WO2020177491A1 - 微动采集设备、无线遥测系统及数据质量监控方法 - Google Patents

微动采集设备、无线遥测系统及数据质量监控方法 Download PDF

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Publication number
WO2020177491A1
WO2020177491A1 PCT/CN2020/072077 CN2020072077W WO2020177491A1 WO 2020177491 A1 WO2020177491 A1 WO 2020177491A1 CN 2020072077 W CN2020072077 W CN 2020072077W WO 2020177491 A1 WO2020177491 A1 WO 2020177491A1
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Prior art keywords
data
micro
motion
module
acquisition device
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PCT/CN2020/072077
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English (en)
French (fr)
Inventor
唐学峰
胡鑫
俞小露
杨阳
陈静
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合肥国为电子有限公司
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Priority claimed from CN201910169075.5A external-priority patent/CN109856675B/zh
Application filed by 合肥国为电子有限公司 filed Critical 合肥国为电子有限公司
Priority to KR1020217031130A priority Critical patent/KR20210131413A/ko
Priority to DE112020000702.2T priority patent/DE112020000702T5/de
Publication of WO2020177491A1 publication Critical patent/WO2020177491A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
    • G01V1/162—Details
    • G01V1/164—Circuits therefore
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/22—Transmitting seismic signals to recording or processing apparatus
    • G01V1/223—Radioseismic systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/25—Acquisition or tracking or demodulation of signals transmitted by the system involving aiding data received from a cooperating element, e.g. assisted GPS
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/24—Recording seismic data
    • G01V1/247—Digital recording of seismic data, e.g. in acquisition units or nodes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/364—Seismic filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V13/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices covered by groups G01V1/00 – G01V11/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
    • G01V1/18—Receiving elements, e.g. seismometer, geophone or torque detectors, for localised single point measurements
    • G01V1/181—Geophones
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2200/00—Details of seismic or acoustic prospecting or detecting in general
    • G01V2200/10—Miscellaneous details
    • G01V2200/12—Clock synchronization-related issues
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2200/00—Details of seismic or acoustic prospecting or detecting in general
    • G01V2200/10—Miscellaneous details
    • G01V2200/14—Quality control
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/12—Signal generation
    • G01V2210/123—Passive source, e.g. microseismics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/14—Signal detection
    • G01V2210/142—Receiver location
    • G01V2210/1425—Land surface
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G01V2210/43—Spectral
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/70—Other details related to processing
    • G01V2210/72—Real-time processing

Definitions

  • the invention relates to the technical field of seismograph electronics, in particular to a micro-motion acquisition equipment, system and data quality monitoring method.
  • Micromotion Under natural conditions, there is a small amplitude and a specific periodic vibration anywhere on the surface of the earth. These signals are usually called micromotion in geophysical prospecting. Micromotion has no specific seismic source and is formed by the collection of incident waves in different directions. to make. Use the array observation system to record the weak ground vibration, and then use the data processing method to extract the Rayleigh wave phase velocity dispersion curve from the micro-motion data, and then invert to obtain the formation shear wave velocity structure information. This geophysical detection method is usually called micro Motion detection. Micro-motion detection can be realized by using natural sources without artificial seismic sources, avoiding environmental damage caused by active earthquakes, and is an important technical means for resource exploration.
  • both cable-based centralized exploration instruments and cable-less storage instruments can be used for micro-motion detection.
  • Centralized exploration often uses cables to connect the shock picks to the data collector, and then disperse the shock picks to the array observation system.
  • the disadvantage of this kind of instrument is that it is restricted by the cable, which makes the field micro-motion array Wiring is difficult, especially in areas with complex terrain, and the labor cost is high, which cannot meet the needs of large-scale array layout and micro-motion detection in complex terrain.
  • the cableless storage instrument is an autonomous node data acquisition station. During the construction process, the collected data is stored in the acquisition station. After the construction is completed, the data stored in the instrument is downloaded, and the final required data is synthesized according to requirements file.
  • the cableless storage instrument is light, convenient and efficient to deploy in the field, and is not affected by the natural environment such as the surface. It can be applied to various complex surface conditions and can be used for micro-motion detection.
  • ordinary non-cable storage instruments are difficult to use for micro-motion detection. This is because the vibration amplitude of micro-motion signals is very small. Any natural site and human factors have a great influence on the signal amplitude, and the collected data will inevitably be affected.
  • Carrying interference from the surrounding environment, such as vehicle communication, human activities, construction vibration, weather changes, etc., the signal acquisition system in ordinary cableless storage instruments may cause noise due to possible zero drift, cable interference, and even some equipment’s own noise signals. Nearly annihilated the micro-motion signal component that can characterize the formation information, resulting in the inability to carry out effective micro-motion exploration, so the hardware design of the data acquisition system is extremely important.
  • the existing cableless storage instruments usually export the data after the construction is completed and then perform data interpretation and analysis, but this working mode lacks remote, real-time, reliable monitoring records and field quality monitoring methods. Therefore, it is not conducive to micro-motion detection.
  • on-site personnel cannot evaluate data quality in time. To ensure data quality, lengthy data collection time is required, which reduces construction efficiency and even leads to rework .
  • Telemetry is a technology that collects and transmits natural source background noise signals at a short distance to a remote computer workstation to achieve remote testing.
  • the telemetry system includes shock picks, communication equipment and data processing equipment.
  • Data acquisition equipment and signal transmission technology are two key technologies of telemetry. The sampling accuracy and reliability of data acquisition equipment, as well as the transmission speed and anti-interference ability of the communication system, determine the performance of the telemetry system.
  • the three commonly used short-range wireless transmission technologies for communication equipment in telemetry systems are ZigBee, Bluetooth (Buletooth) and WiFi. These three wireless transmission technologies have some shortcomings, such as small transmission range, low data transmission rate, and coverage. Limited and poor mobility.
  • WiFi has a fast transmission rate, it can be used to set up base stations in the field to topology the coverage area of the wireless local area network, and establish a connection with the base station through the integration of the WiFi module in the collection station, but it is cumbersome to set up the base station in the actual exploration application, especially for large
  • the range of micro-motion observation system requires more base stations, which will seriously affect the construction progress.
  • its transmission speed and reliability are difficult to effectively guarantee.
  • the purpose of the present invention is to solve the above-mentioned problems in the background technology and improve the accuracy of micro-motion exploration.
  • the present invention adopts a micro-motion acquisition device, which is connected to the output end of the shock pickup, and includes a data acquisition module, a data processing module, a network port forwarding module, a GPS module and a data storage module, and the output of the shock pickup
  • the end is connected with the data acquisition module, the data acquisition module and the GPS module are all connected with the data processing module, and the data processing module, the network port forwarding module and the data storage module are all connected to the embedded Linux system platform.
  • the data acquisition module includes at least one synchronous acquisition channel, each synchronous acquisition channel includes a bandpass filter and an ADC conversion chip, and the bandpass filter and the ADC conversion chip are connected via a variable gain amplifier.
  • the further improvement of the present invention is that the data storage module includes a Flash module and a USB interface module for inserting a U disk.
  • the further improvement point of the present invention is that the network port forwarding module is connected with a server.
