Bayesian Inversion Method Research Articles

Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

SUMMARY Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.

Bayesian acoustic impedance inversion with gamma distribution

Bayesian model is popular in seismic acoustic impedance inversion because of the statistical information from Bayesian model. However, there are many distributions to simulate impedance distribution. Different distribution has different result. In this paper, the Gamma distribution is used as the prior distribution. A Bayesian impedance inversion method is proposed, under the assumption that the prior distribution is a Gamma distribution. Through the test from the field and model data, this method is feasible and this model based on Gamma distribution has a result with tall resolution.

Bayesian Markov-Chain Monte Carlo Inversion of Low-Temperature Thermochronology Around Two 8 − 10 m Wide Columbia River Flood Basalt Dikes

Flood basalt volcanism involves large volumes of magma emplaced into the crust and surface environment on geologically short timescales. The mechanics of flood basalt emplacement, including dynamics of the crustal magma transport system and the tempo of individual eruptions, are not well constrained. Here we study two exhumed dikes from the Columbia River Flood Basalt province in northeast Oregon, USA, using apatite and zircon (U-Th)/He thermochronology to constrain dike emplacement histories. Sample transects perpendicular to the dike margins document transient heating of granitic host rocks. We model heating as due to dike emplacement, considering a thermal model with distinct melt-fraction temperature relationships for basaltic magma and granitic wallrock, and a parameterization of unsteady flow within the dike. We model partial resetting of thermochronometers by considering He diffusion in spherical grains as a response to dike heating. A Bayesian Markov-Chain Monte Carlo framework is used to jointly invert for six parameters related to dike emplacement and grain-scale He diffusion. We find that the two dikes, despite similar dimensions on an outcrop scale, exhibit different spatial patterns of thermochronometer partial resetting away from the dike. These patterns result in distinct predicted emplacement histories. We extend previous modeling of a presumed feeder dike at Maxwell Lake in the Wallowa Mountains of northeastern Oregon, finding posterior probability distribution functions (PDFs) that predict steady heating from sustained magma flow over $1-6$ years and elevated farfield host rock temperatures. This suggests regional-scale heating in the vicinity of Maxwell Lake, which might arise from nearby intrusions. The other dike, within the Cornucopia subswarm, is predicted to have a $1-4$ year thermally active lifespan with an unsteady heating rate suggestive of magma low flow rate compared to Maxwell Lake, in a cool near-surface thermal environment. In both cases, misfit of near-dike partial resetting of thermochronometers by models suggests either heat transfer via fluid advection in host rocks or pulsed magma flow in the dikes. Our results highlight the diversity of dike emplacement histories within the Columbia River Flood Basalt province and the power of Bayesian inversion methods for quantifying parameter trade-offs and uncertainty in thermal models.

A Bayesian statistical approach to inferring particle dynamics from in-situ pump POC and chloropigment data from the Mediterranean Sea

Concentrations of chloropigments and particulate organic carbon (POC) in large-volume in-situ pump samples from the Mediterranean Sea were used to estimate rate constants of processes that control the fate of particles, and specifically chloropigments, in the water column. Here we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and pheopigment degradation, and particle aggregation and disaggregation. We use first-order kinetics to model disaggregation, and use both first-order and second-order kinetics to model aggregation. Using these methods, the estimated small-particle (1–70 μm) POC respiration rate constant was 2.44−1.00+1.69 yr−1 (0.41 yr). The estimated disaggregation and second-order aggregation rate constants were 85.6−36.4+63.4 yr−1 (1.17×10−2 yr) and 2.78−1.17+2.01μM−1 yr−1, respectively. Using the optimal rate constants and the corresponding particle concentrations, disaggregation is ~4.2 times faster than the small-size POC dissolution rate, which indicates that disaggregation is a dominant processes at the time of sampling. More importantly, by comparing our results with those of previous studies, we conclude that sampling methods have less influence than tracers themselves on inferring particle dynamic rate constants. We previously introduced a somewhat similar approach to modeling SV sediment trap data, but large volume pumps are a much more common sample collection method in oceanographic surveys than SV sediment traps, and thus our new model should have a wider applicability.

