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Heterogeneous two-dimensional layered membranes reconstructed from natural or synthetic van der Waals materials enable novel ion transport mechanisms by coupling with the chemistry and optoelectron...

This paper outlines a novel approach for simulating general nonlinear circuits in the time-domain. The proposed approach can be considered as the generalization of the numerical inversion Laplace transform (NILT) which has been used for circuits with only linear elements. The new approach enables the well-known advantages of NILT such the guaranteed numerical stability and the high-order approximation, to be carried to the domain of nonlinear circuit. A numerical example is given to demonstrate the validity of the proposed method.

Aggregation-induced emission enhancement and aggregation-induced chirality inversion are two individual phenomena for the enantiomerically pure organic dyes in the aggregates. Herein we reported for the first time that these two interesting phenomena could be observed simultaneously in the aggregated states of enantiomerically pure S/R-1,1′-binaphthol annulated perylene diimides, in which two perylene diimides moieties were bridged by S/R-1,1′-binaphthol (BINOL) at the bay positions. Owing to the rotatable C2 axes between two naphthol annulated perylene diimides moieties, both of them display intrinsic behaviors of aggregation-induced emission enhancements. At the same time, due to the steric hindrances in the imide and methoxy positions, the neighboring two π-systems of these two unique polycyclic aromatic imides in poor solvents are preferable to adopt a cross-stacking mode and thus form helical X-aggregates of opposite chirality (M/P) with chirality inversion characteristics in their circular dichroism and circularly polarized luminescence spectroscopic studies.

Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is computationally expensive and time-consuming. The calculation of partial derivatives for the Jacobian matrix is by far the most computationally intensive task, as this requires calculation of a significant number of forward responses. We propose to accelerate the inversion process by predicting partial derivatives using an artificial neural network. Network training data for resistivity models for a broad range of geological settings are generated by computing partial derivatives as symmetric differences between two forward responses. Given that certain applications have larger tolerances for modeling inaccuracy and varying degrees of flexibility throughout the different phases of interpretation, we present four inversion schemes that provide a tunable balance between computational time and inversion accuracy when modeling TEM datasets. We improve speed and maintain accuracy with a hybrid framework, where the neural network derivatives are used initially and switched to full numerical derivatives in the final iterations. We also present a full neural network solution where neural network forward and derivatives are used throughout the inversion. In a least-squares inversion framework, a speedup factor exceeding 70 is obtained on the calculation of derivatives, and the inversion process is expedited ~36 times when the full neural network solution is used. Field examples show that the full nonlinear inversion and the hybrid approach gives identical results, whereas the full neural network inversion results in higher deviation but provides a reasonable indication about the overall subsurface geology.

Background: Recently, Particle Swarm Optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions in Transient Electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

The modified numerical inversion of the Laplace transform (dubbed NILT) has been recently proposed as a fast and provably stable numerical simulation for general circuits. Although it has enabled increasing the simulation time step, it highlighted the need for robust approach that can recover the full waveform in between the time points generated by NILT. This paper presents a new approach that addresses this challenge. The proposed approach leverages the NILT framework from a high-order approximation paradigm that computes points on the circuit waveforms to a methodology that computes high order derivatives of the waveforms at the same points. Using those derivatives, an interpolation approach based on Hermite interpolation is used to construct the circuit waveforms on dense points. Numerical experiments are presented to demonstrate the accuracy of both approaches.

The controlled-source electromagnetic (CSEM) method is a crucial tool for near-surface investigations and hydrocarbon exploration because of its economic benefits. Limited resolution is one of the inherent defects of the CSEM method, and to obtain high contrast results a multinary transform function was introduced to CSEM 3-D inversion. The multinary transform function was constituted by superposing several error functions, and the transform function transformed the model parameters from continuously distributed space into a semistep distributed multinary space. To deal with complex geometries, the edge-based finite element (FE) method with an irregular hexahedral grid was applied to the modeling and inverse problem. We used the Gauss-Newton optimization method to minimize the Tikhonov parametric function. The complex chessboard model and the marine CSEM model with complex geometry were used to validate the ability of the new method in improving the resolution of the CSEM method. By comparing the results of conventional, focusing, and multinary inversion methods, the effectiveness of the multinary inversion method in depicting sharp boundaries of different physical properties was proved. Additionally, the comparisons proved that multinary inversion method can, to some extent, overcome the insensitivity to low conductors of the CSEM method.