  • the present invention provides a micro-motion detection wireless telemetry system, including a server, a user client, and a micro-motion array observation system composed of the aforementioned micro-motion acquisition equipment;
  • the data transmission channel between the micro-motion acquisition device and the server, and the data transmission channel between the server and the user client are all established through Internet communication.
  • the further improvement point of the present invention is that the data transmission channel is a socket communication transmission channel.
  • the present invention provides a data quality monitoring method that can process and evaluate collected micro-motion signals in real time, including the following steps:
  • the user client receives the data packet forwarded by the server, and the data packet is added with GPS time stamp information and micro-motion collection device number information;
  • step S4 Determine whether the time length for the data file to receive the sampled data is less than the preset sub-time length L, if not, execute step S5;
  • the further improvement of the present invention lies in that it also includes:
  • the quality of the data collected by the micro-motion acquisition device is evaluated according to the pseudo-color map of the dispersion spectrum.
  • a further improvement of the present invention is that, after the calculation of the spatial autocorrelation coefficient of each station pair in the time period M, the method further includes:
  • the grid error is obtained, specifically:
  • the average value of the measured spatial autocorrelation coefficient is compared with the grid theoretical spatial autocorrelation coefficient to obtain the grid error.
  • the further improvement of the present invention lies in that it also includes:
  • the average error value is color-mapped to update the pseudo-color map of the dispersion spectrum in real time.
  • the further improvement of the present invention lies in that it also includes:
  • the client receives the state information of the micro-motion collection device forwarded by the server, and the state information of the device includes the temperature, power, storage space, and the sampling parameters of the micro-motion collection device;
  • the client terminal displays the state information of the micro-motion acquisition device in a chart form
  • the client dynamically displays the sampled data according to the time stamp and the serial number of the micro-movement collection device.
  • the further improvement of the present invention lies in that it also includes:
  • the client determines whether the data packet is lost during transmission according to the timestamp
  • the client sends a packet loss search instruction to the server, and the packet loss search instruction carries a packet loss timestamp and a micro-motion collection device number;
  • the server parses the packet loss search instruction, and sends the packet loss timestamp and the micro-motion collection device number to the corresponding micro-motion collection device, so that the micro-motion collection device can read the corresponding data packet according to the time stamp and Resend to the server.
  • the micro-motion acquisition device in the present invention receives the GPS satellite standard time signal by adding a GPS module, and automatically and real-timely compares the internal clock signal through the GPS satellite standard time signal After calibration, the synchronization error is less than 15ns, which can ensure the synchronization of data collection by each micro-movement collection device during long-term data collection.
  • the micro-motion detection wireless telemetry system uses the Internet communication method to establish the network connection between the micro-motion acquisition device and the server, as well as the network connection between the server and the client.
  • the wireless telemetry system constitutes is not restricted by region, terrain and transmission distance. As long as there is communication network coverage, the data collection work of the micro-motion collection equipment can be remotely monitored and the working conditions of the field equipment can be reflected in time.
  • the micro-motion detection site calculates the power spectrum of each micro-motion acquisition device within the effective frequency range defined by the user. If the power spectrum is different but the shape is basically similar and the energy is similar, then It shows that the collected micro-motion signals are in a random and stable state in time and space, and the micro-motion signals collected by the micro-motion acquisition device meet the requirements; if the power spectrum of one or more micro-motion acquisition devices is significantly different, it indicates that the The collected micro-motion signals have significant interference, and the field staff can conduct on-site guidance and processing accordingly.
  • Figure 1 is a schematic diagram of the structure of a micro-motion acquisition device
  • Figure 2 is a schematic diagram of the structure of a micro-motion detection wireless telemetry system
  • Figure 3 is a schematic flow chart of a quality monitoring method for micro-motion acquisition equipment
  • Figure 4 is a schematic diagram of the data quality assessment process of a single micro-motion acquisition device
  • Figure 5 is the real-time power spectrum of 10 micro-motion acquisition devices
  • Figure 6 is a flow chart of data quality monitoring of the micro-motion array observation system
  • Fig. 7 is a schematic diagram of a pseudo-color map of real-time dispersion spectrum calculated by an observation system composed of 10 micro-motion acquisition devices.
  • this embodiment discloses a micro-motion acquisition device, which is used to connect to the output end of the shock pickup 10 to collect the micro-motion signal output by the shock pickup 10.
  • the micro-motion acquisition equipment includes a data acquisition module 1, a data processing module 2, a network port forwarding module 3, a GPS module 4 and a data storage module 5.
  • the output end of the vibration pickup 10 is connected to the data acquisition module 1, the data acquisition module 1 and the GPS module 4. All are connected to the data processing module 2, and the data processing module 2, the network port forwarding module 3 and the data storage module 5 are all connected to the embedded Linux system platform.
  • the data acquisition module 1 sends the micro-motion signals collected from the shock pickup 10 to the data processing module 2;
  • the GPS module 4 is used to receive the standard signal of the GPS satellite, and use the standard signal of the GPS satellite as the reference
  • the internal clock signal is corrected in real time and automatically, so that the corrected clock signal is used to synchronize the data collected by the data acquisition module 1, which can ensure the synchronization of the data acquisition of each micro-movement acquisition device during the long-term data acquisition.
  • the GPS time stamp information is added to the data packets collected by the collection module 1 in real time.
  • the data storage module 5 is used to store the Linux system files required for the operation of the embedded Linux system platform 6, and the micro-motion signals collected by the data acquisition module 1 are stored; the network port forwarding module 3 is used to transfer the After adding the number information of the micro-motion acquisition device to the sampling data, it is packaged and sent to the server.
  • the data acquisition module 1 includes at least one synchronous acquisition channel, and each synchronous acquisition channel includes a band-pass filter and an ADC conversion chip, and the band-pass filter and the ADC conversion chip are connected via a variable gain amplifier.
  • three simultaneous acquisition channels are used, and 32-bit ADC chips and components with the same frequency characteristics are used to reduce the quantization noise of the instrument itself, improve the signal-to-noise ratio of the instrument, and make the acquired micro-motion signals more Accurate, the frequency characteristic curve of each acquisition channel is consistent, and each channel has good consistency.
  • the data storage module 5 includes a Flash chip and a USB interface module for inserting a U disk.
  • the Flash chip is used to store the Linux system files required for the operation of the embedded Linux system platform 6 and temporarily store the sampled data
  • the U disk is used to write the sampled data into the U disk in real time.
  • the working process of the micro-motion acquisition device in this embodiment is:
  • the micro-motion acquisition device After the micro-motion acquisition device is powered on, start the embedded Linux system platform 6. After the initialization is completed, send the GPS command to the GPS module 4. After the GPS module 4 gets the command, it enters the search GPS signal state. When the GPS time signal is successfully obtained , Send a feedback signal to the embedded Linux system platform 6, and correct its internal clock signal through the received GPS satellite standard time signal.
  • the embedded Linux system platform 6 configures sampling parameters for the data processing module 2.
  • the sampling parameters include sampling start time, sampling duration, sampling rate, and sampling channel gain.
  • the data acquisition module 1 starts to sample data from the output port of the shock pick 10, and sends the collected sampling data to the data processing module 2.
  • the data processing module 2 performs sampling according to the clock signal of the GPS module 4. Add GPS time stamp information to the data, and integrate the sampling data of 3 channels.
