Parameter Inversion Method Research Articles

Simulation of Load–Sinkage Relationship and Parameter Inversion of Snow Based on Coupled Eulerian–Lagrangian Method

The accurate calibration of snow parameters is necessary to establish an accurate simulation model of snow, which is generally used to study tire–snow interaction. In this paper, an innovative parameter inversion method based on in situ test results is proposed to calibrate the snow parameters, which avoids the damage to the mechanical properties of snow when making test samples using traditional test methods. A coupled Eulerian–Lagrangian (CEL) model of plate loading in snow was established; the sensitivity of snow parameters to the macroscopic load–sinkage relationship was studied; a plate-loading experiment was carried out; and the parameters of snow at the experimental site were inverted. The parameter inversion results from the snow model were verified by the experimental test results of different snow depths and different plate sizes. The results show the following: (1) The material cohesive, angle of friction, and hardening law of snow have great influence on the load–sinkage relationship of snow, the elastic modulus has a great influence on the unloading/reloading stiffness of snow, and the influence of density and Poisson’s ratio on the load–sinkage relationship can be ignored. (2) The correlation coefficient between the inversion result and the matching test data is 0.979, which is 0.304 higher than that of the initial inversion curve. (3) The load–sinkage relationship of snow with different snow depths and plate diameters was simulated by using the model parameter of inversion, and the results were compared with the experimental results. The minimum correlation coefficient was 0.87, indicating that the snow parameter inversion method in this paper can calibrate the snow parameters of the test site accurately.

A Study on the Accurate Equation Inversion Method for Uncoupled Interface Parameters of OA Media Considering Initial Stress

Underground media are generally affected by the initial stress of the overlying strata. At the same time, the uncoupled interface is an important influencing factor of seismic response, as well as an essential reservoir environment, reservoir space, and migration channel. It is significant to explore the parameters of the uncoupled interface under stress for identifying oil and gas reservoirs. As an effective means of predicting reservoir physical parameters, pre-stack seismic inversion uses information on uncoupled interface fractures under stress to improve the accuracy of reservoir parameter prediction, overcome the problem of insufficient interface parameter prediction methods, and promote oil and gas reservoir exploration and development. Based on the precise equations for directly characterizing uncoupled interface fracture weakness parameters, this paper uses the ANNI inversion method to realize the nonlinear inversion method of uncoupled interface fracture parameters. The feasibility and practicability of predicting underground medium parameters from seismic response characteristics have been verified through model testing and actual work area application.

Research on the approximate equation inversion method for the physical properties parameters of OA media with decoupling of stress and fracture effects

Pre-stack seismic inversion is an effective method for predicting reservoir physical parameters. At the same time, azimuth seismic data is a reliable source of information for predicting underground reservoir elastic parameters, fracture parameters, and interface parameters and has rich underground information. However, it is difficult to determine whether the anisotropy of the reservoir is caused by stress or the original fractures. Therefore, distinguishing the influence of fractures and initial stress in the reservoir from azimuth seismic data is the key to predicting reservoir parameters. Because of the problem of insufficient consideration of the influence of stress and fracture weakness in OA media, based on Born's first-order scattering theory and Bayesian inversion framework, an approximate equation for the reflection coefficient decoupling fracture anisotropy and stress-induced anisotropy was constructed, and a linear inversion method based on the approximate equation was developed, realizing the exploration of methods for predicting fracture weakness and stress action parameters. Model testing and actual work area application have verified the feasibility and practicability of predicting underground medium parameters from seismic response characteristics.

