Inversion Process Research Articles

Ruthenium Catalysed Transfer Deuteration of Heteroaromatics in D2O

There is a growing interest in deuterated derivatives in various fields, including the pharmaceutical industry. In this industry, partially hydrogenated heterocycles are also a target. Combining these two concepts, it was studied the reduction of heterocycles with simultaneous deuterium incorporation by transfer deuteration, a process of introducing two deuterium atoms into an organic molecule using non‐D2 sources, avoiding the use of a gas or harmful reagents. Formic acid/sodium formate and D2O as the sole deuterium source were used under mild conditions. The precatalyst was [RuCl(p‐cym)(dmbpy)]BF4, (p‐cym = p‐cymene, dmbpy = 4,4’‐dimethyl‐2,2’‐bipyridine), which is able to generate the “Ru‐D” fragment from D+ by a polarity inversion process (“umpolung”). A wide range of heterocycles were tested. Selective reduction in the nitrogen‐containing ring with deuterium incorporation into the same ring was achieved. The experiments were carried out in a biphasic D2O/toluene medium, which allowed excellent catalyst recycling with a simple work‐up. Through detailed studies for 3‐methylquinoline using DCOOD/DCOONa, the reaction mechanism that involves D+ incorporation at the nitrogen atom and an initial 1,2‐type addition was elucidated. In single‐phase D2O experiments, a distinct selectivity with deuterium incorporation in the non‐hydrogenated ring was observed thus enabling to modulate it by the choice of the medium.

Cold-Atom Particle Collider

A major objective of the strong ongoing drive to realize quantum simulators of gauge theories is achieving the capability to probe collider-relevant physics on them. In this regard, a highly pertinent and sought-after application is the controlled collisions of elementary and composite particles, as well as the scattering processes in their wake. Here, we propose particle-collision experiments in a cold-atom quantum simulator for a 1+1D (one spatial and one temporal dimension) U(1) lattice gauge theory with a tunable topological θ term, where we demonstrate an experimentally feasible protocol to impart momenta to elementary (anti)particles and their meson composites. We numerically benchmark the collisions of moving wave packets for both elementary and composite particles, uncovering a plethora of rich phenomena, such as oscillatory string dynamics in the wake of elementary (anti)particle collisions due to confinement. We also probe string inversion and entropy production processes across Coleman’s phase transition through far-from-equilibrium quenches. We further demonstrate how collisions of composite particles unveil their internal structure. Our work paves the way towards the experimental investigation of collision dynamics in state-of-the-art quantum simulators of gauge theories, and sets the stage for microscopic understanding of collider-relevant physics in these platforms. Published by the American Physical Society 2024

Tectonic Inversion in Sediment-Hosted Copper Deposits: The Luangu Area, West Congo Basin, Republic of the Congo

Complex Neoproterozoic tectonic processes greatly affected the West Congo Basin, resulting in a series of dispersed copper deposits in the Niari Sub-basin, Republic of the Congo. Structural observation and analysis can help in understanding both the transportation pathways for copper accumulation and the detailed tectonic evolution processes. This study examines cases from four copper mine sites in the Luangu region of the Niari Basin, using a set of codes that consider the three regional tectonic regimes (extension, extrusion, and contraction) and three deformation criteria (maximum effective moment criterion, tensile fracture criterion, and the Coulomb criterion). By combining these two aspects, nine new codes are introduced: the extension maximum effective moment criterion (EM), extension tensile fracture criterion (ET), extension Coulomb criterion (EC), strike-slip maximum effective moment criterion (SM), strike-slip tensile fracture criterion (ST), strike-slip Coulomb criterion (SC), compression maximum effective moment criterion (CM), compression tensile fracture criterion (CT), and compression Coulomb criterion (CC). By analyzing and applying these codes to the selected sites, we show that the new codes can present a geometric coordination catering to an exhumation-related inversion process from extension, strike-slipping, to contraction. The existence of SM- and CM-related structures that occurred during regional extrusional and contractional events may indicate a deeper level of exhumation for layers related to copper deposits in the field sites. A new tectonic evolution model is presented, considering the hypothesis of vertical principal stress changes while the two horizontal principal stresses remain relatively constant during copper mineralization affected by the Western Congo Orogen. The application of the nine codes facilitates the determination of interrelations between different tectonic regimes.

