Parameter Inversion Method Research Articles

Optimized inversion method for thermal parameters of concrete dam under the insulated condition

It is crucial to study how thermal characteristics of concrete dam change over time, especially in cold regions, in order to guarantee long-term safety of the engineering projects. In this manuscript, an inverse analysis method by coupling the numerical simulation model and dam safety monitoring data, is proposed to solve the problem that true thermal parameters of the whole structure cannot be obtained by laboratory experiments or field tests of single points. Moreover, a numerical equivalence method of the dam and its insulation layer is introduced, in order to overcome the problem that the thickness of the insulation layer is much smaller than that of the concrete dam, resulting in low accuracy of the simulation model. Thirdly, the particle swarm optimization is introduced, in order to solve the ill-posed problems occurring during the solution process of multi-parameter inverse analysis. Considering the problem that the original particle swarm algorithm is easy to fall into local optimum and stuck edge, this study improves particles velocity and inertia weights. Benchmark function tests are applied to show the performance compared to several typical optimized algorithms. Numerical simulation and engineering verification are used to demonstrate the rationality and feasibility of the proposed method. The cases show that the residual box diagram of two typical measuring points calculated by inversion value is smaller than the median line and mean point calculated by design value. Moreover, the change law of the temperature field of the finite element calculation using the inversion values is closer to the actual measured value than that of the design value. The results indicate that the multi-parameter inverse method is feasible in insulated dam engineering. In addition, the result of the improved PSO optimization converges faster and has a higher fitness value than that of the original algorithm. It suggests that the improved PSO algorithm for thermal parameters inversion has fitting accuracy and rationality for insulated concrete dams in cold regions.

Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

Application of soil parameter inversion method based on BP neural network in foundation pit deformation prediction

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

The global construction of rockfill dams has now surpassed the 300 m height level. Despite great achievements in dam design and construction, monitoring techniques have lagged behind the development of high rockfill dams. Existing deformation monitoring techniques are ill-suited to the high earth and water pressures, and extended monitoring periods are required for ultrahigh rockfill dams. This study introduces, for the first time, the use of a shape accel array (SAA) to monitor internal displacement in a 300 m high earth core rockfill dam. The SAA employs a rope-like array of capacitive MEMS accelerometers for deformation measurement. Compared to conventional monitoring techniques, SAA is a data-intensive monitoring technique. Based on the intensive data obtained from SAA, we employed a parameter inversion method, utilizing multiobjective optimization algorithm, the nondominated sorting genetic algorithm-III (NSGA-III), to inverse the constitutive model parameters of the rockfill dam. The multiobjective parameter inversion method maximizes the use of multisource monitoring data for predicting rockfill dam deformation.

Propagation characteristics of ultrasonic diffusion energy in ballastless track structures with different levels of damage

The composition of concrete, being heterogeneous, leads to the randomization and transformation of high-frequency ultrasonic waves into an incoherent field. This incoherent field can be effectively quantified by ultrasonic diffusion energy, which thereby facilitates the detection of micro-damage in concrete structures using high-frequency ultrasonic waves. However, the propagation characteristics of ultrasonic diffusion energy in multi-layer structures, such as ballastless tracks, remain unclear. To address this issue, a finite difference method was utilized to derive both the difference equation and its corresponding stability condition equation for the ultrasonic diffusion equation within ballastless track structures. This was followed by an introduction of a numerical simulation method for the propagation of ultrasonic waves, as well as the proposal of an inversion method for ultrasonic diffusion parameters. On basis of this, the propagation characteristics of ultrasonic diffusion energy within ballastless track structures with global and local damage were analyzed and an experimental validation was conducted. The results indicate that the delay in peak arrival time, as well as the changes in attenuation behavior of the ultrasonic diffusion energy, are respond to the damage of ballastless tracks. It was also noted that damage to components in the lower layer has a minimal impact on the propagation of ultrasonic diffusion energy in the components above. Additionally, as the frequency increases, the sensitivity of ultrasonic diffusion energy to global damage or damage to individual component in ballastless tracks decreases, while the sensitivity to local defects increases.

A Method for Inverting Shallow Sea Acoustic Parameters Based on the Backward Feedback Neural Network Model

In response to the drawbacks of low efficiency, cumbersome calculation, and easy-to-fall local optimal solutions in existing shallow water acoustic parameters inversion research, this paper proposes a shallow water acoustic parameters inversion method based on a feedback (BP) neural network model. Firstly, the theoretically predicted values of the shallow water sound pressure field are obtained through the fast field method (FFM). Secondly, a relationship model between the predicted sound pressure field and the inversion of ground sound parameter values is established based on the BP neural network model. Finally, the measured sound pressure field data are brought into the neural network model to obtain the inversion results. The application results of the method indicate that, compared to the classical simulated annealing (SA) algorithm, the BP neural network model converts the data-matching process of the optimization algorithm into the construction of a relationship model between the input data and the desired parameters, avoiding repeated matching and optimization processes. Therefore, it can directly, accurately, and efficiently output the inversion results. Under the premise of setting the same accuracy, the iteration number of the BP neural network model is reduced to 2% of the SA algorithm, cutting the calculation time to 30% of the SA algorithm. It has broad application prospects in shallow sea acoustic parameters inversion algorithms.

