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
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
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
An innovative gradient inversion approach employing the natural element method within the framework of least square regularization was proposed to enhance the quantitative interpretation of self-potential(SP)data orig...An innovative gradient inversion approach employing the natural element method within the framework of least square regularization was proposed to enhance the quantitative interpretation of self-potential(SP)data originating from mineral polarization.The results indicated that the natural element method effectively addressed the challenge of subdividing complex resistivity models and aided in the accurate forward calculation of SP.By applying this approach to synthetic SP data and lab-measured SP data associated with redox electrochemical half-cell reactions of iron−copper metal blocks within the geobattery model,the 3D fine structure of buried orebody models was successfully reconstructed and the spatial distribution of SP current sources was mapped.This study significantly contributes to understanding the quantitative relationship between the polarization process of metal deposits and their corresponding SP responses and provides a valuable reference for delineating metal deposits in both terrestrial and marine environments through SP surveys.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wa...In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wave velocity model of the shallow sea bottom by leveraging the seismic wave information at the fluid-solid interface in the ocean,and improve the lateral resolution of marine converted wave data.Given that the dispersion characteristics of surface waves are sensitive to the S-wave velocity of subsurface media,and that Scholte surface waves,which propagate at the interface between liquid and solid media,exist in the data of marine oil and gas exploration,this paper proposes a Scholte wave inversion and modeling method based on oil and gas exploration using ocean bottom nodes.By using the method for calculating the Scholte wave dispersion spectrum based on the Bessel kernel function,the accuracy of dispersion spectrum analysis is improved,and more accurate dispersion curves are picked up.Through the adaptive weighted least squares Scholte wave dispersion inversion algorithm,the Scholte wave dispersion equation for liquid-solid media is solved,and the shear wave velocity model of the shallow sea bottom is calculated.Theoretical tests and applications of realdata have proven that this method can significantly improve the lateral resolution of converted wave data,provide high-quality data for subsequent inversion of marine multi-component oil and gas exploration data and reservoir reflection information,and contribute to the development of marine oil and gas exploration technology.展开更多
Predictions of fluid distribution,stress field,and natural fracture are essential for exploiting unconventional shale gas reservoirs.Given the high likelihood of tilted fractures in subsurface formations,this study fo...Predictions of fluid distribution,stress field,and natural fracture are essential for exploiting unconventional shale gas reservoirs.Given the high likelihood of tilted fractures in subsurface formations,this study focuses on simultaneous seismic inversion to estimate fluid bulk modulus,effective stress parameter,and fracture density in the tilted transversely isotropic(TTI)medium.In this article,a novel PP-wave reflection coefficient approximation equation is first derived based on the constructed TTI stiffness matrix incorporating fracture density,effective stress parameter,and fluid bulk modulus.The high accuracy of the proposed equation has been demonstrated using an anisotropic two-layer model.Furthermore,a stepwise seismic inversion strategy with the L_(P) quasi-norm sparsity constraint is implemented to obtain the anisotropic and isotropic parameters.Three synthetic model tests with varying signal-to-noise ratios(SNRs)confirm the method's feasibility and noise robustness.Ultimately,the proposed method is applied to a 3D fractured shale gas reservoir in the Sichuan Basin,China.The results have effectively characterized shale gas distribution,stress fields,and tilted natural fractures,with validation from geological structures,well logs,and microseismic events.These findings can provide valuable guidance for hydraulic fracturing development,enabling more reliable predictions of reservoir heterogeneity and completion quality.展开更多
The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion i...The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.展开更多
Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model ...Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.展开更多
As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion ...As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.展开更多
Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this...Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.展开更多
In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,...In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.展开更多
Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods rema...Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.展开更多
