Inverse Parameter Research Articles

The behaviour of the deformed Klein-Gordon and Schrödinger equations in 3D-RNCQS and 3D-NRNCQS symmetries under the improved Eckart plus a class of Yukawa potential

We report the new bound state solutions of novel non-central potentials, the improved Eckart plus an improved class of Yukawa potential (IECYP), in the deformation Klein-Gordon equation by using Bopp's shifts method and standard perturbation theory in the symmetries of three-dimensional relativistic noncommutative quantum space-space (3D-RNCQS). We apply a developed scheme to overcome centrifugal terms such as ( and ). The analytical expression of the energy spectra under the IECYP of (NI2, K2, VH, TiH, NiC, TiC, and CuLi) diatomic molecules in 3D-RNCQS and three-dimensional non-relativistic noncommutative quantum space-space (3D-NRNCQS) was obtained to be sensitive to the atomic quantum numbers , the mixed potential depths (), the screening parameter's inverse and non-commutativity parameters . We also discuss various special cases related to our considered combined potentials, which have utility for other physical and chemical systems, and the results are in excellent agreement with the existing literature. We have also studied the effect of deformation in space-space on thermodynamic quantities such as partition function, mean energy, free energy, specific heat, and entropy.

A Preliminary Study on the Inversion Method for the Refraction Structure Parameter from Vortex Electromagnetic Waves

When vortex electromagnetic waves propagate through a turbulent atmosphere, the amplitude and phase of the electromagnetic waves are disturbed, creating the scintillation effect. According to the scintillation index of vortex waves, a new method of retrieving the turbulent refraction structure parameter was proposed using a genetic algorithm, and the feasibility of this method was verified by simulated experiments. The numerical results showed that the inversed value obtained by the genetic algorithm was close to the real parameter when the turbulent inner scale and outer scale were fixed. However, there was a gap between the inversed value and the real parameter when only the turbulent outer scale was fixed. These results suggest that vortex wave data can be used for turbulent refraction structure parameter inversion, and they provide new research directions for atmospheric remote sensing.

Fast inversion method for seepage parameters of core earth-rock dam based on LHS-SSA-MKELM fusion surrogate model

The inversion of seepage parameters is an important research topic in the safety evaluation of earth-rock dams. Core earth-rock dam is a commonly used structural type in earth-rock dams. Most of the existing inversion methods focus on concrete dams, and few of them are aimed at earth-rock dams. These methods have the problems of slow inversion speed or low accuracy. Aiming at the above problems, this paper proposes a novel fast inversion method for the seepage parameters of core earth-rock dams based on the LHS-SSA-MKELM fusion surrogate model. The proposed fusion inversion model optimizes the selection of combination models based on the surrogate model framework, making it more suitable for the inversion calculation of seepage parameters in earth-rock dams. First, based on the seepage theory of earth-rock dams, the finite element numerical simulation model of core earth-rock dam seepage is constructed. The Latin Hypercube Sampling (LHS) method is used to obtain the parameter sample dataset according to the range of the seepage parameters, and the sample dataset is substituted into the finite element model to obtain the corresponding theoretical seepage pressure of different datasets. Then, the Mixed Kernel Extreme Learning Machine (MKELM) is used to establish the mapping relationship between seepage parameters and the theoretical seepage pressure. The MKELM parameters were optimized by using the Sparrow Search Algorithm (SSA) to further improve the accuracy of the surrogate model. Finally, taking the minimum error between the theoretical value and the measured value of the surrogate model as the objective function, the SSA algorithm is used to derive the optimal inversion value of seepage parameters. The results of engineering examples show that the mean absolute percentage error between the measured value of the core earth-rock dam seepage pressure and the simulated value after inversion is only 4.958%. In addition, the average inversion time of the proposed surrogate model is only 6.6 s, which greatly reduces the time-consuming problem of numerical simulation. The proposed method provides a new way for accurate and fast inversion of seepage parameters of core earth-rock dams after long-term operation.

