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Experimental Research on Quantitative Inversion Models of Suspended Sediment Concentration Using Remote Sensing Technology 被引量:1
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作者 Wang Yanjiao Yan Feng +1 位作者 Zhang Peiqun Dong Wenjie 《Chinese Geographical Science》 SCIE CSCD 2007年第3期243-249,共7页
Research on quantitative models of suspended sediment concentration (SSC) using remote sensing technology is very important to understand the scouting and siltation variation in harbors and water channels. Based onl... Research on quantitative models of suspended sediment concentration (SSC) using remote sensing technology is very important to understand the scouting and siltation variation in harbors and water channels. Based onlaboratory study of the relationship between different suspended sediment concentrations and reflectance spectra measured synchronously, quantitative inversion models of SSC based on single factor, band ratio and sediment parameter were developed, which provides an effective method to retrieve the SSC from satellite images. Results show that the bl (430-500nm) and b3 (670-735nm) are the optimal wavelengths for the estimation of lower SSC and the b4 (780-835nm) is the optimal wavelength to estimate the higher SSC. Furthermore the band ratio B2/B3 can be used to simulate the variation of lower SSC better and the B4/B1 to estimate the higher SSC accurately. Also the inversion models developed by sediment parameters of higher and lower SSCs can get a relatively higher accuracy than the single factor and band ratio models. 展开更多
关键词 suspended sediment concentration spectral reflectance inversion model
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Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir 被引量:1
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作者 Rodrigo Penna Wagner Moreira Lupinacci 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1699-1718,共20页
Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation ... Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow. 展开更多
关键词 Flowunits Geostatistical inversion Presalt reservoir 3D reservoir modelling Petrophysical modelling
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Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion
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作者 李玉冰 王建 +3 位作者 苏畅 林伟军 王秀明 骆毅 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第1期362-372,共11页
High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In additi... High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography. 展开更多
关键词 ultrasound brain imaging full waveform inversion high resolution digital body
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Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy 被引量:1
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作者 吕一涵 宋惟然 +1 位作者 侯宗余 王哲 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第7期148-156,共9页
Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a... Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification. 展开更多
关键词 laser-induced breakdown spectroscopy(LIBS) coal ash content quantitative analysis variable selection empirical knowledge partial least squares regression(PLSR)
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Assessing the effects of model parameter assumptions on surface-wave inversion results
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作者 Xuezhen Zhang Xiaodong Song 《Earthquake Science》 2024年第6期529-545,共17页
Surface-wave inversion is a powerful tool for revealing the Earth's internal structure.However,aside from shear-wave velocity(v_(S)),other parameters can influence the inversion outcomes,yet these have not been sy... Surface-wave inversion is a powerful tool for revealing the Earth's internal structure.However,aside from shear-wave velocity(v_(S)),other parameters can influence the inversion outcomes,yet these have not been systematically discussed.This study investigates the influence of various parameter assumptions on the results of surface-wave inversion,including the compressional and shear velocity ratio(v_(P)/v_(S)),shear-wave attenuation(Q_(S)),density(ρ),Moho interface,and sedimentary layer.We constructed synthetic models to generate dispersion data and compared the obtained results with different parameter assumptions with those of the true model.The results indicate that the v_(P)/v_(S) ratio,Q_(S),and density(ρ) have minimal effects on absolute velocity values and perturbation patterns in the inversion.Conversely,assumptions about the Moho interface and sedimentary layer significantly influenced absolute velocity values and perturbation patterns.Introducing an erroneous Mohointerface depth in the initial model of the inversion significantly affected the v_(S) model near that depth,while using a smooth initial model results in relatively minor deviations.The assumption on the sedimentary layer not only affects shallow structure results but also impacts the result at greater depths.Non-linear inversion methods outperform linear inversion methods,particularly for the assumptions of the Moho interface and sedimentary layer.Joint inversion with other data types,such as receiver functions or Rayleigh wave ellipticity,and using data from a broader period range or higher-mode surface waves,can mitigate these deviations.Furthermore,incorporating more accurate prior information can improve inversion results. 展开更多