The complex plate collision process led the South Yellow Sea Basin(SYSB)to go through an intensity tectonic inversion during the Early Cenozoic,leading to a regional unconformity surface development.As a petroliferous...The complex plate collision process led the South Yellow Sea Basin(SYSB)to go through an intensity tectonic inversion during the Early Cenozoic,leading to a regional unconformity surface development.As a petroliferous basin,SYSB saw intense denudation and deposition processes,making it hard to characterize their source-to-sink system(S2S),and this study provided a new way to reveal them quantitatively.According to the seismic interpretation,it was found that two types of tectonic inversion led to the strata shortening process,which was classified according to their difference in planar movements:dip-slip faults and strike-slip ones.As for dip-slip faults,the inversion structure was primarily formed by the dip-slip movement,and many fault-related folds developed,which developed in the North Depression Zone of the SYSB.The strike-slip ones,accompanied by some negative flower structures,dominate the South Depression Zone of the SYSB.To reveal its source-to-sink(S2S)system in the tectonic inversion basin,we rebuild the provenance area with detrital zircon U-Pb data and heavy mineral assemblage.The results show,during the Eocene(tectonic inversion stage),the proximal slump or fan delta from the Central Uplift Zone was prominently developed in the North Depression Zone,and the South Depression Zone is filled by sediments from the proximal area(Central Uplift Zone in SYSB and Wunansha Uplift)and the prograding delta long-axis parallel to the boundary faults.Then,calculations were conducted on the coarse sediment content,fault displacements,catchment relief,sediment migration distance,and discussions about the impact factors of the S2S system developed in various strata shortening patterns with a statistical method.It was found that,within the dip-slip faults-dominated zone,the volume of the sediment routing system and the ratio of coarse-grained sediments merely have a relationship with the amount of sediment supply and average faults break displacement.Compared with the strike-slip faults-dominated zone,the source-to-sink system shows a lower level of sandy sediment influx,and its coarse-grained content is mainly determined by the average faults broken displacement.展开更多
Poly(m-phenylene isophthalamide)(PMIA),a key aromatic polyamide,is widely used for its outstanding mechanical strength,high thermal stability,and excellent insulation properties.However,different applications demand v...Poly(m-phenylene isophthalamide)(PMIA),a key aromatic polyamide,is widely used for its outstanding mechanical strength,high thermal stability,and excellent insulation properties.However,different applications demand varying dielectric properties,so tailoring its dielectric per-formance is essential.PMIA was first synthesized in this study,followed by introducing pores and developing porous PMIA films and PMIA-based composites with reduced dielectric constants.Porous PMIA films were fabricated using the wet phase inversion process with N,N-dimethylac-etamide(DMAC)solvent and water as the non-solvent.The impact of casting solution composi-tion and coagulation bath temperature on pore structures was analyzed.A film produced with 18%PMIA and 5%LiCl in a 35℃coagulation bath achieved the lowest dielectric constant of 1.76 at 1 Hz,48%lower than the standard PMIA film,which had a tensile strength of 18.5 MPa and an initial degradation temperature of 320℃.展开更多
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supported by the National Natural Science Foundation of China(No.42174170)。
文摘An innovative gradient inversion approach employing the natural element method within the framework of least square regularization was proposed to enhance the quantitative interpretation of self-potential(SP)data originating from mineral polarization.The results indicated that the natural element method effectively addressed the challenge of subdividing complex resistivity models and aided in the accurate forward calculation of SP.By applying this approach to synthetic SP data and lab-measured SP data associated with redox electrochemical half-cell reactions of iron−copper metal blocks within the geobattery model,the 3D fine structure of buried orebody models was successfully reconstructed and the spatial distribution of SP current sources was mapped.This study significantly contributes to understanding the quantitative relationship between the polarization process of metal deposits and their corresponding SP responses and provides a valuable reference for delineating metal deposits in both terrestrial and marine environments through SP surveys.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金financially supported by the Scientific Research and Technology Development Project of China National Petroleum Corporation(No.2021ZG02)titled"Development of Seismic Data Processing Software for Ocean Nodes(OBN)"。
文摘In multi-component oil and gas exploration using ocean bottom nodes,converted wave data is rich in lithological and fracture information.One of the urgent problems to be solved is how to construct an accurate shear wave velocity model of the shallow sea bottom by leveraging the seismic wave information at the fluid-solid interface in the ocean,and improve the lateral resolution of marine converted wave data.Given that the dispersion characteristics of surface waves are sensitive to the S-wave velocity of subsurface media,and that Scholte surface waves,which propagate at the interface between liquid and solid media,exist in the data of marine oil and gas exploration,this paper proposes a Scholte wave inversion and modeling method based on oil and gas exploration using ocean bottom nodes.By using the method for calculating the Scholte wave dispersion spectrum based on the Bessel kernel function,the accuracy of dispersion spectrum analysis is improved,and more accurate dispersion curves are picked up.Through the adaptive weighted least squares Scholte wave dispersion inversion algorithm,the Scholte wave dispersion equation for liquid-solid media is solved,and the shear wave velocity model of the shallow sea bottom is calculated.Theoretical tests and applications of realdata have proven that this method can significantly improve the lateral resolution of converted wave data,provide high-quality data for subsequent inversion of marine multi-component oil and gas exploration data and reservoir reflection information,and contribute to the development of marine oil and gas exploration technology.