Comparison of deterministic and stochastic approaches to crosshole seismic travel-time inversions

The Bayesian inversion method is a stochastic approach based on the Bayesian theory. With the development of sampling algorithms and computer technologies, the Bayesian inversion method has been widely used in geophysical inversion problems. In this study, we conduct inversion experiments using crosshole seismic travel-time data to examine the characteristics and performance of the stochastic Bayesian inversion based on the Markov chain Monte Carlo sampling scheme and the traditional deterministic inversion with Tikhonov regularization. Velocity structures with two different spatial variations are considered, one with a chessboard pattern and the other with an interface mimicking the Mohorovicic discontinuity (Moho). Inversions are carried out with different scenarios of model discretization and source–receiver configurations. Results show that the Bayesian method yields more robust single-model estimations than the deterministic method, with smaller model errors. In addition, the Bayesian method provides the posterior probabilistic distribution function of the model space, which can help us evaluate the quality of the inversion result.

Particle Filter Based Monitoring and Prediction of Spatiotemporal Corrosion Using Successive Measurements of Structural Responses.

Prediction of structural deterioration is a challenging task due to various uncertainties and temporal changes in the environmental conditions, measurement noises as well as errors of mathematical models used for predicting the deterioration progress. Monitoring of deterioration progress is also challenging even with successive measurements, especially when only indirect measurements such as structural responses are available. Recent developments of Bayesian filters and Bayesian inversion methods make it possible to address these challenges through probabilistic assimilation of successive measurement data and deterioration progress models. To this end, this paper proposes a new framework to monitor and predict the spatiotemporal progress of structural deterioration using successive, indirect and noisy measurements. The framework adopts particle filter for the purpose of real-time monitoring and prediction of corrosion states and probabilistic inference of uncertain and/or time-varying parameters in the corrosion progress model. In order to infer deterioration states from sparse indirect inspection data, for example structural responses at sensor locations, a Bayesian inversion method is integrated with the particle filter. The dimension of a continuous domain is reduced by the use of basis functions of truncated Karhunen-Loève expansion. The proposed framework is demonstrated and successfully tested by numerical experiments of reinforcement bar and steel plates subject to corrosion.

Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model

In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes.

Recovering the past history of natural recording media by Bayesian inversion

Spatial growth patterns are natural recording media (NRMs) that preserve important historical information, which can be accessed and analyzed to reconstruct past environmental conditions and events. Here, we propose the Bayesian inversion method, which can reconstruct the evolution of target parameters from purely spatial data by extending data assimilation (DA), a method for integrating numerical simulations with time-series observations. Our method converts discrete spatial observation data to time-series data with the help of a law representing the NRM's time-evolution dynamics and Gaussian process regression, enabling us to directly compare the observations with a numerical simulation based on the DA framework. The method's effectiveness is demonstrated using a synthetic inversion problem, namely reconstructing the $\mathit{pressure}--\mathit{temperature}--\mathit{time}$ ($P--T--t$) path of a metamorphic rock from chemical composition profiles of its zoned minerals. The proposed method is broadly applicable to a wide variety of NRMs.

Joint Bayesian inversion for analyzing conservative and reactive breakthrough curves

We present a joint Bayesian inverse method for analyzing conservative and reactive tracer data, which estimates non-parametric transfer functions and effective first-order reaction rate coefficients simultaneously. A stochastic sampling approach based on Markov Chain Monte Carlo (MCMC) methods was used to simulate samples from the posteriors, with a Metropolis within Gibbs sampler used for transfer functions and reaction rate coefficients. Non-negativity constraints were realized by choosing a reflected Gaussian prior for the transfer function and a lognormal prior for the reaction rate coefficient. The approach was tested by synthetic sparse datasets with introduced normal random errors or periodic errors, and then it was applied to the tracer test data collected at Oak Ridge National Laboratory. The results indicate that the developed approach was well capable of characterizing anomalous transfer functions, such as multi-modal functions, and quantifying the uncertainty of both transfer functions and reaction rate coefficients. In particular, compared with the approach of individual inversion of sparse data, the joint inversion approach taking advantage of the sharing of information regarding transport processes across the conservative tracer and reactive tracer data leads to the improved estimation of transfer functions and reaction rate coefficients for sparse datasets.