Physics-guided full waveform inversion using Encoder-Solver convolutional neural networks

Abstract Full Waveform Inversion (FWI) is an inverse problem for estimating the wave velocity distribution in a given domain, based on observed data on the boundaries. The inversion is computationally demanding because we are required to solve multiple forward problems, either in time or frequency domains, to simulate data that are then iteratively fitted to the observed data. We consider FWI in the frequency domain, where the Helmholtz equation is used as a forward model, and its repeated solution is the main computational bottleneck of the inversion process. To ease this cost, we integrate a learning process of an Encoder-Solver preconditioner that is based on convolutional neural networks (CNNs). The Encoder-Solver is trained to effectively precondition the discretized Helmholtz operator given velocity medium parameters. Then, by re-training the CNN between the iterations of the optimization process, the Encoder-Solver is adapted to the iteratively evolving velocity medium as part of the inversion. Without retraining, the performance of the solver deteriorates as the medium changes. Using our light retraining procedures, we obtain the forward simulations effectively throughout the process. We demonstrate our approach to solving FWI problems using 2D geophysical models with high-frequency data.

Metallic material microstructure grain size measurements from backscattering signals in ultrasonic array data sets

Ultrasound backscattering signals from material microstructures can be used to evaluate the material microstructure grain size. This typically involves making pulse-echo immersion measurements at multiple locations using a focused ultrasonic transducer in order to obtain an accurate estimate of the root-mean-square amplitude of the back-scattered signal at a specified focal position. However, this restricts some practical applications of using such techniques in, for example, on-line measurements in high-value manufacturing and in-service inspections where multiple immersion measurements are not feasible to use. The main benefit of using ultrasonic phased arrays is that one array probe at one position can focus ultrasound beams at multiple points using different focal laws either physically or in data postprocessing. Potentially this means that accurate grain size measurements can be obtained from a single array measurement. In this paper, the classic backscattering method for conventional transducers is adapted to be used for full matrix capture datasets from an ultrasonic array. Three-dimensional ultrasonic models are developed in the proposed inverse process to measure material microstructure grain size. Experimental validations were performed on two metallic materials: copper (EN1652) and bright mild steel (BS970). A good agreement is shown between the experimentally measured grain sizes from array data and metallography measurements. Compared to the classic pulse-echo immersion back-scattering measurements, the proposed method enables accurate measurement of grain size in a direct contact configuration at fewer locations. This has potential to make on-line grain size measurements possible.

An Inverse recursive algorithm to retrieve the shape of the inaccessible dielectric objects

A regularized electromagnetic iterative inverse algorithm is formulated and implemented to reconstruct the shape of 2D dielectric objects using the far-field pattern of the scattered field data. To achieve this, an integral operator that maps the unknown boundary of the object onto the far-field pattern of the scattered field is defined and solved for the unknown boundary. The addressed inverse problem has an ill-posed nature and inherits nonlinearity. To overcome these, the proposed solution is linearized via Newton and regularized by Tikhonov in the sense of least squares. Besides, the dominance of the shadow region in the inverse-imaging process is exceeded by considering the superposition of multi-incoming plane waves, leading to less computational cost and a very fast inversion process. Comprehensive numerical analyses are carried out to ascertain the algorithm's feasibility, revealing that it is very efficient and promising.

Photometry and Models of Seven Main-Belt Asteroids

The China Near-Earth Object Survey Telescope (CNEOST) conducted four photometric surveys from 2015 to 2018 using image processing and aperture photometry techniques to obtain extensive light curve data on asteroids. The second-order Fourier series method was selected for its efficiency in determining the rotation periods of the observed asteroids. Our study successfully derived rotation periods for 892 asteroids, with 648 of those matching values recorded in the LCDB (for asteroids with U > 2). To enhance the reliability of the derived spin parameters and shape models, we also amassed a comprehensive collection of published light curve data supplemented by additional photometric observations on a targeted subset of asteroids conducted using multiple telescopes between 2021 and 2022. Through the application of convex inversion techniques, we successfully derived spin parameters and shape models for seven main-belt asteroids (MBAs): (2233) Kuznetsov, (2294) Andronikov, (2253) Espinette, (4796) Lewis, (1563) Noel, (2912) Lapalma, and (5150) Fellini. Our thorough analysis identified two credible orientations for the rotational poles of these MBAs, shedding light on the prevalent issue of “ambiguity in pole direction” that often accompanies photometric inversion processes. CNEOST continues its observational endeavors, and future collected data combined with other independent photometric measurements will facilitate further inversion to better constrain the spin parameters and yield more refined shape models.

