Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel du...Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.展开更多
Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral mole...Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.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
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
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
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
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.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
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
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
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
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
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
Based on drilling,mud logging,core,seismic and imaging logging data,this paper studies the identification and evolution process of negative inversion structures in the Carboniferous buried hills in the No.1 and No.2 f...Based on drilling,mud logging,core,seismic and imaging logging data,this paper studies the identification and evolution process of negative inversion structures in the Carboniferous buried hills in the No.1 and No.2 fault zones of Weixinan Sag,Beibu Gulf Basin,China,and reveals the controls of these structures on high-quality reservoirs.The No.2 fault zone develops significant negative inversion structures in the Carboniferous buried hills,as a result of multi-stage transformations of compressive-tensile stress fields in the period from the Late Hercynian to the Himalayan.The Hercynian carbonates laid the material basis for the formation of high-quality reservoirs.The negative inversion structures mainly control the development of high-quality reservoirs in buried hills through:(1)creating large-scale fractures to increase reservoir space and improve oil-gas flow pathways;(2)regulating stratigraphic differential denudation to highlight dominant lithology for later reservoir transformation;(3)shaping the paleogeomorphological highlands to provide favorable conditions for superficial karstification.The negative inversion structures form a high-quality,composite reservoir space with the synergistic existence of superficial dissolution fractures/cavities and burial-enhanced karst systems through the coupling of fracture network creation,formation denudation screening and multi-stage karst transformation.The research results have guided the breakthrough of the first exploratory well with a daily oil production over 1000 m^(3)in carbonate buried-hill reservoir in the Beibu Gulf Basin,and provide referential geological basis for finding more reserves and achieving higher production in the Carboniferous buried hills in the Weixinan Sag.展开更多
Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fi...Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fit and hard-to-trace assembly stress concentration.Assemblers can only check whether the dimensional tolerance of the component design is exceeded step by step in combination with prior knowledge.Inversion in industrial assembly optimizes assembly and design by comparing real and theoretical results and doing inversion analysis to reduce assembly deviation.The digital twin(DT)technology visualizes and predicts the assembly process by mapping real and virtual model parameters and states simultaneously,expanding parameter range for inversion analysis and improving inversion result accuracy.Problems in improving industrial assembly precision and the significance and research status of DT-driven parametric inversion of assembly tools,processes and object precision are summarized.It analyzes vital technologies for assembly precision inversion such as multi-attribute assembly process parameter sensing,virtual modeling of high-fidelity assembly systems,twin synchronization of assembly process data models,multi-physical field simulation,and performance twin model construction of the assembly process.Combined with human-cyber-physical system,augmented reality,and generative intelligence,the outlook of DT-driven assembly precision inversion is proposed,providing support for DT's use in industrial assembly and precision improvement.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
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
Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-pa...Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-parameter AVO/AVA inversion using only PP-wave angle gathers is often highly ill-posed,leading to instability and inaccuracy in the inverted elastic parameters(e.g.,P-and Swave velocities and bulk density).Seismic AVO/AVA analysis simultaneously using both PP-wave(pressure wave down,pressure wave up)and PS-wave(pressure wave down,converted shear wave up)angle gathers has proven to be an effective method for reducing reservoir interpretation ambiguity associated with using the single wave mode of PP-waves.To avoid the complex PS-wave processing,and the risks associated with PP and PS waveform alignment,we developed a method that predicts PS-wave angle gathers from PP-wave angle gathers using a deep learning algorithm—specifically,the cGAN deep learning algorithm.Our deep learning model is trained with synthetic data,demonstrating a strong fit between the predicted PS-waves and real PS-waves in a test datasets.Subsequently,the trained deep learning model is applied to actual field PP-waves,maintaining robust performance.In the field data test,the predicted PS-wave angle gather at the well location closely aligns with the synthetic PS-wave angle gather generated using reference well logs.Finally,the P-and S-wave velocities estimated from the joint PP and PS AVA inversion,based on field PP-waves and the predicted PS-waves,display a superior model fit compared to those obtained solely from the PP-wave AVA inversion using field PPwaves.Our contribution lies in firstly carrying out the joint PP and PS inversion using predicted PS waves rather than the field PS waves,which break the limit of acquiring PS-wave angle gathers.展开更多