Two-dimensional (2D) nuclear magnetic resonance (NMR) inversion operates with massive echo train data and is an ill-posed problem. It is very important to select a suitable inversion method for the 2D NMR data processing. In this study, we propose a fast, robust, and effective method for 2D NMR inversion that improves the computational efficiency of the inversion process by avoiding estimation of some unneeded regularization parameters. Firstly, a method that combines window averaging (WA) and singular value decomposition (SVD) is used to compress the echo train data and obtain the singular values of the kernel matrix. Subsequently, an optimum regularization parameter in a fast manner using the signal-to-noise ratio (SNR) of the echo train data and the maximum singular value of the kernel matrix are determined. Finally, we use the Butler-Reeds-Dawson (BRD) method and the selected optimum regularization parameter to invert the compressed data to achieve a fast 2D NMR inversion. The numerical simulation results indicate that the proposed method not only achieves satisfactory 2D NMR spectra rapidly from the echo train data of different SNRs but also is insensitive to the number of the final compressed data points.

In this letter, a new full-wave inversion (FWI) scheme is proposed to reconstruct multiple dielectric parameters of 3-D arbitrary anisotropic objects buried in layered media. Three inverse solvers, including the isotropic one, biaxial anisotropic one, and the arbitrary anisotropic one, are cascaded sequentially. The dielectric parameters obtained by the first solver are used as the initial values of the next solver. Meanwhile, the inversion domain is synchronously downsized on the basis of discrepancies between the inverted dielectric parameters and the background ones. Numerical simulations show that, compared with the direct arbitrary anisotropic inverse solver, the cascading inversion scheme not only can produce more reliable reconstructed profiles but also significantly lowers the computational cost. In addition, the antinoise ability of the cascaded solvers is also tested.

In this letter, we concern the acoustic impedance (AI) inversion from the known reflectivity based on the increasingly mature deconvolution techniques. For seismic data with the complicated geological structures, we first construct the regularization model for the AI inversion with the proposed regularizer consisting of the total variation (TV) seminorm and the Frobenius norm of the Hessian. Second, we develop the split Bregman (SB) iterative algorithm in the frequency domain for solving the proposed model. Finally, we verify the effectiveness of our proposed method via synthetic and field data. Experimental results demonstrate that our proposed method can not only preserve the lateral continuity and the impedance interfaces of the inverted AI section well, but also provide a higher resolution impedance section than the other related methods.

Three-dimensional (3-D) inversion technique has become an important and practical approach for magnetotelluric (MT) data interpretation. In this article, we developed a 3-D parallelized MT inversion scheme using the edge-based finite element method and applied the developed method to the newly collected MT data in the Xinjiang Luntai area. The distorted hexahedral element is adopted to incorporate topography into the forward modeling and inversion for complicated scenarios. We use the Gauss-Newton optimization method to minimize the objective functional for MT inversion. The developed algorithm is parallelized using MPI over frequencies and parallel direct solvers when solving the forward and adjoint problems for each frequency. We compare the performance of the least-square QR (LSQR) factorization and preconditioned conjugate gradient (PCG) solvers for the model update within each Gauss-Newton iteration and found that the LSQR solver is more stable. The developed inversion algorithm is validated using several synthetic models. Finally, we applied the inversion algorithm to the subsurface resistivity imaging in the Luntai area. The recovered geoelectric model from full 3-D inversion fits well with the known geological and geophysical information. The recovered model shows a low resistivity layer which may be caused by the salt strata. Besides, the inversion results reveal the movement tectonic in this survey area within a depth of 9 km.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss-Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Full-waveform inversion (FWI) serves as a useful tool to quantitatively investigate the properties of the subsurface. Presently, three-dimensional (3D) elastic FWI uses a finite-difference time-domain (FDTD) approach in numerical simulation. However, such an FDTD scheme often includes only second-order temporal approximations, causing errors in temporal dispersion in the case of a large time-stepping size. Such temporal dispersion will affect the inversion results and reduce the inversion quality. We introduce a unique 3D elastic FWI using a temporal fourth-order finite-difference approximation. A new quasi-stress–velocity elastic equation is solved by the temporal fourth-order and spatial arbitrary even-order FDTD method, and a novel inversion procedure for the convolutional objective function based on this equation is derived. The multiscale strategy is used to enhance the robustness of our algorithm. The forward modeling and FWI examples presented here demonstrate that our method can achieve modeling and inversion with a high degree of accuracy.

