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al., 2014; Eddy et al., 2014; Van Avendonk
                                                                                et al., 2015), were digitized and added to
                                                                                the comparison. With 11–12-km station
                                                                                spacing and the incorporation of coinci-
                                                                                dent seismic reflection data, crustal thick-
                                                                                ness estimates from the GUMBO lines
                                                                                have the highest resolution in this study.

                                                                                DATA AND METHODS
                                                                                  Model comparisons with original figures
                                                                                are complicated by the different choices
                                                                                authors make with respect to presentation:
          models because only a few P-wave velocity   2011; Sutra and Manatschal, 2012). To   color scales and ranges, color palettes, and
          models span the southern U.S. Table 1   evaluate crustal thickness variations    perturbations with respect to a model aver-
          presents pertinent details about models   across the study region, four models were   age or a global standard, etc. Here we plot
          that include the crust: SR16, DNA13,   considered: SLK15, SR16, PnUS2016,    all models on the same scale, in terms of
          PLH15, PM15, and SLK15. The latter four   and LITHO1.0. PnUS2016 (Buehler and   perturbations with respect to the average of
          models were generated via an iterative,   Shearer, 2017) uses Pn arrivals, which are   all models, using a consistent color scale.
          linearized inversion algorithm. The global   P waves that refract just below the Moho   Most models used in this study were
          optimization technique used to generate   and are the first arrivals at regional dis-  downloaded from the IRIS Earth Model
          SR16 makes it unique within our set of   tances, to constrain crustal thickness.   Collaboration (http://ds.iris.edu/ds/
          models, allowing for more formal estimates   PnUS2016 utilizes the seismic velocities   products/emc); others were received via
          of uncertainties. An extended review of   from SR16 to map crustal thickness.   private correspondence. Using MATLAB,
          data types and methods used to generate    LITHO1.0 (Pasyanos et al., 2014) is con-  each velocity model was linearly interpo-
          all eight models examined in this study is   structed by perturbing an initial model   lated onto a three-dimensional (3D) grid
          presented in the GSA Data Repository ;    parameterized vertically as a series of geo-  with 0.2° × 0.2° uniform lateral spacing
                                      1
          for further details readers are referred to   physically identified layers, that is, a com-  and 0.5-km depth spacing. For the southern
          the original publications.         bination of the CRUST1.0 model (Laske    U.S., the domain of interest was bounded
                                             et al., 2012) and the LLNL-G3D model   latitudinally between 26°–37° N and longi-
          CRUSTAL THICKNESS MODELS           (Simmons et al., 2012), to fit surface wave   tudinally between 78°–102° W. Using the
            Crustal thickness varies substantially   dispersion maps over the 5–40 mHz fre-  interpolated shear velocity models, the lat-
          across passive margins, including the   quency band. Additionally, Moho depths   eral root-mean-square velocity, V rms, for
          northern Gulf of Mexico, and hence serves   from velocity models based on results   each model was calculated and compiled to
          as an important parameter in reconstruct-  from the 2010 GUMBO experiments,    create average one-dimensional (1D) veloc-
          ing the tectonic evolution and pre-rift   consisting of four long-offset seismic   ity individual models, and an average 1D
          geometry of such regions (Reston and   refraction profiles in the northern Gulf of   model for the study area (SUSavg; Fig. 2A).
          Morgan, 2004; Huismans and Beaumont,   Mexico (Fig. 1) (Eddy, 2014; Christeson et   This procedure was repeated to create







                                                                                        Figure  2.  (A)  RMS  shear  wave
                                                                                        velocity computed from the eight
                                                                                        velocity models spanning the
                                                                                        southern U.S. (SUS), along with our
                                                                                        average model (SUSavg). (B) Com-
                                                                                        parison of SUSavg with the average
                                                                                        model for the other domains of the
                                                                                        U.S.: WUS—Western U.S.; CUS—
                                                                                        Central U.S.; and EUS—Eastern
                                                                                        U.S., as well as the 1D reference
                                                                                        models, ak135 and PREM.









          1 GSA Data Repository item 2019099, an extended review of data types and methods used to generate all eight models examined in this study, is available online at
          www.geosociety.org/datarepository/2019.

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