The crustal structure of the Arizona Transition Zone and southern Colorado Plateau from multiobservable probabilistic inversion

Abstract

The Arizona Transition Zone is a narrow band that separates two of the main and most contrasting tectonic provinces in western US, namely the southern Colorado Plateau and the southern Basin and Range provinces. As such, the internal crustal structure and physical state of this transitional zone hold clues for understanding (i) the amalgamation of these provinces, (ii) the partitioning of deformation due to both past and present‐day stress fields, and (iii) the role of thermal versus compositional effects in controlling surface observables. Here we employ and expand a novel multiobservable probabilistic inversion method and jointly invert fundamental mode Rayleigh phase velocities, receiver functions, surface heat flow, geoid height, and absolute elevation to obtain an internally consistent 3‐D model of the temperature, density, Vs, and Vp of the Arizona Transition Zone and the southern portions of the Colorado Plateau and Basin and Range. Our results confirm a significant crustal thickening from ∼28 km in the SW of the Arizona Transition Zone and southern Basin and Range to ∼48 km beneath the southern Colorado Plateau. Inverted temperatures agree well with the location of recent volcanism and indicate that the lithosphere‐asthenosphere boundary is not deeper than ∼70 km in most of the region. We find that major pre‐Cambrian surface structures and/or shear zones separate crustal domains with distinct bulk properties, suggesting that the juxtaposed crustal blocks still retain, at least in part, their original characteristics. However, widespread intrusions of significant volumes of mafic magmas have affected these blocks at different depths, locally overprinting their original compositions and creating highly heterogeneous crustal sections. A dominant and large‐scale internal crustal pattern of SW dipping planes/structures is evident in our models, coinciding with the orientation of deep faults previously inferred from earthquake focal mechanisms. While we cannot categorically corroborate the presence of melt or aqueous fluids within the crust, our results are compatible with these scenarios beneath some parts of the Basin and Range, the Mogollon‐Datil, and Springerville volcanic fields.

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

“…For example, up‐weighting the importance of RFs would result in crustal models with more vertical structure than those obtained by up‐weighting, for example, surface waves. As in our previous study (Qashqai et al, ), our main interest is to obtain models that jointly fit the seismic data as well as possible while being simultaneously consistent with, but not strictly controlled by, nonseismic data. We therefore utilize the same scaling (weighting) factors as in Qashqai et al (; see their Appendix A).…”

“…In this study, we employ a recently developed multiobservable probabilistic inversion approach (Afonso, Fullea, Griffin, et al, ; Afonso, Fullea, Yang, et al, ; Guo, Afonso, et al, ; Jones et al, ; Afonso, Rawlinson, et al, ; Qashqai et al, ; Shan et al, ) based on a Bayesian inference framework. In this framework, prior information on both data and model parameters is encoded in a joint probability density function (PDF) denoted by ρ ( d,m ).…”

“…Here we adhere to this idea and characterize the 3‐D thermal, physical and compositional crustal structure of the western and central United States through a simultaneous and internally consistent inversion of complementary geophysical data sets. For this purpose, we apply a recently developed multiobservable probabilistic inversion methodology (e.g., Afonso, Fullea, Griffin, et al, ; Afonso, Fullea, Yang, et al, ; Afonso, Rawlinson, et al, ; Qashqai et al, ) to a region encompassing the Colorado Plateau (CP), some parts of the Basin and Range (BAR), Arizona transition zone (ATZ), SRM, GP, Rio Grande Rift (RGR), and the Central Rocky Mountains (CRM; Figure ). Data used in this study include Rayleigh wave dispersion curves, radial receiver functions (RFs), geoid anomalies, absolute elevation and surface heat flow.…”

Crustal Imaging With Bayesian Inversion of Teleseismic P Wave Coda Autocorrelation

“…We use the Delayed Rejection Adaptive Metropolis (DRAM) algorithm (Haario et al, ) to sample the posterior distribution (equation ). DRAM is a combination of Adaptive Metropolis (AM; Haario et al, ) and Delayed Rejection (DR; Mira, ) algorithms and has been widely used in many geophysical applications (Afonso et al, ; Ball et al, ; Tork Qashqai et al, , ). The DRAM algorithm benefits from the advantages of both DR and AM sampling methods.…”

Fast Stokes Flow Simulations for Geophysical‐Geodynamic Inverse Problems and Sensitivity Analyses Based On Reduced Order Modeling

“…As Baumann and Kaus () pointed out, integrated approaches that jointly invert or model a number of data sets sensitive to the thermochemical structure of the Earth (e.g., Afonso, Fullea, Griffin, et al, ; Afonso, Fullea, Yang, et al, ; Afonso, Moorkamp, et al, ; Khan et al, , ) would constitute a more appropriate and generally applicable approach. In this context, the recent work of Afonso, Fullea, Griffin, et al (); Afonso, Fullea, Yang, et al (); and Afonso, Rawlinson, et al () has made significant progress toward such goal by presenting a multi‐observable probabilistic inversion method that simultaneously invert the most appropriate data sets (with the necessary complementary sensitivities) for the temperature and compositional structure of the lithosphere and upper mantle: Rayleigh wave dispersion data, teleseismic P and S traveltimes, gravity anomalies, geoid height, satellite‐derived gravity gradients, surface heat flow, and absolute elevation; P wave receiver functions have also been implemented recently (Tork Qashqai et al, , ). Although Afonso, Rawlinson, et al () included dynamic contributions to absolute elevation and gravity observables from the instantaneous sublithospheric flow, their implementation of the Stokes forward problem was inefficient and based on a number of simplifying assumptions to make the problem tractable in the probabilistic framework.…”