Inverse Parameter Research Articles

An effective atmospheric correction method for the wide swath of Chinese GF-1 and GF-6 WFV images on lands

Accurate land surface reflectance plays an important role in the accurate inversion of surface parameters, and atmospheric correction plays a decisive role in obtaining accurate reflectance. For GF-1 WFV and GF-6 WFV images, there are two major issues to be addressed, including the spectral differences between nadir with far off-nadir pixels and the spatial variability of atmospheric components for wide imaging. Therefore, this study focuses on these two issues using the Sensor Invariant Atmospheric Correction (SIAC) method. Our results reveal that the SIAC approach improves the correlation accuracy from 0.8868 to 0.9173 for GF-1 WFV image compared with Sentinel-2 reflectance, from 0.9530 to 0.9620 for GF-6 WFV image compared with the results using FLAASH model. For alleviating wide-swathed anisotropy, the directional imaging angle is calculated with the result ranging from 5.6450° to 33.7497°. Furthermore, the atmospheric components have been inversed pixel by pixel with obvious spatial variation. And the correlation of inversed aerosol optical thickness (AOT) and total column water vapor (TCWV) with a spatial resolution of 500 m TCWV with measured results of AERONET (AErosol RObotic NETwork) observation stations are 0.9175 and 0.4442, respectively. These results reveal that the atmospheric correction method works well, which is effective for the wide swath of Chinese GF-1 WFV and GF-6 WFV images on land.

Prediction of the Height of Fractured Water-Conducting Zone: Significant Factors and Model Optimization

Predicting the height of the fractured water-conducting zone (FWCZ) can be challenging due to their significant grey characteristics and the difficulty in scientifically selecting relevant influencing factors. To address this issue, we utilized the Pearson correlation analysis method and the grey entropy correlation analysis method to identify the significant factors and their degree of correlation with the height of FWCZ. Based on this, several constructed models were optimized, and the reliability of the best regression model was verified through parameter inversion analysis. The results indicate that the spatial distribution differences of the main coal mining seams contribute to the complex and variable occurrence conditions of coal seams. This is an important factor that contributes to the significant gray characteristics in predicting the height of FWCZ in the study area. A modeling approach has been proposed for predicting the height of FWCZ. This method is based on analyzing significant factors and conducting a multi-level evaluation of the selected prediction models. The order of correlation between significant influencing factors and the height of FWCZ is as follows: comprehensive hardness of overlying rock > average thickness of sandstone > mining depth > mining height. The results of the multi-level evaluation analysis show that, when using small sample high-quality datasets, the GA-Catboost algorithm has better prediction accuracy compared to the MSR and GA-BP algorithms. The results of the parameter inversion analysis for the GA-Catboost regression prediction model indicate that within the mining height range of 2.5–5.5 m, the ratio of fractured/mining height in the main coal seams is primarily concentrated between 20.45–30.59. In addition, a prediction method was developed to determine the limiting mining height by considering water conservation in coal mining. The relevant research results can provide fundamental theoretical support for ensuring safety in underground production and protecting groundwater in mining areas.

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.

Multi-attribute Probabilistic Neural Network Reservoir Prediction Based on Mudstone Acoustic Time Difference Decompaction Correction

In order to accurately predict reservoirs with similar physical properties between sandstone and surrounding rocks, this study takes Carboniferous-Permian system in southeastern Ordos Basin as an example, and puts forward a multi-attribute probabilistic neural network prediction method based on acoustic time difference compaction correction. Firstly, the time-frequency analysis method is used to divide the frequency of the original acoustic time difference curve, and the low-frequency long-trend correction of all wells is carried out based on the low-frequency components of standard wells. Then, it is frequency-divided and fused with the original high-frequency components to form new acoustic time difference data, and the long-term trend decompaction correction of acoustic time difference is completed, so that the overlapping area of acoustic time difference between reservoir and surrounding rock is reduced. Multi-attribute probabilistic neural network is an algorithm based on sampling points, before reservoir prediction, inversion parameters must be optimized to save calculation time and reduce prediction error. There is a high correlation between longitudinal velocity and lithologic change trend on the inversion profile of sound velocity. The comparison results of various inversions show that the velocity profile of multi-attribute probabilistic neural network has the highest coincidence rate and high resolution with the velocity curve of logging acoustic wave. The example shows that this method has high accuracy in predicting reservoir thickness, especially for the strata with little difference in physical properties between reservoir and surrounding rock.