  • the embedded Linux system platform 6 reads the sampled data in the data processing module 5, and sends the sampled data to the data storage module 5 and the network port forwarding module 3 respectively.
  • the data storage module 5 writes the data into the data file according to the time stamp information. Specify the location and store the data file in the U disk.
  • the network port forwarding module 3 adds the number information of the micro-motion collection device to the sampled data, it is packaged into a data packet and sent to the server.
  • the data processing module 2 uses an FPGA chip, which has the advantages of high clock frequency, small internal delay, and fast running speed.
  • FPGA custom system functions can achieve rapid on-site response and use ping-pong cache technology to overcome The impact of storage rate caused by system performance fluctuations further ensures the real-time performance and reliability of the micro-motion acquisition equipment.
  • this embodiment discloses a micro-motion detection wireless telemetry system, including a server 30, a user client 40, and a 2-fold micro-motion observation system composed of 7 micro-motion acquisition devices 20;
  • the data transmission channel between the micro-motion acquisition device 20 and the server 30, as well as the data transmission channel between the server 30 and the user client 40 are established through Internet communication.
  • This embodiment adopts a client/server (C/S) architecture.
  • the server performs intermediate conversion layer functions during system execution.
  • the client includes a user client and a collection device client, and the user client has a data monitoring function. It is used to monitor the client of the acquisition device, which is a micro-motion array observation system composed of micro-motion acquisition devices.
  • the client/server uses the M2M Internet communication method of 4G communication technology to establish a convenient and fast socket communication method, which is applied to the data transmission channel between the client and the server of the collection device, and the data transmission channel between the user client and the server .
  • the Internet network between the server and the client in this embodiment may also be established through communication technologies such as 3G and 5G.
  • the micro-motion detection wireless telemetry system in this embodiment adopts 3G, 4G, 5G and other communication technologies to establish a system network connection, and mobile communication technology has strong anti-interference ability, high transmission rate, wide network coverage and accessibility.
  • the characteristics of short entry time and low construction cost make the micro-motion detection wireless telemetry system not restricted by region, terrain, distance, etc.
  • 4G signal coverage it can remotely monitor the data collection work and reflect the working conditions of the field equipment in time. Alarms can be set for abnormal conditions such as temperature, power, storage space, etc., to maintain in time, improve efficiency, and provide historical data query, which is displayed through charts.
  • the working process of the micro-motion detection wireless telemetry system is:
  • the micro-motion collection device When the micro-motion collection device is connected to the server-side socket, the micro-motion collection device packages the device status information, including data collection status, temperature, power, and storage space information of the collection device, and sends it to the server. After the server receives the status information of the collection device, it temporarily stores the information according to the collection station number.
  • the data collection status includes the sampling start time, the sampling duration, the sampling rate, and the channel gain.
  • the server checks whether there is a successfully established user client, and if so, forwards the updated device status information to the user client.
  • the user client After the user client is successfully connected to the server, it will first receive the status information of different collection devices. The user client will display the status information of each collection device in the form of a chart, and then the server will forward the real-time sampling data of each collection device. When the user client receives the sampling data information, it first separates the sampling data, time stamp and collection station number in the data packet, and then dynamically displays the sampling data in real time according to the time stamp and collection device number information, and corresponds to different collection stations. For different data files, write the sampled data into the data file, and the location of the written file is calculated according to the timestamp.
  • the write completion signal is sent as a subsequent quality monitoring method for data interpretation instructions.
  • the user client analyzes the sampled data.
  • the user client includes a device status monitoring module and remote data Recovery module, data management module and quality monitoring module;
  • the equipment status monitoring module is used to monitor the status information of the micro-motion acquisition equipment.
  • the status information of the micro-motion acquisition equipment includes the sampling parameters, temperature, power and storage space information of the micro-motion acquisition equipment;
  • the remote data recovery module is used to receive and store the sampled data collected by the micro-motion collection device forwarded by the server;
  • the data management module is used to manage and integrate the sampling data stored in the remote data recovery module, and to carry the time stamp information in the integrated data, and calculate the location where the data is written into the data file to collect different micro-motions
  • the sampling data collected by the device is written into the designated location of the corresponding data file according to the number of the micro-movement collection device;
  • the quality monitoring module is used to analyze the sampled data and evaluate the quality of the micro-motion acquisition equipment.
  • the quality monitoring module includes an equipment quality monitoring unit and a micro-motion signal quality monitoring unit, where:
  • the equipment quality monitoring unit is used to calculate the power spectrum within the user-defined effective frequency range according to the sampled data, and monitor the quality of the micro-motion acquisition equipment according to the power spectrum. Specifically:
  • the time length of the sampled data received by the data file is greater than or equal to L
  • read the sub-length L segment time series data and calculate the power spectrum of the sub-length L time series data as W i .
  • the time length L meets the requirements, repeat the above steps, finally accumulate and average the power spectrum W avj of each time period, and dynamically update and display the W avj power spectrum of each acquisition device in the form of a graph in real time.
  • the power spectrum of the micro-motion signal collected by each micro-motion acquisition device in the effective frequency range if the power spectrum is different, but the shape is basically similar, the energy is similar, indicating that the collected micro-motion signal is in a random and stable state in time and space , The micro-motion signal collected by the collection device meets the requirements. If there is a big difference in the power spectrum of one or more micro-motion acquisition equipment, it indicates that the micro-motion signal collected by the micro-motion acquisition device has significant interference, and the field staff can provide on-site guidance to the on-site micro-motion acquisition equipment based on this information deal with.
  • the micro-motion signal quality monitoring unit is used to draw a pseudo-color map of the dispersion spectrum and evaluate the quality of the data collected by the micro-motion acquisition device based on the dispersion spectrum. Specifically:
  • the sampling period of station pair 1 is 1:00 ⁇ 5:00, and the sampling period of station pair 2 is 2:00 ⁇ 6:00, then the sampling overlap time of station pair 1 and station pair 2 is 2. :00 ⁇ 5:00.
  • the period M in this embodiment is a constant obtained by those skilled in the art through a lot of experiments and used to compare the sampling overlap time with the station.
  • the theoretical spatial autocorrelation coefficient value is calculated using the zero-order standard Bessel function and the station pair spacing. Then calculate the error based on the theoretical spatial autocorrelation value and the actual spatial autocorrelation coefficient average value of the station, and then color-map the calculated error to draw a pseudo-color map of the dispersion spectrum.
  • field personnel can evaluate the effect of the Rayleigh wave dispersion curve based on the continuity and stability of the pseudo-color map of the dispersion spectrum, and can also evaluate whether the exploration depth meets the requirements based on the dispersion curve.
  • the dispersion spectrum is continuous and stable, the data collection work of the micro-motion acquisition device can be terminated, unnecessary and lengthy data acquisition can be avoided, the data collection efficiency of the micro-motion acquisition device can be improved, and rework can be avoided.
  • the user client also judges whether the time stamp is discontinuous during the transmission process. If it is, it indicates that there is network packet loss.
  • the method of judging network packet loss is to compare whether the sequence numbers of the previous and current timestamps are continuous. If continuous, the specified packet loss timestamp is sent to the server.
  • the server obtains the collection device code according to the parsing of the instruction, and then forwards the collection station number to the corresponding collection device, and the collection device will read the data file from the data file according to the time stamp information. Take the corresponding data packet, and send the data packet to the server through the network port forwarding module.