关键词 shear-wave velocity model surface-wave inversion Moho interface sedimentary layer non-linear inversion
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Layer-Valley Hall Effect under Inversion and Time-Reversal Symmetries
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作者 赵交交 刘贵斌 +3 位作者 陈鹏 姚裕贵 张广宇 杜罗军 《Chinese Physics Letters》 SCIE EI CAS CSCD 2024年第6期88-97,共10页
Hall effects have been the central paradigms in modern physics,materials science and practical applications,and have led to many exciting breakthroughs,including the discovery of topological Chern invariants and the r... Hall effects have been the central paradigms in modern physics,materials science and practical applications,and have led to many exciting breakthroughs,including the discovery of topological Chern invariants and the revolution of metrological resistance standard.To date,the Hall effects have mainly focused on a single degree of freedom(Do F),and most of them require the breaking of spatial-inversion and/or time-reversal symmetries.Here we demonstrate a new type of Hall effect,i.e.,layer-valley Hall effect,based on a combined layer-valley Do F characterized by the product of layer and valley indices.The layer-valley Hall effect has a quantum origin arising from the layer-valley contrasting Berry curvature,and can occur in nonmagnetic centrosymmetric crystals with both spatial-inversion and time-reversal symmetries,transcending the symmetry constraints of single Do F Hall effect based on the constituent layer or valley index.Moreover,the layer-valley Hall effect is highly tunable and shows a W-shaped pattern in response to the out-of-plane electric fields.Additionally,we discuss the potential detection approaches and material-specific design principles of layer-valley Hall effect.Our results demonstrate novel Hall physics and open up exotic paradigms for new research direction of layer-valleytronics that exploits the quantum nature of the coupled layer-valley DoF. 展开更多
关键词 quantum inversion CURVATURE
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Establishment of quantitative evaluation system for cochlear implant effectiveness for hearing-impaired children
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作者 Qing-Yuan Feng Song Li +4 位作者 Yong-Mao Cao Chuan-Xin Duan Lu Ma Dan Wu Ze-Zhang Tao 《Journal of Otology》 CAS CSCD 2024年第2期77-84,共8页
Introduction:Cochlear implant is currently the most widely proven interventions for auditory rehabilitation for children with severe sensorineural hearing impairment.However,there are obvious limitations in these curr... Introduction:Cochlear implant is currently the most widely proven interventions for auditory rehabilitation for children with severe sensorineural hearing impairment.However,there are obvious limitations in these current evaluation methods.This study aims to develop an evaluation system for quantitatively evaluating the effectiveness of cochlear implants for hearing-impaired children.Methods:A correspondence questionnaire was developed based on an initial indicator system that was developed based on the literature focused on the evaluation of cochlear implant outcomes in children.Twenty-five experts in otology,clinical audiology,rehabilitation audiology,and mental health from nine provinces in China were consulted.The degree of authority and coordination of experts and the indicators and weights of the quantitative evaluation system were analyzed.Seventy-eight children aged 3–11 years after cochlear implantation were recruited from two centers in Hubei province to evaluate the reliability and validity of the quantitative evaluation system.Results:The opinions of experts converged after the second round of correspondence,and the coordination and authority of the expert consensus were met.The recall rate of the questionnaire was 100%for both rounds.Five secondary indicators,including auditory ability,verbal ability,behavioral assessment,learning capabilities,and quality of life,and 13 tertiary indicators were reserved for the evaluation of cochlear implant effectiveness.The weight of each indicator was calculated.The Cronbach’sαcoefficient of the quantitative evaluation system based on the standardized items was 0.930,and the three extracted common factors could explain 78.86%of the total variance.Conclusions:An expert consensus-based evaluation system that can quantitatively evaluate the effectiveness of cochlear implants in children has been developed with good reliability and validity. 展开更多
关键词 Cochlear implant RELIABILITY quantitative evaluation Validity
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An illustrated guide to:Parsimonious multi-scale full-waveform inversion
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作者 Andreas Fichtner Solvi Thrastarson +1 位作者 Dirk-Philip van Herwaarden Sebastian Noe 《Earthquake Science》 2024年第6期574-583,共10页