Construction, Spectral Modeling, Parameter Inversion-Based Calibration, and Application of an Echelle Spectrometer.

We have developed a compact, asymmetric three-channel echelle spectrometer with remarkable high-spectral resolution capabilities. In order to achieve the desired spectral resolution, we initially establish a theoretical spectral model based on the two-dimensional coordinates of spot positions corresponding to each wavelength. Next, we present an innovative and refined method for precisely calibrating echelle spectrometers through parameter inversion. Our analysis delves into the complexities of the nonlinear two-dimensional echelle spectrogram. We employ a variety of optimization techniques, such as grid exploration, simulated annealing, genetic algorithms, and genetic simulated annealing (GSA) algorithms, to accurately invert spectrogram parameters. Our proposed GSA algorithm synergistically integrates the strengths of global and local searches, thereby enhancing calibration accuracy. Compared to the conventional grid exploration method, GSA reduces the error function by 22.8%, convergence time by 2.16 times, and calibration accuracy by 7.05 times. Experimental validation involves calibrating a low-pressure mercury lamp, resulting in an average spectral accuracy error of 0.0257 nm after performing crucial parameter inversion. Furthermore, the echelle spectrometer undergoes a laser-induced breakdown spectroscopy experiment, demonstrating exceptional spectral resolution and sub-10 ns time-resolved capability. Overall, our research offers a comprehensive and efficient solution for constructing, modeling, calibrating, and applying echelle spectrometers, significantly enhancing calibration accuracy and efficiency. This work contributes to the advancement of spectrometry and opens up new possibilities for high-resolution spectral analysis across various research and industry domains.

Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals

Using intelligent optimization algorithms to retrieve atmospheric duct parameters by monitoring automatic identification system (AIS) signals at sea is a new passive remote sensing technology for atmospheric ducts. To thoroughly compare and analyze the inversion results of different intelligent optimization algorithms and optimize the parameters of the algorithms, this study considered a simulated atmospheric duct environment for atmospheric duct inversion using the genetic, simulated annealing, and particle swarm optimization (PSO) algorithms. The results indicated that the PSO algorithm exhibited the best inversion performance. The inversion results of the simulated annealing particle swarm optimization (SAPSO) and PSO algorithms under different inversion parameters were further statistically analyzed, and the atmospheric duct parameters were obtained from measured AIS signals based on the SAPSO algorithm. The inversion results verified the effectiveness of the proposed algorithm, and they continuously improved with additional calculations in the inversion algorithm. However, the changing trend gradually slowed. Therefore, in practical applications, the inversion time consumption should be balanced with the inversion effect to optimize the inversion parameters.

Bayesian linearized rock-physics amplitude-variation-with-offset inversion for petrophysical and pore-geometry parameters in carbonate reservoirs

Rock-physics amplitude-variation-with-offset (AVO) inversion aims at directly predicting reservoir properties from prestack seismic data. However, carbonate reservoir rocks often develop complex pore structures, which limits the application of conventional inversion methods with fixed pore-geometry parameters. We derive a PP-wave reflectivity equation in terms of porosity, water saturation, and pore-aspect ratio and we develop a rock-physics AVO inversion method to jointly estimate the petrophysical and pore-geometry parameters in carbonates. The reflectivity equation is obtained by linearizing the rock-physics model based on the differential effective medium model and Gassmann’s equation using Taylor series approximation, and we combine the linearization with the Aki-Richards equation. Thanks to the linearized model, the analytical solution to the inverse problem is derived using Bayesian linear theory. Our solution combines simulated annealing to estimate the prior information of the pore geometry and the iterative Bayesian inversion to update the posterior model. The method is tested on a benchmark data set and compared with conventional methods with a fixed aspect ratio. The results show that our method improves the petrophysical results. The method is also compared with two-step and nonlinear inversion methods to demonstrate its advantages. The method is applied to a field seismic section from a carbonate reservoir with a complex pore structure to demonstrate its advantages in terms of prediction accuracy.

