Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensiv...We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensively evaluate the performance of the model by power spectral comparisons,correlation analyses,sensitivity matrix assessments,and comparisons with existing lithospheric field models.Results showed that using near east–west gradient data from MSS-1 significantly enhances the model correlation in the spherical harmonic degree(N) range of 45–60 while also mitigating the decline in correlation at higher degrees(N > 60).Furthermore,the unique orbital characteristics of MSS-1 enable its gradient data to provide substantial contributions to modeling in the mid-to low-latitude regions.With continued data acquisition from MSS-1 and further optimization of data processing methods,the performance of the model is expected to improve.展开更多
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
The sound field driven by piping systems in enclosures may severely affect living comfort,which is frequently encountered in various engineering applications.Managing this sound field relies heavily on the available p...The sound field driven by piping systems in enclosures may severely affect living comfort,which is frequently encountered in various engineering applications.Managing this sound field relies heavily on the available prediction tools at hand,e.g.,the widely used finite element methods are computationally expensive due to the necessity to discretize entire space,analytical models,based on modal expansion method,may offer substantial advantages in terms of computational cost and efficiency.However,deriving eigenmodes of irregular enclosed spaces may be challenging,which impedes accurate and rapid predictions of the sound field in practical applications.This study presents an analytical framework aimed at rapidly and accurately predicting the interior sound field driven by the piping system vibrations in irregular enclosures.Vibration response of the piping system is obtained using the wave approach,and a line dipole source is idealized as the sound source of the piping system vibration.On the basis of eigenmodes of regular enclosures,the Kirchhoff-Helmholtz integral theorem(modal ex-pansion method for irregular enclosures)is introduced to account for the boundaries of irregular enclosures.This theoretical framework is validated through numerical simulations by finite element method and experiments,demonstrating high accuracy and significant efficiency advantages.The proposed method can be further employed to optimize radiated sound fields by tailoring the impedance of space walls or layout of piping systems.This study provides an efficient tool for predicting radiated sound field in general enclosures driven by vibration of piping systems,paving a new path for indoor acoustical optimization.展开更多
The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected t...The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected truly. This paper discusses identification methods and the data credibility evaluation method for formation water in oil and gas fields of petroliferous basins within China. The results of the study show that: (1) the identification methods of formation water include the basic methods of single factors such as physical characteristics, water composition characteristics, water type characteristics, and characteristic coefficients, as well as the comprehensive evaluation method of data credibility proposed on this basis, which mainly relies on the correlation analysis sodium chloride coefficient and desulfurization coefficient and combines geological background evaluation;(2) The basic identifying methods for formation water enable the preliminary identification of hydrochemical data and the preliminary screening of data on site, the proposed comprehensive method realizes the evaluation by classifying the CaCl2-type water into types A-I to A-VI and the NaHCO3-type water into types B-I to B-IV, so that researchers can make in-depth evaluation on the credibility of hydrochemical data and analysis of influencing factors;(3) When the basic methods are used to identify the formation water, the formation water containing anions such as CO_(3)^(2-), OH- and NO_(3)^(-), or the formation water with the sodium chloride coefficient and desulphurization coefficient not matching the geological setting, are all invaded with surface water or polluted by working fluid;(4) When the comprehensive method is used, the data credibility of A-I, A-II, B-I and B-II formation water can be evaluated effectively and accurately only if the geological setting analysis in respect of the factors such as formation environment, sampling conditions, condensate water, acid fluid, leaching of ancient weathering crust, and ancient atmospheric fresh water, is combined, although such formation water is believed with high credibility.展开更多
Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people i...Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.展开更多
The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration an...The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration and development and the requirements of low-carbon development,the focus of exploration anddevelopment in the oil and gas industry is gradually shifting to the exploration and development of renewableenergy sources such as deep sea,deep earth and geothermal energy.The traditional petrophysical evaluation andinterpretation model has encountered great challenges in the face of new evaluation objects.To establish a distributedlogging big data private cloud platform with a unified learning model as the key,which realizes the distributed storageand processing of logging big data,and enables the learning of brand-new knowledge patterns from multi-attributedata in the large function space in the unified logging learning model integrating the expert knowledge and the datamodel,so as to solve the problem of geoengineering evaluation of geothermal fields.Based on the research ideaof“logging big data cloud platform---unified logging learning model---large function space---knowledge learning&discovery---application”,the theoretical foundation of unified learning model,cloud platform architecture,datastorage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storageand processing of data and learning algorithms.New knowledge of geothermal evaluation is found in a large functionspace and applied to Geo-engineering evaluation of geothermal fields.The examples show its good application in theselection of logging series in geothermal fields,quality control of logging data,identification of complex lithologyin geothermal fields,evaluation of reservoir fluids,checking of associated helium,evaluation of cementing quality,evaluation of well-side fractures,and evaluation of geothermal water recharge under the remote logging module ofthe cloud platform.The first and second cementing surfaces of cemented wells in geothermal fields were evaluated,as well as the development of well-side distal fractures,fracture extension orientation.According to the well-sidefracture communication to form a good fluid pathway and large flow rate and long flow diameter of the thermalstorage fi ssure system,the design is conducive to the design of the recharge program of geothermal water.展开更多
Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
Accurate estimation of battery health status plays a crucial role in battery management systems.However,the lack of operational data still affects the accuracy of battery state of health(SOH)estimation.For this reason...Accurate estimation of battery health status plays a crucial role in battery management systems.However,the lack of operational data still affects the accuracy of battery state of health(SOH)estimation.For this reason,a SOH estimation method is proposed based on charging data reconstruction combined with image processing.The charging voltage data is used to train the least squares generative adversarial network(LSGAN),which is validated under different levels of missing data.From a visual perspective,the Gram angle field method is applied to convert one-dimensional time series data into image data.This method fully preserves the time series characteristics and nonlinear evolution patterns,which avoids the difficulties and limited expressive power associated with manual feature extraction.At the same time,the Swin Transformer model is introduced to extract global structures and local details from images,enabling better capture of sequence change trends.Combined with the long short-term memory network(LSTM),this enables accurate estimation of battery SOH.Two different types of batteries are used to validate the test.The experimental results show that the proposed method has good estimation accuracy under different training proportions.展开更多
Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data...Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.展开更多
In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization tec...In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.展开更多
In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm base...In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.展开更多
Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivati...Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.展开更多
In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equ...In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equation. In addition, when the seabed interface is irregular, traditional finite-difference schemes cannot simulate the seismic wave propagation across the irregular seabed interface. Therefore, an acoustic–elastic forward modeling and vector-based P-and S-wave separation method is proposed. In this method, we divide the fluid–solid elastic media with irregular interface into orthogonal grids and map the irregular interface in the Cartesian coordinates system into a horizontal interface in the curvilinear coordinates system of the computational domain using coordinates transformation. The acoustic and elastic wave equations in the curvilinear coordinates system are applied to the fluid and solid medium, respectively. At the irregular interface, the two equations are combined into an acoustic–elastic equation in the curvilinear coordinates system. We next introduce a full staggered-grid scheme to improve the stability of the numerical simulation. Thus, separate P-and S-wave equations in the curvilinear coordinates system are derived to realize the P-and S-wave separation method.展开更多
Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spec...Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.展开更多
In this study, a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer （MODIS） data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape...In this study, a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer （MODIS） data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape. As a case study, the methodology was applied to the whole Tibetan Plateau （TP） area. Four images of MODIS data （i.e., 30 January 2007, 15 April 2007, 1 August 2007, and 25 October 2007） were used in this study for comparison among winter, spring, summer, and autumn. The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform （TORP）. The results show the following： （1） The derived surface heating field for the TP area was in good accord with the land-surface status, showing a wide range of values due to the strong contrast of surface features in the area. （2） The derived surface heating field for the TP was very close to the field measurements （observations）. The APD （absolute percent difference） between the derived results and the field observations was 〈10%. （3） The mean surface heating field over the TP increased from January to April to August, and decreased in October. Therefore, the reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology. The limitations and further improvement of this method are also discussed.展开更多
Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate s...Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.展开更多
Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing th...Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.展开更多
Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seism...Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seismic acquisition is accompanied by the lack of acquisition data,which requires high-precision regularization.The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal,involving sparse transform base optimization and threshold modeling.First,this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency.Second,an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy.Test results show that the method has good adaptability to different seismic data and sparse transform bases.The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency.The parallel computing strategy of curvelet transform combined with OpenMP is further proposed,which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy.Finally,the actual acquisition data are used to verify the proposed method.The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.展开更多
Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric...Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.展开更多
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
基金the support of the National Natural Science Foundation of China (Nos. 42250103, 41974073, and 41404053)the Macao Foundation and the preresearch project of Civil Aerospace Technologies (Nos. D020308 and D020303)funded by China’s National Space Administration, the Specialized Research Fund for State Key Laboratories。
文摘We combine gradient data from the Macao Science Satellite-1(MSS-1),CHAllenging Minisatellite Payload(CHAMP),Swarm-A,and Swarm-C satellites to develop a 110-degree lithospheric magnetic field model.We then comprehensively evaluate the performance of the model by power spectral comparisons,correlation analyses,sensitivity matrix assessments,and comparisons with existing lithospheric field models.Results showed that using near east–west gradient data from MSS-1 significantly enhances the model correlation in the spherical harmonic degree(N) range of 45–60 while also mitigating the decline in correlation at higher degrees(N > 60).Furthermore,the unique orbital characteristics of MSS-1 enable its gradient data to provide substantial contributions to modeling in the mid-to low-latitude regions.With continued data acquisition from MSS-1 and further optimization of data processing methods,the performance of the model is expected to improve.