基金financially supported by the Natural Science Foundation of Sichuan Province(Grant Nos.2023NSFSC0767 and2024NSFSC0809)the China Postdoctoral Science Foundation(Grant No.2024MF750281)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20230326)。
文摘Predictions of fluid distribution,stress field,and natural fracture are essential for exploiting unconventional shale gas reservoirs.Given the high likelihood of tilted fractures in subsurface formations,this study focuses on simultaneous seismic inversion to estimate fluid bulk modulus,effective stress parameter,and fracture density in the tilted transversely isotropic(TTI)medium.In this article,a novel PP-wave reflection coefficient approximation equation is first derived based on the constructed TTI stiffness matrix incorporating fracture density,effective stress parameter,and fluid bulk modulus.The high accuracy of the proposed equation has been demonstrated using an anisotropic two-layer model.Furthermore,a stepwise seismic inversion strategy with the L_(P) quasi-norm sparsity constraint is implemented to obtain the anisotropic and isotropic parameters.Three synthetic model tests with varying signal-to-noise ratios(SNRs)confirm the method's feasibility and noise robustness.Ultimately,the proposed method is applied to a 3D fractured shale gas reservoir in the Sichuan Basin,China.The results have effectively characterized shale gas distribution,stress fields,and tilted natural fractures,with validation from geological structures,well logs,and microseismic events.These findings can provide valuable guidance for hydraulic fracturing development,enabling more reliable predictions of reservoir heterogeneity and completion quality.
基金supposed by the National Nature Science Foundation of China(Grant No.42304131)the Natural Science Foundation of Heilongjiang Province(Grant No.LH2023D012)+1 种基金the Heilongjiang Postdoctoral Fund(Grant No.LBH-Z22092)the Basic Research Fund for Universities in Xinjiang Uygur Autonomous Region(Grant No.XJEDU2023P166)。
文摘The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024MF750281)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20230326)+1 种基金the Natural Science Foundation Project of Sichuan Province(Grant No.2025ZNSFSC1170)Sichuan Science and Technology Program(Grant No.2023ZYD0158).
文摘Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.
基金supported by Heilongjiang Province Basic Research Business Expenses for Universities Heilongjiang University Special Fund Project (Grant No. 2023-KYYWF-1494)the Natural Science Foundation of Jiangxi Province (Grant No. 20212BAB213023)。
文摘As geological exploration conditions become increasingly complex, meeting the requirements of precise geological exploration necessitates the development of a controlled-source audio magnetotelluric (CSAMT) inversion method that considers anisotropy to improve the effectiveness of inversion accuracy and interpretation accuracy of data. This study is based on the 3D fi nite-diff erence forward modeling of axis anisotropy using the reciprocity theorem to calculate the Jacobian matrix by applying the search method to automatically search for the Lagrange operator. The aim is to establish inversion iteration equations to achieve the axis anisotropic Occam's 3D inversion of tensor CSAMT in data space. Further, we obtain an underground axis anisotropic 3D geoelectric model by inverting the impedance data of tensor CSAMT. Two synthetic data examples show that using the isotropic tensor CSAMT algorithm to directly invert data in anisotropic media can generate false anomalies, leading to incorrect geological interpretations. Meanwhile, the proposed anisotropic inversion algorithm can eff ectively improve the accuracy of data inversion in anisotropic media. Further, the inversion examples verify the eff ectiveness and stability of the algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.12474461)the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.JCQY202402)the Goal-Oriented Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.MBDX202113).
文摘Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
基金jointly funded by the National Natural Science Foundation of China(No.U2244220,No.42004125)the China Geological Survey Projects(No.DD20240119,No.DD20243245,No.DD20230114,No.DD20243244)the China Postdoctoral Science Foundation(No.2020M670601)。
文摘In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.
基金sponsorship of the National Natural Science Foundation of China(42430809,42274157,42030103,42404132)the Fund of State Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China)(SKLDOG2024-ZYTS-02)+5 种基金the Postdoctoral Fellowship Program of CPSF(GZB20240850)the Postdoctoral Project of Qingdao(QDBSH20240102082)the Fundamental Research Funds for the Central Universities(24CX07004A,24CX06036A)the CNPC Innovation Fund(2024DQ02-0505,2024DQ02-0136)the Innovation fund project for graduate student of China University of Petroleum(East China)the Fundamental Research Funds for the Central Universities(24CX04002A).