Having been a seemingly unreachable ideal for decades,3-D full-waveform inversion applied to massive seismic datasets has become reality in recent years.Often achieving unprecedented resolution,it has provided new ins... Having been a seemingly unreachable ideal for decades,3-D full-waveform inversion applied to massive seismic datasets has become reality in recent years.Often achieving unprecedented resolution,it has provided new insight into the structure of the Earth,from the upper few metres of soil to the entire globe.Motivated by these successes,the technology is now being translated to medical ultrasound and non-destructive testing.Despite remarkable progress,the computational cost of fullwaveform inversion continues to be a major concern.It limits the amount of data that can be exploited,and it largely inhibits quantitative and comprehensive uncertainty analyses.These notes complement a presentation on recent developments in full-waveform inversion that are intended to reduce computational cost and assimilate more data,thereby improving tomographic resolution.The suite of strategies includes flexible and user-friendly spectral-element simulations,the design of wavefieldadapted meshes that harness prior information on wavefield geometry,dynamic mini-batch optimisation that naturally takes advantage of data redundancies,and collaborative multi-scale updating to jointly constrain crustal and mantle structure. 展开更多
关键词 EARTH MODEL SEISMOLOGY full-waveform inversion
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Linearized waveform inversion for vertical transversely isotropic elastic media:Methodology and multi-parameter crosstalk analysis
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作者 Ke Chen Lu Liu +5 位作者 Li-Nan Xu Fei Hu Yuan Yang Jia-Hui Zuo Le-Le Zhang Yang Zhao 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期252-271,共20页
Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuit... Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs. 展开更多
关键词 ELASTIC ANISOTROPY Least-squares imaging Waveform inversion Computational geophysics
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Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS
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作者 韩伟伟 孙对兄 +7 位作者 张国鼎 董光辉 崔小娜 申金成 王浩亮 张登红 董晨钟 苏茂根 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第6期149-158,共10页
To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis o... To obtain more stable spectral data for accurate quantitative analysis of multi-element,especially for the large-area in-situ elements detection of soils, we propose a method for a multielement quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy(CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I324.75 nm for filtering the experimental data of multiple sample sets. Pre-and post-data filtering,the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection(LOD)values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post-data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis. 展开更多
关键词 laser-induced breakdown spectroscopy SOIL data filtering quantitative analysis multielement
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Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laserinduced breakdown spectroscopy method
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作者 Weiwei HAN Duixiong SUN +7 位作者 Guoding ZHANG Honglin WANG Kai GUO Yuzhuo ZHANG Haoliang WANG Denghong ZHANG Chenzhong DONG Maogen SU 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第9期156-164,共9页
This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to ach... This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve(SC)and calibration-free laser-induced breakdown spectroscopy(CF-LIBS)technology to achieve synchronous,rapid,and accurate measurement of elements in a large number of samples,namely,SC-assisted CF-LIBS.Al alloy standard samples,divided into calibration and test samples,were applied to validate the proposed method.SC was built based on the characteristic line of Pb and Cr in the calibration sample,and the contents of Pb and Cr in the test sample were calculated with relative errors of 6%and 4%,respectively.SC built using Cr with multiple characteristic lines yielded better calculation results.The relative contents of ten elements in the test sample were calculated using CF-LIBS.Subsequently,the SC-assisted CF-LIBS was executed,with the majority of the calculation relative errors falling within the range of 2%-5%.Finally,the Al and Na contents of the Al alloy were predicted.The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples.Furthermore,this quantitative analysis method was successfully applied to soil and Astragalus samples,realizing an accurate calculation of the contents of multiple elements.Thus,it is important to advance the LIBS quantitative analysis and its related applications. 展开更多
关键词 laser-induced breakdown spectroscopy standard curve quantitative analysis multielement
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Self-potential inversion based on Attention U-Net deep learning network
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作者 GUO You-jun CUI Yi-an +3 位作者 CHEN Hang XIE Jing ZHANG Chi LIU Jian-xin 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第9期3156-3167,共12页
Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention an... Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring. 展开更多
关键词 SELF-POTENTIAL attention mechanism U-Net deep learning network inversion landfill
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Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips
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作者 Jie Ren Yunfeng Li +3 位作者 Mingyu Li Xingyu Wu Jiabao Wang Qingxuan Zeng 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期309-316,共8页
Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ ... Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems. 展开更多
关键词 Copper-based azide chips SPECTROPHOTOMETRY Separation method quantitative analysis Ignition ability