基金supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
基金supported by the National Natural Science Foundation of China(Grant Nos.11632003,11972083,11991030,12372088,and U22B2078)Beijing Institute of Technology Research Fund Program for Young Scholars(Grant No.XSQD-202101010).
文摘The sound field driven by piping systems in enclosures may severely affect living comfort,which is frequently encountered in various engineering applications.Managing this sound field relies heavily on the available prediction tools at hand,e.g.,the widely used finite element methods are computationally expensive due to the necessity to discretize entire space,analytical models,based on modal expansion method,may offer substantial advantages in terms of computational cost and efficiency.However,deriving eigenmodes of irregular enclosed spaces may be challenging,which impedes accurate and rapid predictions of the sound field in practical applications.This study presents an analytical framework aimed at rapidly and accurately predicting the interior sound field driven by the piping system vibrations in irregular enclosures.Vibration response of the piping system is obtained using the wave approach,and a line dipole source is idealized as the sound source of the piping system vibration.On the basis of eigenmodes of regular enclosures,the Kirchhoff-Helmholtz integral theorem(modal ex-pansion method for irregular enclosures)is introduced to account for the boundaries of irregular enclosures.This theoretical framework is validated through numerical simulations by finite element method and experiments,demonstrating high accuracy and significant efficiency advantages.The proposed method can be further employed to optimize radiated sound fields by tailoring the impedance of space walls or layout of piping systems.This study provides an efficient tool for predicting radiated sound field in general enclosures driven by vibration of piping systems,paving a new path for indoor acoustical optimization.
基金Supported by the PetroChina Science and Technology Project(2023ZZ0202)。
文摘The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected truly. This paper discusses identification methods and the data credibility evaluation method for formation water in oil and gas fields of petroliferous basins within China. The results of the study show that: (1) the identification methods of formation water include the basic methods of single factors such as physical characteristics, water composition characteristics, water type characteristics, and characteristic coefficients, as well as the comprehensive evaluation method of data credibility proposed on this basis, which mainly relies on the correlation analysis sodium chloride coefficient and desulfurization coefficient and combines geological background evaluation;(2) The basic identifying methods for formation water enable the preliminary identification of hydrochemical data and the preliminary screening of data on site, the proposed comprehensive method realizes the evaluation by classifying the CaCl2-type water into types A-I to A-VI and the NaHCO3-type water into types B-I to B-IV, so that researchers can make in-depth evaluation on the credibility of hydrochemical data and analysis of influencing factors;(3) When the basic methods are used to identify the formation water, the formation water containing anions such as CO_(3)^(2-), OH- and NO_(3)^(-), or the formation water with the sodium chloride coefficient and desulphurization coefficient not matching the geological setting, are all invaded with surface water or polluted by working fluid;(4) When the comprehensive method is used, the data credibility of A-I, A-II, B-I and B-II formation water can be evaluated effectively and accurately only if the geological setting analysis in respect of the factors such as formation environment, sampling conditions, condensate water, acid fluid, leaching of ancient weathering crust, and ancient atmospheric fresh water, is combined, although such formation water is believed with high credibility.