Shock Response Characteristics and Unreacted Equation of State Calibration of GAP/RDX/TEGDN propellant

Abstract Understanding the shock response characteristics of propellants is crucial for studying the safety of the shock wave dominating shock initiation. To study shock response characteristics of high-energy solid propellant (GAP/RDX/TEGDN propellant, GRT propellant) and obtain its unreacted equation of state (EOS) parameters, a shock response experiment (plate impact experiment) was conducted using a caliber 57 mm single stage light-gas gun device. The propellant/window interface particle velocity is measured by using a velocity interferometer system for any reflector (VISAR), and the corresponding mechanical parameters of GRT propellant are obtained by the impedance matching method. The Hugoniot parameters of GRT propellant further were obtained by the parameter inversion method based on Abaqus/explicit, calibrated the unreacted EOS. The experiment results show that: At an impact velocity of 212 m·s−1 and 282 m·s−1, the propellant/window interface particle velocity time-history curve shows a double wave composed of elasticity and viscoplasticity. The shock front rise time in the viscoplasticity wave stage is relatively slow and shows shock viscosity characteristics. The shock viscosity is attributed to the viscous dissipation of each constituent and the wave scattering caused by a wave impedance mismatch between each constituent. Its duration decreases with impact velocity increasing. In addition, the unreacted EOS fitting curve is consistent with the experimental result inversion value and the calibrated parameters are valid.

Study on the stress and deformation characteristics of ultra-deep soft rock tunnel under complex geological conditions

Under asymmetric three-dimensional high in-situ stress and high pore water pressure, tunnels are subjected to complex stress conditions, making them susceptible to failure and posing a threat to water conveyance safety. This study focuses on a large-scale cross-basin water diversion tunnel from Datong river into Huangshui river, which characterized by ultra-deep and complex geological conditions. Based on the field measurement data, a parameter inversion method considering the deformation of tunnel surrounding rock at multiple characteristic points is proposed. Additionally, considering the fluid-structure interaction effects under the influence of asymmetrical three-dimensional high in-situ stress and high pore water pressure, the stress-deformation characteristics of the soft rock tunnel are studied, and the damage evolution characteristics of the surrounding rock under different in-situ stresses and pore water pressures are analyzed. The results manifest that intense mutual extrusion of the surrounding rock, leading to volumetric compression, is the primary cause of the formation of stress concentration and excess pore water pressure. Additionally, the maximum deformation and the most likely location for gushing water both occur at the tunnel’s waist. After the implementation of segment support measures, the deformation control effect on the surrounding rock is remarkable. Notably, the reduction in damage depth across various characteristic locations due to segment support shows minimal variation. However, in terms of the reduction in disturbance depth, the tunnel waist significantly outperforms the top and bottom positions. The damage depth of the tunnel surrounding rock is positively correlated with both in-situ stress and pore water pressure, but it is more significantly influenced by in-situ stress than by pore water pressure. These findings can offer some valuable insights and guidance for future similar tunnel construction project.

Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model

Accurate material performance parameters are the prerequisite for conducting composite material structural analysis and design. However, the complex multiscale structure of ceramic matrix composites (CMCs) makes it extremely difficult to accurately obtain their mechanical performance parameters. To address this issue, a CMC micro-scale constituents (fiber bundles and matrix) elastic parameter inversion method was proposed based on the integration of macro–micro finite element models. This model was established based on the μCT scan data of a plain-woven CMC tensile specimen using the chemical vapor infiltration (CVI) process, which could reflect the real microstructure and surface morphology characteristics of the material. A BP neural network was used to predict the multiscale stiffness, considering the influence of the porous structure on the macroscopic stiffness of the material. The inversion process of the constituent elastic parameters was established using the trust-region algorithm combined with an improved error function. The inversion results showed that this method could accurately invert the CMC constituent elastic parameters with excellent robustness and anti-noise performance. Under four different degrees of deviation in the initial iteration conditions, the inversion error of all parameters was within 1%, and the maximum inversion error was only 2.16% under a 10% high noise level.

A GPR layered media parameters inversion method under rough surface

Abstract Ground Penetrating Radar (GPR) plays an important role in pavement detection and road exploration due to its efficiency. Traditional media parameter inversion algorithms only consider ideal smooth surfaces and neglect the roughness of interfaces, leading to significant errors in practical applications. Therefore, a GPR rough surface-layered medium parameter inversion method based on genetic algorithm is proposed in this paper. The method uses the generalized reflection coefficient and considers the influence of the surface roughness of the medium. Then, a radar echo signal time-domain model is established. Finally, the cost function is optimized by genetic algorithm. The effectiveness and accuracy are validated and analysed by utilizing simulated data through random rough surfaces models.

