Lowermost Mantle Shear‐Velocity Structure From Hierarchical Trans‐Dimensional Bayesian Tomography

Mousavi, Sima; Tkalčić, Hrvoje; Hawkins, Rhys; Sambridge, Malcolm

doi:10.1029/2020jb021557

Cited by 8 publications

(3 citation statements)

References 78 publications

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“…The importance of ongoing improvements to tomographic models (e.g. Tkalčić et al, 2015;Mousavi et al, 2021) and a robust estimate of their uncertainty (e.g. Zaroli, 2019), therefore, cannot be overstated.…”

Section: Outlook: Improving Dynamic Topography Reconstructions Into T...mentioning

confidence: 99%

Observations and models of dynamic topography: Current status and future directions.

Davies¹,

Ghelichkhan²,

Hoggard³

et al. 2022

Preprint

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The slow creeping motion of Earth’s mantle drives transient changes in surface topography across a variety of spatial and temporal scales. Recent decades have seen substantial progress in understanding this so-called `dynamic topography’, with a growing number of studies highlighting its fundamental role in shaping the surface of our planet. In this review, we outline the current frontiers of geodynamical research into dynamic topography. It begins with a summary of ongoing observational, theoretical and computational efforts that aim to quantify the present-day expression of dynamic topography, including its geographical distribution and sensitivity to different components of the mantle's flow regime. Next, observational constraints that shed light on how dynamic topography has changed over time are summarised, and compared with predictions from a range of geodynamical modelling studies, to highlight our current understanding of its evolution through the geological past. Although many model predictions can be reconciled with the available observational constraints, these comparisons demonstrate that there remain inconsistencies, particularly at shorter spatial and temporal scales. These discrepancies allow us to isolate the shortcomings of existing modelling approaches and identify pathways towards improving future reconstructions of dynamic topography through space and time. Such reconstructions are vital if we are to robustly connect the evolution of Earth's surface environments to the processes that are occurring deep within its interior.

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Section: Outlook: Improving Dynamic Topography Reconstructions Into T...mentioning

confidence: 99%

Observations and models of dynamic topography: Current status and future directions.

Davies¹,

Ghelichkhan²,

Hoggard³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“… 2019 ), global core–mantle boundary tomography (Mousavi et al . 2021 ), gravity and magnetic (Ghalenoei et al . 2022 ), among others.…”

Section: Methodsmentioning

confidence: 99%

3-D shallow shear velocity structure of the Jakarta Basin from transdimensional ambient noise tomography

Cummins

Hejrani

et al. 2023

Geophysical Journal International

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Summary Situated on the northern coast of the Indonesian island of Java, Jakarta and its metropolitan area (Greater Jakarta) are subject to significant earthquake hazards from a subduction zone south of Java and nearby active crustal faults. The seismic risk may be even higher because Greater Jakarta resides on a sedimentary basin filled with thick Pliocene-Pleistocene sediments. A comprehensive study of Jakarta Basin's properties and geometry is important for creating robust seismic hazard and risk assessments. The main objective of this study is to develop a 3D model of Jakarta Basin's shallow shear-wave velocity (VS) structure and improve on previous models that did not cover the basin edge due to the extent of data coverage. Between April and October 2018, we deployed a new temporary seismic network to extend the spatial coverage beyond that of a previous deployment in 2013, and sampled 143 locations through sequential deployments of 30 broad-band sensors covering Jakarta and its adjacent areas. We conducted a 2-stage transdimensional Bayesian inversion of Rayleigh wave phase velocity dispersion curves derived from seismic noise. To begin, we applied tomography and constructed 2D phase velocity maps for periods 1–5 s. Then, at each point in a regular grid defined on these maps, we invert each dispersion curve into 1D depth profiles of VS. Finally, these profiles at grid points with ∼2 km spacing are interpolated to form a pseudo-3D VS model. Our results reveal the edge of the Pliocene-Pleistocene sediments along the south. Also, we resolve a basement offset across south Jakarta that we suggest may be related to the western extension of the Baribis Fault (alternatively, the West Java Back-arc Thrust). We recommend using this 3D model of the Jakarta Basin for scenario earthquake ground motion simulations. Such simulations would help establish how important it might be to re-assess seismic hazard and risk in Greater Jakarta so that basin resonance and amplification are considered.

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“…Dispersion inversion using energy likelihood 11Minsley 2011;Ray & Key 2012;Young et al 2013;Piana Agostinetti et al 2015;Saygin et al 2015;Galetti et al 2017;Burdick & Lekić 2017;Biswas & Sen 2017;Zhu & Gibson 2018;Xiang et al 2018; Zhang et al 2020a,b;Estève et al 2021;Mousavi et al 2021;Hallo et al 2021). In this study we use the method to solve the surface wave dispersion inversion problem.In rj-McMC one constructs a (Markov) chain of samples by perturbing the current model m using a proposal distribution q(m |m) to generate a new model m , and by accepting or rejecting this new model with a probability α(m |m) called the acceptance ratio:…”

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confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang

Zheng

Curtis

2022

Geophysical Journal International

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Summary Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis of data recorded on a local array of receivers. The apparent phase velocity at each frequency of the surface waves travelling across the array is that at which the f-c spectrum has maximum amplitude. However, because of potential contamination by other wave arrivals or due to complexities in the velocity structure the f-c spectrum often has multiple maxima at each frequency for each mode. These introduce errors and ambiguity in the picked phase velocities, and consequently the estimated shear velocity structure can be biased, or may not account for the full uncertainty in the data. To overcome this issue we introduce a new method which directly uses the spectrum as the data to be inverted. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood, and efficiently since it removes the need to pick phase velocities. We therefore conclude that the energy likelihood function can be a valuable tool for surface wave dispersion inversion in practice.

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Lowermost Mantle Shear‐Velocity Structure From Hierarchical Trans‐Dimensional Bayesian Tomography

Cited by 8 publications

References 78 publications

Observations and models of dynamic topography: Current status and future directions.

Observations and models of dynamic topography: Current status and future directions.

3-D shallow shear velocity structure of the Jakarta Basin from transdimensional ambient noise tomography

Surface wave dispersion inversion using an energy likelihood function

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