We propose seismic elastic full-waveform inversion (EFWI) using both the multiparametric approximate Hessian and the discrete cosine transform (DCT). EFWI is a promising technology for obtaining physical parameters. EFWI using the multiparametric approximate Hessian can suppress crosstalk artifacts for each physical parameter, but the computational burden increases as the number of physical parameters considered increases. In this study, to reduce the computational burden, DCT was used to compress the model parameters and accelerate EFWI in the truncated DCT domain. EFWI using DCT is possible to increase the calculation efficiency by reducing the total number of unknown variables. We implemented EFWI using monoparametric Hessian and multiparametric Hessian, which reduced the computational burden using DCT and conducted tests through numerical experiments using each Hessian. In numerical result, despite using a small number of DCT coefficients, we confirmed that contamination due to crosstalk artifacts was suppressed when EFWI was performed using the multiparametric approximate Hessian.

We investigate an algorithm for the 3-D inversion of frequency-domain airborne electromagnetic (AEM) data based on the forward modeling and sensitivity calculation by footprint-guided compact finite element method (CFEM). Unlike the conventional approach, the modeling volume in our algorithm for each transmitter-receiver pair is a regular hexahedral that encloses the footprint, rather than a large mesh for the entire survey area or the local mesh with a number of grids extending from the footprint. After the electric fields in the modeling volume are solved by vector finite element method (FEM) with an integral equation boundary condition, the response and sensitivity are explicitly calculated by employing the product of the prepared Green's functions and the vector of electric fields. The accuracy of this footprint-guided CFEM is validated by comparing it against conventional CFEM, and different synthetic models are tested by our inversion algorithm. The inversion tests of synthetic models show the feasibility of the combination of footprint-guided CFEM and Gauss-Newton optimization in recovering models within an acceptable error level, and the inversion results show a good agreement with the true models on both the model geometry and recovered conductivity.

This article presents a novel hybrid electromagnetic inversion method. The traditional 3-D variational Born iterative method (VBIM) is combined with the unsupervised machine-learning expectation maximization (EM). In each iteration, VBIM first outputs the pseudo-randomly distributed model parameters in all discretized cells in the inversion domain. Then the EM algorithm is used to classify them and estimate the mean model parameter values of each homogeneous scatterer or subscatterer supposing that the reconstructed model parameters in all cells comply with the Gaussian mixture model (GMM). At last, partial cells in the inversion domain classified as ``background'' will be removed and the unknowns in the next VBIM iteration are reduced. This process is implemented iteratively until no ``background'' cell can be removed anymore and the data misfit between the measured scattered field and reconstructed field reaches the stop criterion. Finally, the mean value of the model parameter estimated by EM is mandatorily assigned for each homogeneous scatterer or subscatterer. Numerical examples show that the proposed hybrid method works efficiently for the reconstruction of isotropic, anisotropic, homogeneous, or inhomogeneous scatterers. It also has a certain antinoise ability.

Transient electromagnetic (TEM) method is widely used in regional mineral resources surveys, environmental engineering geological surveys and shallow surface geophysical exploration, and so on. However, interpretation and inversion of TEM data is a complicated process. The traditional algorithm of TEM inversion employs the ``smoke ring'' fast imaging method, which can only reflect the approximate morphology of the stratigraphic model, and the inversion accuracy is low. Therefore, this method cannot meet the requirements of high-precision inversion. In this article, we present the firefly algorithm (FA) technology for TEM inversion. First of all, the response of the rectangular loop source TEM based on electric dipole integration was calculated and compared with the analytical solution results of the rectangular loop source and the accuracy of the algorithm was verified. Then, a layered medium model was established. The FA technology and ``smoke ring'' fast imaging method were used to perform inversion calculation, and the influence of random noise on the accuracy of the FA algorithm was analyzed. The results show that the FA has a high degree of fitting to the model, good anti-noise property, and fast search speed. Next, to illustrate the application effect of the FA algorithm in pseudo 2-D inversion, the 2-D model was established. The results show that the FA algorithm can reflect the distribution of the anomalous body more accurately, especially for the low-resistance anomalous body. Finally, we examined the effectiveness of the FA for TEM data processing by inverting survey data and comparing the results with those from the ``smoke ring'' fast imaging and particle swarm optimization (PSO) algorithms. The research works provide new methods and techniques for TEM data processing.