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Characterization of a 4.1 Mb inversion harboring the stripe rust resistance gene YR86 on wheat chromosome 2AL
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作者 Qiang Cao Zhanwang Zhu +13 位作者 Dengan Xu Jianhui Wu Xiaowan Xu Yan Dong Yingjie Bian Fugong Ding Dehui Zhao Yang Tu Ling Wu Dejun Han Caixia Lan Xianchun Xia Zhonghu He Yuanfeng Hao 《The Crop Journal》 SCIE CSCD 2024年第4期1168-1175,共8页
Wheat cultivar Zhongmai 895 was earlier found to carry YR86 in an 11.6 Mb recombination-suppressed region on chromosome 2AL when crossed with Yangmai 16.To fine-map the YR86 locus,we developed two large F2 populations... Wheat cultivar Zhongmai 895 was earlier found to carry YR86 in an 11.6 Mb recombination-suppressed region on chromosome 2AL when crossed with Yangmai 16.To fine-map the YR86 locus,we developed two large F2 populations from crosses Emai 580/Zhongmai 895 and Avocet S/Zhongmai 895.Remarkably,both populations exhibited suppressed recombination in the same 2AL region.Collinearity analysis across Chinese Spring,Aikang 58,and 10+wheat genomes revealed a 4.1 Mb chromosomal inversion spanning 708.5-712.6 Mb in the Chinese Spring reference genome.Molecular markers were developed in the breakpoint and were used to assess a wheat cultivar panel,revealing that Chinese Spring,Zhongmai 895,and Jimai 22 shared a common sequence named InvCS,whereas Aikang 58,Yangmai 16,Emai 580,and Avocet S shared the sequence named InvAK58.The inverted configuration explained the suppressed recombination observed in all three bi-parental populations.Normal recombination was observed in a Jimai 22/Zhongmai 895 F2 population,facilitating mapping of YR86 to a genetic interval of 0.15 cM corresponding to 710.27-712.56 Mb falling within the inverted region.Thirty-three high-confidence genes were annotated in the interval using the Chinese Spring reference genome,with six identified as potential candidates for YR86 based on genome and transcriptome analyses.These results will accelerate map-based cloning of YR86 and its deployment in wheat breeding. 展开更多
关键词 Adult-plant resistance Chromosomal inversion Puccinia striiformis Triticum aestivum
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Novel damage constitutive models and new quantitative identification method for stress thresholds of rocks under uniaxial compression
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作者 DU Kun YI Yang +3 位作者 LUO Xin-yao LIU Kai LI Peng WANG Shao-feng 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第8期2658-2675,共18页
Four key stress thresholds exist in the compression process of rocks,i.e.,crack closure stress(σ_(cc)),crack initiation stress(σ_(ci)),crack damage stress(σ_(cd))and compressive strength(σ_(c)).The quantitative id... Four key stress thresholds exist in the compression process of rocks,i.e.,crack closure stress(σ_(cc)),crack initiation stress(σ_(ci)),crack damage stress(σ_(cd))and compressive strength(σ_(c)).The quantitative identifications of the first three stress thresholds are of great significance for characterizing the microcrack growth and damage evolution of rocks under compression.In this paper,a new method based on damage constitutive model is proposed to quantitatively measure the stress thresholds of rocks.Firstly,two different damage constitutive models were constructed based on acoustic emission(AE)counts and Weibull distribution function considering the compaction stages of the rock and the bearing capacity of the damage element.Then,the accumulative AE counts method(ACLM),AE count rate method(CRM)and constitutive model method(CMM)were introduced to determine the stress thresholds of rocks.Finally,the stress thresholds of 9 different rocks were identified by ACLM,CRM,and CMM.The results show that the theoretical stress−strain curves obtained from the two damage constitutive models are in good agreement with that of the experimental data,and the differences between the two damage constitutive models mainly come from the evolutionary differences of the damage variables.The results of the stress thresholds identified by the CMM are in good agreement with those identified by the AE methods,i.e.,ACLM and CRM.Therefore,the proposed CMM can be used to determine the stress thresholds of rocks. 展开更多
关键词 stress threshold acoustic emission damage constitutive model damage element quantitative method
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Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning
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作者 Jiang HUANGFU Zhiqun HU +2 位作者 Jiafeng ZHENG Lirong WANG Yongjie ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第6期1147-1160,共14页
Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a mult... Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods. 展开更多
关键词 polarimetric radar quantitative precipitation estimation deep learning single-parameter network multi-parameter network
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Probabilistic seismic inversion based on physics-guided deep mixture density network
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作者 Qian-Hao Sun Zhao-Yun Zong Xin Li 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1611-1631,共21页
Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learn... Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data. 展开更多
关键词 Deep learning Probabilistic inversion Physics-guided Deep mixture density network