Pre-stack seismic inversion based on one-dimensional GRU combined with two-dimensional improved ASPP

Abstract Pre-stack seismic inversion is essential to detailed stratigraphic interpretation of seismic data. Recently, various deep learning methods have been introduced into pre-stack inversion, effectively capturing the vertical correlations of seismic data. However, existing deep learning methods face challenges such as insufficient feature extraction, poor lateral continuity, and unclear inversion details. We introduce the atrous spatial pyramid pooling (ASPP) module into the pre-stack inversion process, modifying the connection order and mode of its three components. Additionally, we incorporate a triplet attention module to extract features at different scales and utilize a gate recurrent unit (GRU) module to extract global information. During the network training stage, we employ a multi-gather simultaneous inversion method, combining one-dimensional and two-dimensional inversions. The proposed method is named as IGIT (I for Improved ASPP, G for GRU, I for Initial model and T for Triplet attention). To verify the feasibility of this network model, we evaluate it using the Marmousi2 model, SEAM model, and field data, comparing the results with other deep learning methods. Experimental results demonstrate that the IGIT not only improves lateral continuity but also delivers accurate and clear inversion details. Notably, the inversion effect for density parameters shows significant enhancement.

Performance analysis of mono-stage three-phase DC-AC converter with reduced logic power supply circuits

This paper presents the performance analysis of a mono-stage three-phase DC-AC converter with reduced logic power supply circuits. The power DC-AC power converter of the proposed system is formed by arrangement of one inductor, two power switches, and one capacitor in an opener-like configuration to yield each leg of the inverter. During the inversion process, the number of capacitor elements that charges up (builds electrical energy) or discharges across the power switches will be equal to the number of inductors that discharges or builds up magnetic energy that terminates in between the power switches and vice versa. Under this condition, the proposed system, boosts and converts DC power to AC power and at the same time maintains energy balance in the system in one stage power conversion phenomenon unlike conventional power inverters. The logic power supply circuit used in the proposed system has fewer component counts and performs optical isolation unlike its conventional counterparts which have larger component counts and isolate by electromagnetic induction. The proposed system achieved the following results: lower and cheaper logic power supply circuits, simulated phase voltages of 327.8 V and 326.6 V and 329.8 V at THDs of 0.09879 %, 0.2569 % and 0.2905 % and average phase currents of 6.30A, 6.32A and 6.26A, respectively, and experimented: scaled down phase-A, B, and C average voltages of 66.95 V, 65.6 V and 68.93 V at phase angles of 0°, 120°, and -120° respectively. The performance analysis of the mono-stage three-phase DC-AC converter with reduced logic power supply circuits can be used in home appliances, hospital equipment, communication-based power stations, and drive systems such as electric vehicles.

A NOVEL CONCEPT: THE PRODUCT OF TWO NEGATIVELY DIRECTED NUMBERS IS A NEGATIVELY DIRECTED NUMBER THOUGH THE NEGATIVE OF A NEGATIVE NUMBER IS A POSITIVE NUMBER