Bayesian model averaging and Bayesian inference-based probabilistic inversion method for arch dam zonal material parameters

Inversion analysis based on structural monitoring data is an important part of evaluating the working behaviour of structures. In this paper, a probabilistic inversion method for the elastic modulus of concrete in arch dams based on Bayesian model averaging (BMA) and Bayesian inference is proposed. According to the measured displacement data of an arch dam project, the influence of the separation accuracy of water pressure component, the inversion method and the selection of displacement measuring points on the inversion results of the elastic modulus of the dam body is studied. Example analysis results show that the accuracy of the multi-measurement point displacement and hydraulic pressure component separation model based on BMA-HST (Hydrostatic-seasonal-time) is at least 18.97 % higher than that of the single measurement point, and the relative error of the elastic modulus value of the multi-measurement point zonal inversion based on the Polynomial chaos-Kriging (PCK) proxy model and the Bayesian inference is only 6 % at the maximum, which indicates that the inversion model described in this paper is able to realize the high-precision inversion analysis of the material parameters of the zonal analysis of concrete dams at a relatively low computational cost.

Research on elastic parameter inversion method based on seismic facies-controlled deep learning network

Deep learning has been widely applied in the field of geophysics, and existing research literature has demonstrated that intelligent geophysical inversion methods have high vertical resolution but low horizontal resolution. The reason lies in the fact that existing horizontal constraint methods mainly adopt convolutional models, without fully considering other prior information of seismic data. Within the same sedimentary unit, seismic response characteristics vary gradually due to similar lithology and geological characteristics. Therefore, the seismic facies information extracted from seismic data is integrated into deep learning network to enhance the horizontal prediction stability of the network. Firstly, according to the spatial and temporal characteristics of seismic data, a fusion network of three-dimensional convolutional neural network (3D-CNN), gated recurrent unit (GRU) and attention mechanism is established to improve the vertical resolution of inversion results. Then, seismic facies classification of the target layer is achieved by applying the K-means clustering method. Subsequently, to improve the horizontal resolution of the inversion results, seismic facies classification is transformed into temporal encoding data using the position coding theory in natural language processing, to form a seismic facies-controlled deep learning network. Finally, the deep learning network is trained and tested in the thin interlayer model and practical application adopting a semi-supervised learning method. The results indicate that incorporating seismic facies-controlled technology in the deep learning network can improve the horizontal resolution of the inversion results.

Design and performance study of a multiband metamaterial tunable thermal switching absorption device based on AlCuFe and VO2.

In this paper, we propose a multiband adjustable metamaterial absorption device based on a Dirac semimetal (BDS) AlCuFe and a thermally controlled phase-change material VO2. The absorption device has an axially symmetric structure, resulting in polarization-independent characteristics, and when VO2 is in a high-temperature metal state, ultra-high absorption rates and sensitives at frequencies of M1 = 2.89 THz, M2 = 7.53 THz, M3 = 7.97 THz, and M4 = 9.02 THz are achieved. Using a parameter inversion method, we calculated the impedance of the absorber, proving that it achieves impedance matching and produces perfect absorption in the resonance region. Additionally, we changed the physical and chemical parameters of the absorption device, demonstrating the device's excellent tunability and manufacturing tolerance. Furthermore, by lowering the temperature of VO2 to that of a low dielectric state, additional resonant peaks with ultra-high absorption rates at frequencies M5 = 5.62 THz, M6 = 7.16 THz, M7 = 7.64 THz, and M8 = 8.80 THz were obtained, broadening the absorption band of the device. Lastly, we investigated the detection sensitivity of the device by changing the external refractive index, resulting in a maximum sensitivity of 2229 GHz RIU-1. To sum up, the absorption device has great application potential in the fields of communication, sensing, temperature detection and photoelectric instruments.

