An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)algorithm.The research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,respectively.These results demonstrate the model’s effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model’s superior inversion accuracy over other methods.

GBO algorithm for seismic source parameters inversion

The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer(GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization(MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion.

The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters

With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well＇s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can significantly influence the rocks＇ elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.

Lamé parameters inversion based on elastic impedance and its application

The Connolly （1999） elastic impedance （EI） equation is a function of P-wave velocity, S-wave velocity, density, and incidence angle. Conventional inversion methods based on this equation can only extract P-velocity, S-velocity, and density data directly and the elastic impedance at different incidence angles are not at the same scale, which makes comparison difficult. We propose a new elastic impedance equation based on the Gray et al. （1999） Zoeppritz approximation using Lamé parameters to address the conventional inversion method＇s deficiencies. This equation has been normalized to unify the elastic impedance dimensions at different angles and used for inversion. Lamé parameters can be extracted directly from the elastic impedance data obtained from inversion using the linear relation between Lamé parameters and elastic impedance. The application example shows that the elastic parameters extracted using this new method are more stable and correct and can recover the reservoir information very well. The new method is an improvement on the conventional method based on Connolly＇s equation.

Geoacoustic Inversion for Bottom Parameters in the Deep-Water Area of the South China Sea

Bottom acoustic parameters play an important role in sound field prediction. Acoustic parameters in deep water are not well understood. Bottom acoustic parameters are sensitive to the transmission-loss （TL） data in the shadow zone of deep water. We propose a multiple-step fill inversion method to invert sound speed, density and attenuation in deep water. Based on a uniform liquid hMf-space bottom model, sound speed of the bottom is inverted by using the long range TL at low frequency obtained in an acoustic propagation experiment conducted in the South China Sea （SCS） in summer 2014. Meanwhile, bottom density is estimated combining with the Hamilton sediment empirical relationship. Attenuation coefficients at different frequencies are then estimated from the TL data in the shadow zones by using the known sound speed and density as a constraint condition. The nonlinear relationship between attenuation coefficient and frequency is given in the end. Tile inverted bottom parameters can be used to forecast the transmission loss in the deep water area of SCS very we//.

Prediction method of physical parameters based on linearized rock physics inversion

A linearized rock physics inversion method is proposed to deal with two important issues, rock physical model and inversion algorithm, which restrict the accuracy of rock physics inversion. In this method, first, the complex rock physics model is expanded into Taylor series to get the first-order approximate expression of the inverse problem of rock physics;then the damped least square method is used to solve the linearized rock physics inverse problem directly to get the analytical solution of the rock physics inverse problem. This method does not need global optimization or random sampling, but directly calculates the inverse operation, with high computational efficiency. The theoretical model analysis shows that the linearized rock physical model can be used to approximate the complex rock physics model. The application of actual logging data and seismic data shows that the linearized rock physics inversion method can obtain accurate physical parameters. This method is suitable for linear or slightly non-linear rock physics model, but may not be suitable for highly non-linear rock physics model.

SIMULATION-MEASUREMENT INVERSION OF ENVIRONMENT PARAMETERS DURING WELDING

The manual selection of environment parameters during welding simulation will bring a significant error to the simulation result of welding temperature field. By a combination of finite element method （FEM） and infrared thermography, these environment parameters were inversed mathematically in place of previous manual selection. First, FEM model of the welding process was constructed, and the temperature field was computed with initial environment parameters. Then, a real welding process was conducted and the temperature field was measured by infrared thermography. Last, the simulation and measurement results were compared, and the environment parameters were adjusted continuously with the genetic algorithm （ GA ） until the simulation matched the measurement best. Parameters according to the best-matched simulation results were considered as the most appropriate parameters.

Prestack seismic stochastic inversion based on statistical characteristic parameters

In the conventional stochastic inversion method,the spatial structure information of underground strata is usually characterized by variograms.However,effectively characterizing the heterogeneity of complex strata is difficult.In this paper,multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log data.The spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic parameters.We propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic parameters.According to the random medium theory,this method obtains several statistical characteristic parameters from known seismic and logging data,constructs a prior information model that meets the spatial structure characteristics of the underground strata,and integrates multiparameter constraints into the likelihood function to construct the objective function.The very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the fi nal inversion result.The model test shows that compared with the traditional prior information model construction method,the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information,which can better describe complex underground reservoirs.A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.

Inversion of Oceanic Parameters Represented by CTD Utilizing Seismic Multi-Attributes Based on Convolutional Neural Network

In Recent years,seismic data have been widely used in seismic oceanography for the inversion of oceanic parameters represented by conductivity temperature depth(CTD).Using this technique,researchers can identify the water structure with high horizontal resolution,which compensates for the deficiencies of CTD data.However,conventional inversion methods are modeldriven,such as constrained sparse spike inversion(CSSI)and full waveform inversion(FWI),and typically require prior deterministic mapping operators.In this paper,we propose a novel inversion method based on a convolutional neural network(CNN),which is purely data-driven.To solve the problem of multiple solutions,we use stepwise regression to select the optimal attributes and their combination and take two-dimensional images of the selected attributes as input data.To prevent vanishing gradients,we use the rectified linear unit(ReLU)function as the activation function of the hidden layer.Moreover,the Adam and mini-batch algorithms are combined to improve stability and efficiency.The inversion results of field data indicate that the proposed method is a robust tool for accurately predicting oceanic parameters.

