The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the o...The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.展开更多
Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppr...Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.展开更多
With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs ...With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs can be finely characterized,and the seismic inversion results can clearly characterize the location of stratigraphic boundaries and meet the needs of accurate geological description.Specifically,for pre-stack AVO inversion,it is required to be able to distinguish smaller geological targets in the depth or time domain,and clearly depict the vertical boundaries of the geological objects.In response to the above requirements,we introduce the preprocessing regularization of the adaptive edge-preserving smooth filter into the pre-stack AVO elastic parameter inversion to clearly invert the position of layer boundary and improve the accuracy of the inversion results.展开更多
The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth...The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain.It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain.We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet.The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations.Incorporating the weighted superposition principle,a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain.An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm,which avoids additional data conversions between the time and depth domains.The impedance inversions of the synthetic and field seismic data in the depth domain show good results,which demonstrates that seismic inversion in the depth domain is feasible.The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data.It is advantageous for further studies concerning the stability,accuracy,and efficiency of seismic inversions in the depth domain.展开更多
Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid cha...Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid characterization. In this paper, starting with the exact Zoeppritz equation that relates P-and S-wave moduli, a coefficient that describes the reflections of P-and converted waves is established. This method effectively avoids error introduced by approximations or indirect calculations, thus improving the accuracy of the inversion results. Considering that the inversion problem is ill-posed and that the forward operator is nonlinear, prior constraints on the model parameters and modified low-frequency constraints are also introduced to the objective function to make the problem more tractable. This modified objective function is solved over many iterations to continuously optimize the background values of the velocity ratio, which increases the stability of the inversion process. Tests of various models show that the method effectively improves the accuracy and stability of extracting P and S-wave moduli from underdetermined data. This method can be applied to provide inferences for reservoir exploration and fluid extraction.展开更多
For a typical marine shale reservoir in the Jiaoshiba area, Sichuan Basin of China, P-impedance is sensitive for identifying lithology but not suitable for indicating good shale reservoirs. In comparison, density is a...For a typical marine shale reservoir in the Jiaoshiba area, Sichuan Basin of China, P-impedance is sensitive for identifying lithology but not suitable for indicating good shale reservoirs. In comparison, density is an important quantity, which is sensitive for identifying the organic-rich mud shale from non-organic-rich mud shale. Due to the poor data quality and incidence angle range, density cannot be easily inverted by directly solving the ill-posed pre-stack seismic inversion in this area. Meanwhile, the traditional density regularizations implemented by directly using the more robust P-impedance inversion tend to be inaccurate for recovering density for this shale reservoir. In this paper, we combine the P-impedance and the minus uranium to construct the pseudo-P-impedance（PIp） at well locations. The PIp is observed to be sensitive for identifying organic-rich mud shale and has a good correlation with density in this area. We employ the PIp–density relation into the pre-stack inversion framework to estimate density. Three types of regularization are tested on both numerical and field data： These are no regularization, traditional regularization and the proposed approach. It is observed that the proposed method is better for recovering the density of organic-rich mud shale in the Jiaoshiba area.展开更多
Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based c...Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based controller.This paper,the second part of a two-part series of surveys on INDI,aims to summarize the modern trends in INDI and its related components.Besides a comprehensive components specification,it addresses their most common challenges,compares different variants,and discusses proposed advances.Further important aspects of INDI are gain design,stability,and robustness.This paper also provides an overview of research conducted concerning these aspects.This paper is written in a tutorial style to familiarize researchers with the essential specifics and pitfalls of INDI and its components.At the same time,it can also serve as a reference for readers already familiar with INDI.展开更多