Soil parameter inversion modeling using deep learning algorithms and its application to settlement prediction: a comparative study

Soil parameter inversion modeling using deep learning algorithms and its application to settlement prediction: a comparative study

Finite element simulation of formation testing while drilling accounting for thermal-hydro-mechanical coupling

Formation testing while drilling (FTWD) is useful for acquiring dynamic formation parameters in real time. However, the FTWD is inevitably affected by the thermal-hydro-mechanical (T-H-M) coupling effect as the current drilling depth and the formation temperature continue to increase. In order to clarify the mechanisms of the effect of T-H-M coupling on the pressure response of FTWD, a novel T-H-M coupling mathematical model of the pressure response of FTWD was proposed. The parametric studies of the pressure response of FTWD under T-H-M coupling condition were simulated using the finite element method (FEM). The influence of different borehole wall conditions and coupling conditions on the pressure response of FTWD were compared, and the effect of T-H-M coupling on the interpretation of formation parameters was also discussed. The results indicated that the stress perturbation of drilling has little effect on the FTWD, but the change in formation pressure and temperature around the wellbore will significantly affect the pressure response of FTWD. The T-H-M process can create the “coupling skin” effect in the formation, which further leads to the greater pressure drop in the pressure drawdown stage and the lower growth rate in the pressure recovery stage. Formation permeability and temperature differential have a large effect on the pressure response of FTWD, but in situ stress and probe orientation have almost no effect. When the inversion of the original formation parameters is performed using the formation rate analysis (FRA) method, the near-wellbore supercharging can lead to an overestimation of the original formation pressure and the T-H-M coupling can lead to an underestimation of the original formation mobility. The model established in this paper is more in line with the actual test conditions, and the results can provide theoretical support for parameter optimization and data interpretation of FTWD.

Parametric elastic full waveform inversion with convolutional neural network

Parametric elastic full waveform inversion with convolutional neural network

Construction of the Global Reference Atmospheric Profile Database

Atmospheric profiles are important input parameters for atmospheric radiative transfer models and atmospheric parameter inversions. The construction of regionally representative reference atmospheric profiles can provide basic data for global atmospheric and environmental research. Most reference atmospheric profile databases commonly used lag behind in updating frequency. These databases usually have limited spatial and temporal resolution and differ greatly from the real atmospheric state. To present the real atmospheric state, this article constructs the Global Reference Atmospheric Profile Database (GRAP) based on ACE-FTS satellite products of 2021 and 2022, AIRS satellite products and ERA5 reanalysis data of 2022 u6sing a random forest regression model and a hierarchical mean algorithm. The radiance spectrum of FY-3E HIRAS-II using different profile databases was simulated and compared with the measured spectrum. The results show that GRAP spectral simulations fit better with the measured HIRAS-II spectrum. Comparing the CO2, CH4, O3 and N2O profiles of GRAP, AFGL, MIPAS, RTTOV and NDACC ground station profiles in equatorial, mid-latitude summer and polar winter, the results show that GRAP has high spatial and temporal resolution and better fits the current real atmospheric state. Comparing the temperature profiles of eight regions in China, the results illustrate that GRAP is a better representation of the state of the atmosphere in the Chinese region. GRAP can provide fundamental atmospheric data for radiative transfer studies and atmospheric parameter inversions.

Terahertz Selective Active Electromagnetic Absorption Film Based on Single-layer Graphene

In this paper, a terahertz selective active electromagnetic frequency absorption film based on single-layer graphene is proposed. The film can achieve electromagnetic wave absorption rate higher than 0.9 at 2.4-4 THz and 7-8.6 THz in the THz band. And, through parameter inversion analysis of the relative impedance, it is found that the relative impedance of the film in the strong absorption part matches the free space impedance, which is consistent with the classical theory. The absorption mechanism of the high absorption of the film is also studied by analyzing the electric field distribution and the pattern changed simulation of graphene. The film has good stability at different incident angles and shows strong polarization incoherence. The characteristics of the film can be tuned by tuning the physical parameters, graphene Fermi energy and relaxation time. Interestingly, under some parameters, the film will change the original absorption mode. This paper introduces five absorption modes: double bandwidth absorption mode, two different single bandwidth absorption mode and two different double peak and bandwidth absorption mode. To sum up, the film has strong application value in aerospace, military, medical and other fields.

Machine learning algorithm inversion experiment and pollution analysis of water quality parameters in urban small and medium-sized rivers based on UAV multispectral data.