Broadband Seismic Inversion for Low-Frequency Component of the Model Parameter

Seismic inversion is an important approach in parameters estimation in the fields of geosciences. The low-frequency component of the model parameter plays an important role in seismic inversion. The emergence of broadband seismic data acquisition and processing technologies is pushing the attention of the low frequency to a new level. With the review of the research status of the estimation of the low-frequency models in history and the low-frequency component contained in the complex frequency domain, a novel broadband seismic Bayesian inversion approach in the complex frequency domain is proposed to implement the estimation of the low-frequency component of the model parameter. The proposed approach makes full use of the advantage of broadband seismic data and the low-frequency component of the damped wave fields in the complex frequency domain. The kernel function of the proposed inversion approach is built with Bayesian inference. Synthetic examples demonstrate the feasibility and robustness of the proposed inversion approach in the estimation of the low-frequency component of the model parameter. A field data example verifies the feasibility and reasonability of the proposed inversion approach in application. Finally, the estimated low-frequency component of the model parameter is utilized as the initial model for the suggested seismic Bayesian inversion method in time domain. Model and field data examples further verify the effectiveness and superiority of the proposed inversion method in the final estimation of the model parameter by comparing with the conventional inversion approach.

Petrophysical parameters prediction and uncertainty analysis in tight sandstone reservoirs using Bayesian inversion method

Petrophysical parameters are of great importance in the evaluation and characterization for reservoirs, especially for the unconventional reservoirs with complex properties. The geophysical inversion is an efficient and economic method to obtain the petrophysical parameters. In this paper, Bayesian inversion method is presented to predict petrophysical model with conventional well logs. Statistical analysis results of accepted Markov chain Monte Carlo (MCMC) samples are used to study the uncertainty of forecasted parameters, since the MCMC is a powerful approach to obtain adequate samples obeying the posterior distribution of Bayesian inversion. The proposed method is applied to reservoirs of the Xiashihezi Formation which are typical tight sandstone layers in the Ordos Basin. Model prediction and corresponding uncertainty analysis are presented in detail at a specific depth. The interactive effects of multiple petrophysical parameters are investigated by correlation coefficients. Then, the accuracy and reliability of predicted model is validated by both forward log responses and core data of the whole depth interval. According to the results and discussions, it can be concluded that: (1) a reasonable prior information of model parameters will simplify the inversion problem, which provides much conveniences of statistical analysis of the MCMC samples; (2) the weak correlation between each two petrophysical parameters indicates that it is reasonable and feasible to disregard dependence of parameters; (3) synthetic logs calculated by predicted model are in good agreement with observed well logs, which implies the precision and credibility of Bayesian inversion; (4) the predicted porosity, permeability and minerals content are consistent with core data, verifying the effectiveness and reliability of proposed method and inversion results; (5) it is an advantage of Bayesian inversion to locate the most probable reservoirs with the extreme value.

Estimation of fluid indicator and dry fracture compliances using azimuthal seismic reflection data in a gas-saturated fractured reservoir