The Pamir Plateau,at the northwestern margin of the Tibetan Plateau,is a key region for investigating continental collision and plateau uplifting.To probe its deep structure,we collected seismic data from 263 stations...The Pamir Plateau,at the northwestern margin of the Tibetan Plateau,is a key region for investigating continental collision and plateau uplifting.To probe its deep structure,we collected seismic data from 263 stations across 11 research projects.We applied cross-correlation to noise data and extracted surface wave dispersion data from cross-correlation functions.The extracted dispersion data were subsequently inverted using a 3-D transdimensional Bayesian inversion method(rj-3 DMcMC).The inversion result reveals several crustal low-velocity zones(LVZs).Their formation is likely related to crustal thickening,the exposure of gneiss domes,and thicker sedimentary sequences compared to surrounding areas.In the lower crust and upper mantle,the LVZs in southern Pamir and southeastern Karakoram evolve into high-velocity zones,which expand northeastward with increasing depth.This suggests northward underthrusting of the Indian Plate.We also analyzed the Moho using both the standard deviation of S-wave velocity and the S-wave velocity structure.Results show that significant variations in velocity standard deviation reliably delineate the Moho interface.展开更多
Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnos...Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.展开更多
Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex str...Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.展开更多
Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this cha...Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.展开更多
Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elas...Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.展开更多
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit...Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.展开更多
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al...In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.展开更多
Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leve...Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leveraging complex ML models,such as those developed using field inversion and machine learning(FIML),to dynamically adjust the constants within the baseline RANS model.However,the ML models often overlook the fundamental calibrations of the RANS turbulence model.Consequently,the basic calibration of the baseline RANS model is disrupted,leading to a degradation in the accuracy,particularly in basic wall-attached flows outside of the training set.To address this issue,a modified version of the Spalart-Allmaras(SA)turbulence model,known as Rubber-band SA(RBSA),has been proposed recently.This modification involves identifying and embedding constraints related to basic wall-attached flows directly into the model.It is shown that no matter how the parameters of the RBSA model are adjusted as constants throughout the flow field,its accuracy in wall-attached flows remains unaffected.In this paper,we propose a new constraint for the RBSA model,which better safeguards the law of wall in extreme conditions where the model parameter is adjusted dramatically.The resultant model is called the RBSA-poly model.We then show that when combined with FIML augmentation,the RBSA-poly model effectively preserves the accuracy of simple wall-attached flows,even when the adjusted parameters become functions of local flow variables rather than constants.A comparative analysis with the FIML-augmented original SA model reveals that the augmented RBSA-poly model reduces error in basic wall-attached flows by 50%while maintaining comparable accuracy in trained separated flows.These findings confirm the effectiveness of utilizing FIML in conjunction with the RBSA model,offering superior accuracy retention in cardinal flows.展开更多
Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition....Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.展开更多
Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeli...Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.展开更多
In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events cau...In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events caused by fault slip and their potential effects on rockbursts.Through Bayesian inversion,it is determined that the sources near fault FQ14 have a significant shear component.Additionally,we analyzed the stress and displacement fields of high-energy events,along with the hypocenter distribution of aftershocks,which aided in identifying the slip direction of the critically stressed fault FQ14.We also performed forward modeling to capture the complex dynamics of fault slip under varying friction laws and shear fracture modes.The selection of specific friction laws for fault slip models was based on their ability to accurately replicate observed slip behavior under various external loading conditions,thereby enhancing the applicability of our findings.Our results suggest that the slip behavior of fault FQ14 can be effectively understood by comparing different scenarios.展开更多
With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,inc...With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,including poor timeliness,inadequate handling of imbalanced data,and low accuracy when dealing with these events.This paper proposes a method based on CD-Pix2Pix model for inverting short-duration heavy rainfall events,aiming to improve the accuracy of inversion.The method integrates the attention mechanism network CSM-Net and the Dropblock module with a Bayesian optimized loss function to improve imbalanced data processing and enhance overall performance.This study utilizes multisource heterogeneous data,including radar composite reflectivity,FY-4B satellite data,and ground automatic station rainfall observations data,with China Meteorological Administration Land Data Assimilation System(CLDAS)data as the target labels fror the inversion task.Experimental results show that the enhanced method outperforms conventional rainfall inversion methods across multiple evaluation metrics,particularly demonstrating superior performance in Threat Score(TS,0.495),Probability of Detection(POD,0.857),and False Alarm Ratio(FAR,0.143).展开更多
The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic...The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.展开更多
基金supported by National Basic Research Program(2006CB202304)of Chinaco-supported by the National Basic Research Program of China(Grant No.2011CB201103)the National Science and Technology Major Project of China(Grant No.2011ZX05004003)
文摘The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.
