Inversion Identification Research Articles

A new method for selecting and evaluating the hyperelastic model of tread rubber material under rolling contact condition

The selection and evaluation of the hyperelastic model of tread rubber material were of great value and significance to the clarification and application of the mechanical properties of tread rubber material. In this article, a non-contact tread rubber material rolling test bench was established. The improved digital image correlation method was used to calculate the strain rate distribution of the marked point under different test conditions. Combined with the stress distribution of the marked point, the rolling dynamic stress-strain characteristics of tread rubber material were obtained. Based on rubber material models under different tensile test modes, the parameter inversion identification of the hyperelastic model was completed, and the influence of the tensile test mode on the parameter inversion of the tread rubber hyperelastic model was revealed. A new method for selecting and evaluating the hyperelastic model of tread rubber material under rolling contact conditions was proposed. The selection criterion for the hyperelastic model of tread rubber material was developed under different rolling conditions. The experimental and analytical results show that the nonlinear degree of the constitutive relation of the hyperelastic model decreased with the increase in rolling speed. Compared with hyperelastic models under different tensile modes, the fitting error of the Yeoh model was less than 3%, and the overall fitting error of the Yeoh model was less than 2%. Therefore, the Yeoh model had better stability and accuracy and was more suitable for describing the rolling mechanical properties of tread rubber under rolling contact conditions.

Study on the optimal design of specimens for stiffness coefficients identification of glass fiber-reinforced polymer composites by virtual fields method

Glass fiber-reinforced polymer composites are important structural materials and are widely used in structure engineering. In this study, a new V-notch non-standard tensile specimen is proposed. All the in-plane stiffness coefficients of glass fiber-reinforced polymer composites could be obtained by the virtual fields method with only one uniaxial tensile test. First, the special virtual fields method for inversion of elastic constitutive parameters of orthotropic materials in the uniaxial tensile test was introduced. The optimization of the geometrical design of the specimen was conducted using finite element simulation experiments. Batch modeling calculation was conducted within the designed range of the geometric parameters of the specimen. The generated ideal strain field data were substituted into the virtual fields method to invert and identify the stiffness coefficients. The optimized geometry of the specimen was determined according to the objective function of minimum error. Through a tensile experiment on glass fiber composites, the influence of specimen deformation on the identification results was assessed, and the load level suitable for parameter identification was determined. Based on the results, it can be concluded that the inversion identification accuracy meets the requirements of engineering measurement.

Частота инверсий в хромосомах Т-лимфоцитов у облученных жителей Южного Урала

It is well-known that ionizing radiation is among factors increasing the rate of chromosomal rearrangements. The inversion rate was poorly understood due to difficulty of inversion identification by the conventional differential staining method. A comprehensive study of chromatin and its complex rearrangements has become possible with the use of the high-tech molecular genetic method, fluorescence in situ hybridization (FISH). The study was aimed to assess frequency of inversions involving the chromosome telomeric regions in 36 residents of the South Urals, almost all of them were affected by combined chronic exposure. The calculated individualized cumulative external and internal doses were 0.0001–4.7 Gy. Inversions were identified by fluorescence staining of the chromosome telomeric region. It was found that chromatid inversions were more abundant than chromosomal variants (9 : 0.3 per 100 cells (p < 0.001). No relationship between the studied parameters and the absorbed dose, sex and age at the time of the examination was revealed.

Recent Advances and Challenges in the Inverse Identification of Thermal Diffusivity of Natural Ice in China

The ice thermal parameters are the key to reasonably simulating ice phenology, distribution, and thickness, but they have always been a “vulnerable group” in ice research. Technically, it may seem simple to obtain accurate ice thermal property parameters, but in reality, there are numerous impact factors, requiring a rigorous research process. In the 1980s, the thermal conductivity of ice was explored in the field and laboratory, after which there has been no significant progress in China. In this century, mathematics is introduced, after which the inversion identification and analysis with the time-series data of the vertical temperature profiles of ice layers by in situ testing are carried out. The in situ thermal diffusivities of different natural ices were obtained and cross-validated with the inversion identification results. Both natural freshwater ice and sea ice exhibited differences in the thermal diffusivity of the pure ice chosen for the current simulations due to impurities within the unfrozen water among the ice crystals, but the trends are consistent with the results of a small number of laboratory tests on different types of saltwater frozen ice. In this paper, the inversion identification results of the thermal diffusivity of typical ice were selected, and the factors constraining the thermal diffusivities were analyzed. The importance of parameterizing the thermal diffusivity in the phase transition zone of ice under the trend of global warming was illustrated. Future research ideas on the physical mechanism, application value, and parameterization scheme of the thermal diffusivity of natural ice were envisaged.

GPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines

Ground penetrating radar (GPR) enables infrastructure inspection using consecutive and long survey lines. However, the existing GPR data processing methods may lead to distortions or dislocations in the reconstructed shapes of the detected objects or even inconsistencies of the inverted dielectric values when performing inversion or object identification directly using GPR data obtained from the consecutive and long survey lines. To overcome these issues, this study proposed a novel deep neural network (DNN) architecture named GPRI2Net to simultaneously reconstruct the permittivity maps and categorize the object class labels from the GPR data of consecutive and long survey lines. GPRI2Net combined a convolutional neural network (CNN) based on DenseUnet and a recurrent neural network (RNN) based on bidirectional convolutional long short-term memory (Bi-ConvLSTM) to exploit the contextual information in and between the B-Scan segments extracted from the GPR data of a consecutive and long survey line. In addition, GPRI2Net performed inversion and object identification simultaneously using one network, which highly shared features between the two tasks and greatly reduced the computational complexity. Validation experiments were performed at two levels: first, using synthetic data based on the tunnel liner defects model and then using a sandbox model test in a realistic scenario. The results demonstrated that GPRI2Net can reconstruct consecutive permittivity maps and categorize the object class labels from GPR data with different dominant frequencies and survey line lengths and achieved a superior performance using the synthetic data compared to several other methods. Moreover, GPRI2Net also achieved satisfactory results using real-world GPR data.

K-BP neural network-based strain field inversion and load identification for CFRP

The strain information and loads conditions of composite wings are important basis for aircraft health evaluation. In this paper, firstly to demonstrate the accuracy of Fiber Bragg Grating (FBG) sensors and provide guidance for the following study, experiment is carried out to study the effect of the adhesive layer thickness on the strain transfer. Then finiteelementanalysissoftware ABAQUS is applied to analyze the strain distribution, and the simulation data is used to fit the pseudo-Kriging interpolation model to invert the strain information of crucial points. Finally, K-BP model is proposed by combining the interpolation model with back propagation (BP) neural network, which can be used to improve the accuracy on both strain field inversion and load identification. The results show that the proposed model can achieve great load identification results with fewer samples and optimize the interpolation parameters at the same time, which could provide important basis for evaluating the accuracy of strain field.

Prestack Inversion Identification of Dolomite Reservoirs in the Fourth Member of the Sinian Dengying Formation in Moxi Area, Sichuan Basin, SW China

The dolomite reservoir of the fourth member of Dengying Formation in Moxi area of Sichuan Basin is thin, is fast in lateral variation, and has P-impedance difference from the surrounding rock; it is difficult to identify and predict the dolomite reservoir and fluid properties by conventional poststack seismic inversion. Through the correlation analysis of core test data and logging P-S-wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges and solved the issue that some wells in the study area have no S-wave logging data. AVO forward analysis reveals that whether the gas reservoir of dolomite reservoir is located at the top of the fourth member of Dengying Formation is the main factor affecting the variation of AVO type. Through cross-plotting analysis of elastic parameters, it is found that P-S-wave velocity ratio and fluid factor are sensitive parameters to gas-bearing property of dolomite reservoir in the study area. By comparing the inversion results of prestack parameters such as density, P-wave impedance, S-wave impedance, P-S-wave velocity ratio, and fluid factor, it is found that the gas-bearing prediction of dolomite reservoir by using P-S-wave velocity ratio and fluid factor obtained from simultaneous prestack inversion had the highest coincidence rate with actual drilling data. At last, according to the distribution characteristics of fluid factor and P-S-wave velocity ratio, the favorable gas-bearing areas of dolomite reservoir in the fourth member of Dengying Formation in the study area are finely predicted, and the next favorable exploration areas were pointed out.