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Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification
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作者 Fábio Júnior Damasceno Fernandes Leonardo Teixeira +1 位作者 Antonio Fernando Menezes Freire Wagner Moreira Lupinacci 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期918-935,共18页
We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin. This integration of seismic and well-derived ... We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin. This integration of seismic and well-derived information enhances reservoir characterization. Stochastic inversion and Bayesian classification are powerful tools because they permit addressing the uncertainties in the model. We used the ES-MDA algorithm to achieve the realizations equivalent to the percentiles P10, P50, and P90 of acoustic impedance, a novel method for acoustic inversion in presalt. The facies were divided into five: reservoir 1,reservoir 2, tight carbonates, clayey rocks, and igneous rocks. To deal with the overlaps in acoustic impedance values of facies, we included geological information using a priori probability, indicating that structural highs are reservoir-dominated. To illustrate our approach, we conducted porosity modeling using facies-related rock-physics models for rock-physics inversion in an area with a well drilled in a coquina bank and evaluated the thickness and extension of an igneous intrusion near the carbonate-salt interface. The modeled porosity and the classified seismic facies are in good agreement with the ones observed in the wells. Notably, the coquinas bank presents an improvement in the porosity towards the top. The a priori probability model was crucial for limiting the clayey rocks to the structural lows. In Well B, the hit rate of the igneous rock in the three scenarios is higher than 60%, showing an excellent thickness-prediction capability. 展开更多
关键词 Stochastic inversion Bayesian classification Porosity modeling Carbonate reservoirs Igneous rocks
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Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods
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作者 Ming Li Yuhang Liu +1 位作者 Yiyuan Sun Kefeng Liu 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2024年第5期110-120,共11页
The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and co... The mesoscale eddy(ME)has a significant influence on the convergence effect in deep-sea acoustic propagation.This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone(CZ)characteristics.Based on the Gaussian vortex model,we construct various sound propagation scenarios under different eddy conditions,and carry out sound propagation experiments to obtain simulation samples.With a large number of samples,we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters.The sensitivity of eddy indicators to the CZ is quantitatively analyzed.Then,we adopt the machine learning(ML)algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters.Through the research,we can express the influence of ME on the CZ quantitatively,and achieve the rapid prediction of CZ parameters in ocean eddies.The prediction accuracy(R)of the CZ distance(mean R:0.9815)is obviously better than that of the CZ width(mean R:0.8728).Among the three ML algorithms,Gradient Boosting Decision Tree has the best prediction ability(root mean square error(RMSE):0.136),followed by Random Forest(RMSE:0.441)and Extreme Learning Machine(RMSE:0.518). 展开更多
关键词 convergence zone mesoscale eddy statistic analysis quantitative prediction machine learning
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True-temperature inversion algorithm for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization
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作者 Mei Liang Zhuo Sun +3 位作者 Jiasong Liu Yongsheng Wang Lei Liang Long Zhang 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2024年第1期55-62,共8页
Herein,a method of true-temperature inversion for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization is proposed for difficult inversion problems with unknown emissivity.Fractional-order... Herein,a method of true-temperature inversion for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization is proposed for difficult inversion problems with unknown emissivity.Fractional-order calculus has the inherent advantage of easily jumping out of local extreme values;here,it is introduced into the particle-swarm algorithm to invert the true temperature.An improved adaptive-adjustment mechanism is applied to automatically adjust the current velocity order of the particles and update their velocity and position values,increasing the accuracy of the true temperature values.The results of simulations using the proposed algorithm were compared with three algorithms using typical emissivity models:the internal penalty function algorithm,the optimization function(fmincon)algorithm,and the conventional particle-swarm optimization algorithm.The results show that the proposed algorithm has good accuracy for true-temperature inversion.Actual experimental results from a rocket-motor plume were used to demonstrate that the true-temperature inversion results of this algorithm are in good agreement with the theoretical true-temperature values. 展开更多
关键词 Fractional-order particle swarm True-temperature inversion algorithm Multi-wavelength pyrometer
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