基金supported by Fundamental Research Funds for the Central Universities,CHD300102264715National Key Research and Development Program of China under Grant 2021YFA0716902Natural Science Basic Research Program of Shaanxi 2024JCYBMS-199。
文摘Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.
基金support of China national key project 41904130 and key research project 041020080060.
文摘With the development of exploration of oil and gas resources,the requirements for seismic inversion results are getting more accurate.In particular,it is hoped that the distribution patterns of oil and gas reservoirs can be finely characterized,and the seismic inversion results can clearly characterize the location of stratigraphic boundaries and meet the needs of accurate geological description.Specifically,for pre-stack AVO inversion,it is required to be able to distinguish smaller geological targets in the depth or time domain,and clearly depict the vertical boundaries of the geological objects.In response to the above requirements,we introduce the preprocessing regularization of the adaptive edge-preserving smooth filter into the pre-stack AVO elastic parameter inversion to clearly invert the position of layer boundary and improve the accuracy of the inversion results.
基金supported by the National Natural Science Foundation of China(No.41574130,41874143 and 41374134)the National Science and Technology Major Project of China(No.2016ZX05014-001-009)the Sichuan Provincial Youth Science&Technology Innovative Research Group Fund(No.2016TD0023)
文摘The extensive application of pre-stack depth migration has produced huge volumes of seismic data,which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain.It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain.We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet.The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations.Incorporating the weighted superposition principle,a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain.An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm,which avoids additional data conversions between the time and depth domains.The impedance inversions of the synthetic and field seismic data in the depth domain show good results,which demonstrates that seismic inversion in the depth domain is feasible.The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data.It is advantageous for further studies concerning the stability,accuracy,and efficiency of seismic inversions in the depth domain.
基金supported by the National Science and Technology Major Project(No.2016ZX05047-002-001)
文摘Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid characterization. In this paper, starting with the exact Zoeppritz equation that relates P-and S-wave moduli, a coefficient that describes the reflections of P-and converted waves is established. This method effectively avoids error introduced by approximations or indirect calculations, thus improving the accuracy of the inversion results. Considering that the inversion problem is ill-posed and that the forward operator is nonlinear, prior constraints on the model parameters and modified low-frequency constraints are also introduced to the objective function to make the problem more tractable. This modified objective function is solved over many iterations to continuously optimize the background values of the velocity ratio, which increases the stability of the inversion process. Tests of various models show that the method effectively improves the accuracy and stability of extracting P and S-wave moduli from underdetermined data. This method can be applied to provide inferences for reservoir exploration and fluid extraction.
基金NSFC and Sinopec Joint Key Project (U1663207)the China Geology Survey Project (DD20160195)+2 种基金973 Program (2014CB239104)National Key S&T Projects (2017ZX05049002)China Postdoctoral Science Foundation for the financial support
文摘For a typical marine shale reservoir in the Jiaoshiba area, Sichuan Basin of China, P-impedance is sensitive for identifying lithology but not suitable for indicating good shale reservoirs. In comparison, density is an important quantity, which is sensitive for identifying the organic-rich mud shale from non-organic-rich mud shale. Due to the poor data quality and incidence angle range, density cannot be easily inverted by directly solving the ill-posed pre-stack seismic inversion in this area. Meanwhile, the traditional density regularizations implemented by directly using the more robust P-impedance inversion tend to be inaccurate for recovering density for this shale reservoir. In this paper, we combine the P-impedance and the minus uranium to construct the pseudo-P-impedance（PIp） at well locations. The PIp is observed to be sensitive for identifying organic-rich mud shale and has a good correlation with density in this area. We employ the PIp–density relation into the pre-stack inversion framework to estimate density. Three types of regularization are tested on both numerical and field data： These are no regularization, traditional regularization and the proposed approach. It is observed that the proposed method is better for recovering the density of organic-rich mud shale in the Jiaoshiba area.