Amplitude variation with offset and azimuth (AVOA) inversion is a powerful tool used to predict the fluid and fracture information by utilizing the observable seismic reflection amplitudes. Understanding the effect of in situ fluid and fracture characteristics on seismic response is significant in a fractured reservoir. Our goal is to demonstrate an AVOA inversion approach to utilize the observable azimuthal seismic reflection data to predict the fluid and fracture properties in a gas-saturated fractured reservoir. Starting from Gassmann’s anisotropic poroelasticity theory and the linear-slip model, we first derive a linearized PP-wave reflection coefficient in terms of fluid, rigidity, density, and dry fracture compliance parameters based on the scattering function and perturbations in dry stiffness matrix of a horizontal transversely isotropic (HTI) rock containing a system of aligned vertical fractures. The derived equation builds a linearized relationship between the characteristics of fluid and fracture and the seismic response of azimuthal seismic reflection amplitudes. Incorporating the convolution model and Bayesian inversion, we propose a feasible Bayesian AVOA inversion method to estimate the fluid and fracture parameters directly. Cauchy and Gaussian probability distribution are used for the a priori information of model parameters and the likelihood function, respectively, and the nonlinear iteratively reweighted least squares (IRLS) strategy is finally utilized to solve the maximum a posteriori solutions of model parameters. The synthetic examples containing a moderate noise demonstrate the feasibility of inversion method, and the real data illustrates the inversion stabilities of fluid and fracture parameters in a gas-saturated fractured reservoir.

Unsaturated soil slope characterization with Karhunen–Loève and polynomial chaos via Bayesian approach

Field measured data reflect real response of soil slopes under rainfall infiltration and can provide representative estimates of in situ soil properties. In this study, an efficient probabilistic back analysis method for characterization of spatial variability of soil properties is used to investigate the effects of field responses with various monitoring schemes on characterization of spatial variability in unsaturated soil slope. A hypothetical heterogeneous slope of spatially varied saturated hydraulic conductivity subjecting to steady-state rainfall infiltration is analyzed as a numerical example. The spatially varied soil saturated hydraulic conductivity is parameterized by the Karhunen–Loeve expansion (KLE) with a given covariance. The random variables corresponding to the truncated KLE terms are considered as variables to be estimated with Bayesian inverse method. Synthetic pore water pressure data corrupted with artificial noise are utilized as measurement data. Nine schemes with various locations, spacings and depths of monitoring sections are discussed. The results show that the local variability can be reduced substantially around the monitoring points of pore pressure. The spatial variability can be estimated more accurately with a smaller spacing of measurement points. When measurement points are installed with a spacing of 16.5 m, the posterior average COV of ks field is around 2% and the RMSE of the MAP field is only 5.90 × 10− 7 m/s. For schemes with different depths, the RMSEs of the MAP field does not change much but the posterior uncertainty of the estimated field is reduced with the increase of borehole depth.

Reliability enhancement of mixed-domain seismic inversion with bounding constraints

ABSTRACTSeismic inversion works as one of pragmatic and effective approaches to estimate the subsurface parameters. One robust Bayesian sparse inversion method incorporating the mixed-domain convolution with model bounding constraints is proposed in this study. The time-domain response and partial frequency components are utilized in mixed-domain seismic inversion to improve the resolution and stability of seismic inversion. First, the objective updated function is yielded with Bayesian inference in joint time and frequency domain. And, the sparse constraint is incorporated into the objective function to improve robustness of inversion algorithm. Conventional seismic inversion methods always don’t focus on the lower and upper bounding constraints on subsurface model parameters, due to which unrealistic predicted results may arise. To get rid of the problem, the bounding constraints on P-wave impedance elaborated by logarithmic or inverse hyperbolic tangent formulas is introduced in mixed-domain seismic inversion as it renders to reduce the unrealistic parameters and enhance the reliability of prediction effectively. In addition, the synthetic examples demonstrate the effectiveness and robustness of the proposed inversion algorithm. Finally, one field case is studied carefully and the estimated parameters with bounding constraints at the borehole-side location can preserve a high degree of agreements with real logging data to verify the practicability of the bounding-constraining mixed-domain Bayesian inversion.