To examine and analyze the applicability of UAV multispectral images to urban river monitoring, this paper, taking the Fuyang River in the urban area of Handan Municipality as the object, the orthogonal image data of the river in different seasons were acquired by unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and at the same time, the water samples were collected for physical and chemical indexes detection. Based on the image data, a total of 51 modeling spectral indexes were obtained by constructing three forms of band combinations ranging from the difference index (DI), ratio index (RI), and normalization index (NDI) and combining six single-band spectral values. Through the partial least squares (PLS), random forest (RF), and lasso prediction models, six fitting models of water quality parameters were constructed: turbidity (Turb), suspended, substance (SS), chemical oxygen demand (COD), ammonia nitrogen (NH4-N), total nitrogen (TN), and total phosphorus (TP). After verifying the results and evaluating the accuracy, the following conclusions were drawn: (1) The inversion accuracy of the three types of models is generally the same-summer is better than spring, and winter is the worst. (2) Water quality parameter inversion model based on two kinds of machine learning algorithms has more prominent advantages than PLS. RF model has good performance in the inversion accuracy and generalization ability of water quality parameters in different seasons. (3) The prediction accuracy and stability of the model are positively correlated to a certain extent with the size of the standard deviation of sample values. To sum up, by using the multispectral image data acquired by UAV and adopting the prediction models built upon machine learning algorithms, water quality parameters in different seasons can be predicted in different degrees.

A method for computing inverse parametric PDE problems with random-weight neural networks

We present a method for computing the inverse parameters and the solution field to inverse parametric partial differential equations (PDE) based on randomized neural networks. This extends the local extreme learning machine technique originally developed for forward PDEs to inverse problems. We develop three algorithms for training the neural network to solve the inverse PDE problem. The first algorithm (termed NLLSQ) determines the inverse parameters and the trainable network parameters all together by the nonlinear least squares method with perturbations (NLLSQ-perturb). The second algorithm (termed VarPro-F1) eliminates the inverse parameters from the overall problem by variable projection to attain a reduced problem about the trainable network parameters only. It solves the reduced problem first by the NLLSQ-perturb algorithm for the trainable network parameters, and then computes the inverse parameters by the linear least squares method. The third algorithm (termed VarPro-F2) eliminates the trainable network parameters from the overall problem by variable projection to attain a reduced problem about the inverse parameters only. It solves the reduced problem for the inverse parameters first, and then computes the trainable network parameters afterwards. VarPro-F1 and VarPro-F2 are reciprocal to each other in some sense. The presented method produces accurate results for inverse PDE problems, as shown by the numerical examples herein. For noise-free data, the errors of the inverse parameters and the solution field decrease exponentially as the number of collocation points or the number of trainable network parameters increases, and can reach a level close to the machine accuracy. For noisy data, the accuracy degrades compared with the case of noise-free data, but the method remains quite accurate. The presented method has been compared with the physics-informed neural network method.

Creep mechanical characteristics and nonlinear viscoelastic‐plastic creep model of sandstone after high temperature heat treatment

AbstractThis work studies the creep mechanical characteristics and nonlinear viscoelastic‐plastic creep model of sandstone treated at different temperatures. The peak strain, uniaxial compressive strength (UCS), and Young's modulus of sandstone treated at different temperatures are discussed as well as creep strain and long‐term strength. The conventional uniaxial compression curves and uniaxial creep curves of sandstone treated at different temperatures are compared. Moreover, a nonlinear generalized Kelvin model is developed to simulate the decay creep deformation; a time‐triggered nonlinear viscoplastic model is proposed to simulate the accelerated creep deformation. By combining the time‐triggered nonlinear viscoplastic model with the nonlinear generalized Kelvin model, plastic element, and viscous element, a nonlinear viscoelastic‐plastic creep model that can well reflect the entire creep process of sandstone treated at different temperatures is established. According to the results of model validation and parameter inversion, the new nonlinear creep model can be employed to predict the creep instability of rocks under high temperature environment.