A New Inversion Method of Sedimentary Strata For Deriving Double Parameters and Its Applications

The inverse problem of wave equation is the importance of study not only in seismic prospecting but also in applied mathematics. With the development of the research, the inverse methods of 1 - D wave equations have been trending towards the multiple parameters inversion . We have obtained an inverse method with double -parameter, in which medium density and wave velocity can be derived simultaneously. In this paper, to increase the inverse accuracy, the method is improved as follows. Firstly, the formula in which the Green Function is omitted are derived and used. Secondly, the regularizing method is reasonable used by choosing the stable function. With the new method, we may derive elastic parameter and medium density or medium density and wave velocity. Thus, lithology parameters for seismic prospecting may be obtained.After comparing the derived values from the new method with that from previous method, we obtain the new method through which substantially improve the derived accuracy . The new method has been applied to real depths inversion for sedimentary strata and volcanic rock strata in Chaoyanggou Terrace of Songliao Basin in eastern China. According to the inverse results,the gas - bearing beds are determlned.

PARAMETERS INVERSION OF FLUID-SATURATED POROUS MEDIA BASED ON NEURAL NETWORKS

The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.

An Optimization Method for Elasto－plastic Inversion of Parameters in Rock Mechanics

ＡｎＯｐｔｉｍｉｚａｔｉｏｎＭｅｔｈｏｄｆｏｒＥｌａｓｔｏ－ｐｌａｓｔｉｃｉｎｖｅｒｓｉｏｎｏｆＰａｒａｍｅｔｅｒｓｉｎＲｏｃｋＭｅｃｈａｎｉｃｓＢｙＳｕｎＪｕｎａｎｄＨｕａｎｇＷｅｉ（ＴｏｎｇｊｉＵｎｉｖｅｒｓｉｔｙ，Ｓｈａｎｇｈａｉ２０００９２）...

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

The dispersion characteristics of shallow water can be described by the dispersion curves,which contain substantial ocean parameter information.A fast ocean parameter inversion method based on dispersion curves with a single hydrophone is presented in this paper.The method is achieved through Bayesian theory.Several sets of dispersion curves extracted from measured data are used as the input function.The inversion is performed by matching a replica calculated with a dispersion formula.The bottom characteristics can be described by the bottom reflection phase shift parameter P.The propagation range and the depth can be inverted quickly when the seabed parameters are represented by on parameter P.The inversion results improve the inversion efficiency of the seabed parameters.Consequently,the inversion efficiency and accuracy are improved while the number of inversion parameters is decreased and the computational speed of replica is increased.The inversion results have lower error than the reference values,and the dispersion curves calculated with inversion parameters are also in good agreement with extracted curves from measured data;thus,the effectiveness of the inversion method is demonstrated.

THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.

Geoacoustic Inversion for Bottom Parameters via Bayesian Theory in Deep Ocean

We develop a new approach to estimating bottom parameters based on the Bayesian theory in deep ocean. The solution in a Bayesian inversion is characterized by its posterior probability density （PPD）, which combines prior information about the model with information from an observed data set. Bottom parameters are sensitive to the transmission loss （TL） data in shadow zones of deep ocean. In this study, TLs of different frequencies from the South China Sea in the summer of 2014 are used as the observed data sets. The interpretation of the multidimensional PPD requires the calculation of its moments, such as the mean, covariance, and marginal distributions, which provide parameter estimates and uncertainties. Considering that the sensitivities of shallow- zone TLs vary for different frequencies of the bottom parameters in the deep ocean, this research obtains bottom parameters at varying frequencies. Then, the inversion results are compared with the sampling data and the correlations between bottom parameters are determined. Furthermore, we show the inversion results for multi- frequency combined inversion. The inversion results are verified by the experimental TLs and the numerical results, which are calculated using the inverted bottom parameters for different source depths and receiver depths at the corresponding frequency.

A new approach for separating mixed model parameters:application to simultaneous inversion of earthquake source parameters

A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system,the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the tradeoffs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history,source geometry,and hypocentral location from regional seismograms,although it is applicable to any geophysical inversion.

Reservoir parameter inversion based on weighted statistics

Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.

Study of prestack elastic parameter consistency inversion methods

The three parameters of P-wave velocity, S-wave velocity, and density have remarkable differences in conventional prestack inversion accuracy, so study of the consistency inversion of the ＂three parameters＂ is very important. In this paper, we present a new inversion algorithm and approach based on the in-depth analysis of the causes in their accuracy differences. With this new method, the inversion accuracy of the three parameters is improved synchronously by reasonable approximations and mutual constraint among the parameters. Theoretical model calculations and actual data applications with this method indicate that the three elastic parameters all have high inversion accuracy and maintain consistency, which also coincides with the theoretical model and actual data. This method has good application prospects.

Parameter optimization of gravity density inversion based on correlation searching and the golden section algorithm

For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.

Cauchy prior distribution-based AVO elastic parameter estimation via weakly nonlinear waveform inversion

Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.