Tight sandstone has become an important area in gas exploration.In this study,we propose a 3D seismic reservoir parameter inversion method for tight gas-bearing sandstone reservoirs using dual neural networks.The firs...Tight sandstone has become an important area in gas exploration.In this study,we propose a 3D seismic reservoir parameter inversion method for tight gas-bearing sandstone reservoirs using dual neural networks.The first network referred to as the inversion network,receives seismic data and predicts reservoir parameters.At well locations,these predictions will be validated based on actual reservoir parameters to evaluate errors.For non-well locations,synthetic seismic data are generated by the application of rock physics forward modeling and seismic reflection coefficient equations.The errors are then calculated by comparing synthetic seismic data with actual seismic data.During the rock physics forward modeling,pseudo reservoir parameters are derived by perturbing the actual reservoir parameters,which are then used to generate pseudo elastic parameters through the modeling.Both the actual and pseudo parameters are then used to train the second network,referred to as the rock physics network.By incorporating the rock physics network,the method effectively alleviates issues such as gradient explosion that may arise from directly integrating rock physics computations into the network,while the inclusion of pseudo parameters enhances the network's generalization capability.The proposed method enables the direct inversion of porosity,clay conte nt,and water saturation from pre-stack seismic data using deep learning,thereby achieving quantitative predictions of reservoir rock physical parameters.The application to the field data from tight sandstone gas reservoirs in southwestern China demonstrates the method has the good capability of indicating the gas-bearing areas and provide high resolution.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
基金supported by the National Key R&D Program of China(Grant Nos.2023YFC3008300&2023YFC3008305)the National Natural Science Foundation of China(Grant No.42172320)+1 种基金the Key Laboratory of Mountain Hazards and Engineering Resilience,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant Nos.KLMHER-Z06&KLMHER-T07)the Science and Technology Research Program of Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHE-CXTD.04).
文摘Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.
基金financially supported by the National Natural Science Foundation of China (Nos.22171165 and 22371170)Natural Science Foundation of Shandong Province (No.ZR2022MB080)Scientific and Technological Frontiers in Project of Henan Province(No.242102110192)。
文摘Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.
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.
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 Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
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 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 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Supported by the Hainan Provincial Science and Technology Special Project(ZDYF2025GXJS013)CNOOC Zhanjiang Branch Project(CCL2023ZJFN0540).
文摘Based on drilling,mud logging,core,seismic and imaging logging data,this paper studies the identification and evolution process of negative inversion structures in the Carboniferous buried hills in the No.1 and No.2 fault zones of Weixinan Sag,Beibu Gulf Basin,China,and reveals the controls of these structures on high-quality reservoirs.The No.2 fault zone develops significant negative inversion structures in the Carboniferous buried hills,as a result of multi-stage transformations of compressive-tensile stress fields in the period from the Late Hercynian to the Himalayan.The Hercynian carbonates laid the material basis for the formation of high-quality reservoirs.The negative inversion structures mainly control the development of high-quality reservoirs in buried hills through:(1)creating large-scale fractures to increase reservoir space and improve oil-gas flow pathways;(2)regulating stratigraphic differential denudation to highlight dominant lithology for later reservoir transformation;(3)shaping the paleogeomorphological highlands to provide favorable conditions for superficial karstification.The negative inversion structures form a high-quality,composite reservoir space with the synergistic existence of superficial dissolution fractures/cavities and burial-enhanced karst systems through the coupling of fracture network creation,formation denudation screening and multi-stage karst transformation.The research results have guided the breakthrough of the first exploratory well with a daily oil production over 1000 m^(3)in carbonate buried-hill reservoir in the Beibu Gulf Basin,and provide referential geological basis for finding more reserves and achieving higher production in the Carboniferous buried hills in the Weixinan Sag.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3304200)National Natural Science Foundation of China(Grant No.52205288)+1 种基金China Postdoctoral Science Foundation(Grant Nos.2024T170795,2024M762815)Zhejiang Provincial Key Research and Development Program(Grant No.2024C01029)。
文摘Assembly precision greatly influences the performance of complex high-end equipment.The traditional industrial assembly process and deviation transfer are implicit and uncertain,causing problems like poor component fit and hard-to-trace assembly stress concentration.Assemblers can only check whether the dimensional tolerance of the component design is exceeded step by step in combination with prior knowledge.Inversion in industrial assembly optimizes assembly and design by comparing real and theoretical results and doing inversion analysis to reduce assembly deviation.The digital twin(DT)technology visualizes and predicts the assembly process by mapping real and virtual model parameters and states simultaneously,expanding parameter range for inversion analysis and improving inversion result accuracy.Problems in improving industrial assembly precision and the significance and research status of DT-driven parametric inversion of assembly tools,processes and object precision are summarized.It analyzes vital technologies for assembly precision inversion such as multi-attribute assembly process parameter sensing,virtual modeling of high-fidelity assembly systems,twin synchronization of assembly process data models,multi-physical field simulation,and performance twin model construction of the assembly process.Combined with human-cyber-physical system,augmented reality,and generative intelligence,the outlook of DT-driven assembly precision inversion is proposed,providing support for DT's use in industrial assembly and precision improvement.
