Data processing of the Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system and application to South China Sea data

The Kuiyang-ST2000 deep-towed high-resolution multichannel seismic system was designed by the First Institute of Oceanography,Ministry of Natural Resources(FIO,MNR).The system is mainly composed of a plasma spark source(source level:216 dB,main frequency:750 Hz,frequency bandwidth:150-1200 Hz)and a towed hydrophone streamer with 48 channels.Because the source and the towed hydrophone streamer are constantly moving according to the towing configuration,the accurate positioning of the towing hydrophone array and the moveout correction of deep-towed multichannel seismic data processing before imaging are challenging.Initially,according to the characteristics of the system and the towing streamer shape in deep water,travel-time positioning method was used to construct the hydrophone streamer shape,and the results were corrected by using the polynomial curve fitting method.Then,a new data-processing workflow for Kuiyang-ST2000 system data was introduced,mainly including float datum setting,residual static correction,phase-based moveout correction,which allows the imaging algorithms of conventional marine seismic data processing to extend to deep-towed seismic data.We successfully applied the Kuiyang-ST2000 system and methodology of data processing to a gas hydrate survey of the Qiongdongnan and Shenhu areas in the South China Sea,and the results show that the profile has very high vertical and lateral resolutions(0.5 m and 8 m,respectively),which can provide full and accurate details of gas hydrate-related and geohazard sedimentary and structural features in the South China Sea.

Combination of Tsoft and ET34-ANA-V80 software for the preprocessing and analysis of tide gauge data in French Polynesia

Since 2008 a network of five sea-level monitoring stations was progressively installed in French Polynesia.The stations are autonomous and data,collected at a sampling rate of 1 or 2 min,are not only recorded locally,but also transferred in real time by a radio-link to the NOAA through the GOES satellite.The new ET34-ANA-V80 version of ETERNA,initially developed for Earth Tides analysis,is now able to analyze ocean tides records.Through a two-step validation scheme,we took advantage of the flexibility of this new version,operated in conjunction with the preprocessing facilities of the Tsoft software,to recover co rrected data series able to model sea-level variations after elimination of the ocean tides signal.We performed the tidal analysis of the tide gauge data with the highest possible selectivity(optimal wave grouping)and a maximum of additional terms(shallow water constituents).Our goal was to provide corrected data series and modelled ocean tides signal to compute tide-free sea-level variations as well as tidal prediction models with centimeter precision.We also present in this study the characteristics of the ocean tides in French Polynesia and preliminary results concerning the non-tidal variations of the sea level concerning the tide gauge setting.

The development of data acquisition and processing application system for RF ion source

As the key ion source component of nuclear fusion auxiliary heating devices, the radio frequency （RF） ion source is developed and applied gradually to offer a source plasma with the advantages of ease of control and high reliability. In addition, it easily achieves long-pulse steady-state operation. During the process of the development and testing of the RF ion source, a lot of original experimental data will be generated. Therefore, it is necessary to develop a stable and reliable computer data acquisition and processing application system for realizing the functions of data acquisition, storage, access, and real-time monitoring. In this paper, the development of a data acquisition and processing application system for the RF ion source is presented. The hardware platform is based on the PXI system and the software is programmed on the LabVIEW development environment. The key technologies that are used for the implementation of this software programming mainly include the long-pulse data acquisition technology, multi- threading processing technology, transmission control communication protocol, and the Lempel-Ziv-Oberhumer data compression algorithm. Now, this design has been tested and applied on the RF ion source. The test results show that it can work reliably and steadily. With the help of this design, the stable plasma discharge data of the RF ion source are collected, stored, accessed, and monitored in real-time. It is shown that it has a very practical application significance for the RF experiments.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

Application and evaluation of layering shear method in LADCP data processing

The current velocity observation of LADCP(Lowered Acoustic Doppler Current Profiler)has the advantages of a large vertical range of observation and high operability compared with traditional current measurement methods,and is being widely used in the field of ocean observation.Shear and inverse methods are now commonly used by the international marine community to process LADCP data and calculate ocean current profiles.The two methods have their advantages and shortcomings.The shear method calculates the value of current shear more accurately,while the accuracy in an absolute value of the current is lower.The inverse method calculates the absolute value of the current velocity more accurately,but the current shear is less accurate.Based on the shear method,this paper proposes a layering shear method to calculate the current velocity profile by“layering averaging”,and proposes corresponding current calculation methods according to the different types of problems in several field observation data from the western Pacific,forming an independent LADCP data processing system.The comparison results have shown that the layering shear method can achieve the same effect as the inverse method in the calculation of the absolute value of current velocity,while retaining the advantages of the shear method in the calculation of a value of the current shear.