  • this embodiment discloses a data quality monitoring method, which is used by the user client to process the data packets sent by the micro-motion array observation system, including the following steps S1 to S7:
  • the user client receives the data packet forwarded by the server, and the data packet is added with GPS time stamp information and micro-motion collection device number information;
  • sampling parameters include sampling start time, sampling duration, and sampling gain of each channel.
  • the sampling parameters are set by the embedded Linux system platform 6 before the data acquisition module 1 performs data acquisition.
  • step S4 Determine whether the time length for the data file to receive the sampled data is less than the preset sub-time length L, if not, execute step S5;
  • W preAvj is the average value of the previous power spectrum
  • W i is the power spectrum of the current sub-time length L
  • W avj is the average value of the current power spectrum
  • the W avj power spectrum of each acquisition device is displayed as a graph, and the above process is repeated in turn to dynamically display the updated power spectrum of each acquisition device in real time.
  • this embodiment also evaluates the quality of the data collected by the observation array system by monitoring the dispersion spectrum, including the following steps:
  • micro-motion acquisition equipment if there are micro-motion acquisition device 1, micro-motion acquisition device 2 and micro-motion acquisition device 3, the result of the pairwise combination is (micro-motion acquisition device 1, micro-motion acquisition device 2 ), (micro-motion acquisition device 1, micro-motion acquisition device 3) and (micro-motion acquisition device 2, micro-motion acquisition device 3).
  • each micro-movement exploration equipment calculates the distance between each station pair.
  • the station pairs classify the spatial autocorrelation coefficients of the array pairs with the same distance and calculate the average value to obtain Average value of measured spatial autocorrelation coefficients at different intervals;
  • the average value of the measured spatial autocorrelation coefficient is compared with the grid theoretical spatial autocorrelation coefficient to obtain the grid error.
  • the effective frequency range and phase velocity range are initialized, and the frequency and phase velocity are gridded to obtain effective frequency grid data and phase velocity grid data.
  • the theoretical spatial autocorrelation coefficient under each grid is calculated.
  • the calculation formula is to use the first kind of zero-order Bessel function, as follows:
  • f is the effective frequency
  • v r (f) is the phase velocity
  • r is the distance between the two micro-motion acquisition devices in the station alignment
  • this embodiment also dynamically updates the pseudo-color map of the dispersion spectrum in real time, which specifically includes:
  • the error values of the pseudo-dispersion spectrum of each sub-time are linearly superimposed, and the average value is calculated, and the average value is used as the final pseudo-color dispersion spectrum.
  • a micro-motion detection array system composed of 10 acquisition devices uses the dispersion spectrum pseudo-color map obtained by the above method, respectively Real-time image of 1 minute dispersion spectrum pseudo-color map (see Figure 7-(a)), 5-minute real-time dispersion spectrum pseudo-color map (see Figure 7-(b)), 15-minute dispersion spectrum pseudo-color map in real time Figure (see Figure 7-(c)) and the real-time graph of the 20-minute dispersion spectrum pseudo-color map (see Figure 7-(d)).
  • the color gray value represents the error value of different sizes. The darker the gray value, the greater the error, and the smaller the gray value, the smaller the error; the frequency of the circular hollow connection according to the minimum error at different frequencies Scattered curve.
  • the dispersion spectrum pseudo-color map of the dispersion spectrum it can be found that when the total sampling time is less than 5 minutes, the dispersion spectrum and the dispersion curve are different and unstable, which indicates that the sampling time does not meet the requirements and needs to be collected for a period of time.
  • the sampling time exceeds 15 minutes, the dispersion spectrum pseudo-color map and dispersion curve are stable, so the sampling time can be regarded as meeting the requirements.
  • the dispersion spectrum pseudo-color map has excellent continuity, so the quality of on-site micro-motion data is high. This quality assessment method provides a basis for later data processing, and at the same time avoids rework.
  • the user client performs the following processing on the sampled data forwarded by the server:
  • the client receives the state information of the micro-motion collection device forwarded by the server, and the state information of the device includes the temperature, power, storage space, and the sampling parameters of the micro-motion collection device;
  • the client terminal displays the state information of the micro-motion acquisition device in a chart form
  • the client dynamically displays the sampled data according to the time stamp and the serial number of the micro-movement collection device.
  • state information and sampling data of the micro-movement acquisition device are dynamically displayed to facilitate the user's visual observation.
  • the user client performs the following processing on the sampled data forwarded by the server:
  • the client determines whether the data packet is lost during transmission according to the timestamp
  • the client sends a packet loss search instruction to the server, and the packet loss search instruction carries a packet loss timestamp and a micro-motion collection device number;
  • the server parses the packet loss search instruction, and sends the packet loss timestamp and the micro-motion collection device number to the corresponding micro-motion collection device, so that the micro-motion collection device can read the corresponding data packet according to the time stamp and Resend to the server.
  • time stamp information it is judged whether the sampled data has packet loss during transmission, so as to ensure the integrity of the sampled data transmission, and to ensure the accuracy of equipment quality monitoring and sampling data quality monitoring.
  • the micro-motion acquisition device in the present invention receives the GPS satellite standard time signal by adding a GPS module, and automatically corrects the internal clock signal in real time through the GPS satellite standard time signal.
  • the synchronization error is less than 15ns, which can be used for long-term data In the collection, the synchronization of data collection by each micro-motion collection device is ensured, which has industrial practicability.