Linearized algorithms based on the Born approximation are well-known and popular techniques for quantitative seismic imaging and inversion. However, linearization methods usually suffer from some significant problems, such as the computational cost for the required number of iterations, requirement for background models, and uncertain and unstable multiparameter extraction, which make the methods difficult to implement in practical applications. To avoid these problems, we have developed an angle-domain generalized Radon transform (AD-GRT) inversion in 2D elastic isotropic media. This AD-GRT is an approximate transform between the seismic data and an angle-domain model, which acts as a scattering function, and the seismic data can be reconstructed accurately, even when the background models are incorrect. The density and Lamé moduli perturbation parameters can be extracted stably from the inverted angle-domain scattering function. Deconvolution of the source wavelet is taken into account to remove the effect of the wavelet and improve the resolution and accuracy of the inversion results. The derived AD-GRT inversion is noniterative and is as efficient as the traditional elastic GRT method. The additional dimension of the angle domain has little effect on the computational cost of the AD-GRT, as opposed to other extended-domain inversion/migration methods. Our method also can be used to solve nonlinear Born inversion problems using iteration, which can significantly improve their convergence rate. Three numerical examples illustrate that the angle-domain scattering function inversion, data reconstruction, and multiparameter extraction using the presented AD-GRT inversion are effective.

We have adopted a new tool to invert magnetotelluric data for the 1D model based on deep Q-networks (DQN), which works as a stochastic optimization method. By transforming the inversion problem into a Markov decision process, the tool learns by trial and error to find the optimal path for updating the model to fit the observed data. The DQN method converges to the target through different paths (e.g., Bayesian or other stochastic methods) and can partially provide the probability distribution of the inversion results, which can be used for uncertainty estimation. The DQN search space gradually decreases as the learning experience progresses, accelerating the single inversion and approximating the optimal result. To check the effectiveness of the DQN inversion, the five- and eight-layer models were separately designed to test the robustness of the DQN for the initial model and the noise level. A further comparison with Occam’s method and the Bayesian method indicated that our DQN obtained more robust inversion results for data contaminated by different noise levels. The inversion results with the survey data from Zhagaitunuoergong area, Inner Mongolia, China, well reveals the shape of the interface basement.

Transient electromagnetic method (TEM) is one of the major tools to image the subsurface resistivity. The gradient-based inversion of TEM data only provides a unique solution using a subjectively defined regularization penalty, leaving the uncertainty of the solution unaddressed. The Bayesian method can be used to estimate the model parameters, as well as quantify their uncertainty. However, it requires far higher computational costs than gradient-based inversion, which limits the Bayesian inversion of TEM data to 1-D assumptions. We propose an accelerated Bayesian method based on Markov chain Monte Carlo (MCMC) subposteriors to perform full 2-D inversion of TEM data. A robust scheme is designed to divide the model space of a TEM profile into subspaces so that independent MCMC chains can be used to update the parameters in each subspace in parallel. The division is based on the coverage of the source loops using a cumulative sensitivity matrix. Then, the subposteriors obtained at each subspace are merged to approximate the full posterior of the model space using a weighting strategy. A numerical test of a 2-D valley model is used to validate the proposed method. The Bayesian inversion successfully obtained posterior that converges to the true model. The median model makes a good inference of the model parameters, while the probability density function gives their uncertainty estimates. The statistical model of interest can be further extracted from the model ensemble. The proposed method provides an effective framework for Bayesian inversion of TEM data with a multidimensional forward operator.

Prestack amplitude variation with offset (AVO) inversion can provide abundant reservoir information underground, which is always implemented trace-by-trace. However, it cannot guarantee the lateral accuracy of the inversion results. To utilize the lateral difference of the angle gathers and improve the lateral resolution, the difference angle gathers are introduced. Based on the Bayes inversion framework, the objective function considering the difference angle gathers is first constructed. Then, the effect of difference angle gathers on inversion results is analyzed, which is essential to improve the accuracy of the inversion results. To further figure out the applicable conditions of difference angle gathers, different forward operators are analyzed including a nonlinear operator and a linear operator. The used nonlinear operator is the exact Zoeppritz's equation. The linear operator is a linear perturbation equation based on the elastic inverse-scattering theory. Due to the difference of angle gathers in adjacent traces, the linear forward operator may cause a deviation of the updated parameters. By comparison, the exact Zoeppritz's equation as the nonlinear forward operator has better applicability and precision. Based on the proposed method, the elastic parameters are obtained from seismic data. Numerical examples show that the inverted elastic parameters of the proposed method have a higher horizontal resolution, and the details in the inversion profile can be better highlighted.

