scholarly journals Temperature and Salinity Inverted for a Mediterranean Eddy Captured With Seismic Data, Using a Spatially Iterative Markov Chain Monte Carlo Approach

2021 ◽  
Vol 8 ◽  
Author(s):  
Wuxin Xiao ◽  
Katy Louise Sheen ◽  
Qunshu Tang ◽  
Jamie Shutler ◽  
Richard Hobbs ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Seismic Data ◽  
Imaging Method ◽  
Hydrographic Data ◽  
Prior Model ◽  
Acoustic Reflection ◽  
Starting Model ◽  
The Impact

Ocean submesoscale dynamics are thought to play a key role in both the climate system and ocean productivity, however, subsurface observations at these scales remain rare. Seismic oceanography, an established acoustic imaging method, provides a unique tool for capturing oceanic structure throughout the water column with spatial resolutions of tens of meters. A drawback to the seismic method is that temperature and salinity are not measured directly, limiting the quantitative interpretation of imaged features. The Markov Chain Monte Carlo (MCMC) inversion approach has been used to invert for temperature and salinity from seismic data, with spatially quantified uncertainties. However, the requisite prior model used in previous studies relied upon highly continuous acoustic reflection horizons rarely present in real oceanic environments due to instabilities and turbulence. Here we adapt the MCMC inversion approach with an iteratively updated prior model based on hydrographic data, sidestepping the necessity of continuous reflection horizons. Furthermore, uncertainties introduced by the starting model thermohaline fields as well as those from the MCMC inversion itself are accounted for. The impact on uncertainties of varying the resolution of hydrographic data used to produce the inversion starting model is also investigated. The inversion is applied to a mid-depth Mediterranean water eddy (or meddy) captured with seismic imaging in the Gulf of Cadiz in 2007. The meddy boundary exhibits regions of disrupted seismic reflectivity and rapid horizontal changes of temperature and salinity. Inverted temperature and salinity values typically have uncertainties of 0.16°C and 0.055 psu, respectively, and agree well with direct measurements. Uncertainties of inverted results are found to be highly dependent on the resolution of the hydrographic data used to produce the prior model, particularly in regions where background temperature and salinity vary rapidly, such as at the edge of the meddy. This further advancement of inversion techniques to extract temperature and salinity from seismic data will help expand the use of ocean acoustics for understanding the mesoscale to finescale structure of the interior ocean.

A two-step inversion approach for seismic-reservoir characterization and a comparison with a single-loop Markov-chain Monte Carlo algorithm

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. R227-R244 ◽  
Author(s):  
Mattia Aleardi ◽  
Fabio Ciabarri ◽  
Timur Gukov
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Reservoir Characterization ◽  
Gaussian Mixture ◽  
Petrophysical Properties ◽  
Mcmc Algorithm ◽  
Prior Model ◽  
Single Loop ◽  
Seismic Reservoir

We have evaluated a two-step Bayesian algorithm for seismic-reservoir characterization, which, thanks to some simplifying assumptions, is computationally very efficient. The applicability and reliability of this method are assessed by comparison with a more sophisticated and computer-intensive Markov-chain Monte Carlo (MCMC) algorithm, which in a single loop directly estimates petrophysical properties and lithofluid facies from prestack data. The two-step method first combines a linear rock-physics model (RPM) with the analytical solution of a linearized amplitude versus angle (AVA) inversion, to directly estimate the petrophysical properties, and related uncertainties, from prestack data under the assumptions of a Gaussian prior model and weak elastic contrasts at the reflecting interface. In particular, we use an empirical, linear RPM, properly calibrated for the investigated area, to reparameterize the linear time-continuous P-wave reflectivity equation in terms of petrophysical contrasts instead of elastic constants. In the second step, a downward 1D Markov-chain prior model is used to infer the lithofluid classes from the outcomes of the first step. The single-loop (SL) MCMC algorithm uses a convolutional forward modeling based on the exact Zoeppritz equations, and it adopts a nonlinear RPM. Moreover, it assumes a more realistic Gaussian mixture distribution for the petrophysical properties. Both approaches are applied on an onshore 3D seismic data set for the characterization of a gas-bearing, clastic reservoir. Notwithstanding the differences in the forward-model parameterization, in the considered RPM, and in the assumed a priori probability density functions, the two methods yield maximum a posteriori solutions that are consistent with well-log data, although the Gaussian mixture assumption adopted by the SL method slightly improves the description of the multimodal behavior of the petrophysical parameters. However, in the considered reservoir, the main difference between the two approaches remains the very different computational times, the SL method being much more computationally intensive than the two-step approach.

