A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets

Afonso, Juan Carlos; Salajegheh, F.; Szwillus, Wolfgang; Ebbing, Jörg; Gaina, Carmen

doi:10.1093/gji/ggz094

Cited by 86 publications

(116 citation statements)

References 130 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…Because of the natural variability of [U, Th, K] in the crust and the difficulty with mapping their 3‐D distribution, the uncertainty on bulk abundances is unlikely to change significantly. Joint inversions of multiple data sets, including teleseismic

V_{P}

and

V_{S}

, surface waves, gravity, surface heat flow, and topography/geoid have the most potential for significant advances in our understanding of the structure and composition of the crust (e.g., Afonso et al, , ). Improving our understanding of the physical and chemical nature of the bulk‐crust will not have the same impact on reduction in the geoneutrino signal uncertainty as improvements in the near‐field.…”

Section: Resultsmentioning

confidence: 99%

Reference Models for Lithospheric Geoneutrino Signal

2020

View full text Add to dashboard Cite

Debate continues on the amount and distribution of radioactive heat producing elements (i.e., U, Th, and K) in the Earth, with estimates for mantle heat production varying by an order of magnitude. Constraints on the bulk‐silicate Earth's (BSE) radiogenic power also places constraints on overall BSE composition. Geoneutrino detection is a direct measure of the Earth's decay rate of Th and U. The geoneutrino signal has contributions from the local ( ∼40%) and global ( ∼35%) continental lithosphere and the underlying inaccessible mantle ( ∼25%). Geophysical models are combined with geochemical data sets to predict the geoneutrino signal at current and future geoneutrino detectors. We propagated uncertainties, both chemical and physical, through Monte Carlo methods. Estimated total signal uncertainties are on the order of ∼20%, proportionally with geophysical and geochemical inputs contributing ∼30% and ∼70%, respectively. We find that estimated signals, calculated using CRUST2.0, CRUST1.0, and LITHO1.0, are within physical uncertainty of each other, suggesting that the choice of underlying geophysical model will not change results significantly, but will shift the central value by up to ∼15%. Similarly, we see no significant difference between calculated layer abundances and bulk crustal heat production when using these geophysical models. The bulk crustal heat production is calculated as 7 ± 2 TW, which includes an increase of 1 TW in uncertainty relative to previous studies. Combination of our predicted lithospheric signal with measured signals yield an estimated BSE heat production of 21.5 ± 10.4 TW. Future improvements, including uncertainty attribution and near‐field modeling, are discussed.

show abstract

V_{P}

and

V_{S}

Section: Resultsmentioning

confidence: 99%

Reference Models for Lithospheric Geoneutrino Signal

2020

View full text Add to dashboard Cite

show abstract

“…The input data are again the vertical velocities within the region of interest

normalΓ

, which for this example is larger,

\pm

100 km from the reference LAB value. The lithospheric structure (LAB depth and temperatures) of the reference model is based on the global lithospheric model by Afonso et al (). Sublithospheric temperatures are computed based on the work of Stixrude and Lithgow‐Bertelloni () for a reference adiabatic gradient, with anomalies with respect to that gradient from the work of Steinberger and Becker ().…”

Section: Numerical Examplesmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Markov chain Monte Carlo (MCMC) methods have become standard in Bayesian inference and multi‐observable inversions in almost every discipline of the Earth sciences. In the case of geodynamic and/or coupled geophysical‐geodynamic inverse problems, however, the computational cost associated with the solution of large‐scale 3‐D Stokes forward problems has rendered probabilistic formulations impractical. Here we present a novel and extremely efficient method to produce ultrafast solutions of the 3‐D Stokes problem for MCMC simulations. Our approach combines the individual benefits of Reduced Basis techniques, goal‐oriented error formulations, and MCMC algorithms to produce an accurate and computationally efficient surrogate for the forward problem. Importantly, the surrogate adapts itself during the MCMC simulation according to the history of the chain and the goals of the inversion. This maximizes the efficiency of the forward problem and removes the need for preinversion off‐line computations to build a surrogate. We demonstrate the benefits and limitations of the method with several numerical examples and show that in all cases the computational cost is of the order of <1% compared to a traditional MCMC approach. The method is general enough to be applied to a range of problems, including uncertainty quantification/propagation, adjoint‐based geodynamic inversions, sensitivity analyses in mantle convection problems, and in the creating surrogate models for complex forward problems (e.g., heat transfer, seismic tomography, and magnetotellurics).

show abstract

“…-Solid Earth: The step towards global, fully data informed, model data is also made in geophysics. For instance, recently Afonso et al (2019) used an inversion approach to develop a 3D model that fully describes multiple parameters in the Earth interior, including e.g. crustal and lithospheric thickness, average crustal density, and a depth-dependent density of the lithospheric mantle, among other variables.…”

Section: Priorities For Future Developmentsmentioning

confidence: 99%

Earth system data cubes unravel global multivariate dynamics

Mahecha¹,

Gans²,

Brandt³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing inter-disciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. Latest developments in machine learning, causal inference, and model data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.

show abstract

A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets

Cited by 86 publications

References 130 publications

Reference Models for Lithospheric Geoneutrino Signal

Reference Models for Lithospheric Geoneutrino Signal

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

Earth system data cubes unravel global multivariate dynamics

Contact Info

Product

Resources

About