Quest for consistency, symmetry, and simplicity — The legacy of Albert Tarantola

Mosegaard, Klaus

doi:10.1190/geo2010-0328.1

Cited by 48 publications

(59 citation statements)

References 30 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We further employ the autocorrelation method proposed by Mosegaard and Tarantola (1995) to select 1,000 independent models, which are finally used to calculate the maximum probability model. At this stage, the code randomly generates 160,000 trial models to estimate the first-stage posterior probability.…”

Section: Joint Inversion Of Surface and Bw Datamentioning

confidence: 99%

Joint Inversion of Rayleigh Wave Phase Velocity, Particle Motion, and Teleseismic Body Wave Data for Sedimentary Structures

Niu

Yang

et al. 2019

Geophysical Research Letters

View full text Add to dashboard Cite

Mapping seismic structures of sedimentary basins is of great importance to better evaluate energy resources and seismic hazards. In this study, we develop a joint Bayesian Monte Carlo nonlinear inversion method that utilizes the complementary nature of Rayleigh wave phase velocity, Rayleigh wave particle motion, and teleseismic P wave in responding to sedimentary structures. Synthetic tests demonstrate that our joint inversion can retrieve the input sedimentary models better than inversions with surface or body wave data alone. We apply our method to waveform data of two broadband stations inside the Songliao Basin in northeast China to invert for sedimentary structures beneath the two stations. We verify our results by successfully isolating the Moho P‐to‐S conversion from sediment reverberations in surface receiver functions with a wavefield‐downward‐continuation technique. We further demonstrate that we can extend our method to single‐station inversions by excluding the Rayleigh wave phase velocity in the inversion.

show abstract

Section: Joint Inversion Of Surface and Bw Datamentioning

confidence: 99%

Joint Inversion of Rayleigh Wave Phase Velocity, Particle Motion, and Teleseismic Body Wave Data for Sedimentary Structures

Niu

Yang

et al. 2019

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…Alternatively, deterministic derivative-based methods, such as quasi-Newton or Gauss-Newton, can be applied. Therefore, our inverse problem formulation is based on a mixed scheme that combines a stochastic optimization technique known as Covariance Matrix Adaptation Evolution Strategy (CMAES) (Hansen & Ostermeier, 2001), with McMC methods (e.g., Mosegaard & Tarantola, 1995). Therefore, our inverse problem formulation is based on a mixed scheme that combines a stochastic optimization technique known as Covariance Matrix Adaptation Evolution Strategy (CMAES) (Hansen & Ostermeier, 2001), with McMC methods (e.g., Mosegaard & Tarantola, 1995).…”

Section: Stochastic Inversionmentioning

confidence: 99%

Stochastic Inversion of Geomagnetic Observatory Data Including Rigorous Treatment of the Ocean Induction Effect With Implications for Transition Zone Water Content and Thermal Structure

et al. 2018

View full text Add to dashboard Cite

In this paper we estimate and invert local electromagnetic (EM) sounding data for 1‐D conductivity profiles in the presence of nonuniform oceans and continents to most rigorously account for the ocean induction effect that is known to strongly influence coastal observatories. We consider a new set of high‐quality time series of geomagnetic observatory data, including hitherto unused data from island observatories installed over the last decade. The EM sounding data are inverted in the period range 3–85 days using stochastic optimization and model exploration techniques to provide estimates of model range and uncertainty. The inverted conductivity profiles are best constrained in the depth range 400–1,400 km and reveal significant lateral variations between 400 km and 1,000 km depth. To interpret the inverted conductivity anomalies in terms of water content and temperature, we combine laboratory‐measured electrical conductivity of mantle minerals with phase equilibrium computations. Based on this procedure, relatively low temperatures (1200–1350°C) are observed in the transition zone (TZ) underneath stations located in Southern Australia, Southern Europe, Northern Africa, and North America. In contrast, higher temperatures (1400–1500°C) are inferred beneath observatories on islands, Northeast Asia, and central Australia. TZ water content beneath European and African stations is ∼0.05–0.1 wt %, whereas higher water contents (∼0.5–1 wt %) are inferred underneath North America, Asia, and Southern Australia. Comparison of the inverted water contents with laboratory‐constrained water storage capacities suggests the presence of melt in or around the TZ underneath four geomagnetic observatories in North America and Northeast Asia.

show abstract

“…Since his seminal work on the full-waveform inverse problem, Albert Tarantola had the vision that realistic a priori information for inversion could be learned from a large collection of "training images" of the subsurface (Mosegaard, 2011). In this paper we demonstrate how this is made possible by using the extended Metropolis algorithm in conjunction with a priori information defined by a geostatistical algorithm using sequential Gibbs sampling.…”

Section: Introductionmentioning

confidence: 98%

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

2012

Self Cite

View full text Add to dashboard Cite

We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework for a full-waveform inversion strategy that integrates the extended Metropolis algorithm with sequential Gibbs sampling such that arbitrary complex geostatistically defined a priori information can be included. At the same time we show how temporally and/or spatially correlated data uncertainties can be taken into account during the inversion. The suggested inversion strategy is tested on synthetic tomographic crosshole ground-penetrating radar full-waveform data using multiple-point-based a priori information. This is, to our knowledge, the first example of obtaining a posteriori realizations of a full-waveform inverse problem. Benefits of the proposed methodology compared with deterministic inversion approaches include: (1) The a posteriori model variability reflects the states of information provided by the data uncertainties and a priori information, which provides a means of obtaining resolution analysis. (2) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional methods do. Moreover, an adequate knowledge of data uncertainties and a priori information is required to obtain meaningful uncertainty estimates. The latter may be a key challenge when considering field experiments, which will not be addressed here.

show abstract

Quest for consistency, symmetry, and simplicity — The legacy of Albert Tarantola

Cited by 48 publications

References 30 publications

Joint Inversion of Rayleigh Wave Phase Velocity, Particle Motion, and Teleseismic Body Wave Data for Sedimentary Structures

Joint Inversion of Rayleigh Wave Phase Velocity, Particle Motion, and Teleseismic Body Wave Data for Sedimentary Structures

Stochastic Inversion of Geomagnetic Observatory Data Including Rigorous Treatment of the Ocean Induction Effect With Implications for Transition Zone Water Content and Thermal Structure

Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

Contact Info

Product

Resources

About