A residual-driven adaptive Gaussian mixture approximation for Bayesian inverse problems

In this article, we develop a residual-driven adaptive Gaussian mixture approximation (RD-AGMA) for Bayesian inverse problems. The posterior distribution is often non-Gaussian in practical Bayesian inference. To obtain a good approximation of the posterior, we provide the adaptive Gaussian mixture approximation (GMA) based on a residual. For GMA, the clustering of ensemble samples provides the predictor of means, covariances and weights by smoothed expectation–maximization (SmEM). SmEM can overcome the singularity of covariance matrix for small ensemble size. Then the parameters of GMA are updated by an iterative ensemble smoother (IES). To enhance clustering efficiency, the ensemble samples for the clustering are updated by IES as well. Since the goal of inverse problems is to minimize the residual between the observation data and the model response, the adaptive GMA of the posterior is constructed through a residual threshold. The mixture components with large residuals will be discarded in the adaptive procedure. When the prior is incorporated into the likelihood model, small residuals can drive the AGMA close to the true posterior. In the proposed method, a large number of samples can be efficiently drawn from the posterior distribution of GMA. A few numerical examples are presented to demonstrate the efficacy of RD-AGMA with applications in multimodal inversion and channel identification for subsurface flow problems in porous media.

An efficient method to identify thermal conductivity of orthotropic material based on BP neural network algorithm

BP neural network algorithm is used to identify the three-dimensional orthotropic thermal conductivity. The finite volume method is used to solve the forward heat transfer problem, and the training samples for neural network inversion identification are obtained. According to the identification results, the temperature field is reconstructed. Through the analysis of the calculation results, it is found that the accurate identification of three-dimensional orthotropic thermal conductivity can be realized by using BP neural network algorithm. When the temperature measurement error is less than 0.5 °C, the maximum identification error of thermal conductivity is only 3.5%.

Prestack inversion identification of organic reef gas reservoirs of Permian Changxing Formation in Damaoping area, Sichuan Basin, SW China

Organic reef reservoirs in the platform margin of Kaijiang-Liangping trough in Damaoping area, Sichuan Basin are thin in single layer, fast in lateral variation, and have small P-impedance difference from the surrounding rock, it is difficult to identify and predict the reservoirs and fluid properties by conventional post-stack inversion. Through correlation analysis of core test data and logging P-S wave velocity, this work proposed a formula to calculate the shear wave velocity in different porosity ranges, and solved the issue that some wells in the study area have no S-wave data. AVO forward analysis reveals that formation porosity is the main factor affecting the variation of AVO type, the change of water saturation cannot affect the AVO type, but it has an effect on the change range of AVO. Through cross-plotting analysis of elastic parameters, it is found that fluid factor is a parameter sensitive to gas-bearing property of organic reef reservoir in the study area. By comparing results of post-stack impedance inversion, post-stack high frequency attenuation property, pre-stack simultaneous inversion and AVO anomaly analysis of angle gathers, it is found that the gas-bearing prediction of organic reef reservoirs by using fluid factor derived from simultaneous pre-stack inversion had the highest coincidence rate with actual drilling data. At last, according to the characteristics of fluid factor distribution, the favorable gas-bearing area of the organic reef reservoir in Changxing Formation was predicted, and the organic reef trap at the top of Changxing Formation in Block A of Damaoping area was sorted out as the next exploration target.

A modified shear-wave velocity estimation method based on well-log data

Shear wave (S-wave) velocity is crucial for AVO inversion and fluid identification. Estimating S-wave velocity based on the rock physical model is commonly necessary for practical reservoir prediction. However, it is difficult to obtain some crucial parameters such as the pore aspect ratio accurately in some conventional estimation methods. Additionally, estimating S-wave velocity usually needs to use some optimization algorithms that depend on the initial values, which may decrease the accuracy of the estimation results. To solve these difficulties, we propose a concise S-wave velocity estimation method based on the notion of the consolidation parameter. We introduce a novel parameter defined as the product of consolidation parameter and porosity for calculating S-wave velocity, and propose a simple equation about the novel parameter after derivation. We solve the simple equation and obtain the novel parameter without needing any optimization algorithm. Then, the S-wave velocity is estimated accurately by using the novel parameter. The calculation of the proposed method is concise, because the optimization algorithm is needless, and the conventional parameters, such as the consolidation parameter and the pore-aspect ratio, can be disregarded. We test the estimation method by using well-log data from an oil field, where the lithology is complex. In the data test, the estimation error of the proposed method decreases compared with the error of the conventional method.

SFPSO algorithm-based multi-scale progressive inversion identification for structural damage in concrete cut-off wall of embankment dam

As an important part of dam seepage system, the quality of concrete cut-off wall is of great importance. In order to obtain effective feedback on the quality of the wall with limited observation data, this paper introduces the idea of multi-scale parameter inversion. Meanwhile, considering the instability of this ill-posed inversion problem, a regularized coefficient is derived to control the reliability of the final solution. Afterwards, we deduce the analytic sensitivity expression of the seepage head, and simplify the expression based on the adjoint state theory, which greatly improves the computational efficiency of the ill-posed multi-scale parameter inversion. Then, EM clustering algorithm, multi-scale data edit and semi-supervised fuzzy particle swarm optimization algorithm (SFPSO) are proposed to optimize and classify the parameter samples. Finally, we can make an effective identification of the damage inside the cut-off wall through clustering ensemble and progressive learning of the previous sample classification. To verify the accuracy and reliability of this method, we compare the calculation results with actual experimental data. The results show that the proposed multi-scale progressive inversion method is in good agreement with the experimental results and can be used to identify the damage of the actual cut-off wall.