Landsat 8 and Sentinel-2 Fused Dataset for High Spatial-Temporal Resolution Monitoring of Farmland in China’s Diverse Latitudes

Crop growth and development exhibit high temporal heterogeneity. It is crucial to capture the dynamic characteristics of crop growth using intensive time-series data. However, single satellites are limited by revisit cycles and weather conditions to provide dense time-series data for earth observations. However, up until now, there has been no proposed remote sensing fusion product that offers high spatial-temporal resolution specifically for farmland monitoring. Therefore, focusing on the demands of farmland remote sensing monitoring, identifying quantitative conversion relationships between multiple sensors, and providing high spatial-temporal resolution products is the first step that needs to be addressed. In this study, a fused Landsat 8 (L8) Operational Land Imager (OLI) and Sentinel-2 (S-2) multi-spectral instruments (MSI) data product for regional monitoring of farmland at high, mid, and low latitudes in China is proposed. Two image pairs for each study area covering different years were acquired from simultaneous transits of L8 OLI and S-2 MSI sensors. Then, the isolation forest (iForest) algorithm was employed to remove the anomalous pixels of image pairs and eliminate the influence of anomalous data on the conversion relationships. Subsequently, the adjustment coefficients for multi-source sensors at mixed latitudes with high spatial resolution were obtained using an ordinary least squares regression method. Finally, the L8-S-2 fused dataset based on the adjustment coefficients is proposed, which is suitable for different latitude farming areas in China. The results showed that the iForest algorithm could effectively improve the correlation between the corresponding spectral bands of the two sensors at a spatial resolution of 10 m. After the removal of anomalous pixels, excellent correlation and consistency were obtained in three study areas, and the Pearson correlation coefficients between the corresponding spectral bands almost all exceeded 0.88. Furthermore, we mixed the six image pairs of the three latitudes to obtain the adjustment coefficients derived for integrated L8 and S-2 data with high-spatial-resolution. The significance and accuracy quantification of the adjustment coefficients were thoroughly examined from three dimensions: qualitative and quantitative analyses, and spatial heterogeneity assessment. The obtained results were highly satisfactory, affirming the validity and precision of the adjustment coefficients. Finally, we applied the adjustment coefficients to crop monitoring in three latitudes. The normalized difference vegetation index (NDVI) time-series curves drawn by the integrated dataset could accurately describe the cropping system and capture the intensity changes of crop growth within the high, middle, and low latitudes of China. This study provides valuable insights into enhancing the application of multi-source remote sensing satellite data for long-term, continuous quantitative inversion of surface parameters and is of great significance for crop remote sensing monitoring.

Quantum Control of a Nonlinear Time-Dependent Interaction of a Damped Three-Level Atom

We investigate some new aspects of the nonlinear interaction between a three-level Ξ-type atom and bimodal field. The photon-assisted atomic phase damping, detuning parameter, Kerr nonlinearity and the time-dependent coupling have been considered. The general solution has been obtained by using the Schrődinger equation when the atom and the field are initially prepared in the excited state and coherent state, respectively. The atomic population inversion and concurrence are discussed. It is shown that the time-dependent coupling parameter and the detuning parameter can be considered as quantum controller parameters of the atomic population inversion and quantum entanglement in the considered model.

New framework for land surface temperature and emissivity inversion based on hyperspectral thermal infrared data

ABSTRACT Hyperspectral thermal infrared (HTIR) data offers enormous potential for land surface temperature (LST) and emissivity (LSE) inversion. Methods to fully use a large amount of spectral information, while avoiding data redundancy, is constantly being explored. Therefore, a new framework for LST and LSE inversion using HTIR data is proposed in this study. First, the stepwise iteration method based on information content was used to select channels that were more sensitive to land surface information. Principal component analysis (PCA) and a two-step machine learning method were then used to retrieve the LST and LSE based on the selected channels. The findings demonstrate that the LST and spectra of the LSE can be obtained simultaneously based on the proposed framework. An LST inversion accuracy of 1.5K and an emissivity inversion accuracy of 0.017 can be achieved. However, an increase in the number of channels does not result in superior outcomes. Satisfactory inversion results can be obtained by selecting only 10–35 channels with the highest information content. In conclusion, this framework can be used to guide channel selection and land surface parameters inversion for HTIR sensors.

Visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber surface with hyperspectral imaging technology

The application of high temperature vulcanized (HTV) silicone rubber materials in transmission lines insulators has been extensive. However, with the accumulation of pollution and moisture on the surface of HTV silicone rubber, the insulation reliability gradually decreases, which may even lead to flashover. In this paper, hyperspectral imaging technology is utilized as a rapid and effective tool for the visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber. Based on the analysis of reflection spectra, it has been determined that the pollution degree has a positive effect and the moisture content has a negative effect on the reflection characteristics of HTV silicone rubber, respectively. Further quantitative analysis of reflectance values at various moisture conditions reveals that exponential relationships can be observed between reflectance values and moisture contents at a certain wavelength. At the same time, linear correlations between reflectance values and pollution degrees (quantified as equivalent salt deposit density (ESDD)) at a certain wavelength are also proposed. By integrating the exponential and linear relationships, a double-exponential model is formulated to concurrently characterize the ESDD and moisture content (MC). Furtherly, from the perspective of engineering application, to enhance the model’s robustness, a set of equations combining spectral indexes and spectral shape feature parameters is initially formulated for the calculating of ESDD and MC. Given the synchronous inversion and visualization capabilities of pollution and moisture parameters of the polluted HTV silicone rubber surface, the visual characterization model serves as a valuable tool for protecting against pollution flashover failures and designing power equipment insulation.

Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems

In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly. Traditional inverse methods often involve tedious computational processes, require significant computational effort, and exhibit slow convergence speeds. The recently proposed Physics-Informed Neural Network (PINN) has shown great potential in solving inverse problems. Therefore, in this paper, we propose a transfer learning-based coupling of the Smoothed Finite Element Method (S-FEM) and PINN methods for the inversion of parameters in elastic-plasticity problems. The aim is to improve the accuracy and efficiency of parameter inversion for different elastic-plastic materials with limited data. High-quality small datasets were synthesized using S-FEM and subsequently combined with PINN for pre-training purposes. The parameters of the pre-trained model were saved and used as the initial state for the PINN model in the inversion of new material parameters. The inversion performance of the coupling of S-FEM and PINN is compared with the coupling of the conventional Finite Element Method (FEM) and PINN on a small data set. Additionally, we compared the efficiency and accuracy of both the transfer learning-based and non-transfer learning-based methods of the coupling of S-FEM and PINN in the inversion of different material parameters. The results show that: (1) our method performs well on small datasets, with an inversion error of essentially less than 2%; (2) our approach outperforms the coupling of conventional FEM and PINN in terms of both computational accuracy and computational efficiency; and (3) our approach is at least twice as efficient as the coupling of S-FEM and PINN without transfer learning, while still maintaining accuracy. Our method is well-suited for the inversion of different material parameters using only small datasets. The use of transfer learning greatly improves computational efficiency, making our method an efficient and accurate solution for reducing computational cost and complexity in practical engineering applications.

Robust quantitative susceptibility mapping via approximate message passing with parameter estimation.

For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE. On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model

Forest growing stock volume (GSV) is an essential aspect of ecological carbon stock monitoring. The successive launches of spaceborne microwave satellites have provided a broader way to use microwave remote sensing to monitor forest accumulation. Currently, the inversion parameterization models of active microwave remote sensing stock volume mainly include the interferometric water cloud (IWCM), BIOMASAR, and Siberia. Among them, the IWCM introduces backscattering and coherence, the BIOMASAR model only introduces backscattering, and the Siberia model only introduces coherence. Although these three models combine the backscatter coefficient and coherence of SAR to estimate volume accumulation, the performance of the models has not been evaluated at the same time in the same area. Therefore, this article starts from the perspective of the three combinations of coherence and backscattering, relies on three models that do not require measured data, and evaluates the accuracy of the models’ overall inversion of GSV. In addition, we combine precipitation meteorological information, vegetation types, and seasonal variation to separately explore model performance. The comparison results show that the IWCM model is relatively stable in the process of stock volume inversion and is more sensitive to the vegetation types of coniferous and deciduous forests. The influence of seasons and precipitation on the model is weak, and the accuracy of the multi-time-series model is slightly improved. The Siberia model has a good storage volume inversion effect in this study area, but the multiple time series did not improve the model accuracy. The BIOMASAR model is simple, and its performance was slightly inferior in this study area. Precipitation can negatively affect BIOMASAR. The model results for multiple time series outperform those for single time. In summary, the stability of IWCM is more suitable for research with unknown information. The BIOMASAR model is simple, does not require coherence calculations, and is ideal for the estimation of large-scale national or world-level storage distributions. The Siberian model performs better in small regions and smaller spatiotemporal baselines.