Abstract

一种微动采集设备(20)、系统及数据质量监控方法，属于地震仪电子技术领域，微动采集设备(20)连接在拾震器(10)的输出端，包括数据采集模块(1)、数据处理模块(2)、网口转发模块(3)、GPS模块(4)和数据存储模块(5)，拾震器(10)输出端连接数据采集模块(1)，数据采集模块(1)和GPS模块(4)均与数据处理模块(2)连接，数据处理模块(2)、网口转发模块(3)和数据存储模块(5)均连接至嵌入式Linux系统平台(6)。通过GPS卫星标准的时间信号自动地、实时地对内部时钟信号进行校正，可在长时间数据采集中确保各微动采集设备进行数据采集的同步性。

Description

微动采集设备、无线遥测系统及数据质量监控方法 技术领域
本发明涉及地震仪电子技术领域，特别涉及一种微动采集设备、系统及数据质量监控方法。
 背景技术
在自然条件下，地球表面的任何地方均存在一种微小的振幅和特定的周期振动，这些信号在物探中通常称之为微动，微动没有特定的震源，由不同方向的入射波集合而成。利用台阵观测系统记录地面微弱振动，再利用数据处理方法从微动数据中提取瑞雷波相速度频散曲线，进而反演获得地层横波速度结构信息，这种地球物理探测方法通常称为微动探测。微动探测利用天然源即可实现，无需人工震源，避免了有源地震对环境造成的损坏，是资源勘探的重要技术手段。
目前，基于线缆的集中式勘探仪器和无缆存储式仪器均可用于微动探测。集中式勘探常利用线缆将各拾震器连接在数据采集器上，再将拾震器分散布设台阵观测系统，这种仪器不足在于收到线缆的约束，使得野外微动台阵布线困难，尤其是复杂地形地区，人力成本高，无法满足大范围台阵布设及复杂地形微动探测的需求。
无缆存储式仪器是一种自主式节点数据采集站，在施工过程中，采集的数据存储在采集站中，施工结束后，将仪器中存储的数据进行下载，再根据要求合成最终需要的数据文件。
首先，无缆存储式仪器设备轻便，野外布设方便、高效，不受地表等自然环境的影响，能够适用各种复杂地表条件需求，可用于微动探测需求。但普通的无缆存储式仪器是难以用于微动探测的，这是由于微动信号振动幅值很微小，任何自然现场和人为因素对信号幅值的影响很大，采集的数据中难免会携带周围环境的干扰，如车辆通信、人类活动、施工振动、天气变化等，普通的无缆存储式仪器中的信号采集系统可能的零点漂移、电缆干扰等引起噪声，甚至有些设备自身噪声信号已近湮没了能表征地层信息的微动信号成分，导致无法进行有效的微动勘探，因此数据采集系统的硬件设计显得极为重要。
其次，目前已有的无缆存储式仪器通常是当施工结束后，导出数据再进行数据解释分析，但这种工作模式缺乏远程、实时、可靠的监控记录和野外现场质量监控手段。因此也不利于微动探测，同时考虑到微动探测的影响因素众多，现场人员无法及时评价数据质量，为了保证数据质量，需进行冗长的数据采集时间，从而降低了施工效率，甚至会导致返工。
为了解决现场微动数据质量监控，远程遥测终端控制器作为分布式监控系统中常用的一种，其应用越来越普及。遥测是将天然源背景噪声信号近距离采集传输至远距离的计算机工作站来实现远距离测试的技术。在地震勘探系统中，遥测系统包括拾震器、通信设备和数据处理设备。数据采集设备和信号传输技术是遥测的两项关键技术，数据采集设备的采样精度、可靠性以及通信系统的传输速度和抗干扰能力等决定了遥测系统的性能。
目前，遥测系统中的通信设备常用的三种近距离无线传输技术有ZigBee、蓝牙（Buletooth）和WiFi，这三种无线传输技术均存在一些不足，如传输范围小、数据传输率低、覆盖范围有限和移动性不佳等状态。虽然WiFi具有传输速率快，在野外可以用架设基站的方式来拓扑无线局域网络的覆盖面积，通过采集站中集成WiFi模块与基站建立连接，但是在实际勘探应用汇总基站架设繁琐，尤其是对大范围的微动台阵观测系统，需要更多的基站，将严重影响施工进度，同时在地形复杂的野外环境下，其传输速度及可靠性难以得到有效保证。
 技术问题
本发明的目的在于解决上述背景技术中存在的问题，提高微动勘探的准确性。
 技术解决方案
为实现以上目的，本发明采用一种微动采集设备，其连接在拾震器的输出端，包括数据采集模块、数据处理模块、网口转发模块、GPS模块和数据存储模块，拾震器输出端连接数据采集模块，数据采集模块和GPS模块均与数据处理模块连接，数据处理模块、网口转发模块和数据存储模块均连接至嵌入式Linux系统平台。
本发明进一步改进点在于，数据采集模块包括至少一路同步采集通道，每路同步采集通道包括带通滤波器和ADC转换芯片，带通滤器和ADC转换芯片经可变增益放大器连接。
本发明进一步改进点在于，所述数据存储模块包括Flash模块和用于供U盘插入的USB接口模块。
本发明进一步改进点在于，所述网口转发模块连接有服务器。
另一方面，本发明提供一种微动探测无线遥测系统，包括服务器、用户客户端和由上述的微动采集设备组成的微动台阵观测系统；
所述微动采集设备与服务器之间的数据传输通道，以及服务器与用户客户端之间的数据传输通道均通过互联网通信方式建立。
本发明进一步改进点在于，所述数据传输通道为socket通信传输通道。
另一方面，本发明提供一种数据质量监控方法，可实时处理与评估采集的微动信号，包括如下步骤：
S1、所述用户客户端接收所述服务器转发的所述数据包，该数据包中添加有GPS时间戳信息和微动采集设备编号信息；
S2、从所述数据包中分离出时间戳、微动采集设备编号和微动采集设备实时采集的采样数据，所述采样数据为微动采集设备按照设定的采样参数进行采样得到；
S3、按照微动采集设备编号所对应的数据文件，将所述采样数据存储到对应的数据文件中；
S4、判断数据文件接收到采样数据的时间长度是否小于预先设置的子时间长度L，若否则执行步骤S5；
S5、读取该子时间长度L内的采样数据，并根据读取的采样数据计算该子时间长度L内采样数据的功率谱；
S6、重复执行上述步骤S3~ S5，并将至少两次功率谱累加求平均值，得到微动采集设备所对应的功率谱平均值；
S7、将各微动采集设备所对应的功率谱平均值以曲线图形式进行显示，并根据功率谱平均值对所述微动采集设备的采集质量进行评估。
本发明进一步改进点在于，还包括：
将所述微动采集设备两两组成台站对；
判断所述各数据文件接收到采样数据的时间长度所重叠的时间段是否大于或等于设定的时间段M；
若是，则计算各台站对在时间段M内的空间自相关系数；
将用户自定义的微动信号有效频率范围和相速度范围进行网格化，得到有效频率网格数据和相速度网格数据；
利用零阶标准贝塞尔函数，对所述台站对中两微动采集设备之间的距离、有效频率网格数据和相速度网格数据进行处理，得到网格理论空间自相关系数；
将所述空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差；
将网格误差进行颜色映射，得到频散谱伪色彩图；
根据频散谱伪色彩图对所述微动采集设备采集的数据质量进行评价。
本发明进一步改进点在于，在所述计算各台站对在时间段M内的空间自相关系数之后，还包括：
根据预先设置的各微动探勘设备的位置坐标，计算各台站对间距；
在所有台站对中，将间距相同的台站对的空间自相关系数归类，并计算其平均值，进而得到不同间距的实测空间自相关系数；
相应地，所述将空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差，具体为：