A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system

Fracture network developed in hot dry rock (HDR) primarily controls the flow and heat transport in enhanced geothermal system (EGS). Characterization of fracture network in deep geothermal reservoir is among the most challenging tasks. An interpretation framework for explicit delineating complex fracture network is proposed based on induced micro-seismic events, hydraulic stimulation and tracer test data, which are often available in EGS sites. It is processed by (1) the spatial distribution of fracture intensity, size and hydraulic aperture constrained by the hydraulic diffusivity derived from the spatio-temporal distribution of micro-seismic events, (2) numerical models and long short-term memory (LSTM) neural network models for the hydraulic stimulation and tracer test, and (3) parameter inversion model based on the multi-objective Harris hawks optimization (MOHHO) algorithm searching for optimal fracture network parameters minimizing the deviations between model predictions and field observations. This method has been applied to the Habanero EGS site, Australia. The prediction accuracy of the injection wellhead pressure and produced tracer concentration of the inversed micro-seismicity mapping DFN model is improved by 61.16% and 67.35%, respectively, compared with the stochastic DFN model. These results emphasize the importance to integrate induced micro-seismicity monitoring data for the characterization of hydraulically stimulated fracture network.

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

The resistivity method and time-domain-induced polarization (TDIP) are two branches of electric exploration that are used to solve problems in mineral exploration, hydrogeology and engineering geology. In recent years, integrating different physical parameters for joint inversion to improve the accuracy of inversion results has been extensively examined; however, three-dimensional joint inversion of the two methods above has not been realized. To further address this issue, in this research, we used the limited-memory BFGS (L-BFGS) method to develop a three-dimensional joint inversion algorithm of the resistivity method and TDIP based on the cross-gradient constraints. In the new algorithm, the resistivity method and TDIP inversion were iteratively updated alternately to ensure that the inversion results can simultaneously meet the two conditions of obtaining minimum data misfits and finding structural similarity. The three-dimensional synthetic dataset inversion results showed that the models obtained by joint inversion are more accurate in the recovery of both the boundaries and the values of the anomalies. Especially in the background of high noise, joint inversion has higher resolution for the target body. The joint inversion algorithm was also successfully applied to a groundwater detection practice in Beijing, China, in which the practicability of the algorithm was confirmed.

Improvement of pBRDF Model for Target Surface Based on Diffraction and Transmission Effects

The polarised Bidirectional Reflectance Distribution Function (pBRDF) model relates the properties of target materials to the polarisation information of the incident and reflected light. The Priest–Germer (P-G) model was the first strictly pBRDF model to be officially released; however, some shortcomings remain. In this study, we first analyse the assumption framework of the P-G model, analyse the assumption framework to determine the imperfections in the framework, supplement the boundary conditions of the model for diffraction and transmission effects, and propose and construct a polarised pBTDF model based on the existing P-G model and parameter inversion; the output results of the model are compared with the experimental data through simulation. The results show that the intensity relative error and Degree of Linear Polarisation relative error of the target can be reduced by more than 40%, using the improved model, proving its accuracy and precision.

Petrophysical parameters inversion for heavy oil reservoir based on a laboratory-calibrated frequency-variant rock-physics model

