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.

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.

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//.

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 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.

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 high resolution inversion method for fluid factor with dynamic dryrock V_(P)/V_(S) ratio squared

As an important indicator parameter of fluid identification,fluid factor has always been a concern for scholars.However,when predicting Russell fluid factor or effective pore-fluid bulk modulus,it is necessary to introduce a new rock skeleton parameter which is the dry-rock VP/VS ratio squared(DVRS).In the process of fluid factor calculation or inversion,the existing methods take this parameter as a static constant,which has been estimated in advance,and then apply it to the fluid factor calculation and inversion.The fluid identification analysis based on a portion of the Marmousi 2 model and numerical forward modeling test show that,taking the DVRS as a static constant will limit the identification ability of fluid factor and reduce the inversion accuracy.To solve the above problems,we proposed a new method to regard the DVRS as a dynamic variable varying with depth and lithology for the first time,then apply it to fluid factor calculation and inversion.Firstly,the exact Zoeppritz equations are rewritten into a new form containing the fluid factor and DVRS of upper and lower layers.Next,the new equations are applied to the four parameters simultaneous inversion based on the generalized nonlinear inversion(GNI)method.The testing results on a portion of the Marmousi 2 model and field data show that dynamic DVRS can significantly improve the fluid factor identification ability,effectively suppress illusion.Both synthetic and filed data tests also demonstrate that the GNI method based on Bayesian deterministic inversion(BDI)theory can successfully solve the above four parameter simultaneous inversion problem,and taking the dynamic DVRS as a target inversion parameter can effectively improve the inversion accuracy of fluid factor.All these results completely verified the feasibility and effectiveness of the proposed method.

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.

Inversion and III-Posed Problem Solutions in Atmospheric Remote Sensing

With the swift advances in earth observation,satellite remote sensing and application of atmospheric radiation theory have been developed in the past decades,atmospheric sensing inversion with its algorithms is getting more and more importance.It is known that since a remote sensing equation falls into an integral equation of the first kind,thus leading to the fact that it is ill-posed and particularly the solution is unsteady,tremendous difficulties arise from the retrieval.This paper will present a simple review on the inversion techniques with some necessary remarks,before introducing the successful efforts with respect to such equations and the encouraging solutions achieved in recent decades by researchers of the world.

Influence of correlation scale errors on aquifer hydraulic conductivity inversion precision

In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivity field of a heterogeneous aquifer based on synthetic experiments.By increasing the numbers of observation wells and pumping tests,we analyzed the difference between the estimated and true values of hydraulic conductivity with different correlation scale errors.The relationships between the observation well number and the error in inversion results,and between the pumping test number and the error in inversion results were investigated.The results show that,if the amount of observed head data is insufficient,there will be errors in inversion results with changing correlation scale.Due to the existence of correlation scale error,the improvement of inversion precision gradually slows down with the increase of the amount of observed head data,which indicates that too much observed head data causes data redundancy.Therefore,for the synthetic experiments described in this paper,the observation well number should be less than 41,the pumping test number should be less than 17,and a more suitable method should be selected according to the precision requirements of specific situations in practical engineering.

Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.

A parameter inversion for sea bridge based on high-resolution polarimetric synthetic aperture radar

Each reflection return of a bridge over water is displayed as wide stripe in a high-resolution synthetic aperture radar （SAR） image, which lead to difficulties in a parameter inversion. Therefore, a method of bridge parameter inversion is proposed for high-resolution full polarimetric SAR （PolSAR）. First, the single, double and triple- bounce returns from each component of the bridge are distinguished by the polarization scattering features. Then the reasons which lead to the backscatter echoes oft_he bridge over water being displayed as stripes are analyzed, using a principle of microwave reflection, as well as an extraction method for each reflection return, and a parameter retrieval method is obtained. Finally, the parameters of the bridge, including the height （top and bottom surfaces of the sea bridge）, width, thickness, span, and height of the bridge tower, are retrieved using full polarimetric AIRSAR data. When a comparison of the measured data is completed, the results indicate that the proposed method can invert the parameters with a high accuracy, and that the inversion error of the bridge height （bottom surface） is only 1.3%. Moreover, the results also show that for the high-resolution SAR, the C and L-band images have the same ability in regards to parameter retrieval.

Inversion of Seawater Physical Properties Based on Allied Elastic Impedance

Inversion of seawater physical parameters (temperature, salinity and density) from seismic data is an important part of Seismic Oceanography, which was raised recent years to study physical oceanography. However present methods have problems that inversion accuracy is not high or inverted parameters are incomprehensive. To overcome these problems, this paper derives Allied Elastic Impedance (AEI), from which we can extract acoustic velocity and density of seawater directly. Furthermore this paper proposes a method to fit temperature and salinity with acoustic velocity and density respectively, breaking through the limitation that temperature and salinity can only be extracted from acoustic velocity. After applying it to model and real data, we find that this method not only solves the problem that ocean density is hard to extract, but also increases accuracy of other parameters, with the temperature and salinity resolution of 0.06°C and 0.02 psu respectively. All results show that AEI is promising in inversion of seawater physical parameters.