In this paper, the author proved that the product of two negatively directed numbers is a negatively directed number. This article is the outcome of previously published (2021-2024) ten (10) articles of this author. It is true that the negative of a negative number is a positive number. It has been done by applying the inversion process to a negative number. The process of inversion does not satisfy the basic concept of multiplication. Multiplication is defined as the adding of a number concerning another number repeatedly. So, the process of inversion does not comply with the fundamental concept of multiplication. According to the Theory of Dynamics of Numbers there exist three types of numbers: (1) Neutral or discrete numbers (2) Positively directed numbers (3) Negatively directed numbers. In general, we use four types of operators: addition (+), subtraction (-), multiplication (x), and division (÷) in mathematical calculations. Besides these, we use the negative sign (-) as an inversion operator. The positive sign (+) and negative sign (-) also represent the direction of neutral or discrete numbers. In this paper, the author introduced Fermat's Last Theorem: xn + yn = zn for n = 2 in Bhattacharyya's Theorem to prove that the product of two negatively directed numbers is a negatively directed number using the concept of the Theory of Dynamics of Numbers. In this paper, the author framed new laws of multiplication and inversion. Also, the author has given a comparative study between the conventional method of multiplication and the present concept of multiplication citing some practical examples. The author has become successful in finding the root of a quadratic equation in real numbers even if the discriminant, b2 – 4ac < 0 without using the concept of the imaginary number and also in determining the radius of a circle even if g2 + f2 < c, in real number without using the concept of complex numbers. With one example the author proved that this theorem is applicable in ‘Calculus’ also.

Full-Waveform Inversion Imaging of Cortical Bone Using Phased Array Tomography.

Classic ultrasound bone imaging modalities usually demand either a prior knowledge or an advanced estimation on speed of sound (SoS), which not only renders to a burdensome imaging process but also supplies a limited resolution. To overcome these drawbacks, this article proposed a frequency-domain full-waveform inversion (FDFWI) modality using phased array tomography for high-accuracy cortical bone imaging. A transmission scenario of ultrasound wave in 2-D space was presented in the frequency domain to simulate the forward wavefield propagation. Iterations in the inversion process were performed by matching the simulation wavefield to the experimental one from low to high discrete frequency points. Moreover, the association between the maximum initial frequency and the initial SoS model was explored to prevent the occurrence of cycle-skipping phenomenon, which could lead to the outcomes being trapped in local minima. The feasibility and effectiveness of the proposed imaging scheme were testified by simulation, phantom, and ex-vivo studies, with mean relative errors of cortical part being 3.18%, 8.71%, and 9.36%, respectively. It is verified that the proposed FDFWI method is an effective way for parametric imaging of cortical bone without any prior knowledge of sound speed.

Speckle-free holography with a diffraction-aware global perceptual model

Computer-generated holography (CGH) based on neural networks has been actively investigated in recent years, and convolutional neural networks (CNNs) are frequently adopted. A convolutional kernel captures local dependencies between neighboring pixels. However, in CGH, each pixel on the hologram influences all the image pixels on the observation plane, thus requiring a network capable of learning long-distance dependencies. To tackle this problem, we propose a CGH model called Holomer. Its single-layer perceptual field is 43 times larger than that of a widely used 3×3 convolutional kernel, thanks to the embedding-based feature dimensionality reduction and multi-head sliding-window self-attention mechanisms. In addition, we propose a metric to measure the networks’ learning ability of the inverse diffraction process. In the simulation, our method demonstrated noteworthy performance on the DIV2K dataset at a resolution of 1920×1024, achieving a PSNR and an SSIM of 35.59 dB and 0.93, respectively. The optical experiments reveal that our results have excellent image details and no observable background speckle noise. This work paves the path of high-quality hologram generation.

Machine learning – enabled inverse design of shell-based lattice metamaterials with optimal sound and energy absorption

ABSTRACT Currently, the development in shell-based lattice, is increasingly focused on multifunctionality, with growing interest in combining sound and energy absorption. However, few studies have explored the multi-objective inverse design process. Herein, we propose a new approach using machine learning (ML) to optimise both the mechanical and acoustic performances of shell-based lattices. Firstly, the K-Nearest Neighbour and Artificial Neural Network are employed to predict the properties of different configurations. Then the non-dominated sorting genetic algorithm is employed to generate the desired structures. Finally, the lightweight metamaterials generated achieve optimal multifunctional performances (an energy absorption capacity of 50% higher than typical Gyroid structure and a sound absorption coefficient near 1 at specific frequency band). Besides, the potential trade-off phenomenon of mechanical and acoustic properties is also presented by our work. Overall, this work presents a new concept to use ML and genetic algorithm for multi-functional inverse design for shell lattice metamaterials.

