Spatially Varying Stress State in the Central U.S. From Bayesian Inversion of Focal Mechanism and In Situ Maximum Horizontal Stress Orientation Data

Carlson, Grace; Johnson, K. M.; Chuang, R. Y.; Rupp, John A.

doi:10.1002/2017jb015158

Cited by 9 publications

(2 citation statements)

References 67 publications

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“…To rectify this problem, Hardebeck and Michael (2006) developed a damped least squares method of focal mechanism data, which minimized the difference in the stress state between adjacent subareas. This was further extended to a nonlinear inversion with a fully Bayesian formalism (Carlson et al., 2018). Iwata (2018) also constructed a spatially continuous inversion method of P‐wave first motion data.…”

Section: Introductionmentioning

confidence: 99%

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

2022

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This study presents a new method of centroid moment tensor (CMT) data inversion to estimate time‐dependent regional stress fields. The Gaussian process (GP) is applied to resolve the computational difficulty of the existing basis function expansion method when analyzing high‐dimensional data, including time dependence. A critical step in the formulation is an analytical derivation of the relationship of the covariance function, which is a key ingredient of GP, between CMT data and the model stress field based on an observation equation. The validity and efficiency of the proposed method are verified through applications to CMT data in and around Japan after the 2011 Tohoku earthquake. The estimated stress field exhibits small‐scale heterogeneity in space and long‐term stability in time for most regions. Additionally, significant temporal variations are observed around the margin of the focal region of the 2011 event, with opposite changes on landward and oceanward sides. GP would be particularly effective for geophysical inversions of high‐dimensional data distributed over a broad region.

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Section: Introductionmentioning

confidence: 99%

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

2022

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“…The inversion results of traditional optimize methods are all prone to being trapped by local minima, can only determine the best-fit model and not provide quantitatively nonlinear uncertainty estimation of the awaiting inversion parameters. In recent years, the Bayesian inversion method underwent considerable development and significantly contributed to estimating bottom properties and their uncertainties based on a Bayesian formulation [15][16][17][18][19]. The Bayesian inversion method is a global optimization algorithm based on the probability theory, applied mathematics, and optimization theory.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Zheng

Zhu

Wang

et al. 2020

Sensors

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Geoacoustic parameter inversion is a crucial issue in underwater acoustic research for shallow sea environments and has increasingly become popular in the recent past. This paper investigates the geoacoustic parameters in a shallow sea environment using a single-receiver geoacoustic inversion method based on Bayesian theory. In this context, the seabed is regarded as an elastic medium, the acoustic pressure at different positions under low-frequency is chosen as the study object, and the theoretical prediction value of the acoustic pressure is described by the Fast Field Method (FFM). The cost function between the measured and modeled acoustic fields is established under the assumption of Gaussian data errors using Bayesian methodology. The Bayesian inversion method enables the inference of the seabed geoacoustic parameters from the experimental data, including the optimal estimates of these parameters, such as density, sound speed and sound speed attenuation, and quantitative uncertainty estimates. The optimization is carried out by simulated annealing (SA), and the Posterior Probability Density (PPD) is given as the inversion result based on the Gibbs Sampler (GS) algorithm. Inversion results of the experimental data are in good agreement with both measured values and estimates from Genetic Algorithm (GA) inversion result in the same environment. Furthermore, the results also indicate that the sound speed and density in the seabed have fewer uncertainties and are more sensitive to acoustic pressure than the sound speed attenuation. The sea noise could increase the variance of PPD, which has less influence on the sensitive parameters. The mean value of PPD could still reflect the true values of geoacoustic parameters in simulation.

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Atmospheric River Signatures in Radiosonde Profiles and Reanalyses at the Dronning Maud Land Coast, East Antarctica

et al. 2020

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Atmospheric rivers (ARs) are an important component of the hydrological cycle linking moisture sources in lower latitudes to the Antarctic surface mass balance. We investigate AR signatures in the atmospheric vertical profiles at the Dronning Maud Land coast, East Antarctica, using regular and extra radiosonde measurements conducted during the Year of Polar Prediction Special Observing Period November 2018 to February 2019. Prominent AR events affecting the locations of Neumayer and Syowa cause a strong increase in specific humidity extending through the mid-troposphere and a strong low-level jet (LLJ). At Neumayer, the peak in the moisture inversion (up to 4 g kg −1) is observed between 800 and 900 hPa, while the LLJ (up to 32 m s −1) is concentrated below 900 hPa. At Syowa the increase in humidity is less pronounced and peaks near the surface, while there is a substantial increase in wind speed (up to 40 m s −1) between 825 and 925 hPa. Moisture transport (MT) within the vertical profile during the ARs attains a maximum of 100 g kg −1 m s −1 at both locations, and is captured by both ERA-Interim and ERA5 reanalysis data at Neumayer, but is strongly underestimated at Syowa. Composites of the enhanced MT events during 2009−19 show that these events represent an extreme state of the lower-tropospheric profile compared to its median values with respect to temperature, humidity, wind speed and, consequently, MT. High temporal-and vertical-resolution radiosonde observations are important for understanding the contribution of these rare events to the total MT towards Antarctica and improving their representation in models.

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Spatially Varying Stress State in the Central U.S. From Bayesian Inversion of Focal Mechanism and In Situ Maximum Horizontal Stress Orientation Data

Cited by 9 publications

References 67 publications

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

Bayesian Inversion for Geoacoustic Parameters in Shallow Sea

Atmospheric River Signatures in Radiosonde Profiles and Reanalyses at the Dronning Maud Land Coast, East Antarctica

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