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funded by the National Natural Science Foundation of China(No.42325403 and No.42122029)the Deep Earth Probe and Mineral Resources Exploration—National Science and Technology Major Project(No.2024ZD1004201)+3 种基金the Young Expert of Taishan Scholars Project(No.tsqn202408095)the Independent Innovation Research Project(Science and Engineering)—Youth Fund(No.24CX06012A)of China University of Petroleumthe Qingdao Postdoctoral Funding Program(No.QDBSH20240202023)the CNPC Investigations on fundamental experiments and advanced theoretical methods in geophysical prospecting applications(2022DQ0604-02)。
文摘Seismic AVO/AVA(amplitude-versus-offset or amplitude-versus-angle)analysis,based on prestack seismic angle gathers and the Zoeppritz equation,has been widely used in seismic exploration.However,conducting the multi-parameter AVO/AVA inversion using only PP-wave angle gathers is often highly ill-posed,leading to instability and inaccuracy in the inverted elastic parameters(e.g.,P-and Swave velocities and bulk density).Seismic AVO/AVA analysis simultaneously using both PP-wave(pressure wave down,pressure wave up)and PS-wave(pressure wave down,converted shear wave up)angle gathers has proven to be an effective method for reducing reservoir interpretation ambiguity associated with using the single wave mode of PP-waves.To avoid the complex PS-wave processing,and the risks associated with PP and PS waveform alignment,we developed a method that predicts PS-wave angle gathers from PP-wave angle gathers using a deep learning algorithm—specifically,the cGAN deep learning algorithm.Our deep learning model is trained with synthetic data,demonstrating a strong fit between the predicted PS-waves and real PS-waves in a test datasets.Subsequently,the trained deep learning model is applied to actual field PP-waves,maintaining robust performance.In the field data test,the predicted PS-wave angle gather at the well location closely aligns with the synthetic PS-wave angle gather generated using reference well logs.Finally,the P-and S-wave velocities estimated from the joint PP and PS AVA inversion,based on field PP-waves and the predicted PS-waves,display a superior model fit compared to those obtained solely from the PP-wave AVA inversion using field PPwaves.Our contribution lies in firstly carrying out the joint PP and PS inversion using predicted PS waves rather than the field PS waves,which break the limit of acquiring PS-wave angle gathers.
基金the supports provided by the CNPC Science and Technology Major Project(Grant Nos.2023ZZ05,2023ZZ0505,2023ZZ18-03)Changqing Oilfield Science and Technology Major Project(Grant No.2023DZZ01)。
文摘Tight sandstone has become an important area in gas exploration.In this study,we propose a 3D seismic reservoir parameter inversion method for tight gas-bearing sandstone reservoirs using dual neural networks.The first network referred to as the inversion network,receives seismic data and predicts reservoir parameters.At well locations,these predictions will be validated based on actual reservoir parameters to evaluate errors.For non-well locations,synthetic seismic data are generated by the application of rock physics forward modeling and seismic reflection coefficient equations.The errors are then calculated by comparing synthetic seismic data with actual seismic data.During the rock physics forward modeling,pseudo reservoir parameters are derived by perturbing the actual reservoir parameters,which are then used to generate pseudo elastic parameters through the modeling.Both the actual and pseudo parameters are then used to train the second network,referred to as the rock physics network.By incorporating the rock physics network,the method effectively alleviates issues such as gradient explosion that may arise from directly integrating rock physics computations into the network,while the inclusion of pseudo parameters enhances the network's generalization capability.The proposed method enables the direct inversion of porosity,clay conte nt,and water saturation from pre-stack seismic data using deep learning,thereby achieving quantitative predictions of reservoir rock physical parameters.The application to the field data from tight sandstone gas reservoirs in southwestern China demonstrates the method has the good capability of indicating the gas-bearing areas and provide high resolution.
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.