Status of UnDifferenced and Uncombined GNSS Data Processing Activities in China

With the continued development of multiple Global Navigation Satellite Systems(GNSS)and the emergence of various frequencies,UnDifferenced and UnCombined(UDUC)data processing has become an increasingly attractive option.In this contribution,we provide an overview of the current status of UDUC GNSS data processing activities in China.These activities encompass the formulation of Precise Point Positioning(PPP)models and PPP-Real-Time Kinematic(PPP-RTK)models for processing single-station and multi-station GNSS data,respectively.Regarding single-station data processing,we discuss the advancements in PPP models,particularly the extension from a single system to multiple systems,and from dual frequencies to single and multiple frequencies.Additionally,we introduce the modified PPP model,which accounts for the time variation of receiver code biases,a departure from the conventional PPP model that typically assumes these biases to be time-constant.In the realm of multi-station PPP-RTK data processing,we introduce the ionosphere-weighted PPP-RTK model,which enhances the model strength by considering the spatial correlation of ionospheric delays.We also review the phase-only PPP-RTK model,designed to mitigate the impact of unmodelled code-related errors.Furthermore,we explore GLONASS PPP-RTK,achieved through the application of the integer-estimable model.For large-scale network data processing,we introduce the all-in-view PPP-RTK model,which alleviates the strict common-view requirement at all receivers.Moreover,we present the decentralized PPP-RTK data processing strategy,designed to improve computational efficiency.Overall,this work highlights the various advancements in UDUC GNSS data processing,providing insights into the state-of-the-art techniques employed in China to achieve precise GNSS applications.

Studies of an event-building algorithm of the readout system for the twin TPCs in HFRS

The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.

Enhancing the vertical resolution of lunar penetrating radar data using predictive deconvolution

The Yutu-2 rover onboard the Chang’E-4 mission performed the first lunar penetrating radar detection on the farside of the Moon.The high-frequency channel presented us with many unprecedented details of the subsurface structures within a depth of approximately 50 m.However,it was still difficult to identify finer layers from the cluttered reflections and scattering waves.We applied deconvolution to improve the vertical resolution of the radar profile by extending the limited bandwidth associated with the emissive radar pulse.To overcome the challenges arising from the mixed-phase wavelets and the problematic amplification of noise,we performed predictive deconvolution to remove the minimum-phase components from the Chang’E-4 dataset,followed by a comprehensive phase rotation to rectify phase anomalies in the radar image.Subsequently,we implemented irreversible migration filtering to mitigate the noise and diminutive clutter echoes amplified by deconvolution.The processed data showed evident enhancement of the vertical resolution with a widened bandwidth in the frequency domain and better signal clarity in the time domain,providing us with more undisputed details of subsurface structures near the Chang’E-4 landing site.

System for Control, Data Collection and Processing in 8 mm Portable Microwave Radiometer-Scatterometer

In this paper we describe a system used to control,collect and process data in an 8 mm portable microwave radiometer scatterometer .We focus on hardware and software design of the system based on a PIC16F874 chip. The system has been successfully used in an 8 mm portable microwave radiometer scatterometer. Compared with other similar systems, the system modularization, miniaturization and intelligentization are improved so as to meet portable instrument requirements.

Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.

Fast Data Processing of a Polarimeter-Interferometer System on J-TEXT

A method of fast data processing has been developed to rapidly obtain evolution of the electron density profile for a multichannel polarimeter-interferometer system（POLARIS）on J-TEXT. Compared with the Abel inversion method, evolution of the density profile analyzed by this method can quickly offer important information. This method has the advantage of fast calculation speed with the order of ten milliseconds per normal shot and it is capable of processing up to 1 MHz sampled data, which is helpful for studying density sawtooth instability and the disruption between shots. In the duration of a flat-top plasma current of usual ohmic discharges on J-TEXT, shape factor u is ranged from 4 to 5. When the disruption of discharge happens, the density profile becomes peaked and the shape factor u typically decreases to 1.

A Review of the Status and Development Strategies of Computer Science and Technology Under the Background of Big Data

This article discusses the current status and development strategies of computer science and technology in the context of big data.Firstly,it explains the relationship between big data and computer science and technology,focusing on analyzing the current application status of computer science and technology in big data,including data storage,data processing,and data analysis.Then,it proposes development strategies for big data processing.Computer science and technology play a vital role in big data processing by providing strong technical support.

Adaptive Method’s Application for the Identification Systems in Dynamic Weighing

In this work,the possibility of adaptive algorithm in WIM(weight-in-motion)systems,in which fibre optic sensors are used,is shown.Appointment of dynamic weighing device consists in determining the weight and type of vehicle.In this work an algorithm for processing the input data and fiber optic sensor to create the database used in the algorithm is presented.The results of the algorithm for the identification of vehicles are given.The conclusions are made and options of increasing the accuracy of the identification algorithm are considered.