Inversion of source process and related studies of the Taiwan Strait earthquake using genetic algorithm

pplying genetic algorithm to inversion of seismic moment tensor solution and using the data of P waveform from digital network and initial motion directions of P waves of Taiwan network stations, we studied the moment tensor solutions and focal parameters of the earthquake of M=7.3 on 16 September of 1994 in Taiwan Strait and other four quakes of ML5.8 in the near region (21°～26°N, 115°～120°E). Among the five earthquakes, the quake of M=7.3 on September 16, 1994 in Taiwan Strait is the strongest one in the southeastern coast area since Nan′ao earthquake of M=7.3 in 1918. The results show that moment tensor solution of M=7.3 earthquake is mainly doublecouple component, and is normal fault whose fault plane is near NW. The strike of the fault plane resembles that of the distributive bands of earthquakes before the main event and fracture pattern shown by aftershocks. The tension stress axis of focal mechanism is about horizontal, near in NE strike, the compressive stress axis is approximately vertical, near in NWW strike. It seems that this quake is controlled by the force of Philippine plate′s pressing Eurasian plate in NW direction. But from the viewpoint of P axis of near vertical and T axis of near horizontal, it is a normal fault of strong tensibility. There are relatively big difference between focal mechanism solution of this quake and those of the four other strong quakes. The complexity of source mechanism solution of these quakes represents the complexity of the process of the strait earthquake sequences.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Real-Time Automatic Scaling Method of Oblique Ionogram Parameters Based on Morphological Operator and Inversion Technique

Automatic scaling ionogram can get the parameters of ionogram which are vital to ionosphere detecting. In this paper, a new method is proposed to scale F2 layer trace automatically from oblique ionogram based on morphological operator and inversion technique. This method is verified through the comparison of actual detecting data with statistical analysis. The results show that the proposed automatic scaling method has high acceptable rate and is suitable for scaling oblique ionogram with different high angle wave states. It is fast and precise to fit O-mode echoes in F2 layer without the influence from F1 layer. This method could be applied in real-time ionospheric oblique sounding research with high reliability and versatility.

Theoretical dispersion curves for borehole real-valued wave modes in vertically transverse isotropic formations

The dispersion curves of real-valued modes in a fluid-filled borehole are widely used in acoustic well logging.The accurate dispersion curves are the precondition of theoretical analysis and inversion process.Generally,these curves can be obtained by solving the conventional dispersion equation for isotropic formations and most vertically transverse isotropy(VTI)formations.However,if the real-valued solutions exist when the radial wavenumbers for the formation quasi-P and quasi-S equals to each other,the existed methods based on the conventional dispersion equation could lead to incorrect results for some VTI formations.Few studies have focused on the influence of these real-valued solutions on dispersion curve extraction.To remove these real-valued solutions,we have proposed a modified dispersion equation and its corresponding solving process.When solving the dispersion equation,the Scholte wave velocity of VTI formation at high frequency is used as the initial guess.The two synthetic examples including fast and slow VTI formations validate that these real-valued solutions do not contribute to the wavefield,and the new dispersion curve extraction method is suitable for all kinds of VTI formations.Consequently,the method can provide reliable dispersion curves for both theoretical analysis and anisotropic parameters inversion in VTI formations.

Deformation early-warning index for heightened gravity dam during impoundment period

The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,particularly of heightened projects in the impoundment period.Herein,a new method for monitoring the safety status of heightened dams is proposed based on the deformation monitoring data of a dam structure,a statistical model,and finite-element numerical simulation.First,a fast optimization inversion method for estimation of dam mechanical parameters was developed,which used the water pressure component extracted from a statistical model,an improved inversion objective function,and a genetic optimization iterative algorithm.Then,a finite element model of a heightened concrete gravity dam was established,and the deformation behavior of the dam with rising water levels in the impoundment period was simulated.Subsequently,mechanical parameters of aged dam parts were calculated using the fast optimization inversion method with simulated deformation and the water pressure deformation component obtained by the statistical model under the same conditions of water pressure change.Finally,a new earlywarning index of dam deformation was constructed by means of the forward-simulated deformation and other components of the statistical model.The early-warning index is useful for forecasting dam deformation under different water levels,especially high water levels.

A smart productivity evaluation method for shale gas wells based on 3D fractal fracture network model

The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility.

A study on improving anti-interference capability of inversion method reconstructing layered sea bottom parameters

The remote measurement of the vertical distribution of acoustical parameters of the sea bottom is an inverse method in acoustics. The existing methods can be divided into two types: the wave method and the ray method. The former usually does not apply to cases with discontinuity in acoustical parameter profiles[1] , whereas the latter usually ignores the contributions made by multiple reflections, resulting in the appearance of pseudo interfaces. However, under certain practical circumstances, the ray method can produce satisfactory results[2,3]. In this paper, at first we use the conception of equitime layer thickness to obtain the pulse response iteration method. The anti-interference capability of this method is poor. In order to improve this capability, we develope the sample iteration method and the sample-averaging iteration method. The sample-averaging iteration method introduces a way of statistical averaging, which can further improve the anti-interference capability.