International audience; The augmented Lagrangian (AL) method provides a flexible and efficient framework for solving extended-space full-waveform inversion (FWI), a constrained nonlinear optimization problem whereby we seek model parameters and wavefields that minimize the data residuals and satisfy the wave-equation constraint. The AL-based wavefield reconstruction inversion, also known as iteratively refined wavefield reconstruction inversion, extends the search space of FWI in the source dimension and decreases the sensitivity of the inversion to the initial model accuracy. Furthermore, it benefits from the advantages of the alternating direction method of multipliers, such as generality and decomposability for dealing with nondifferentiable regularizers, e.g., total variation regularization, and large-scale problems, respectively. In practice, any extension of the method aiming at improving its convergence and decreasing the number of wave-equation solves would have great importance. To achieve this goal, we recast the method as a general fixed-point iteration problem, which enables us to apply sophisticated acceleration strategies such as Anderson acceleration. The accelerated algorithm stores a predefined number of previous iterates and uses their linear combination together with the current iteration to predict the next iteration. We investigate the performance of our accelerated algorithm on a simple checkerboard model and the benchmark Marmousi II and 2004 BP salt models through numerical examples. These numerical results confirm the effectiveness of our algorithm in terms of convergence rate and the quality of the final estimated model.

The opening up of space flights is going on with physical discoveries. One of them was a spinning object cyclic inversion revealed on the MIR space station classified in 1985. Later, the NASA International Space Station openly showed the same effect. This physical effect was an object of stare studying by physicists and mathematicians. They developed only approximated and numerical models on the level of assumptions. The inversion of the spinning objects in the condition of free flight is the subject of gyroscope theory. The mass of the spinning object at the orbital flight generates the system of the interrelated inertial torques that results from the action of the inertial torques produced by the curvilinear motion of the object around the earth. This system of the torques acting on the spinning object at an orbital flight manifests its cyclic inversions, which is the gyroscopic effects. The theory of the gyroscopic effects describes the method of application of the system of the inertial torques, the physics of all gyroscopic effects that manifested by any rotating objects under any condition of their motions.

SUMMARY Attention is paid to joint inversion of multiple geophysical data because of its advantages on weakening the non-uniqueness of inversion and further enhancing comprehensive interpretation. Due to the good correlation between rock velocity and density, seismic and gravity data have been widely used in joint inversion. However, the joint inversion of pre-stack seismic reflection and gravity data remains underdeveloped at the exploration scale. Without a quantitive relation between velocity and density, we develop a structure-based joint inversion using seismic reflection traveltimes, slopes and Bouguer gravity anomaly data simultaneously for building both velocity and density models. In our method, cubic B-spline interpolation is used to parametrize the common knots of velocity and density models. Incorporating seismic slopes into the joint inversion framework, we build a composite objective function which minimizes the weighted-sum of seismic/gravity data misfits, regularization and structural constraint terms. By subdividing the knot spacing, a multiscale strategy is alternative to increase the stability of inversion. First, we describe the methodology, followed by three synthetic examples to illustrate the feasibility and benefits of the method. Examination of the convergence curves via inversion suggests that the desired solution is more likely to be obtained with gentle convergence of each term, thus it can be used as an indicator for weight adjustment. Additionally, locations of scattering points and acoustic impedance can be obtained as by-products. Compared with the inversion of the respective data, the joint inversion exhibits the complementary characteristics of seismic and gravity data, improves the distribution and structural features of the resulting physical properties, especially in deep and complex tectonic situations.