Oil and gas prediction basing on seismic inversion of elastic properties in Chaoshan depression, south China sea

The marine Mesozoic is widely distributed in the northeastern waters of the South China Sea and is an important field for oil-gas exploration in the South China Sea. The Chaoshan Depression is the largest residual depression in this sea. At a previous well, LF35-1-1, no oil and gas have been discovered with then pre-drilling prediction techniques. Post-drill analysis shows that the physical properties of the Mesozoic reservoir are not favorable there. So, in accurate prediction of the oil-gas reservoirs is necessary. Since the drilling at the LF35-1-1, extensive surveys and studies have been carried out which shows a number of favorable trapping structures. In the middle low bulge of the Chaoshan Depression, the DS-A structures found with potential reservoirs, complete trap structures, and dual source hydrocarbon supply on both sides, making it the most favorable zone for oil-gas accumulation. We apply the state of art prediction techniques for it using pre-stack seismic raw gather. The sensitivity analysis results of reservoir physical properties indicate that the difference in P- wave velocity between sand and mudstone is 500 m/s, the difference in density is 0.02 g/cm3, and the Poisson’s ratio ranges between 0.11 and 0.33. The Mesozoic sandstone reservoirs in the Chaoshan Depression have characteristics of high velocity and low Poisson’s ratio, and the P-wave velocity, density, and Poisson’s ratio are the main sensitive parameters for predicting reservoir and its oil-gas bearing properties. The density inversion, P-wave impedance inversion, and S-wave impedance inversion jointly characterize the “wedge-shaped” sand body in the DS-A structural area, with a maximum thickness of over 400 m and an area of ∼130 km2. The overlap of the sand body contour map and Poisson’s ratio inversion results indicates that the “wedge-shaped” sand body is an oil-gas bearing sand body. It can be concluded that pre-stack elastic parameter inversion is an effective method for reservoir prediction in deep-sea no-well exploration areas. It has the characteristics of high signal-to-noise ratio, strong stability and reliability, and high accuracy, which is conducive to reduce the non-uniqueness and uncertainty of seismic inversion. The inversion results predict that the DS-A structure is an oil-gas bearing structure.

Amplitude variation with incidence and azimuth stepwise inversion with coherence-attribute constraints for anisotropic parameters

With the development of 5D (3D + offset + azimuth) seismic technology, the stable acquisition of anisotropy information from wide-azimuth seismic data has become a key scientific problem in the seismic inversion of fractured reservoirs. The amplitude variation with incidence and azimuth (AVAZ) inversion method using wide-azimuth seismic data is an effective way to predict the anisotropic information of the subsurface medium. However, the conventional AVAZ inversion method suffers from too many parameters to be estimated, large variation in contribution, and inversion instability. Therefore, an AVAZ inversion method with coherence-attribute constraints is developed to solve the problem of unstable inversion of anisotropic parameters. First, we use seismic coherence attributes to build a fracture-probability-distribution model containing anisotropic information of the subsurface medium, which can be used to simulate large-scale subsurface fractures and faults. Then, it is added to the objective function as an anisotropic information constraint to improve the reliability and stability of the anisotropic inversion. Furthermore, an AVAZ inversion method in a Bayesian framework is implemented by using wide-azimuth seismic data. Gaussian distribution and a smoothing background model are added to the objective function to improve the reasonableness and stability of the inversion. In addition, we develop a stepwise optimization inversion method for isotropic and anisotropic parameters, prioritizing the inversion of parameters that contribute significantly to the reflection coefficient, and then using the results of the previous inversion as the initial values for the next inversion step to achieve multiparameter inversion. This method can reduce the number of the estimated parameters and thus improve the stability of the inversion of the anisotropic parameters. Field data examples indicate that this method produces suitable inversion results even at moderate levels of noise. Therefore, we can conclude that the proposed method has good applicability and stability in predicting the anisotropy parameters of fractured shale reservoirs.