Inversion of Underground Target by Satellite Gravity Gradient Signal Considering Terrain Interference

This paper explores using satellite gravity gradiometry to detect underground targets. It discusses measured signals including target body, terrain interference, and global background signals. Formulas and principles for calculating these signals are provided. Simulation results show that a 5 million cubic meter sphere generates a gravity gradient signal of to at 200 km altitude. Terrain interference signals are around for a 100 km × 100 km area. The paper presents a parametric inversion method based on full tensor gravity gradient signals for target detection. Inversion experiments consider noise and terrain deduction, with 100 iterations for error analysis.

The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data

Abstract. This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration.

An Inversion Method for Surrounding Rock Parameters of Tunnels Based on a Probabilistic Baseline Model under a Constructional Environment

An Inversion Method for Surrounding Rock Parameters of Tunnels Based on a Probabilistic Baseline Model under a Constructional Environment

Pre-stack Seismic Probabilistic Inversion Method for Lithofacies and Elastic Parameters of Volcanic Reservoir

Pre-stack Seismic Probabilistic Inversion Method for Lithofacies and Elastic Parameters of Volcanic Reservoir

Intelligent Inversion Analysis of Drilling Gas Kick Characteristic Parameters and Advanced Predictions of Development Trends

Summary Gas kicks are a common and complex occurrence in oil and gas exploration and development. Obtaining the characteristic parameters promptly and accurately after a gas kick occurs is of utmost importance. The kick characteristic parameters are essential foundational data for accurately understanding the multiphase flow state within the wellbore, analyzing kick development trends, and formulating well-killing design. Traditional methods for determining gas kick characteristic parameters often involve trial estimation, a process that can introduce significant arbitrariness. Alternatively, using specialized equipment for measurement and analysis is an option, but it can be time-consuming and limited by technology and cost constraints. This paper proposes an intelligent inversion method for kick characteristic parameters based on surface logging data, including standpipe pressure and mud pit volume. First, a gas kick simulation model is developed based on wellbore multiphase flow theory. This model can accurately simulate the changes in surface logging parameters that occur when a gas kick occurs. Next, the particle swarm optimization algorithm is used to optimize the kick parameters, such as the gas-influx index and pore pressure, in conjunction with the gas kick simulation model. The optimization evaluation criterion is the Fréchet distance, which is used to identify the calculated curve that is most similar to the actual logging parameter change curve. Subsequently, kick trends can be predicted using the simulation model based on these kick parameters. The case analysis demonstrates that the method proposed in this paper can rapidly acquire the gas kick characteristic parameters based on the early change characteristics of logging parameters. It effectively simulates and reproduces the true distribution of wellbore fluids, enabling advanced prediction of kick development. This approach helps in preparing preventive measures in advance, reducing the risk of accidents, and minimizing financial losses.

A new approach for seepage parameter inversion of earth–rockfill dams based on an improved sparrow search algorithm

This paper introduces a novel seepage parameter inversion method for earth–rockfill dams. The method utilizes pore water pressure data and employs a radial basis function (RBF) neural network as a surrogate model, which is optimized with the improved chaos sparrow search algorithm (ICSSA) using a hybrid strategy. The improved surrogate model (ICSSA–RBF) establishes a nonlinear relationship between the seepage parameter and pore water pressure. Unlike the original algorithm, the algorithm proposed in this paper can avoid three principal problems: the optimization search falling into a local optimal value, the population diversity decreasing during the iteration process, and the RBF neural network being prone to overfitting. The ICSSA, which is proficient in recognizing an objective function's significant values, is also chosen for identifying the parameters. To validate the effectiveness of the proposed approach, four classical test functions, a numerical model, and an actual engineering project are considered for the comparative analysis. The study outcomes reveal that ICSSA–RBF exhibits an exceptional level of prediction accuracy, with a mean relative error of less than 5‰. The findings also affirm the potential of the proposed approach in parameter identification and its superiority in facilitating the evaluation of seepage parameters in earth–rockfill dams.