Word2Vec inversion and traditional text classifiers for phenotyping lupus

BackgroundIdentifying patients with certain clinical criteria based on manual chart review of doctors’ notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR). This task can be automated using text classifiers based on Natural Language Processing (NLP) techniques along with pattern recognition machine learning (ML) algorithms. The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus (SLE) in comparison with a newer Bayesian word vector method.MethodsWe obtained clinical notes for patients with SLE diagnosis along with controls from the Rheumatology Clinic (662 total patients). Sparse bag-of-words (BOWs) and Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs) matrices were produced using NLP pipelines. These matrices were subjected to several different NLP classifiers: neural networks, random forests, naïve Bayes, support vector machines, and Word2Vec inversion, a Bayesian inversion method. Performance was measured by calculating accuracy and area under the Receiver Operating Characteristic (ROC) curve (AUC) of a cross-validated (CV) set and a separate testing set.ResultsWe calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00% with an AUC of 0.900, the shallow neural network with CUIs to be 92.10% with an AUC of 0.970, the random forest with BOWs to be 95.25% with an AUC of 0.994, the random forest with CUIs to be 95.00% with an AUC of 0.979, and the Word2Vec inversion to be 90.03% with an AUC of 0.905.ConclusionsOur results suggest that a shallow neural network with CUIs and random forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task. The Word2Vec inversion method failed to significantly beat the ICD-9 code classification, but yielded promising results. This method does not require explicit features and is more adaptable to non-binary classification tasks. The Word2Vec inversion is hypothesized to become more powerful with access to more data. Therefore, currently, the shallow neural networks and random forests are the desirable classifiers.

Bayesian rupture imaging in a complex medium: The 29 May 2012 Emilia, Northern Italy, earthquake

AbstractWe develop a new approach to image earthquake rupture from strong motion data. We use a large data set of aftershock waveforms, interpolated over the seismic fault to obtain Green's function approximations. Next we deploy a Bayesian inversion method to characterize the slip distribution, the rupture velocity, the slip duration, and their uncertainties induced by errors in the Green's functions. The method is applied to the 29 May 2012 Mw 6 Emilia earthquake, which ruptured a fault buried below the Po Plain sediments (Northern Italy). Despite the particularly complex wave propagation, the near‐field strong motion observations are well reproduced with 15 rupture parameters. The rupture and slip velocities were notably slow (~0.5 Vs and <0.5 m/s, respectively), implying that the fault was difficult to break. This method opens some perspectives for earthquake rupture studies in areas where numerical simulations suffer from imprecise knowledge of the velocity structure.

Effects of sand-shale anisotropy on amplitude variation with angle (AVA) modelling: The Sawan gas field (Pakistan) as a key case-study for South Asia's sedimentary basins

Amplitude variation with angle (AVA) is a technique widely used in the characterisation of hydrocarbon reservoirs and assumes the Earth’s crust to be an isotropic medium. Yet, anisotropy is ubiquitous in stratigraphic sequences and has first-order effects on seismic AVA responses when investigating subsurface prospects. This work analyses the effects of anisotropic strata on AVA responses using the Lower Goru Formation, middle Indus basin (Pakistan) as a case study. In the study area, shale intervals are interbedded with reservoir sands of the Sawan gas field. Shales in this field form laminae or are dispersed within reservoir sands, making the Lower Goru Formation an example of a vertically transversely isotropic (VTI) medium. In this work, we calculate the effective (saturated) mechanical properties of the Lower Goru Formation based on rock physics templates; the Backus (1962) average typically designed for layered media, combined with the empirical relations of Brown and Korringa (1975) and Wood (1955). The input data used in our rock physics modelling is based on detailed petrophysical analyses of well data. Using the saturated effective mechanical properties of the Lower Goru Formation, we generate angle-dependent reflection coefficient curves (and seismic AVA responses) based on exact and approximate solutions, for both isotropic and anisotropic reservoir scenarios. Our results suggest that the effects of lithological anisotropy are more pronounced in places with thick shale beds within reservoir sands. Conversely, angle-dependent reflection curves, and seismic AVA responses based on isotropic or anisotropic cases, give similar solutions in the presence of thin shale beds. As a corollary of this work, we present a Bayesian inversion method for the estimation of porosity in VTI media.

Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)

Abstract. The rate at which low-lying sandy areas in temperate regions, such as the Campine Plateau (NE Belgium), have been eroding during the Quaternary is a matter of debate. Current knowledge on the average pace of landscape evolution in the Campine area is largely based on geological inferences and modern analogies. We performed a Bayesian inversion of an in situ-produced 10Be concentration depth profile to infer the average long-term erosion rate together with two other parameters: the surface exposure age and the inherited 10Be concentration. Compared to the latest advances in probabilistic inversion of cosmogenic radionuclide (CRN) data, our approach has the following two innovative components: it (1) uses Markov chain Monte Carlo (MCMC) sampling and (2) accounts (under certain assumptions) for the contribution of model errors to posterior uncertainty. To investigate to what extent our approach differs from the state of the art in practice, a comparison against the Bayesian inversion method implemented in the CRONUScalc program is made. Both approaches identify similar maximum a posteriori (MAP) parameter values, but posterior parameter and predictive uncertainty derived using the method taken in CRONUScalc is moderately underestimated. A simple way for producing more consistent uncertainty estimates with the CRONUScalc-like method in the presence of model errors is therefore suggested. Our inferred erosion rate of 39 ± 8. 9 mm kyr−1 (1σ) is relatively large in comparison with landforms that erode under comparable (paleo-)climates elsewhere in the world. We evaluate this value in the light of the erodibility of the substrate and sudden base level lowering during the Middle Pleistocene. A denser sampling scheme of a two-nuclide concentration depth profile would allow for better inferred erosion rate resolution, and including more uncertain parameters in the MCMC inversion.

Seismic constraints on magma evolution beneath Mount Baekdu (Changbai) volcano from transdimensional Bayesian inversion of ambient noise data

AbstractThe magmatic process of continental intraplate volcanism (CIV) is difficult to understand due to heterogeneous interactions with the crust and the lithospheric upper mantle. Mount Baekdu (Changbai) volcano (MBV) is one of the prominent CIVs in northeast Asia that has shown a complex history of eruptions and associated magmatic structures. In addition, the relationship between the crustal magmatic structures and upper mantle phenomena are enigmatic due to the lack of consistent seismic constraints for the lithospheric structure. To enhance comprehensive understanding of the MBV magma evolution, we image the lithospheric structure beneath the MBV and surrounding regions using ambient noise data and the following two approaches: (1) multiple measures of ambient noise dispersion are acquired through different methods and (2) a transdimensional Bayesian inversion method is utilized to obtain unbiased results in joint analysis of the multiple data sets. The estimated Earth structure shows a thick crust (~40 km) and a crustal anomaly with relatively high S wave velocity in the depth range 20–40 km. This type of structure extends to ~100 km north from the MBV and is accompanied by the shallow and rapid S wave velocity decrease beneath the mantle lid (~80 km). Through a comparison with previous P wave models, we interpret this structure as a consequence of compositional partitioning by mafic underplating and overlying cooled felsic layers as a result of fractional crystalization.

Internal rotation of 13 low-mass low-luminosity red giants in theKeplerfield

The Kepler space telescope has provided time series of red giants of such unprecedented quality that a detailed asteroseismic analysis becomes possible. For a limited set of about a dozen red giants, the observed oscillation frequencies obtained by peak-bagging together with the most recent pulsation codes allowed us to reliably determine the core/envelope rotation ratio. The results so far show that the current models are unable to reproduce the rotation ratios, predicting higher values than what is observed and thus indicating that an efficient angular momentum transport mechanism should be at work. Here we provide an asteroseismic analysis of a sample of 13 low-luminosity low-mass red giant stars observed by Kepler during its first nominal mission. These targets form a subsample of the 19 red giants studied previously Corsaro et al. 2015, which not only have a large number of extracted oscillation frequencies, but also unambiguous mode identifications We aim to extend the sample of red giants for which internal rotation ratios obtained by theoretical modeling of peak-bagged frequencies are available. We also derive the rotation ratios using different methods, and compare the results of these methods with each other. We built seismic models using a grid search combined with a Nelder-Mead simplex algorithm and obtained rotation averages employing Bayesian inference and inversion methods. We compared these averages with those obtained using a previously developed model-independent method. We find that the cores of the red giants in this sample are rotating 5 to 10 times faster than their envelopes, which is consistent with earlier results. The rotation rates computed from the different methods show good agreement for some targets, while some discrepancies exist for others.