基金Supported by the National Key Research and Development Program of China(Nos.2021YFC3101801,2023YFC3008200)the National Natural Science Foundation of China(Nos.42476219,41976200)+6 种基金the National Foreign Experts Program(No.S20240134)the Innovative Team Plan of the Department of Education of Guangdong Province(No.2023KCXTD015)the Tropical Ocean Environment in Western Coastal Waters Observation and Research Station of Guangdong Province(No.2024B1212040008)the Independent Research Project of the Southern Ocean Laboratory(No.SML2022SP301)the Shandong Innovation and Development Research Institute Think Tank Projectthe Guangdong Ocean University Scientific Research Program(No.060302032106)the Start-up Fund for Ph D Researchers(No.060302032104)。
文摘Typhoon Chaba was the most intense typhoon to strike western Guangdong since Typhoon Mujigae in 2015.According to the National Disaster Reduction Center of China,in the morning of July 7,2022,over 1.5 million people in Guangdong,Guangxi,and Hainan were affected by Typhoon Chaba.The typhoon also caused the“Fukui 001”ship to be in distress in the waters near Yangjiang,Guangdong,on July 2,resulting in big casualties.Studies have indicated that wind field forecast for Typhoon Chaba was not accurate.To better simulate typhoon events and assess their impacts,we proposed the use of a model wind field(Fujita-Takahashi)integrated with the Copernicus Marine and Environmental Monitoring Service(CMEMS)data to reconstruct effectively the overall wind field of Typhoon Chaba.The simulation result aligns well with the observations,particularly at the Dashu Island Station,showing consistent trends in wind speed changes.However,certain limitations were noted.The model shows that the attenuation of wind speed is slower when typhoon neared land than that observed,indicating that the model has a high simulation accuracy for the ocean wind field,but may have deviations near coastal areas.The result is accurate for open sea but deviated for near land due to the land friction effect.Therefore,we recommend to adjust the model to improve the accuracy for near coasts.
文摘The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration and development and the requirements of low-carbon development,the focus of exploration anddevelopment in the oil and gas industry is gradually shifting to the exploration and development of renewableenergy sources such as deep sea,deep earth and geothermal energy.The traditional petrophysical evaluation andinterpretation model has encountered great challenges in the face of new evaluation objects.To establish a distributedlogging big data private cloud platform with a unified learning model as the key,which realizes the distributed storageand processing of logging big data,and enables the learning of brand-new knowledge patterns from multi-attributedata in the large function space in the unified logging learning model integrating the expert knowledge and the datamodel,so as to solve the problem of geoengineering evaluation of geothermal fields.Based on the research ideaof“logging big data cloud platform---unified logging learning model---large function space---knowledge learning&discovery---application”,the theoretical foundation of unified learning model,cloud platform architecture,datastorage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storageand processing of data and learning algorithms.New knowledge of geothermal evaluation is found in a large functionspace and applied to Geo-engineering evaluation of geothermal fields.The examples show its good application in theselection of logging series in geothermal fields,quality control of logging data,identification of complex lithologyin geothermal fields,evaluation of reservoir fluids,checking of associated helium,evaluation of cementing quality,evaluation of well-side fractures,and evaluation of geothermal water recharge under the remote logging module ofthe cloud platform.The first and second cementing surfaces of cemented wells in geothermal fields were evaluated,as well as the development of well-side distal fractures,fracture extension orientation.According to the well-sidefracture communication to form a good fluid pathway and large flow rate and long flow diameter of the thermalstorage fi ssure system,the design is conducive to the design of the recharge program of geothermal water.
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
基金supported in part by the National Natural Science Foundation of China(under Grant 62473309,62203352)the Shaanxi Outstanding Youth Science Fund Project(under Grant 2024JC-JCQN-68)+1 种基金the Xi’an Science and Technology Plan Project(under Grant 24GXFW0050)the Xi’an Key Laboratory(under Grant 24ZDSY0015).
文摘Accurate estimation of battery health status plays a crucial role in battery management systems.However,the lack of operational data still affects the accuracy of battery state of health(SOH)estimation.For this reason,a SOH estimation method is proposed based on charging data reconstruction combined with image processing.The charging voltage data is used to train the least squares generative adversarial network(LSGAN),which is validated under different levels of missing data.From a visual perspective,the Gram angle field method is applied to convert one-dimensional time series data into image data.This method fully preserves the time series characteristics and nonlinear evolution patterns,which avoids the difficulties and limited expressive power associated with manual feature extraction.At the same time,the Swin Transformer model is introduced to extract global structures and local details from images,enabling better capture of sequence change trends.Combined with the long short-term memory network(LSTM),this enables accurate estimation of battery SOH.Two different types of batteries are used to validate the test.The experimental results show that the proposed method has good estimation accuracy under different training proportions.
基金financially supported by National 863 Program (Grants No.2006AA 09A 102-09)National Science and Technology of Major Projects ( Grants No.2008ZX0 5025-001-001)
文摘Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets （POCS） based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the anti- noise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.