Unlocking thin sand potential: a data-driven approach to reservoir characterization and pore pressure mapping

The gradual decline of production of the major fields of the Indus Basin has influenced the field pressure. Therefore, the prospects need to be assessed by the petro-elastic relationship. This study highlights the value-addition for identifying gas pockets within the E-Sand of the Lower Goru Formation through estimating and populating petro-elastic properties. A probabilistic neural network (PNN) was employed to develop the relationship of pore pressure to the inverted properties. The outcomes will help identify the high-pressure areas in accordance with the petro-elastic relationship for optimizing production strategy. In order to evaluate the petro-elastic relationship, stochastic seismic inversion was employed to holistically capture the reservoir’s variability. Pore pressure volumetric was estimated from inverted attributes using a PNN, a non-linear algorithm that was more suited to heterogeneous reservoirs. The main objective of the research is achieved via enhanced resolution of elastic properties attained through the integration of stochastic inversion and PNN processes. The high-resolution elastic properties including P-impedance, S-impedance, and Vp/Vs ratio can delineate the heterogeneities more profoundly. The petrophysical volumes obtained via enhanced inverted properties combined with pore-pressure through PNN illuminated the potential facies and the correlations in predicting the properties. The procedure provides a linkage of elastic, petrophysical, and geomechanical properties that is helpful for professionals covering a broad field of the petroleum sector. The developed workflow can be followed globally where problems occur regarding heterogeneities and early depletion.

Enhancing accuracy and robustness in FBS decoupling through Tikhonov regularization and unit balancing

Frequency-based Substructuring (FBS) enables the prediction of transfer functions for an assembled system based on those of a single-unit system. Conversely, FBS decoupling derives the frequency response function (FRF) of a single-unit system from the assembled system. This approach is valuable when obtaining the transfer functions of a system is challenging due to boundary conditions and deformation conditions. FBS decoupling can enhance accuracy by using additional indicator sensors. However, this often results in significant noise due to the ill-posed FRF matrix inversion. In this paper, Tikhonov regularization is employed to reduce noise-induced errors. Furthermore, this paper addresses the issue of inadequate Tikhonov regularization caused by the inherent scaling differences between the rotational and translational components within the FRF matrix. To rectify this challenge, a unit balancing method is proposed to mitigate the influence of scaling differences. This method enhances the accuracy and reliability of FRF matrix inversion processes, contributing to enhanced noise reduction and robustness in practical applications.

Collaborative Seismic Impedance Inversion under Bayesian Parameter Estimation

Abstract At present, the industry mainly divides seismic inversion methods into deterministic inversion and deterministic inversion based on the methods and principles used in inversion. There are two types of geostatistical inversion. Deterministic inversion is the process of directly modifying the model quantity through optimization algorithms under given initial constraint conditions to obtain a definite solution; Geostatistical inversion uses random simulation methods to obtain inversion results through simulation and selection. The seismic data did not participate in the direct modification of the model, but rather served as a matching optimization constraint for the random model. Starting from the Bayesian formula, this article unifies deterministic inversion and geostatistical inversion within the Bayesian parameter estimation framework. From a theoretical framework, it analyzes the similarities and differences between deterministic inversion and geostatistical inversion, and proposes a method that combines deterministic inversion and geostatistical inversion - seismic wave impedance co inversion. Sequential Gaussian simulation and model constrained inversion are used as examples of the two major methods, The correctness of the research results and the feasibility and effectiveness of the proposed new inversion method and process have been verified through the application of the Marmousi theoretical model and actual data from Fudong, Fukang Depression, Xinjiang. It has been proven that the proposed method can improve the accuracy and precision of inversion. The inversion method and process used in this study obtained high-resolution and high-precision inversion results, which have good application prospects.