Inverse Evaluation of Monopile Pile–Soil Interaction Parameters Using Random Search

To deal with the uncertainties in modeling offshore wind turbines, we propose a parameter inversion method for the pile–soil interaction model based on structural health monitoring results and the numerical model. The proposed parameter inversion method has a numerical model, an objective function selected using both the numerical and identified results, and an inverse optimization using a random search algorithm in the assumed parameter space. The parameter results in the minimum optimization objective function are identified as the in situ parameter. The proposed method is confirmed to converge after some number of iterations, depending on what the initial parameter values are. However, different initial parameter cases may converge to slightly different optimal parameters, implying that the pile results are sensitive to geological parameters. Moreover, a comparison with the original design results shows design redundancy or risks. Though the proposed method has several flaws, it can shed light on the influence of parameter uncertainties on offshore wind turbines.

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.

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.

Study on the Evolution Law of Overlying Strata Structure in Stope Based on “Space–Air–Ground” Integrated Monitoring Network and Discrete Element

The geological environmental damage caused by coal mining has become a hot issue in current research. Especially in the western mining area, the size of the mining working face is large, the mining intensity is high, while the surface movement and deformation are more intense and wider. Therefore, it is necessary to effectively monitor the surface using appropriate means and carrying out research on the overlying strata structure of the stope. In this paper, by using advantages of various subsidence monitoring technologies and the technical framework of the Internet of Things (IoT), a “space–air–ground” integrated collaborative monitoring network is constructed. The evolution law of overlying strata structure is studied based on discrete element simulations and theoretical analysis. Furthermore, a discrete element mechanical parameter inversion method is proposed. The main results, using numerical simulations, are as follows: The mean square error of monitoring surface subsidence is 33.2 mm, the mean square error of mechanical parameter inversion is 13.4 mm, and relative error is as low as 3.8%. The surface subsidence law of adjacent mining under different working face widths and interval coal pillar widths is revealed. The Boltzmann function model of surface subsidence ratio changing with width–depth ratio and the calculation formula of width reduction coefficient of adjacent mining working face are inverted. The critical failure width of the interval coal pillar is determined as 20.5 m. Based on the theory of “arch–beam” structure and numerical simulation results, the overlying strata structure model of adjacent mining in the mining area is constructed. The research results can provide technical support or theoretical reference for mining damage monitoring, subsidence control, and prediction in western mines.

Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers

The remote sensing inversion of the water quality parameters of a complex river network in the absence of historical ground data is a difficult problem in the field of remote sensing. In this paper, a sub-regional inversion method for typical water quality parameters is presented for a complex river network using Gaofen-1 satellite data. Qidong’s rivers were selected as the survey region, and different band combination models and datasets on different river sub-regions were used to perform the remote sensing inversion, which realized the inversion of the permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) in the rivers. The results show that all the coefficients of determination (R^2) of the inversion models are larger than 0.5, indicating an increase of about 0.4 when compared with the inversion method of the whole region, indicating good relevance. Water quality data and satellite data collected at different times were used for validation, which showed good results. On the basis of the water quality inversion, the key polluted areas were extracted in combination with on-site surveys to find the pollution source in order to verify the results of the inversion. The sub-region inversion method proposed in this paper can be used for the remote sensing inversion of the water quality parameters of complex river networks in the absence of historical ground data.

Characterization of Thermo-mechanical parameters of Nickel-Based single crystal superalloys under high-temperature by modified I-DIC method

Understanding the evolution law of thermo-mechanical parameters of nickel-based single crystal superalloys under high-temperature is significant for modeling, application, and structural optimization. In this study, a modified integrated digital image correlation (I-DIC) method is developed to characterize its thermo-mechanical properties. Particle Swarm Optimization (PSO) and Gradient-Based optimization algorithm are introduced to the modified I-DIC method. The orthotropic thermo-mechanical constitutive model for nickel-based single crystal superalloys is evaluated and the relative five constitutive parameters at temperatures ranging from 600 ℃ to 1000 ℃ are inversed simultaneously by the modified I-DIC method. Comparison between experiments and simulations confirms that the suggested model is reliable and the modified parameter inversion method is accurate and effective. The results show that the parameters vary with temperature. The coefficient of thermal expansion (CTE) and Poison’s ratio increased with increasing temperature, while Young’s modulus and shear modulus decreased with increasing temperature.