基金This work is supported by the research project (grant No. G20000467) of the Institute of Geology and Geophysics, CAS and bythe China Postdoctoral Science Foundation (No. 2004036083).
文摘In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.
基金The National Natural Science Foundation of China(No.61231002,61273266,61571106)the Foundation of the Department of Science and Technology of Guizhou Province(No.[2015]7637)
文摘In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.
基金supported by the National Science and Technology Major Projects (2008ZX05025)the Project of National Oil and Gas Resources Strategic Constituency Survey and Evaluation of the Ministry of Land and Resources,China (XQ-2007-05)
文摘Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.
基金financially supported by the Natural Science Foundation of China(No.41774133)the Open Funds of SINOPEC Key Laboratory of Geophysics(No.wtyjy-wx2017-01-04)National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2016ZX05024-003-011)
文摘In marine seismic exploration, ocean-bottom cable techniques accurately record the multicomponent seismic wavefield; however, the seismic wave propagation in fluid–solid media cannot be simulated by a single wave equation. In addition, when the seabed interface is irregular, traditional finite-difference schemes cannot simulate the seismic wave propagation across the irregular seabed interface. Therefore, an acoustic–elastic forward modeling and vector-based P-and S-wave separation method is proposed. In this method, we divide the fluid–solid elastic media with irregular interface into orthogonal grids and map the irregular interface in the Cartesian coordinates system into a horizontal interface in the curvilinear coordinates system of the computational domain using coordinates transformation. The acoustic and elastic wave equations in the curvilinear coordinates system are applied to the fluid and solid medium, respectively. At the irregular interface, the two equations are combined into an acoustic–elastic equation in the curvilinear coordinates system. We next introduce a full staggered-grid scheme to improve the stability of the numerical simulation. Thus, separate P-and S-wave equations in the curvilinear coordinates system are derived to realize the P-and S-wave separation method.
文摘Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.
基金performed under the auspices of the Chinese National Key Programme for Developing Basic Sciences (Grant No. 2010CB951701)the Innovation Projects of the Chinese Academy of Sciences (Grant No. KZCX2-YW-Q11-01)+1 种基金the National Natural Science Foundation of China (Grant Nos. 40825015and 40810059006)EU-FP7 project "CEOP-AEGIS"(Grant No. 212921)
文摘In this study, a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer （MODIS） data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape. As a case study, the methodology was applied to the whole Tibetan Plateau （TP） area. Four images of MODIS data （i.e., 30 January 2007, 15 April 2007, 1 August 2007, and 25 October 2007） were used in this study for comparison among winter, spring, summer, and autumn. The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform （TORP）. The results show the following： （1） The derived surface heating field for the TP area was in good accord with the land-surface status, showing a wide range of values due to the strong contrast of surface features in the area. （2） The derived surface heating field for the TP was very close to the field measurements （observations）. The APD （absolute percent difference） between the derived results and the field observations was 〈10%. （3） The mean surface heating field over the TP increased from January to April to August, and decreased in October. Therefore, the reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology. The limitations and further improvement of this method are also discussed.
基金supported by the funding of the Key Laboratory of Aerodynamic Noise Control(No.ANCL20190103)the State Key Laboratory of Aerodynamics,China(No.SKLA20180102)+1 种基金the Aeronautical Science Foundation of China(Nos.2018ZA52002,2019ZA052011)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD).
文摘Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.
基金funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam "Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin" (Grant No. TNMT.2017.08.06)
文摘Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
基金supported by the National Science and Technology Major project(No.2016ZX05024001003)the Innovation Consortium Project of China Petroleum,and the Southwest Petroleum University(No.2020CX010201).
文摘Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments.However,the irregular seismic acquisition is accompanied by the lack of acquisition data,which requires high-precision regularization.The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal,involving sparse transform base optimization and threshold modeling.First,this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency.Second,an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy.Test results show that the method has good adaptability to different seismic data and sparse transform bases.The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency.The parallel computing strategy of curvelet transform combined with OpenMP is further proposed,which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy.Finally,the actual acquisition data are used to verify the proposed method.The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.
基金This work is supported by the National Key Research and Development Program of China(Grant No.2021YFE0105500)the National Natural Science Foundation of China(Grant No.61671248)+2 种基金the Key Research and Development Plan of Jiangsu Province,China(Grant No.BE2018719)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No.SJCX19_0309)the Advantage Discipline Information and Communication Engineering of Jiangsu Province,China.
文摘Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.