Heavy oil has high density and viscosity, and exhibits viscoelasticity. Gassmann's theory is not suitable for materials saturated with viscoelastic fluids. Directly applying such model leads to unreliable results for seismic inversion of heavy oil reservoir. To describe the viscoelastic behavior of heavy oil, we modeled the elastic properties of heavy oil with varying viscosity and frequency using the Cole-Cole-Maxwell (CCM) model. Then, we used a CCoherent Potential Approximation (CPA) instead of the Gassmann equations to account for the fluid effect, by extending the single-phase fluid condition to two-phase fluid (heavy oil and water) condition, so that partial saturation of heavy oil can be considered. This rock physics model establishes the relationship between the elastic modulus of reservoir rock and viscosity, frequency and saturation. The viscosity of the heavy oil and the elastic moduli and porosity of typical reservoir rock samples were measured in laboratory, which were used for calibration of the rock physics model. The well-calibrated frequency-variant CPA model was applied to the prediction of the P- and S-wave velocities in the seismic frequency range (1–100 Hz) and the inversion of petrophysical parameters for a heavy oil reservoir. The pre-stack inversion results of elastic parameters are improved compared with those results using the CPA model in the sonic logging frequency (∼10 kHz), or conventional rock physics model such as the Xu-Payne model. In addition, the inversion of the porosity of the reservoir was conducted with the simulated annealing method, and the result fits reasonably well with the logging curve and depicts the location of the heavy oil reservoir on the time slice. The application of the laboratory-calibrated CPA model provides better results with the velocity dispersion correction, suggesting the important role of accurate frequency dependent rock physics models in the seismic prediction of heavy oil reservoirs.

Sea Clutter Feature Prediction and Parameters Inversion Using Deep Learning Model

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry such as radar detection, remote sensing, SAR imaging, and situational awareness. In this paper, a deep neural network sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015-2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multi-scale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the LSTM model and deep neural network (DNN) are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results shown that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this paper can help improve the ability of sea surface remote sensing and sea clutter suppression.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

Cation Ordering in Spinel from Calcium-Aluminum-Rich Inclusions in Carbonaceous Chondrites NWA 2364 and NWA 6991 to Quantify Temperature in the Early Solar System

Abstract Minerals extracted from two calcium-aluminum-rich inclusions, one each from NWA 2364 and NWA 6991 CV3 chondrite meteorites, were examined using micro X-ray diffraction, 27Al magic angle spinning nuclear magnetic resonance spectroscopy (MAS NMR) and Triple Quantum (3Q) MAS NMR. In situ examination by micro X-ray diffraction was used to confirm the presence of spinel (MgAl2O4) and to identify co-existing minerals. Aluminum-27 3Q MAS NMR was used to confirm the identity of co-existing minerals by their NMR signature in this two-dimensional experiment and to ensure that their NMR peaks did not overlap with those attributed to spinel. Aluminum-27 MAS NMR was used to quantitatively measure cation ordering between the tetrahedral ([4]Al) and octahedral ([6]Al) sites in the spinel. The measured cation distribution was used to calculate the inversion parameter, x, for each sample: x = 0.077 ± 0.007 for NWA 2364, x = 0.027 ± 0.001 for NWA 6991 (gehlenite-rich fraction), and x = 0.052 ± 0.003 for NWA 6991 (Al-bearing diopside-rich fraction). The measured cation-ordering data were input into six literature calibration curves to estimate the temperature of formation or the most recent equilibration temperature of the spinel. The NWA 2364 sample yielded temperatures between 420 and 707 K and the NWA 6991 sample yielded temperatures between 153 and 615 K, depending on the calibration curve used. These temperatures are lower than expected for nebular condensation temperatures, however, reordering may have occurred during cooling from high temperatures, so these values may be taken to represent temperature minima. The calculated spinel-related temperatures may thus represent equilibration temperatures related to subsequent nebular or CV chondrite parent body processes.

Application of GA-BP in Displacement Force Inverse Analysis and Mechanical Parameter Inversion of Deep Foundation Pits

Aiming at the defects of various existing displacement inverse analysis methods, using the nonlinear mapping ability of neural network and the global random search ability of genetic algorithm, this paper proposes a displacement inverse analysis method based on optimized Genetic Algorithm- Back Propagation (GA-BP) for deep foundation pit support. The method changes the method that BP algorithm relies on the guidance of gradient information to adjust the network weights, but uses the characteristics of global search of genetic algorithm to find the most suitable network connection rights and network structure, etc. to achieve the purpose of optimization. Firstly, the deformation mechanism of deep foundation pit is analyzed, its failure mode is summarized, and the calculation method of lateral rock and soil pressure is sorted out according to the code. The theory and characteristics of BP neural network and genetic algorithm are discussed, and the method of using genetic algorithm to optimize BP neural network is proposed to improve the prediction accuracy. In view of the shortcomings of GA-BP neural network prediction model in training sample pretreatment and hidden layer structure design, the optimal normalization interval was determined by correlation coefficient regression analysis, and the analytical expression of the number of neurons in hidden layer was derived by statistical principle, and the value range of the optimal number of neurons in single hidden layer was proposed. Combined with the actual engineering, the mechanical parameters inversion and displacement force inverse analysis are performed using this method, and the results show that the optimized GA-BP has higher prediction accuracy compared with BP neural network and GA-BP, and the deviation of the displacement prediction value at each depth is kept within 0.2 mm, the absolute error interval width is 0.07 mm, and the maximum relative error is 1.35% at 4.0 m depth.