Combining Multi-Dimensional SAR Parameters to Improve RVoG Model for Coniferous Forest Height Inversion Using ALOS-2 Data

This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses the InSAR phase center height histogram and Gaussian function to fit the normalized extinction coefficient curve so as to reflect the vertical structure of the heterogeneous forest. Combining polarization decomposition based on the physical model and the PolInSAR parameter inversion method, the ground and volume coherence matrices can be separated based on the polarization characteristics and interference coherence diversity. By combining the new abovementioned parameters, the semi-empirical improved RVoG inversion model can be used to both quantify the effects of temporal decorrelation on coherence and phase errors and avoid the effects of small vertical wavenumbers on the large temporal baseline of spaceborne data. The model provided robust inversion for the height of the coniferous forest and enhanced the parameter estimation of the forest structure. This study addressed the influence of vertical structure differences on the extinction coefficient, though the coherence of the ground and volume in sparse vegetation areas could not be accurately estimated, and the oversensitivity of temporal decorrelation caused by inappropriate vertical wavenumbers. According to this method we used spaceborne L-band ALOS-2 PALSAR data on the Saihanba forest in Hebei Province acquired in 2020 for the purpose of height inversion, with a temporal baseline range of 14–70 days and the vertical wavenumber range of 0.01–0.03 rad/m. The results are further validated using sample data, with R2 reaching 0.67.

Field experiment of stress sensitivity effect in the Mabidong CBM block, southern Qinshui Basin, China

Coalbed methane (CBM) has made significant contributions to global energy supply and carbon neutrality. The stress sensitivity effect (SSE) is an important factor that influences CBM production, and it indicates that reservoir properties change with stress. Although there is abundant research on the SSE in CBM, most studies are based on theoretical analyses or laboratory physical simulations. Only few studies focused on the SSE of CBM during field production. Therefore, this study aims to investigate the SSE through a field experiment. To this end, a field well from the Mabidong CBM block in the southern Qinshui Basin is selected. Then, the corresponding experimental installations are set up. A parametric inversion method based on the Newman product method and type curve matching is established to interpret the experimental data. The entire experimental process includes six stages of pressure build tests, lasting for 290 days. The field experiment data are inverted using the parametric inversion method. The SSE in the CBM during production is revealed by analyzing the variations in the formation parameters of each stage. The results indicate that both the permeability and fracture half-length are related to the average pressure. The average pressure declines as the production proceeds, and this leads to an increase in the effective stress and a decrease in the permeability and fracture half-length. Relationships between permeability, fracture half-length, and average pressure are derived, and they can be used to better interpret the dynamic production data of CBM wells in the Mabidong Block. Moreover, these results indicate that decreasing the flow rate can mitigate the SSE, especially when the gas desorbs. Thus, during CBM production, the flow rate needs to be strictly controlled for maintaining a stable pressure drop and reducing reservoir damage caused by the SSE.

An Unsupervised Inversion Method for Seismic Brittleness Parameters Driven by the Physical Equation

Brittleness is an important parameter characterizing the fracturing properties of shale reservoir, which can be predicted by the pre-stack seismic inversion. In order to overcome the low efficiency and ill-posed problems of the traditional pre-stack brittleness inversion, we propose a new unsupervised deep learning (DL) inversion method for seismic brittleness parameters based on the physical equation. This method integrates DL framework and the physical equation, and provides a DL inversion strategy without actual labels. We first input the original seismic data into the Fastformer network, and use the low-frequency model as the physical constraint to predict the brittle parameters. Then, the prediction results of brittleness parameters are sent to the forward modeling module (a linear approximation equation) to calculate the synthetic seismic data. Next, the error between the calculated seismic data and original seismic data is used to update the network prediction results. The network parameters are iteratively optimized to minimize the error, and the brittle prediction parameters are finally output. In the whole training process, it is not necessary to use the real brittle parameters as the labels. Through this method, the effect of approximate unsupervised learning is obtained. Finally, we apply the proposed method to the synthetic data and field data, and compared with the results inverted by the traditional L1 method. The experimental results show that the proposed method has higher inversion accuracy and efficiency than the traditional L1 method, which has a great potential in the practical application.