文摘Reservoirs with a group of vertical fractures in a vertical transversely isotropic(VTI)background are considered as orthorhombic(ORT)medium.However,fracture detection in ORT medium using seismic inversion methods remains challenging,as it requires the estimation of more than eight parameters.Assuming the reservoir to be a weakly anisotropic ORT medium with small contrasts in the background elastic parameters,a new azimuthal elastic impedance equation was first derived using parameter combinations and mathematical approximations.This equation exhibited almost the same accuracy as the original equation and contained only six model parameters:the compression modulus,anisotropic shear modulus,anisotropic compression modulus,density,normal fracture weakness,and tangential fracture weakness.Subsequently,a stepwise inversion method using second-order derivatives of the elastic impedance was developed to estimate these parameters.Moreover,the Thomsen anisotropy parameter,epsilon,was estimated from the inversion results using the ratio of the anisotropic compression modulus to the compression modulus.Synthetic examples with moderate noise and field data examples confirm the feasibility and effectiveness of the inversion method.The proposed method exhibited accuracy similar to that of previous inversion strategies and could predict richer vertical fracture information.Ultimately,the method was applied to a three-dimensional work area,and the predictions were consistent with logging and geological a priori information,confirming the effectiveness of this method.Summarily,the proposed stepwise inversion method can alleviate the uncertainty of multi-parameter inversion in ORT medium,thereby improving the reliability of fracture detection.
基金sponsored by the National Natural Science Foundation of China-Youth Science Fund(No.42402150)the Major State Science and Technology Research Program(No.2016ZX05024002-002)the Chinese Scholarship Council(CSC)。
文摘The complex plate collision process led the South Yellow Sea Basin(SYSB)to go through an intensity tectonic inversion during the Early Cenozoic,leading to a regional unconformity surface development.As a petroliferous basin,SYSB saw intense denudation and deposition processes,making it hard to characterize their source-to-sink system(S2S),and this study provided a new way to reveal them quantitatively.According to the seismic interpretation,it was found that two types of tectonic inversion led to the strata shortening process,which was classified according to their difference in planar movements:dip-slip faults and strike-slip ones.As for dip-slip faults,the inversion structure was primarily formed by the dip-slip movement,and many fault-related folds developed,which developed in the North Depression Zone of the SYSB.The strike-slip ones,accompanied by some negative flower structures,dominate the South Depression Zone of the SYSB.To reveal its source-to-sink(S2S)system in the tectonic inversion basin,we rebuild the provenance area with detrital zircon U-Pb data and heavy mineral assemblage.The results show,during the Eocene(tectonic inversion stage),the proximal slump or fan delta from the Central Uplift Zone was prominently developed in the North Depression Zone,and the South Depression Zone is filled by sediments from the proximal area(Central Uplift Zone in SYSB and Wunansha Uplift)and the prograding delta long-axis parallel to the boundary faults.Then,calculations were conducted on the coarse sediment content,fault displacements,catchment relief,sediment migration distance,and discussions about the impact factors of the S2S system developed in various strata shortening patterns with a statistical method.It was found that,within the dip-slip faults-dominated zone,the volume of the sediment routing system and the ratio of coarse-grained sediments merely have a relationship with the amount of sediment supply and average faults break displacement.Compared with the strike-slip faults-dominated zone,the source-to-sink system shows a lower level of sandy sediment influx,and its coarse-grained content is mainly determined by the average faults broken displacement.
文摘Poly(m-phenylene isophthalamide)(PMIA),a key aromatic polyamide,is widely used for its outstanding mechanical strength,high thermal stability,and excellent insulation properties.However,different applications demand varying dielectric properties,so tailoring its dielectric per-formance is essential.PMIA was first synthesized in this study,followed by introducing pores and developing porous PMIA films and PMIA-based composites with reduced dielectric constants.Porous PMIA films were fabricated using the wet phase inversion process with N,N-dimethylac-etamide(DMAC)solvent and water as the non-solvent.The impact of casting solution composi-tion and coagulation bath temperature on pore structures was analyzed.A film produced with 18%PMIA and 5%LiCl in a 35℃coagulation bath achieved the lowest dielectric constant of 1.76 at 1 Hz,48%lower than the standard PMIA film,which had a tensile strength of 18.5 MPa and an initial degradation temperature of 320℃.