Layered Media Parameter Inversion Method Based on Deconvolution Autoencoder and Self-Attention Mechanism Using GPR Data

Layered Media Parameter Inversion Method Based on Deconvolution Autoencoder and Self-Attention Mechanism Using GPR Data

Hydraulic Fracture Parameter Inversion Method for Shale Gas Wells Based on Transient Pressure-Drop Analysis during Hydraulic Fracturing Shut-in Period

Hydraulic Fracture Parameter Inversion Method for Shale Gas Wells Based on Transient Pressure-Drop Analysis during Hydraulic Fracturing Shut-in Period

Parameter inversion method for target plate constitutive model based on BP neural network

In this paper, a neural network-based inversion method that can be used to streamline the acquisition of polymethyl methacrylate (PMMA) material constitutive model parameters for explosive cutting simulations is presented. This approach mitigates the need for multiple tests, a requirement of traditional methods. Initially, three categories of the damage, penetration depth, impact fracture thickness and spallation damage thickness, were distinguished and quantified by cutting the14 mm PMMA target plate with a 2.5 mm wide linear shaped charge, and the adjustment interval for the constitutive model parameters could be determined by combining the explosive cutting experiment data and the empirical parameters of the Johnson Holmquist Ceramics (JH-2) constitutive model. Subsequently, the numerical simulations of the cutting process of the performed target plate tests were carried out using LS-DYNA, and a dataset of target plate damage incorporating the three types of damage data was amassed. Ultimately, a neural network model correlating the parameters of the PMMA target plate constitutive model with the damage data was developed and trained using the plate damage dataset, and several supplementary experiments as well as finite element numerical simulations involving a 4.2 mm wide linear shaped charge cutting a 19 mm PMMA target plate were conducted. The numerical results displayed minimal variance from the experimental results in terms of fracture characteristics and damage data, suggesting that the inverted JH-2 constitutive model parameters can be effectively applied to PMMA target plate explosive cutting simulations. The parameter inversion method allows for the acquisition of more accurate material constitutive model parameters with fewer experiments and will be beneficial for further theoretical studies and practical applications of target plates in the future.

Unbalanced optimal transport for full waveform inversion in visco-acoustic media

Abstract As a high-precision parameter inversion method, visco-acoustic full waveform inversion (QFWI) is widely used in the inversion of parameters such as velocity and quality factor Q in visco-acoustic media. Conventional QFWI, using the L2 norm as the objective function, is susceptible to face the cycle-skipping problem, especially with inaccurate initial models. Lately, adopting the optimal transportation (OT) distance as the objective function in QFWI (OT-QFWI) has become one of the most promising solutions. In OT-QFWI, converting oscillatory seismic data into a probability distribution that satisfies equal-mass and non-negativity conditions is essential. However, seismic data in visco-acoustic media face challenges in meeting the equal-mass assumption, primarily due to the attenuation effect (amplitude attenuation and phase distortion) associated with the quality factor Q. Unbalanced optimal transportation (UOT) has shown potential in solving equal-mass assumption. It offers the advantage of relaxing equal-mass requirements through entropy regularization. Owing to this advantage, UOT can mitigate the attenuation effect caused by inaccurate quality factor Q during the inversion. Simultaneously, the Sinkhorn algorithm can quickly solve the UOT distance through CUDA programming. Accordingly, we propose a UOT-based QFWI (UOT-QFWI) method to improve the accuracy of two-parameter inversion. The proposed method mitigates the impact of inaccurate quality factor Q by introducing the UOT distance to calculate the objective function, thereby helping to obtain more accurate inverted parameters. Experimental tests on the 1D Ricker wavelet and 2D synthetic model are used to validate the effectiveness and robustness of our proposed method.