A matching method for integrating multiscale components to model elastic parameters

ABSTRACTSeismic inversion and reservoir identification are complicated problems with integrated data sets and need a velocity or density model to ensure the stability of the results. However, an elastic parameter model, such as velocity or density, is difficult to obtain with high precision by conventional migration or interpolation, especially during the early exploration stage, which contains few available logging data. The kriging method has proved to be an effective technique for mineral exploration and petroleum geophysics, and has led to a series of expansive techniques in earth science. In this paper, we proposed a new matching method, base value compensation (BVC), for integrating multiscale data sets into model elastic parameters. Considering the variety of original information, we used multiple constrain cokriging for three types of data set at different scales. These three data sets come from logging, seismic attributes and sedimentary facies information. The P-wave velocity model is stable and more representative of the subsurface condition, but we found that this method produces few points with anomalous values in the density model. Those abnormal points cause substantial loss of geological detail at sedimentary boundaries. In most modelling methods, this shortcoming is due to the fact that these three input parameters have significantly different properties. The proposed method can effectively solve this problem by matching the input data sets at a similar observational scale before modelling. We demonstrated this method using a case study in the South China Sea. The decrease in abnormal points in the final modelling results verifies the effectiveness of BVC.

Simultaneous anisotropy inversion and type identification in the frequency domain for flexural waves in horizontal transverse isotropic media

The anisotropy of elastic waves has been widely used to obtain information about the formation structure in geosciences research. The splitting of flexural waves is generally applied to evaluate anisotropy using geophysical inversion methods. However, most of these methods must be combined with other methods, such as dispersion analysis, to distinguish stress-induced and intrinsic anisotropies. The objective function proposed by Tang and Chunduru has been improved by a new objective function that introduces an amplitude ratio of the slow to fast flexural waves in a horizontal transverse isotropic formation. By considering the amplitude difference in flexural wave splitting under stress-induced or intrinsic anisotropy, the new method can perform anisotropy inversion and type identification in the frequency domain. The calculated azimuth of the fast flexural wave as a function of frequency is used to distinguish the anisotropy type. The results from synthetic examples indicate that the intrinsic anisotropy commonly leads to a smooth azimuth curve, whereas the stress-induced anisotropy leads to a sharp step change. Therefore, the distribution of the azimuth in the frequency domain is a better indicator of the anisotropy type than the traditional slowness dispersion curve. Moreover, a new objective function and a new quality indicator for simultaneous anisotropy inversion and type identification have been developed and validated by synthetic and field data sets.

Local zone-wise elastic-plastic constitutive parameters of Laser-welded aluminium alloy 6061 using digital image correlation

The mechanical properties of aluminium alloys can be affected by the local high temperature in laser welding. In this paper, an inversion identification method of local zone-wise elastic-plastic constitutive parameters for laser welding of aluminium alloy 6061 was proposed based on full-field optical measurement data using digital image correlation (DIC). Three regions, i.e., the fusion zone, heat-affected zone, and base zone, of the laser-welded joint were distinguished by means of microstructure optical observation and micrometer hardness measurement. The stress data were obtained using a laser-welded specimen via a uniaxial tensile test. Meanwhile, the local strain data of the laser-welded specimen were obtained by the DIC technique. Thus, the stress–strain relationship for different local regions was established. Finally, the constitutive parameters of the Ramberg–Osgood model were identified by least-square fitting to the experimental stress–strain data. Experimental results revealed that the mechanical properties of the local zones of the welded joints clearly weakened, and these results are consistent with the results of the hardness measurement.

Identification of Constitutive Parameters of Laser Welded Aluminum Alloy Using Digital Image Correlation

An inversion identification method of local wise elastic-plastic constitutive parameters for laser welding of aluminum alloys 6061 was proposed based on full-field optical measurement data using digital image correlation (DIC). Three regions, fusion zone, heat affected zone and base zone of the laser welded joint were distinguished by means of microstructure optical observation and micrometer hardness measurement. The stress data was obtained by the laser welded specimen under uniaxial tensile test, meanwhile, the local strain data of the laser welded specimen was measured by the DIC technique, so the stress-strain relationship for different local regions was established. At last, the constitutive parameters of Ramberg-Osgood model were identified by least-square fitting to the experimental stress-strain data. Experimental results reveal that the mechanical properties for the local zones of welded joint are obviously weakened, which are consistent with the hardness measurement.

