Physical State and Structure of the Crust Beneath the Western‐Central United States From Multiobservable Probabilistic Inversion

Abstract

The Southern Rocky Mountains (SRM) have the highest regional elevations, but a comparable crustal thickness to that of the Great Plains. Isolating the contributions of the crust and upper mantle in supporting the topography of the SRM has been a topic of considerable controversy for the past two decades, leading to contrasting hypotheses. In order to constrain the relative contributions from the crust and mantle, reliable models of the physical state of the crust and upper mantle are necessary. Here we employ a recently developed multiobservable Bayesian inversion approach to study the present‐day physical state (i.e., Vs,Vp, density, Vp/Vs, crustal thickness, and temperature) of the crust in a region encompassing the western‐central United States. We image highly heterogeneous temperature and compositional structures, which combine in a nonintuitive way with total crustal thickness to create the observed topography and control other geophysical signatures. In the SRM, topography is supported by a combination of crustal and mantle contributions. South of 38°N, mantle sources (both static and dynamic) constitute the main support to the high elevations, whereas to the north of this latitude, crustal structure provides the main contribution. In the Colorado Plateau, Arizona transition zone, and Basin and Range provinces, most of the topography seems to be supported by mantle sources (within the lithospheric mantle and/or dynamic). At middle to lower crustal depths beneath the SRM, we image high temperatures, high Vp/Vs values, anomalously low crustal velocities, and low densities. These observations correlate with high electrical conductivities and the location of recent volcanism, suggesting the occurrence of melt/fluids. We do not find low Vp/Vs values at middle/low crustal depths beneath the SRM that may suggest felsic, weak lithologies.

The Lithospheric Structure of the Saharan Metacraton From 3‐D Integrated Geophysical‐Petrological Modeling

“…Still, the mechanisms and the extent of the lithospheric evolution remain ambiguous. Joint inversion of seismological and gravity data in a Bayesian manner is an option to further exploit the solution space (e.g., Afonso et al., 2019; Tork Qashqai et al., 2018). Ideally, such analysis should be combined with geodynamic, numerical modeling to explore different possible scenarios of the evolution of the SMC.…”

Inferring Crustal Temperatures Beneath Italy From Joint Inversion of Receiver Functions and Surface Waves

“…Interestingly, this is the area where also the RFs fit is rather poor. Tork Qashqai et al () have pointed out similar difficulties in jointly fitting dispersion curves and RFs for certain locations in the central western United States where major structural complexities are expected.…”

Moho structure of Australia from probabilistic inversion of teleseismic P-wave coda autocorrelation

“…We use Delayed Rejection Adaptive Metropolis (DRAM) algorithm (Haario et al, 2006) to invert the stacked autocorrelograms. DRAM is a combination of Adaptive Metropolis (AM) (Haario et al, 2001) and Delayed Rejection (DR) (Mira, 2001) algorithms and has been widely used in many geophysical applications (Ball et al, 2014;Afonso et al, 2016;Tork Qashqai et al, 2018).…”