scholarly journals Accessing the syndemic of COVID-19 and malaria intervention in Africa

2020 ◽  
Author(s):  
Benyun Shi ◽  
Jinxin Zheng ◽  
Shang Xia ◽  
Shan Lin ◽  
Xinyi Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Malaria Transmission ◽  
Vectorial Capacity ◽  
Insecticide Treated Nets ◽  
Transmission Potential ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
Epidemiological Parameters

Abstract Background: The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. Methods: We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results: In this paper, we conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R_0 and the duration of infection D_I) of COVID-19 in each country are estimated as follows: Ethiopia (R_0=1.57, D_I=5.32), Nigeria (R_0=2.18, D_I=6.58), Tanzania (R_0=2.47, D_I=6.01), and Zambia (R_0=2.12, D_I=6.96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions: By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential. Keywords : COVID-19 pandemic; Non-pharmaceutical interventions; Particle Markov chain Monte Carlo; Insecticide-treated nets; Vectorial capacity; Malaria transmission potential

Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. R69-R78 ◽  
Author(s):  
Anne Louise Larsen ◽  
Marit Ulvmoen ◽  
Henning Omre ◽  
Arild Buland
Keyword(s):  
Markov Chain ◽  
Seismic Data ◽  
Recursive Algorithm ◽  
Noise Model ◽  
Prior Model ◽  
Likelihood Model ◽  
Band Limited ◽  
Prediction And Simulation ◽  
The Impact ◽  
Vertical Correlation

A technique for lithology/fluid (LF) prediction and simulation from prestack seismic data is developed in a Bayesian framework. The objective is to determine the LF classes along 1D profiles through a reservoir target zone. A stationary Markov-chain prior model is used to model vertical continuity of LF classes along the profile. The likelihood relates the LF classes to the elastic properties and to the seismic data, and it introduces vertical correlation because the seismic data are band-limited. An approximation of the likelihood model provides an approximate posterior model that is a Markov chain. The approximate posterior can be assessed by an exact and efficient recursive algorithm. The LF inversion approach is evaluated on a synthetic 1D profile that is inspired by a North Sea sandstone reservoir. With a realistic wavelet-colored noise model and a S/N ratio of three in the seismic data, the results are reliable. The LF classes and the interfaces between zones are largely correct. The prediction uncertainty increases if the number of zones increases and zone thicknesses decreases. The study clearly demonstrates the impact of a vertically coupled prior Markov model for the LF classes.

scholarly journals Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Georges Bresson ◽  
Anoop Chaturvedi ◽  
Mohammad Arshad Rahman ◽  
Shalabh
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Measurement Error ◽  
Mean Field ◽  
Variational Bayes ◽  
Seemingly Unrelated Regression ◽  
Estimation Methods ◽  
Unrelated Regression ◽  
The Impact

Abstract Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates for different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting.

scholarly journals Bayesian molecular dating as a “doubly intractable” problem

2017 ◽  
Author(s):  
Stéphane Guindon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Inference ◽  
Molecular Dating ◽  
Death Process ◽  
Divergence Times ◽  
Calibration Data ◽  
Fossil Data ◽  
The Impact

1AbstractThis study focuses on a conceptual issue with Bayesian inference of divergence times using Markov chain Monte Carlo. The influence of fossil data on the probabilistic distribution of trees is the crux of the matter considered here. More specifically, among all the phylogenies that a tree model (e.g., the birth-death process) generates, only a fraction of them “agree” with the fossil data at hands. Bayesian inference of divergence times using Markov Chain Monte Carlo requires taking this fraction into account. Yet, doing so is challenging and most Bayesian samplers have simply overlooked this hurdle so far, thereby providing approximate estimates of divergence times and tree process parameters. A generic solution to this issue is presented here. This solution relies on an original technique, the so-called exchange algorithm, dedicated to drawing samples from “doubly intractable” distributions. A small example illustrates the problem of interest and the impact of the approximation aforementioned on tree parameter estimates. The analysis of land plant sequences and multiple fossils further illustrates the importance of proper mathematical handling of calibration data in order to derive accurate estimates of node age.

scholarly journals A Bayesian space–time model for clustering areal units based on their disease trends

Biostatistics ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 681-697 ◽  
Author(s):  
Gary Napier ◽  
Duncan Lee ◽  
Chris Robertson ◽  
Andrew Lawson
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Disease Risk ◽  
Population Level ◽  
Research Literature ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Increased Risk ◽  
The Impact

Summary Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)$^3$) algorithm. The effectiveness of the (MC)$^3$ algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.

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