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.

Impact of land use on water quality in buffer zones at different scales in the Poyang Lake, middle reaches of the Yangtze River basin

The water quality of Poyang Lake (PYL) is significantly influenced by land use which is a crucial factor that can exhibit complex changes in the environment and can serve as an indicator of the intensity of human activity. Therefore, this study analyzed the spatial and temporal distribution characteristics of nutrients and investigated the effects of land-use factors on water quality in the PYL during 2016–2019. The main conclusions are as follows: (1) Although there was some variation in the accuracy of the water quality inversion models (random forest (RF), support vector machine (SVM), and multiple statistical regression models), they were homogeneous. In particular, ammonia nitrogen (NH3-N) concentration from band (B) 2 and B2–B10 regression model was more consistent with each other. In contrast, the overall concentration levels from the combined B9/(B2–B4) triple-band regression model were relatively low, with approximately 0.03 mg/L in most areas of PYL. (2) The optimal inversion method varied for different water quality parameters. For instance, RF obtained better inversion of total phosphorus (TP) and total nitrogen (TN), with the fitting coefficient (r2) of 0.78 and 0.81, respectively; SVM had higher accuracy in the inversion of permanganate index (CODMn), with r2 of approximately 0.61; the accuracy of multi-band combined regression model in the inversion of each water quality parameter was at a higher level. (3) The influence of land use on water quality at different scales of buffer zone was different. In general, the correlation between water quality parameters and land use was higher at large spatial scales (1000–5000 m) than at small spatial scales (100 m, 500 m). A common feature of all hydrological stations was the significant negative correlation between crops, buildings, and water quality at all buffer scales. This study is of great practical significance for promoting water environment management and water quality health in the PYL.

Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

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.

Research on infrared hyperspectral remote sensing cloud detection method based on deep learning

ABSTRACT Infrared hyperspectral is susceptible to clouds, and accurately identifying whether the hyperspectral infrared sounder data is polluted by clouds is of great significance for numerical weather prediction and atmospheric parameter inversion. Since the complex spectral characteristics of clouds, the existing spectral threshold methods and machine learning methods have the difficulties of undetermined threshold and clear field of view (FOV) missed and false detections. In order to improve the cloud recognition accuracy of infrared hyperspectral data, three end-to-end cloud detection models combining deep neural network (DNN) and convolutional neural network (CNN) and long short-term memory network (LSTM) are proposed. In this paper, taking the High Spectral Infrared Atmospheric Sounder (HIRAS) equipped with Fengyun-3D (FY-3D) satellite as the research object, based on the same platform Moderate Resolution Spectral Imager-II (MERSI-II) cloud mask (CLM) product, the HIRAS Cloud dataset is established, and the accuracy test and qualitative analysis are carried out by using the test datasets and Typhoon Siamba, July 3, 2022, as well as the earth observation scene under the conditions of ice and snow surface. The test datasets analysis results show that the cloud detection accuracy of CNN and CNN-LSTM model is stable at 0.96, and the false alarm rate of cloud is 0.035 and 0.036, respectively, and the detection ability of DNN model is slightly inferior to the former two in the same hidden layer, with an accuracyof 0.94. In further qualitative research, we found that the CNN-LSTM model has high accuracy and robustness in infrared hyperspectral cloud detection, and the detection results in a variety of surface scenarios are consistent with the actual situation of whether clouds occur in the FOV of the instrument. Compared with CLM products, it can better identify clear ocean scenes, and provide fast and efficient cloud detection reference for data assimilation systems.