文摘Incremental Nonlinear Dynamic Inversion(INDI)is a control approach that has gained popularity in flight control over the past decade.Besides the INDI law,several common additional components complement an INDI-based controller.This paper,the second part of a two-part series of surveys on INDI,aims to summarize the modern trends in INDI and its related components.Besides a comprehensive components specification,it addresses their most common challenges,compares different variants,and discusses proposed advances.Further important aspects of INDI are gain design,stability,and robustness.This paper also provides an overview of research conducted concerning these aspects.This paper is written in a tutorial style to familiarize researchers with the essential specifics and pitfalls of INDI and its components.At the same time,it can also serve as a reference for readers already familiar with INDI.
基金supported by the National Natural Science Foundation of China(Grant No.42174126)the Alliance of International Science Organizations(ANSO)Project(Grant No.ANSO-CR-PP2022-04)+1 种基金the Deep Earth Probe and Mineral Resources Exploration National Science and Technology Major Project(Grant Nos.2024ZD1002206,2024ZD1002201)Key R&D Program of Xinjiang Uyghur Autonomous Region(Grant No.2024B03013-2)。
文摘The Pamir Plateau,at the northwestern margin of the Tibetan Plateau,is a key region for investigating continental collision and plateau uplifting.To probe its deep structure,we collected seismic data from 263 stations across 11 research projects.We applied cross-correlation to noise data and extracted surface wave dispersion data from cross-correlation functions.The extracted dispersion data were subsequently inverted using a 3-D transdimensional Bayesian inversion method(rj-3 DMcMC).The inversion result reveals several crustal low-velocity zones(LVZs).Their formation is likely related to crustal thickening,the exposure of gneiss domes,and thicker sedimentary sequences compared to surrounding areas.In the lower crust and upper mantle,the LVZs in southern Pamir and southeastern Karakoram evolve into high-velocity zones,which expand northeastward with increasing depth.This suggests northward underthrusting of the Indian Plate.We also analyzed the Moho using both the standard deviation of S-wave velocity and the S-wave velocity structure.Results show that significant variations in velocity standard deviation reliably delineate the Moho interface.
基金supported by the National Key Research and Development Program of China(2022YFA1404400)the National Natural Science Foundation of China(62122072,12174368,61705216,62405306)+4 种基金Anhui Provincial Department of Science and Technology(202203a07020020,18030801138)Anhui Provincial Natural Science Foundation(2308085QA21,2408085QF187)the USTC Research Funds of the Double First-Class Initiative(YD2090002015)the Institute of Artificial Intelligence at Hefei Comprehensive National Science Center(23YGXT005)the Fundamental Research Funds for the Central Universities(WK2090000083).
文摘Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.
基金We would like to acknowledge the sponsorship of the National Natural Science Foundation of China(42004092,42030103,41974119)Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001-6)Young Elite Scientists Sponsorship Program by CAST(2021QNRC001).
文摘Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.
基金funded by the National Key R&D Program of China(Grant No.2022YFC2903904)the National Natural Science Foundation of China(Grant Nos.51904057 and U1906208).
文摘Due to the heterogeneity of rock masses and the variability of in situ stress,the traditional linear inversion method is insufficiently accurate to achieve high accuracy of the in situ stress field.To address this challenge,nonlinear stress boundaries for a numerical model are determined through regression analysis of a series of nonlinear coefficient matrices,which are derived from the bubbling method.Considering the randomness and flexibility of the bubbling method,a parametric study is conducted to determine recommended ranges for these parameters,including the standard deviation(σb)of bubble radii,the non-uniform coefficient matrix number(λ)for nonlinear stress boundaries,and the number(m)and positions of in situ stress measurement points.A model case study provides a reference for the selection of these parameters.Additionally,when the nonlinear in situ stress inversion method is employed,stress distortion inevitably occurs near model boundaries,aligning with the Saint Venant's principle.Two strategies are proposed accordingly:employing a systematic reduction of nonlinear coefficients to achieve high inversion accuracy while minimizing significant stress distortion,and excluding regions with severe stress distortion near the model edges while utilizing the central part of the model for subsequent simulations.These two strategies have been successfully implemented in the nonlinear in situ stress inversion of the Xincheng Gold Mine and have achieved higher inversion accuracy than the linear method.Specifically,the linear and nonlinear inversion methods yield root mean square errors(RMSE)of 4.15 and 3.2,and inversion relative errors(δAve)of 22.08%and 17.55%,respectively.Therefore,the nonlinear inversion method outperforms the traditional multiple linear regression method,even in the presence of a systematic reduction in the nonlinear stress boundaries.