Wave Parameter Inversion From Motion-Affected Echoes Using Shipboard Coherent Microwave Radar

Shipboard coherent microwave radar has been a rapidly developing tool for ocean wave measurements. However, the radial velocity estimated from echoes collected with a shipboard radar is significantly affected by the forward speed and six-degrees-of-freedom (six-DOF) motion of the ship. Accordingly, the performance of ocean wave parameter inversion using such radar degrades. To address this problem, the distribution of energy modulated by the ship motion in the wavenumber-frequency spectrum is illustrated, and wave parameters inversion method based on shipboard coherent S-band radar is proposed. An adaptive filter is designed according to the distribution of the energy components modulated by the ship motion in the wavenumber-frequency spectrum. The proposed method filters out the non-wave components and preserves the wave field components. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber-frequency spectrum to obtain the spatial-temporal radial velocities. Then the wave height spectrum is estimated from the radial velocities based on the direct relationship between the radial velocity spectrum and the wave height spectrum. Later, the significant wave height and mean wave period can be derived from the wave height spectrum. A dataset collected with a shipboard coherent S-band radar in the South China Sea in December 2020 is analyzed. The ship deployed with the radar sailed around a wave buoy according to a route plan for two days in that experiment. Comparisons between the radar-estimated and buoy-measured measurements in cases of ship motion are conducted. The results indicate that the proposed method can invert wave parameters well.

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones.

Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model

The mining of underground coal resources can trigger geological hazards such as subsidence basins, cave-in pits, and step cracks. In China, the probability integral method (PIM), the most popular method for predicting surface movement deformation caused by coal resource mining, has a prediction accuracy that is mainly influenced by both the measurement data (i.e., quantity and quality) from ground movement observatories and the parameter inversion method. To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. The results of the study show that (1) the RF-AGA-ENN model is used to obtain PIM values with MAXRE values between 1.94% and 9.18%, AVERY values between 0.98% and 3.98%, and RMSE values between 0.0050 and 0.9632. (2) Compared with the PIM parameters obtained from BP neural network, RF-ENN, RF-ACO-ENN, and RF-GA-ENN models, the PIM parameters obtained from the RF-AGA-ENN model have better stability and accuracy. (3) According to the PIM parameters obtained by the RF-AGA-ENN model, the predicted and measured values of surface settlement at the 11111 working face have a high degree of agreement. In summary, the RF-AGA-ENN model to obtain the PIM parameters has good application value.

A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone

A fast inversion method for ocean parameters based on dispersion curves with a single hydrophone

Experimental and simulation research on microscopic damage of HTPB propellant under tension-shear loading

In order to study the influence of loading conditions on propellant mesoscopic failure morphology, scanning electron microscopy was carried out to observe the morphology of the failure section of a hydroxyl-terminated polybutadiene propellant under different temperatures, loading speeds, and loading conditions. Using the finite element method, a mesoscopic model of a composite solid propellant was constructed, and the mechanical parameters of the propellant particle–matrix interface were obtained based on the parameter inversion method. A meso-model of a hydroxyl-terminated polybutadiene propellant under different loading conditions was constructed, and numerical results were compared with experimental results to analyze the meso-damage. With decreasing temperature and increasing loading speed, the level of propellant damage increased, and the particle–matrix interface became more prone to damage under the combined action of tensile and shear loads. In addition, with an increasing loading angle, ammonium perchlorate particles were more prone to dewetting.

A Joint Method for Wave and Wind Field Parameter Inversion Combining SAR with Wave Spectrometer Data

Synthetic aperture radar (SAR) and wave spectrometer are common methods for observing the ocean wind field and waves on the sea surface, but both have limitations. Due to the influence of velocity bunching modulation, SAR wave observation is limited by the azimuth cut-off phenomenon. Meanwhile, SAR relies on an external wind direction source, so it is difficult for SAR to observe wind fields independently. There is no azimuthal cut-off phenomenon when the spectrometer observes the sea surface, but its azimuth resolution is much lower than SAR. Combining the above characteristics, the joint inversion of SAR and wave spectrometer for sea-state parameters becomes possible. In this paper, a joint method for wind field and wave parameter inversion combining SAR with spectrometer data is proposed. In this method for wave parameter inversion, the wave spectrum of the spectrometer was used as a first-guess spectrum of SAR wave spectrum inversion, the fit wave spectrum obtained by joint inversion and the modified wave spectrum of the spectrometer were fused to form a new spectrum, and the wave parameters were calculated. For wind field parameter inversion, wind direction was obtained using a wave spectrometer, and was used as the input of the SAR wind field inversion. Wind speed was obtained using the CMOD5.N method. Collocated data from Sentinel-1 SAR, the wave spectrometer, the National Data Buoy Center (NDBC) buoy, and the European Centre for Medium-Range Weather Forecasting (ECMWF) are used to verify the proposed method. Sea parameters retrieved from the spectrometer and SAR are compared to the buoy and ECMWF data. The results show that the root mean square errors of significant wave height, mean wave period, wind direction and wind speed are 0.37 m, 1.02 s, 22.7° and 1.06 m/s with ECMWF data, and 0.35 m, 0.78 s, 18.22° and 0.92 m/s with buoy data, respectively. By comparing the inversion results with the L2 products of SAR and SWIM, it can be concluded that the inversion accuracy of the joint method is higher in the middle and low sea conditions. Therefore, the joint inversion method for wind field and wave parameters proposed in this paper has good results, which verifies the accuracy of the joint inversion method.