将所述实测空间自相关系数平均值与网格理论空间自相关系数进行比较，得网格误差。
本发明进一步改进点在于，还包括：
将各时间段M所对应的网格误差求平均处理，得到平均误差值；
将平均误差值进行颜色映射，以实时更新频散谱伪色彩图。
本发明进一步改进点在于，还包括：
所述客户端接收经所述服务器转发的所述微动采集设备的状态信息，该设备的状态信息包括微动采集设备的温度、电量、存储空间以及所述采样参数；
所述客户端以图表形式对所述微动采集设备的状态信息进行显示；
所述客户端按照所述时间戳和所述微动采集设备的编号，将所述采样数据进行动态显示。
本发明进一步改进点在于，还包括：
所述客户端根据所述时间戳判断所述数据包在传输过程中是否出现丢包情况；
若是，则所述客户端发送丢包查找指令至所述服务器，该丢包查找指令携带有丢包时间戳和微动采集设备编号；
所述服务器对所述丢包查找指令进行解析，并将丢包时间戳和微动采集设备编号发送至对应的微动采集设备，以供微动采集设备按照时间戳读取对应的数据包并重新发送至所述服务器。
 有益效果
与现有技术相比，本发明存在以下技术效果：本发明中的微动采集设备通过增加GPS模块接收GPS卫星标准的时间信号，通过GPS卫星标准的时间信号自动地、实时地对内部时钟信号进行校正，其同步误差小于15ns，可在长时间数据采集中确保各微动采集设备进行数据采集的同步性。
微动探测无线遥测系统采用互联网通信方式建立微动采集设备与服务器之间的网络连接，以及服务器与客户端之间的网络连接，构成的无线遥测系统不受地域、地形以及传输距离的限制，只要有通信网络覆盖，就能远程监控微动采集设备的数据采集工作，及时反映现场设备的工作情况。
另外，在采集微动信号微动探测现场实施质量监控过程中，计算在用户定义的有效频率范围内各微动采集设备的功率谱，若功率谱虽有差异但形态基本相似，能量相近，则说明采集的微动信号在时间和空间上为随机稳定状态，该微动采集设备采集的微动信号满足要求；若某个或多个微动采集设备的功率谱存在较大差异，则表明该采集的微动信号存在显著干扰，现场工作人员可据此进行现场指导处理。
 附图说明
下面结合附图，对本发明的具体实施方式进行详细描述：
图1是一种微动采集设备的结构示意图；
图2是一种微动探测无线遥测系统的结构示意图；
图3是一种微动采集设备的质量监控方法的流程示意图；
图4是单台微动采集设备的数据质量评估流程示意图；
图5是10台微动采集设备的实时功率谱图；
图6是微动台阵观测系统数据质量监控流程图；
图7是由10台微动采集设备组成的观测系统所计算的实时频散谱伪彩色图示意图。
 本发明的最佳实施方式
为了更进一步说明本发明的特征，请参阅以下有关本发明的详细说明与附图。所附图仅供参考与说明之用，并非用来对本发明的保护范围加以限制。
如图1所示，本实施例公开了一种微动采集设备，该微动采集设备用于连接在拾震器10的输出端，对拾震器10输出的微动信号进行采集。微动采集设备包括数据采集模块1、数据处理模块2、网口转发模块3、GPS模块4和数据存储模块5，拾震器10输出端连接数据采集模块1，数据采集模块1和GPS模块4均与数据处理模块2连接，数据处理模块2、网口转发模块3和数据存储模块5均连接至嵌入式Linux系统平台。
其中，数据采集模块1将从拾震器10所采集的微动信号发送至数据处理模块2；GPS模块4用于通过接收GPS卫星的标准信号，并以GPS卫星的标准信号为基准，对其内部时钟信号进行实时地、自动的校正，从而利用校正后的时钟信号同步数据采集模块1采集的数据，可在长时间的数据采集中确保各微动采集设备数据采集的同步性，并在数据采集模块1实时采集的数据包中添加GPS时间戳信息。
数据存储模块5用于存储嵌入式Linux系统平台6运行所需的Linux系统文件，并将数据采集模块1采集到的微动信号进行存储；网口转发模块3用于在有可用网络时，将采样数据添加微动采集设备编号信息后，打包发送至服务器。
具体地，数据采集模块1包括至少一路同步采集通道，每路同步采集通道包括带通滤波器和ADC转换芯片，带通滤器和ADC转换芯片经可变增益放大器连接。
较佳地，本实施例中采用三路同步采集通道，均采用32-bitADC芯片及频率特性相同的元器件，以降低仪器自身的量化噪声，提高仪器信噪比，使得获取的微动信号更加精确，各采集通道的频率特性曲线一致，各通道具有良好的一致性。
具体地，数据存储模块5包括Flash芯片和用于供U盘插入的USB接口模块。其中，Flash芯片用于存储嵌入式Linux系统平台6运行所需要的Linux系统文件并将采样数据暂存，U盘用于将采样数据实时写入U盘中。
具体地，本实施例中的微动采集设备的工作过程为：
微动采集设备上电后，启动嵌入式Linux系统平台6，初始化完成后，发送GPS指令到GPS模块4，GPS模块4在获取指令后，进入搜索GPS信号状态，当成功获取到GPS时间信号后，发送反馈信号到嵌入式Linux系统平台6，并通过接收到的GPS卫星的标准时间信号对其内部时钟信号进行校正。
嵌入式Linux系统平台6对数据处理模块2进行采样参数配置，该采样参数包括采样开始时间、采样时长、采样率和采样通道增益。当采样参数配置完成后，数据采集模块1开始从拾震器10输出端口采样数据，并将采集到的采样数据发送到数据处理模块2，数据处理模块2依据GPS模块4的时钟信号，在采样数据中添加GPS时间戳信息，并整合3个通道的采样数据。嵌入式Linux系统平台6再读取数据处理模块5中的采样数据，将采样数据分别发送到数据存储模块5和网口转发模块3，数据存储模块5根据时间戳信息将数据写入数据文件的指定位置，并将数据文件存储在U盘中，网口转发模块3在采样数据中添加微动采集设备的编号信息后，再打包成数据包发送至服务器。
需要说明的是，数据处理模块2采用FPGA芯片，FPGA芯片具有时钟频率高、内部延迟小、运行速度快等优点，利用FPGA自定义系统功能，可实现现场快速响应，并运用乒乓缓存技术，克服系统性能波动带来的存储速率的影响，进一步保证微动采集设备的实时性与可靠性。
 本发明的实施方式
实施例一
如图2所示，本实施例公开了一种微动探测无线遥测系统，包括服务器30、用户客户端40和由7台上述微动采集设备20组成的2重圆微动台阵观测系统；所述微动采集设备20与服务器30之间的数据传输通道，以及服务器30与用户客户端40之间的数据传输通道均通过互联网通信方式建立。
本实施例采用客户端/服务端（C/S）方式架构，服务器端在系统执行过程中执行中间转换层功能，客户端包括用户客户端和采集设备客户端，用户客户端具有数据监控功能，用于对采集设备客户端进行监控，该采集设备客户端由微动采集设备组成的微动台阵观测系统。客户端/服务端利用4G通信技术的M2M互联网通信方式，建立方便快捷的socket通信方式，应用于采集设备客户端与服务器端之间的数据传输通道，以及用户客户端与服务器中间的数据传输通道。
应当理解的是，本实施例中服务端与客户端之间的互联网网络还可以通过3G、5G等通信技术建立。
需要说明的是，本实施例中的微动探测无线遥测系统由于采用3G、4G、5G等通信技术建立系统网络连接，由于移动通信技术具有抗干扰能力强、传输速率高、网络覆盖面广以及接入时间短、建设成本低等特点，使得微动探测无线遥测系统不受地域、地形、距离等限制，只要有4G信号覆盖，就能远程监控数据采集工作，及时反映现场设备工作情况，对于设备温度、电量、存储空间等异常情况可设置报警，及时维护、提高效率，并提供历史数据查询，通过图表进行显示。