基金the sponsorship of National Natural Science Foundation of China(42325403)Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project of China(2024ZD1004201)。
文摘Subsurface reservoirs commonly exhibit layered structures.Conventional amplitude variation with angle(AVA)inversion,which relies on the Zoeppritz equation and its approximations,often fails to accurately estimate elastic parameters because it assumes single-interface models and ignores multiple reflections and transmission losses.To address these limitations,this study proposes a novel prestack time-frequency domain joint inversion method that utilizes the reflection matrix method(RMM)as the forward operator.The RMM accurately simulates wave propagation in layered media,while the joint inversion framework minimizes the misfit between observed and synthetic data in both the time and frequency domains.By incorporating Bayesian theory to optimize the inversion process,the method effectively balances contributions from both time-domain waveforms and frequency-domain spectral information through a weighting factor.Tests on both synthetic data and field data demonstrate that the proposed method outperforms conventional AVA inversion and time-domain waveform inversion in accuracy and robustness.Furthermore,the method demonstrates good robustness against variations in initial models,random noise,and coherent noise interference.This study provides a practical and effective approach for high-precision reservoir characterization,with potential applications in complex layered media.
基金the sponsorship of the Key Technology for Geophysical Prediction of Ultra-Deep Carbonate Reservoirs(P24240)the National Natural Science Foundation of China(U24B2020)the National Science and Technology Major Project of China for New Oil and Gas Exploration and Development(Grant No.2024ZD1400102)。
文摘Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
基金sponsored by China Geological Survey Project(DD20243193 and DD20230206508).
文摘In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.
基金supported by the National Natural Science Foundation of China(Grant Nos.12388101,12372288,U23A2069,and 92152301).
文摘Machine learning(ML)techniques have emerged as powerful tools for improving the predictive capabilities of Reynolds-averaged Navier-Stokes(RANS)turbulence models in separated flows.This improvement is achieved by leveraging complex ML models,such as those developed using field inversion and machine learning(FIML),to dynamically adjust the constants within the baseline RANS model.However,the ML models often overlook the fundamental calibrations of the RANS turbulence model.Consequently,the basic calibration of the baseline RANS model is disrupted,leading to a degradation in the accuracy,particularly in basic wall-attached flows outside of the training set.To address this issue,a modified version of the Spalart-Allmaras(SA)turbulence model,known as Rubber-band SA(RBSA),has been proposed recently.This modification involves identifying and embedding constraints related to basic wall-attached flows directly into the model.It is shown that no matter how the parameters of the RBSA model are adjusted as constants throughout the flow field,its accuracy in wall-attached flows remains unaffected.In this paper,we propose a new constraint for the RBSA model,which better safeguards the law of wall in extreme conditions where the model parameter is adjusted dramatically.The resultant model is called the RBSA-poly model.We then show that when combined with FIML augmentation,the RBSA-poly model effectively preserves the accuracy of simple wall-attached flows,even when the adjusted parameters become functions of local flow variables rather than constants.A comparative analysis with the FIML-augmented original SA model reveals that the augmented RBSA-poly model reduces error in basic wall-attached flows by 50%while maintaining comparable accuracy in trained separated flows.These findings confirm the effectiveness of utilizing FIML in conjunction with the RBSA model,offering superior accuracy retention in cardinal flows.
基金supported by National Science and Technology Major Project(Grant No.2017ZX05018-001)。
文摘Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.
