a depth of 400 m. The shallow surveys included both active and passive methods, normally using a linear array of 48 sensors with a 1 m sensor spacing. The MASW (multichannel analysis of surface waves) method was employed with a sledgehammer source and the MAM (Microtremor Array Measurement) method utilized ambient noise. Velocities of a peat layer in the upper 3 m to 7 m were as low as 40 m/s. Velocity of an underlying sandy unit generally reached 200-250 m/s at a depth of about 20 m. At open field sites away from levees, fundamental mode Rayleigh wave dispersion curves were clear and velocity could be estimated to depths of 20-30 m. At sites on or along levees, however, higher modes were dominant and depth penetration was limited to about 10 m. Deeper velocity measurements were performed at Twitchell and Sherman Islands using nodal seismographs to record ambient noise. Triangular arrays were used with a maximum radius of 231 meters, providing velocity estimates to a depth of 200 m at Sherman Island and 400 m at Twitchell. Two seismic reflection profiles, 193 m and 245 m long, were recorded on Bouldin Island with the aim of imaging deeper stratigraphy. Basemaps with line locations and sample field records are included. Introduction The Sacramento-San Joaquin Delta consists of a network of river channels that originate in the Sierra Nevada and drain through the Carquinez Strait to San Francisco Bay. Because its outlet is constricted, deposits of peat and mud up to 10 m thick accumulated in the central Delta during the Holocene. These estuarine and floodplain deposits are underlain by eolian sands and Pleistocene alluvial fan deposits (Atwater and Belknap, 1980). Delta islands have experienced 3 to 8 meters of subsidence during the past century due to oxidation and compaction of peat (Mount and Twiss, 2005). Projected sea level rise over the next century will contribute to an ongoing landward shift of the freshwater-saltwater interface, and increase the risk of flooding due to levee failure or overtopping. Ground motion due to an earthquake on a nearby fault could be sufficient to cause multiple levee failures (URS, 2009). Seismic shear wave velocity (VS) in the upper 30 m may be used to determine Uniform Building Code (UBC)/ National Earthquake Hazard Reduction Program (NEHRP) site class (BSSC, 2001). At a typical site, S waves from earthquakes experience a decrease in S wave velocity near the surface, which tends to cause amplification of ground motion. Knowledge of shear wave velocities is needed to accurately estimate site-specific variations in ground motion. The main goal of this study was to measure near surface (to 30 m depth) S wave velocities (VS) by conducting surface wave surveys (Figure 1). This was done at most sites using short linear arrays of sensors. Deeper (to 500 m depth) VS was measured at two sites using nodal seismographs to form larger areal arrays. 1 Figure 1. Schematic velocity model showing S-waves from earthquake and surface sources. Upgoing waves from the earthquake experience a velocity decrease near the surface and amplification of ground motion. S wave velocities of the near-surface layer may be measured from surface waves generated by either an artificial source (active method) or ambient noise (passive method). Method: A combination of active and passive seismic surface wave methods was used to determine shear wave velocity VS for site characterization at nine sites in the Sacramento Delta (Figures 1 and 2). Seismic reflection surveys Active surveys were conducted using the MASW (multichannel analysis of surface waves) method (Xia et al., 1999) with a linear array and sledgehammer source. Passive surveys were conducted using the MAM (microtremor array method) with ambient noise (Okada, 2003). Active and passive data were recorded at most sites using a linear array of 48 vertical-component 4.5 Hz geophones. Geophone spacing was 1 m and spread length was 47 m. The target depth of these surveys was 30 m. For active surveys, several sledgehammer blows were recorded at a few different offsets ranging from 1 m to 10 m from each end of the recording spread. Data were processed using SeisImager SW (Geometrics, 2009). Field records and frequency-velocity spectra were inspected and for each site the record with the most coherent dispersion curve was selected for further processing. For passive surveys, about 20 one- minute records were recorded at each site using the same linear array as was used for the active survey. Coherence was computed for each sensor spacing. The spatial autocorrelation (SPAC) method was used to prepare frequency-velocity spectra. Dispersion curves were picked from both active and passive data, combined, and edited. For additional discussion of the method and more data examples, see Craig and Hayashi (2016). 2 Figure 2. Basemap showing locations of velocity surveys in the Sacramento Delta. Yellow dots are 2017 sites (SHN, SHS, SHE = Sherman Island; TWW, TWC, TWN, TWS = Twitchell Island; BON, BOE = Bouldin Island). Red dots are 2015 sites (SIA = Sherman Island, WEB = Webb Tract, EMR = Empire Tract, SRB = Bethel Island, SMB = Sandmound Boulevard, HOL = Holland Tract, BAC = Bacon Island, CC = Clifton Court Forebay). 3 Velocity-depth profiles were determined from dispersion curves. An initial velocity model was prepared by mapping each frequency-velocity pick from the dispersion curve to a depth equal to one-third the wavelength. A non-linear inversion was then performed to obtain a final velocity model. The inversion seeks to minimize the RMS error between the observed dispersion curve and a theoretical fundamental mode dispersion curve (Hayashi and Craig, 2017). Frequency-velocity spectra and dispersion curves: Example frequency-velocity (f-v) spectra and dispersion curves from site TWN are discussed below. The spectrum from the active method (Figure 3a) displays energy, and the fundamental mode dispersion curve appears as a dark blue curved ridge of maximum energy, with red picks. The spectrum from the passive method (Figure 3b) was prepared using the spatial autocorrelation (SPAC) method, and shows the error between theoretical and observed coherence. Although the spectra from the active and passive methods appear similar, the dispersion curve of the passive spectrum corresponds to the zone of minimum error rather than maximum energy. Dispersion curves picked from the active and passive spectra were merged for the next step in data processing, the determination of a 1D velocity-depth model. In general, the dispersion curves from the two methods were in good agreement. As may be seen in the f-v spectra for site TWN velocities tend to be more tightly constrained at higher frequencies using the active method (Figure 3a) and at lower frequencies using the passive method (Figure 3b). a) b) Figure 3. Velocity-frequency spectra for site TWN with picked dispersion curve. a) active method. b) passive method. Dark blue curved region with red picks is fundamental mode dispersion curve. F-v spectra for additional sites are shown below. All have clear fundamental mode dispersion curves that are seen in both active and passive data. These include TWE (Figure 4), BON (Figure 5), and BOE (Figure 6). 4 a) b) Figure 4. Velocity-frequency spectra for site TWE with picked dispersion curve. a) active method. b) passive method. a) b) Figure 5. Velocity-frequency spectra for site BON with picked dispersion curve. a) active method. b) passive method. Dark blue curved region with red picks is fundamental mode dispersion curve. 5 a) b) Figure 6. Velocity-frequency spectra for site BOE with picked dispersion curve. a) active method. b) passive method. 6 Observed and theoretical dispersion curves Combined and edited dispersion curves from the active and passive methods are shown below in Figure 7, along with the theoretical dispersion curves corresponding to the best fit models. a) b) c) d) e) f) Figure 7. Combined dispersion curves from active and passive methods (red) and theoretical dispersion curve (black). a) BOE, b) BON, c) SHN, d) SHS, e) TWN, and f (TWE). Minimum frequencies range from about 2 Hz to 5 Hz. Velocities range from about 35 m/s to 250 m/s. 7 Higher modes In some cases, f-v spectra contain appreciable higher modes in addition to the fundamental mode. This tends to be the case for surveys on or along engineered structures such as levees.