具体地，微动探测无线遥测系统的工作过程为：
当微动采集设备连接到服务器端socket后，微动采集设备将设备状态信息包括数据采集状态、采集设备的温度、电量、存储空间信息打包发送到服务端。服务器端接收到采集设备状态信息后，按照采集站编号暂存该信息，所述的数据采集状态包括采样开始时间、已采样时长、采样率和通道增益。服务端检查是否有建立成功的用户客户端，若有则将更新的设备状态信息转发至用户客户端。
当用户客户端连接到服务端成功后，首先将接收到不同采集设备的状态信息，用户客户端将以图表的形式显示各采集设备的状态信息，然后服务端转发各采集设备的实时采样数据，当用户客户端接收到采样数据信息时，先分离该数据包中的采样数据、时间戳和采集站编号，再根据时间戳和采集设备编号信息将采样数据动态实时显示，同时按照不同采集站对应不同的数据文件，将采样数据写入数据文件中，写入文件的位置根据时间戳计算得到。
当用户客户端的采样数据写入文件完成后，发送写入完成信号，作为后续的质量监控方法进行数据解释指令，用户客户端对采样数据进行解析，其中用户客户端包括设备状态监控模块、远程数据回收模块、数据管理模块和质量监控模块；
设备状态监控模块用于监控微动采集设备状态信息，微动采集设备的状态信息包括微动采集设备的采样参数、温度、电量及存储空间信息；
远程数据回收模块用于接收服务器转发的微动采集设备所采集的采样数据并进行存储；
数据管理模块用于将远程数据回收模块所存储的采样数据进行管理和整合，并在整合得到的数据中携带的时间戳信息，计算得到数据写入数据文件的位置，以将不同的微动采集设备所采集的采样数据按照微动采集设备编号写入对应数据文件的指定位置中；
在数据管理模块将采样数据写入数据文件完成后，发送数据写入完成指令至质量监控模块；
质量监控模块用于对采样数据进行解析，进行微动采集设备质量评价。
具体地，质量监控模块包括设备质量监控单元和微动信号质量监控单元，其中：
（1）设备质量监控单元用于根据采样数据计算在用户自定义有效频率范围内的功率谱，并根据功率谱对微动采集设备的质量进行监控。具体包括：
首先设置子时间长度L，当数据文件接收到的采样数据时间长度大于或等于L时，读取子长度L段时序数据并计算该子长度L时间序列数据的功率谱记为W i，当下个子时间长度L满足要求，重复上述步骤，最后累积并平均各时间段的功率谱W avj，并将各采集设备的W avj功率谱以曲线图的形式实时动态更新显示。
根据各微动采集设备采集的微动信号在有效频率范围内的功率谱，若功率谱虽有差异，但形态基本相似，能量相近，说明采集的微动信号在时间和空间上为随机稳定状态，该采集设备采集的微动信号满足要求。若某个或多个微动采集设备的功率谱存在较大差异，表明该微动采集设备采集的微动信号存在显著干扰，现场工作人员可根据该信息对现场的微动采集设备进行现场指导处理。
（2）微动信号质量监控单元用于绘制频散谱伪色彩图，并根据频散谱评估微动采集设备采集数据的质量。具体包括：
设置各采集设备的位置坐标，并将微动采集设备两两组成台站对，当所有台站对的采样时间重叠时段大于或等于M时，分别计算各台站对之间的空间自相关系数，再将间距相同的台站对空间自相关系数进行平均，得到空间自相关系数平均值。需要说明的是，这里所述的间距相同指的台站对中两微动采集设备之间的距离相同。
比如台站对1的采样时间段1:00~5:00，台站对2的采样时间段是2:00~6:00，那么台站对1与台站对2的采样重叠时间就是2:00~5:00。应当理解的是，本实施例中的时段M是本领域技术人员经过大量实验得到的一个用于与台站对采样重叠时间进行比较的常数。
根据用户自定义有效频率范围和相速度范围，利用零阶标准贝塞尔函数和台站对间距计算理论空间自相关系数数值。然后根据理论空间自相关数值与台站对实际的空间自相关系数平均值计算误差，在将计算误差进行颜色映射，绘制出频散谱伪彩色图。
在实际应用中，现场人员可根据频散谱伪色彩图连续性和稳定性来评价瑞雷波频散曲线的效果，也根据频散曲线可评估如勘探深度是否满足要求。在频散谱连续且稳定时，可终止微动采集设备的数据采集工作，避免进行不必要的冗长数据采集，提高了微动采集设备的数据采集效率，避免返工。
进一步地，用户客户端还判断传输过程中时间戳是否出现不连续情况，若是则表明出现网络丢包情况，判断网络丢包的方法是通过对比前一次和当前时间戳的序号是否连续，若不连续，则发送指定的丢包时间戳到服务端，服务器根据解析该指令得到采集设备编码，在将根据采集站编号转发到对应到的采集设备，采集设备将根据时间戳信息将从数据文件读取对应的数据包，并将该数据包通过网口转发模块发送到服务端。
实施例二
如图3所示，本实施例公开了一种数据质量监控方法，用于用户客户端用于对微动台阵观测系统所发送的数据包进行处理，包括如下步骤S1至S7：
S1、所述用户客户端接收所述服务器转发的所述数据包，该数据包中添加有GPS时间戳信息和微动采集设备编号信息；
S2、从所述数据包中分离出时间戳、微动采集设备编号和微动采集设备实时采集的采样数据，所述采样数据为微动采集设备按照设定的采样参数进行采样得到；
需要说明的是，采样参数包括采样起始时间、采样时长以及各通道的采样增益，该采样参数在数据采集模块1进行数据采集之前由嵌入式Linux系统平台6设置。
S3、按照微动采集设备编号所对应的数据文件，将所述采样数据存储到对应的数据文件中；
需要说明的是，采样数据存储于数据文件的具体位置根据时间戳信息计算得到。
S4、判断数据文件接收到采样数据的时间长度是否小于预先设置的子时间长度L，若否则执行步骤S5；
S5、读取该子时间长度L内的采样数据，并根据读取的采样数据计算该子时间长度L内采样数据的功率谱，重复执行上述步骤S3~ S4；
S6、将前后两次功率谱累加求平均值，得到微动采集设备所对应的功率谱平均值；
S7、将各微动采集设备所对应的功率谱平均值呈曲线图形式进行显示，并根据功率谱平均值对所述微动采集设备的采集质量进行评估。
需要说明的是，如图4所示，以单个微动采集设备质量监控过程为例：首先设置子时间长度L，当采集设备的数据文件最新存储时间长度大于或等于L时，读取
Figure 316158dest_path_image001
个数据点，其中SampleRate为采样率，计算该段时序数据的功率谱并记录为W i，然后线性叠加前一次功率谱并计算平均值，其表达式如下：
Figure 764457dest_path_image002
，
式中：W preAvj为前一次功率谱平均值，W i为当前子时间长度L的功率谱，W avj为当前功率谱平均值。
最后将各采集设备的W avj功率谱以曲线图显示出来，依次重复上述流程，可动态实时显示更新后各采集设备的功率谱。
如图5所示，从计算得到的10台微动采集设备的实时功率谱图可以看出，在有效频率范围内，功率谱虽有差异，但形态基本相似，所有采集设备的能量分布在3-5 Hz和 8-22 Hz范围内，表明各微动采集设备采集的微动信号在时间和空间上为随机稳定状态，设备采集的微动信号满足要求。若某个或多个采集设备的功率谱存在较大差异，则该采集的微动信号存在显著干扰，现场工作人员可根据该信息进行现场指导处理。
如图6所示，本实施例还通过对频散谱的监控来对观测台阵系统采集的数据质量进行评价，包括如下步骤：
a）将所述微动采集设备两两组成台站对；