基金National Natural Science Foundation of China under Grant 42276055National Key Research and Development Program under Grant 2022YFC2803503Fundamental Research Funds for the Central Universities under Grant 202262008.
文摘Full waveform inversion methods evaluate the properties of subsurface media by minimizing the misfit between synthetic and observed data.However,these methods omit measurement errors and physical assumptions in modeling,resulting in several problems in practical applications.In particular,full waveform inversion methods are very sensitive to erroneous observations(outliers)that violate the Gauss–Markov theorem.Herein,we propose a method for addressing spurious observations or outliers.Specifically,we remove outliers by inverting the synthetic data using the local convexity of the Gaussian distribution.To achieve this,we apply a waveform-like noise model based on a specific covariance matrix definition.Finally,we build an inversion problem based on the updated data,which is consistent with the wavefield reconstruction inversion method.Overall,we report an alternative optimization inversion problem for data containing outliers.The proposed method is robust because it uses uncertainties.This method enables accurate inversion,even when based on noisy models or a wrong wavelet.
基金the Graduate Innovation Program of China University of Mining and Technology,the Fundamental Research Funds for the Central Universities(Grant No.2023WLKXJ017)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX23_2776)the Shandong Energy Group(Grant No.SNKJ2022BJ03-R28)。
文摘In this study,we employed Bayesian inversion coupled with the summation-by-parts and simultaneousapproximation-term(SBP-SAT)forward simulation method to elucidate the mechanisms behind mininginduced seismic events caused by fault slip and their potential effects on rockbursts.Through Bayesian inversion,it is determined that the sources near fault FQ14 have a significant shear component.Additionally,we analyzed the stress and displacement fields of high-energy events,along with the hypocenter distribution of aftershocks,which aided in identifying the slip direction of the critically stressed fault FQ14.We also performed forward modeling to capture the complex dynamics of fault slip under varying friction laws and shear fracture modes.The selection of specific friction laws for fault slip models was based on their ability to accurately replicate observed slip behavior under various external loading conditions,thereby enhancing the applicability of our findings.Our results suggest that the slip behavior of fault FQ14 can be effectively understood by comparing different scenarios.
基金Key Project of the NSFC Joint Fund(U20B2061)Innovation Development Special Project(CXFZ2024J001,CXFZ2023J013)+3 种基金Key Open Fund of the Laboratory of Hydrometeorology,China Meteorological Administration(23SWQXZ001)Open Research Fund of Anyang National Climate Observatory(AYNCOF202401)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX24_0478)Zhejiang Provincial Natural Science Foundation Project(LZJMD25D050002)。
文摘With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,including poor timeliness,inadequate handling of imbalanced data,and low accuracy when dealing with these events.This paper proposes a method based on CD-Pix2Pix model for inverting short-duration heavy rainfall events,aiming to improve the accuracy of inversion.The method integrates the attention mechanism network CSM-Net and the Dropblock module with a Bayesian optimized loss function to improve imbalanced data processing and enhance overall performance.This study utilizes multisource heterogeneous data,including radar composite reflectivity,FY-4B satellite data,and ground automatic station rainfall observations data,with China Meteorological Administration Land Data Assimilation System(CLDAS)data as the target labels fror the inversion task.Experimental results show that the enhanced method outperforms conventional rainfall inversion methods across multiple evaluation metrics,particularly demonstrating superior performance in Threat Score(TS,0.495),Probability of Detection(POD,0.857),and False Alarm Ratio(FAR,0.143).
基金The National Key Research and Development Program under contract No. 2024YFB3909702the National Natural Science Foundation of China under contract Nos 42474014, 41931076, and 42388102+2 种基金the Scientific and Technology Inmo-vation Program of Laoshan Laboratory under contract Nos LSKJ202205100 and LSKJ202205105the Independent Research Project of State Key Laboratory of Geo-information Engineering under contract SKLGIE2023-ZZ-8the Scientific Research Project of Chinese Academy of Surveying and Mapping under contract No. AR2501。
文摘The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.