Gradient-based traveltime tomography, which aims to minimize the difference between modeled and observed first-arrival times, is a highly nonlinear optimization problem. Stabilization of this inverse problem often requires using regularization. Although regularization helps avoid local minima solutions, it might cause low-resolution tomograms because of its inherent smoothing property. However, although conventional ray-based tomography can be robust in terms of the uniqueness of the solution, it suffers from the limitations inherent in ray tracing, which limits its use in complex media. To mitigate the aforementioned drawbacks of gradient and ray-based tomography, we have approached the problem in a novel way leveraging data-driven inversion techniques based on training deep convolutional neural networks (DCNN). Because DCNN often face challenges in detecting high-level features from the relatively smooth traveltime data, we use this type of network to map horizontal changes in observed first-arrival traveltimes caused by a source shift to lateral velocity variations. The relationship between them is explained by a linearized eikonal equation. Construction of the velocity models from this predicted lateral variation requires information from, for example, a vertical well log in the area. This vertical profile is then used to build a tomogram from the output of the network. The synthetic and field data results verify that the suggested approach reliably estimates the velocity models. Because of the limited depth penetration of first-arrival traveltimes, the method is particularly favorable for near-surface applications.

The study of entropies of hypergroups in scientific disciplines such as chemistry, physics, geometry and coding theory helps us to calculate the chaos of the scientific processes of phenomena. In this respect, different entropies on hypergroups have been defined and their systemic properties have been investigated. This paper introduces the notion of hypernormed entropy on topological hypernormed hypergroups and provides some interesting examples. The study investigates the fundamental properties of this entropy such as invariance under conjugation, invariance under inversion, the logarithmic law, monotonicity for subflows and continuity for direct limits.

BIKE is a Key Encapsulation Mechanism selected as an alternate candidate in NIST’s PQC standardization process, in which performance plays a significant role in the third round. This paper presents FPGA implementations of BIKE with the best area-time performance reported in literature. We optimize two key arithmetic operations, which are the sparse polynomial multiplication and the polynomial inversion. Our sparse multiplier achieves time-constancy for sparse polynomials of indefinite Hamming weight used in BIKE’s encapsulation. The polynomial inversion is based on the extended Euclidean algorithm, which is unprecedented in current BIKE implementations. Our optimized design results in a 5.5 times faster key generation compared to previous implementations based on Fermat’s little theorem.Besides the arithmetic optimizations, we present a united hardware design of BIKE with shared resources and shared sub-modules among KEM functionalities. On Xilinx Artix-7 FPGAs, our light-weight implementation consumes only 3 777 slices and performs a key generation, encapsulation, and decapsulation in 3 797 μs, 443 μs, and 6 896 μs, respectively. Our high-speed design requires 7 332 slices and performs the three KEM operations in 1 672 μs, 132 μs, and 1 892 μs, respectively.

Objetiva-se relatar e descrever um caso de impactação e inversão de caninos superiores permanentes, discutindo-se o seu valor para o processo de identificação humana. A pesquisa foi realizada com esqueleto pertencente à Coleção de Esqueletos Identificados do Centro de Estudos em Antropologia Forense (CEAF) da Faculdade de Odontologia da Universidade de Pernambuco (FOP/UPE). Inicialmente, o esqueleto foi montado em posição anatômica e posteriormente procedeu-se à descrição dos ossos presentes e das alterações observadas, bem como às fotografias da ossada. O esqueleto pertencia a indivíduo do sexo feminino, com 82 anos de idade à morte e apresentou, ao exame macroscópico, a impactação e total inversão de ambos os caninos permanentes superiores. Os referidos dentes apresentavam-se localizados lateralmente e no terço médio da crista zigomático-alveolar da maxila, em posição verticalmente invertida (com as coroas voltadas para as bases das órbitas e raízes voltadas para os rebordos alveolares). Essa variação apresenta uma baixa frequência de ocorrência na população e, assim como todas as outras anomalias, funcionam como fatores individualizantes, tornando os indivíduos ainda mais únicos. Quando apropriadamente registradas em prontuário odontológico, servem como excelentes fontes de dados ante mortem, no processo de identificação humana, a partir do confronto com as informações post mortem coletados nos exames necroscópicos. Por isso, é importante que, cada vez mais, os Cirurgiões-dentistas adquiram a consciência do valor dos prontuários clínicos e cumpram com o dever de elaborá-lo e mantê-lo atualizado, explicitamente expresso no Código de Ética Odontológica.