SEMANTIC INTERPRETATION OF INSAR ESTIMATES USING OPTICAL IMAGES WITH APPLICATION TO URBAN INFRASTRUCTURE MONITORING

Abstract. Synthetic aperture radar interferometry (InSAR) has been an established method for long term large area monitoring. Since the launch of meter-resolution spaceborne SAR sensors, the InSAR community has shown that even individual buildings can be monitored in high level of detail. However, the current deformation analysis still remains at a primitive stage of pixel-wise motion parameter inversion and manual identification of the regions of interest. We are aiming at developing an automatic urban infrastructure monitoring approach by combining InSAR and the semantics derived from optical images, so that the deformation analysis can be done systematically in the semantic/object level. This paper explains how we transfer the semantic meaning derived from optical image to the InSAR point clouds, and hence different semantic classes in the InSAR point cloud can be automatically extracted and monitored. Examples on bridges and railway monitoring are demonstrated.

Quantification Study on Bone Growth and Remodeling Adaptation Model Based on Experiment of Rapid-Growing Rats

In the 19th century, Julius Wolff put forward the law of bone transformation, it has been widely known that the trabecular structure of cancellous bone depend on the mechanical characteristic of cancellous bone. At present, in the field of bone remodeling and biomechanics, FE method and computer simulation are playing important role in simulating and predicting the bone mineral density or bone structure. To establish a quantized biological model of bone growth and remodeling adaptation, which integrates animal experiments, unknown parameter inversion identification of mathematical functions and technique of computer simulation. The 80 rats were randomly divided into three groups: 15 rats were in normal control groups, 45 in experiment groups, 20 in validation and prediction groups. By designing a new animal experiment, we investigate the effects of stress environments on bone growth and remodeling of rapid growing rats. And gather the bone mineral density (BMD) of proximal femur in the same interval for the unknown parameters (B and K) inversion of bone growth. The model of bone growth and remodeling advanced in this paper can not only numerically simulate the relationship between outer stimulus and the femur BMD variation of rapid growing rats, but also predict the growth trend of rat femur under different stress environments in its whole lifecycle.In this paper, we showed that the modeling ideas and methods for human bone reconstruction tips to provide clue and reference to establishing human model of bone growth and remodeling.

Research on the Rate-Dependent Fracture Energy of Al/HTPB Adhesive Interface

The mode-I fracture of adhesive surface was studied by uniaxial tensile test of the double cantilever beam (DCB) adhesive joints which were made from aluminium sheet and Hydroxyl-terminated polybutadienec (HTPB). The load - displacement curves which characterize the response of the macroscopic fracture show a significant rate-dependent property, i.e. the peak load and corresponding displacement increase with the increase of the loading rates. The critical energy release rate has been defined in two waysnon-linear initiation point (NL) and the maximum load point based on the experimental curves, was gained by the corrected beam theory of fracture mechanics. In addition, the cohesive parameters-fracture energy was also decided by the inversion identification method. The fracture energy obtained numerical simulation is slightly greater than the value obtained by the corrected beam theory, but both results indicate the fracture energy of the adhesive interface also increases continuously with increasing loading rates.

Application of Pre-Stack Inversion to a Complex Reservoir Prediction: a Case Study in LG34 Area, Tarim Basin

The Donghe Sandstone formation (DS fm. for simplicity) presents an excellent thin condensate gas reservoir in LG34 well area, Tarim Basin. Unfortunately, drilling results show that the reservoir is thin, deeply buried (below 5000m) and mix lithologies. Pre-stack inversion and fluid identification is implemented in this area. The feasibility and validity of pre-stack inversion for this kind of reservoir is tested, since that application of pre-stack inversion in this area is for the first time. A new rock physics model is used for estimating rock velocities and elastic attributes, and cross-plots of Poisson's ratio (σ) again LambdaRho (λρ) is found to be more favorable for lithofacies identification. Then, pre-stack inversion which uses seismic data with careful amplitude processing is performed to predict the reservoir. The inversion results agree and tie with most wells, suggesting that the pre-stack inversion is viable process for this complex lithological reservoir prediction in the area.