A global approach to detecting and characterizing water leakage in a concrete bridge deck: Parametric study to validate an adapted full-waveform inversion method

The objective of this study is to address issues related to defects in waterproofing membranes through the use of Ground Penetration Radar to detect water leakage into the concrete layer of bridge decks. Given that processing radar signals based solely on temporal data introduces significant estimation errors, an advanced method optimized from Full-Waveform Inversion (FWI) is employed. This method accounts for several unknown factors, including boundary conditions, antenna positioning inaccuracies, and approximations inherent in 2D modeling during the inversion process. The method is adaptable and capable of reconstructing both the dielectric and geometric parameters of a multilayered structure with any number of layers and unknown parameters using just a few A-scans (up to 10, depending on the number of parameters and layers). In contrast, other methods typically require several hundred A-scans. This efficiency is achieved due to the two-dimensional nature of the layer system. Additionally, the simplicity of the structure facilitates a much faster and more straightforward inversion algorithm, hence making these features especially advantageous for practical applications. The optimized Full-Waveform Inversion approach allows for an accurate determination of unknown parameters within a multilayered medium. The high accuracy of this method is validated through a direct comparison with experiments, wherein the exact parameters are known. Such an approach enables the attainment of a low relative error using the currently available measurement device.

Research on multi-wave joint elastic modulus inversion based on improved quantum particle swarm optimization

Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation. Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus, which can introduce cumulative errors and reduce the accuracy of inversion results. To address these issues, this paper introduces the analytical solution of the Zoeppritz equation into the inversion process. The equation is re-derived and expressed in terms of Young's modulus, Poisson's ratio, and density. Within the Bayesian framework, we construct an objective function for the joint inversion of PP and PS waves. Traditional gradient-based algorithms often suffer from low precision and the computational complexity. In this study, we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping, Lévy flights, and Gaussian mutation to optimize the quantum particle swarm optimization (QPSO), named improved quantum particle swarm optimization (IQPSO). The IQPSO demonstrates superior global optimization capabilities. We test the proposed inversion method with both synthetic and field data. The test results demonstrate the proposed method's feasibility and effectiveness, indicating an improvement in inversion accuracy over traditional methods.

Bayesian calculation of degradation-based burn-in policy for heterogeneous item under two-dimensional warranty

Two-dimensional (2D) warranty service has been greatly used in many heavy equipment and consumer durables. The items with 2D warranty are usually highly reliable and their heterogeneity is unavoidable. Degradation-based failure model is often used to characterize failure modes for highly reliable items. This paper proposes a degradation-based burn-in model for heterogeneous items with renewing 2D warranty. We consider that the degradation of item is modeled through the inverse Gaussian process and that the item is replaced by a new one as soon as it fails within warranty period. We screen the items based on the degradation information of the items during burn-in to enhance the reliability of items passed burn-in. The models based on performance and cost are proposed to analyze the burn-in procedures from different perspectives. Firstly, we investigate the properties of the optimal policies for the three burn-in models. Secondly, we further present a Bayesian method to solve the uncertainty of parameters in the model. The Bayesian method allows us to incorporate prior knowledge and update our beliefs based on observed data, providing more accurate and reliable estimates. Finally, we give an example of the gallium arsenide laser devices to illustrate the benefits of proposed model and method.

Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network

Inverse design of metamaterial structures with customized strain-dependent Poisson’s ratio has significant potential across various applications. However, achieving precise control over these mechanical properties presents a challenge due to the complex relationship between geometry and mechanical performance. Here, we present a novel data-driven approach utilizing a constrained generative inverse design network (CGIDN) to address this challenge. The CGIDN uses backpropagation to efficiently navigate the design space and achieve target mechanical properties with high accuracy. Our method starts by generating a comprehensive dataset of Poisson’s ratio-strain curves for various geometries incorporating cuts. These curves are then compressed using principal component analysis (PCA) to reduce dimensionality while preserving essential features. A deep neural network (DNN) is then trained to map input geometric parameters to these principal components, with the architecture optimized using grid search. The CGIDN facilitates the inverse design process by recommending geometric parameters for unit cell designs that match specified target Poisson’s ratio-strain curves. We validated the effectiveness of our approach through Finite Element Analysis (FEA) and experimental verification. The FEA results for the designed unit cells showed high agreement with the target and predicted curves, demonstrating the accuracy of the CGIDN model. Further, tensile tests on specimens confirmed that the inverse-designed structures reproduced the desired mechanical behavior upon scale-up. Our method, which enables efficient and accurate design of metamaterials with tailored mechanical properties, holds promise for applications in wearable devices, soft robotics, and advanced sensor systems