Stochastic petrophysical inversion is a method used to predict reservoir properties from seismic data. Recent advances in stochastic optimization allow generating multiple realizations of rock and fluid properties conditioned on seismic data. To match the measured data and represent the uncertainty of the model variables, many realizations are generally required. Stochastic sampling and optimization of spatially correlated models are computationally demanding. Monte Carlo methods allow quantifying the uncertainty of the model variables but are impractical for high-dimensional models with spatially correlated variables. We have developed a Bayesian approach based on an efficient implementation of the Markov chain Monte Carlo (MCMC) method for the inversion of seismic data for the prediction of reservoir properties. Our Bayesian approach includes an explicit vertical correlation model in the proposal distribution. It is applied trace by trace, and the lateral continuity model is imposed by using the previously simulated values at the adjacent traces as conditioning data for simulating the initial model at the current trace. The methodology is first presented for a 1D problem to test the vertical correlation, and it is extended to 2D problems by including the lateral correlation and comparing two novel implementations based on sequential sampling. Our method is applied to synthetic data to estimate the posterior distribution of the petrophysical properties conditioned on the measured seismic data. The results are compared with an MCMC implementation without lateral correlation and demonstrate the advantage of integrating a spatial correlation model.

The linear Gauss-Markov model for waveform-based moment tensor inversion often relies on the overdetermined least-squares method. It needs a proper stochastic model of the observables for accurate and precise estimates of the unknown parameters. Furthermore, estimating the level and distribution of random errors in the observed waveforms is challenging due to assessing the minimum-variance unbiased estimator (MVUE). Hence, according to the considerable effects of random data errors in assessing the uncertainty of the moment tensor components, this paper aims to describe an MVUE of the data covariance matrix and its application on uncertainty quantification of the moment tensor. The used mathematical prescription allows us to use the covariance matrix for the three-component noise records at every station and all possible cross-correlations among the recorded noise wavefield. To illustrate the proposed method's performance, we conducted tests with synthetic data using configuration of the 2018 M-w 6.8 Zakynthos (Ionian Sea, Greece) earthquake. Both uncorrelated and correlated random noise traces were added to the synthetic waveform data in amounts between 5 and 20 per cent of the maximum amplitude. In order to test the efficiency of the method, we considered three different structures of covariance matrix: (i) diagonal matrix (contains a variance of individual measurements at seismic stations), (ii) block-diagonal matrix (considering cross-covariance among three components at each station), and (iii) full covariance matrix. Test results are presented by comparison of the moment tensor inversion outcomes with known noise levels of generated synthetic data and with synthetic focal mechanisms, the ability of the estimated full covariance matrix in illustrating the minimum variance of parameters (namely, minimum posterior uncertainties), unbiased of the parameters, and values of the cross-correlations between the components of each station and also among stations. Finally, we applied the method to the real waveforms of the Zakynthos earthquake having inferred focal mechanism of strike/dip/rake angles 13/40/171 (deg) with 33 per cent double couple (DC) and -61 per cent compensated linear vector dipole component (CLVD). The focal mechanism solution has strike/dip/rake angles 19/34/177 (deg) with 69 per cent DC and -23 per cent CLVD when using our estimated full covariance matrix., Geophysical Journal International, 229 (1), ISSN:0956-540X, ISSN:1365-246X

We have developed a 2D anisotropic magnetotelluric (MT) inversion algorithm that uses a limited-memory quasi-Newton (QN) method for bounds-constrained optimization. This algorithm solves the inverse problem, which is nonlinear, by iterative minimization of linearized approximations of the classic Tikhonov regularized objective function. The QN approximation for the Hessian matrix is only implemented for the data-misfit term of the objective function; the part of the Hessian matrix for the regularization is explicitly computed. This adjustment results in a better approximation for the data-misfit term in particular. The inversion algorithm considers arbitrary anisotropy, and it is extended for special cases including azimuthal and vertical anisotropy. The algorithm is shown to be stable and converges rapidly for several simple anisotropic models. These synthetic tests also confirm that the anisotropic inversion produces a correct anomaly with different but equivalent anisotropic parameters. We also consider a complex 2D anisotropic model; the successful results for this model further confirm that the inversion algorithm presented here, which uses the novel modified limited-memory QN approach, is capable of solving the 2D anisotropic MT inverse problem. Finally, we have evaluated a practical application on MT data collected in northern Tibet to demonstrate the effectiveness and stability of our algorithm.