Research on Chinese Antarctic Data Directory System^->─Collecting,processing,examining and submitting data directory

Based on the general framework of ADDS( Antarctic Data Directory System) established by SCAR- COMNAP ad hoc Planning Group on Antarctic data management,the CN- ADDS( Chinese Antarctic Data Directory System) projectis go- ing on,of which the research and activity keeps to the available method and technique in ADDS development and allows for the Chinese specific status in Antarctic data man- agementas well.Atpresent,authoring and submitting timely Antarctic data directory in China is one of the key issuesthatisto be dealtwith necessarily.This paper aims at studying the technical procedure in collecting,processing,examining and submitting data directory. In additional,italso discusses the efficient collection of data directory, which needs the effort of administrative and technical support.

Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

Magnetic field data processing methods of the China Seismo-Electromagnetic Satellite

The High Precision Magnetometer(HPM) on board the China Seismo-Electromagnetic Satellite(CSES) allows highly accurate measurement of the geomagnetic field; it includes FGM(Fluxgate Magnetometer) and CDSM(Coupled Dark State Magnetometer)probes. This article introduces the main processing method, algorithm, and processing procedure of the HPM data. First, the FGM and CDSM probes are calibrated according to ground sensor data. Then the FGM linear parameters can be corrected in orbit, by applying the absolute vector magnetic field correction algorithm from CDSM data. At the same time, the magnetic interference of the satellite is eliminated according to ground-satellite magnetic test results. Finally, according to the characteristics of the magnetic field direction in the low latitude region, the transformation matrix between FGM probe and star sensor is calibrated in orbit to determine the correct direction of the magnetic field. Comparing the magnetic field data of CSES and SWARM satellites in five continuous geomagnetic quiet days, the difference in measurements of the vector magnetic field is about 10 nT, which is within the uncertainty interval of geomagnetic disturbance.

A machine learning framework for low-field NMR data processing

Low-field(nuclear magnetic resonance)NMR has been widely used in petroleum industry,such as well logging and laboratory rock core analysis.However,the signal-to-noise ratio is low due to the low magnetic field strength of NMR tools and the complex petrophysical properties of detected samples.Suppressing the noise and highlighting the available NMR signals is very important for subsequent data processing.Most denoising methods are normally based on fixed mathematical transformation or handdesign feature selectors to suppress noise characteristics,which may not perform well because of their non-adaptive performance to different noisy signals.In this paper,we proposed a“data processing framework”to improve the quality of low field NMR echo data based on dictionary learning.Dictionary learning is a machine learning method based on redundancy and sparse representation theory.Available information in noisy NMR echo data can be adaptively extracted and reconstructed by dictionary learning.The advantages and application effectiveness of the proposed method were verified with a number of numerical simulations,NMR core data analyses,and NMR logging data processing.The results show that dictionary learning can significantly improve the quality of NMR echo data with high noise level and effectively improve the accuracy and reliability of inversion results.

Data processing and initial results of Chang'e-3 lunar penetrating radar

To improve our understanding of the formation and evolution of the Moon, one of the payloads onboard the Chang＇e-3 （CE-3） rover is Lunar Penetrating Radar （LPR）. This investigation is the first attempt to explore the lunar subsurface structure by using ground penetrating radar with high resolution. We have probed the subsur- face to a depth of several hundred meters using LPR. In-orbit testing, data processing and the preliminary results are presented. These observations have revealed the con- figuration of regolith where the thickness of regolith varies from about 4 m to 6 m. In addition, one layer of lunar rock, which is about 330 m deep and might have been accumulated during the depositional hiatus of mare basalts, was detected.

A novel technique for automatic seismic data processing using both integral and local feature of seismograms

A novel technique for automatic seismic data processing using both integral and local feature of seismograms was presented in this paper. Here, the term integral feature of seismograms refers to feature which may depict the shape of the whole seismograms. However, unlike some previous efforts which completely abandon the DIAL approach, i.e., signal detection, phase identifi- cation, association, and event localization, and seek to use envelope cross-correlation to detect seismic events directly, our technique keeps following the DIAL approach, but in addition to detect signals corresponding to individual seismic phases, it also detects continuous wave-trains and explores their feature for phase-type identification and signal association. More concrete ideas about how to define wave-trains and combine them with various detections, as well as how to measure and utilize their feature in the seismic data processing were expatiated in the paper. This approach has been applied to the routine data processing by us for years, and test results for a 16 days＇ period using data from the Xinjiang seismic station network were presented. The automatic processing results have fairly low false and missed event rate simultaneously, showing that the new technique has good application prospects for improvement of the automatic seismic data processing.

Cost of Multicast Logical Key Tree Based on Hierarchical Data Processing

How to design a multicast key management system with high performance is a hot issue now. This paper will apply the idea of hierarchical data processing to construct a common analytic model based on directed logical key tree and supply two important metrics to this problem： re-keying cost and key storage cost. The paper gives the basic theory to the hierarchical data processing and the analyzing model to multieast key management based on logical key tree. It has been proved that the 4-ray tree has the best performance in using these metrics. The key management problem is also investigated based on user probability model, and gives two evaluating parameters to re-keying and key storage cost.