具体地，举例说明微动采集设备组队过程：若有微动采集设备1、微动采集设备2和微动采集设备3，两两组合结果为（微动采集设备1，微动采集设备2），（微动采集设备1，微动采集设备3）和（微动采集设备2，微动采集设备3）。
b）判断所述各数据文件接收到采样数据的时间长度所重叠的时间段是否大于或等于设定的时间段M；
c）若是，则计算各台站对在时间段M内的空间自相关系数；
d）将用户自定义的微动信号有效频率范围和相速度范围进行网格化，得到有效频率网格数据和相速度网格数据；
e）利用零阶标准贝塞尔函数，对所述台站对中两微动采集设备之间的距离、有效频率网格数据和相速度网格数据进行处理，得到网格理论空间自相关系数；
f）将所述空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差；
g）将网格误差进行颜色映射，得到频散谱伪色彩图；
h）根据频散谱伪色彩图对所述微动采集设备采集的数据质量进行评价。
具体地，在步骤c）：计算各台站对在时间段M内的空间自相关系数之后，还包括：
根据预先设置的各微动探勘设备的位置坐标，计算各台站对间距，在所有台站对中，将间距相同的台阵对的空间自相关系数归类，并计算其平均值，进而得到不同间距的实测空间自相关系数平均值；
相应地，步骤f）：将空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差，具体为：
将所述实测空间自相关系数平均值与网格理论空间自相关系数进行比较，得网格误差。
进一步地，各台阵对在该子时间M的空间自相关系数的计算公式如下：
Figure 216298dest_path_image003
，
式中：*表示复数共轭，S A和 S O分别表示微动信号的傅里叶变换，r表示台站对中两微动采集设备之间的距离，
Figure 877087dest_path_image004
，f表示有效频率。然后将台站对距离相同的空间自相关系数计算平均值。
进一步地，初始化有效频率范围和相速度范围，并将频率和相速度网格化，得到有效频率网格数据和相速度网格数据。根据各台站对的距离r和有效频率网格数据、相速度网格数据，计算各网格下的理论空间自相关系数，其计算公式是利用第一类零阶贝塞尔函数，如下：
Figure 347251dest_path_image005
，
式中：f为有效频率，v r(f)为相速度，r表示台站对中两微动采集设备之间的距离，
Figure 435293dest_path_image006
为第一类零阶贝塞尔函数。
进一步地，本实施例还对频散谱伪色彩图进行实时动态更新，具体包括：
分别线性叠加各子时间的伪频散谱误差值，在计算平均值，以平均值作为最终的频散谱伪色彩图。
现场人员可根据实时更新的频散谱评估微动勘探数据质量，如图7所示，由10台采集设备组成的微动探测台阵系统，利用上述方法得到的频散谱伪色彩图，分别为1分钟频散谱伪色彩图实时图（参见图7-（a））、5分钟频散谱伪色彩图实时图（参见图7-（b））、15分钟频散谱伪色彩图实时图（参见图7-（c））和20分钟频散谱伪色彩图实时图（参见图7-（d））。图7中，颜色灰度值表示不同大小的误差值，灰度值越暗则误差越大，灰度值越小则误差越小；圆形空心连线根据不同频率下误差最小值得到的频散曲线。
通过频散谱伪色彩图可以发现，当总采样时间小于5分钟时，频散谱和频散曲线具有差异且不稳定，表明采样时间未满足要求，需要继续采集一段时间。当采样时间超过15分钟时，频散谱伪色彩图和频散曲线已稳定，因此可视为采样时间已满足要求，同时频散谱伪色彩图连续性优，故现场微动数据质量高，该质量评估方法为后期数据处理提供了依据，同时也避免了返工。
进一步地，用户客户端对服务器端转发的采样数据还进行如下处理：
客户端接收经所述服务器转发的所述微动采集设备的状态信息，该设备的状态信息包括微动采集设备的温度、电量、存储空间以及所述采样参数；
所述客户端以图表形式对所述微动采集设备的状态信息进行显示；
所述客户端按照所述时间戳和所述微动采集设备的编号，将所述采样数据进行动态显示。
需要说明的是，通过将微动采集设备的状态信息和采样数据进行动态显示，以便用户进行直观观察。
进一步地，在上述对微动采集设备进行质量监控以及对采样数据质量进行监控之前，用户客户端对服务器端转发的采样数据还进行如下处理：
所述客户端根据所述时间戳判断所述数据包在传输过程中是否出现丢包情况；
若是，则所述客户端发送丢包查找指令至所述服务器，该丢包查找指令携带有丢包时间戳和微动采集设备编号；
所述服务器对所述丢包查找指令进行解析，并将丢包时间戳和微动采集设备编号发送至对应的微动采集设备，以供微动采集设备按照时间戳读取对应的数据包并重新发送至所述服务器。
需要说明的是，通过根据时间戳信息来判断采样数据在传输过程中是否出现丢包情况，以保证采样数据传输的完整性，以保证设备质量监控和采样数据质量监控的准确性。
以上所述仅为本发明的较佳实施例，并不用以限制本发明，凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
 工业实用性
本发明中的微动采集设备通过增加GPS模块接收GPS卫星标准的时间信号，通过GPS卫星标准的时间信号自动地、实时地对内部时钟信号进行校正，其同步误差小于15ns，可在长时间数据采集中确保各微动采集设备进行数据采集的同步性，具有工业实用性。

Claims (12)

  1. 一种微动采集设备，其特征在于，其连接在拾震器（10）的输出端，包括数据采集模块（1）、数据处理模块（2）、网口转发模块（3）、GPS模块（4）和数据存储模块（5），拾震器（10）输出端连接数据采集模块（1），数据采集模块（1）和GPS模块（4）均与数据处理模块（2）连接，数据处理模块（2）、网口转发模块（3）和数据存储模块（5）均连接至嵌入式Linux系统平台（6）。
  2. 如权利要求1所述的微动采集设备，其特征在于，所述数据采集模块（1）包括至少一路同步采集通道，每路同步采集通道包括带通滤波器和ADC转换芯片，带通滤器和ADC转换芯片经可变增益放大器连接。
  3. 如权利要求1或2所述的微动采集设备，其特征在于，所述数据存储模块（5）包括Flash模块和用于供U盘插入的USB接口模块。
  4. 如权利要求2所述的微动采集设备，其特征在于，所述网口转发模块（3）连接有服务器。
  5. 一种微动探测无线遥测系统，其特征在于，包括服务器、用户客户端和由所述权利要求1-4任一项所述的微动采集设备组成的微动台阵观测系统；
    所述微动采集设备与服务器之间的数据传输通道，以及服务器与用户客户端之间的数据传输通道均通过互联网通信方式建立。
  6. 如权利要求5所述的微动探测无线遥测系统，其特征在于，所述数据传输通道为socket通信传输通道。
  7. 一种数据质量监控方法，其特征在于，用于对如权利要求5-6任一项所述的微动台阵观测系统所发送的数据包进行处理，包括如下步骤：
    S1、所述用户客户端接收所述服务器转发的所述数据包，该数据包中添加有GPS时间戳信息和微动采集设备编号信息；
    S2、从所述数据包中分离出时间戳、微动采集设备编号和微动采集设备实时采集的采样数据，所述采样数据为微动采集设备按照设定的采样参数进行采样得到；
    S3、按照微动采集设备编号所对应的数据文件，将所述采样数据存储到对应的数据文件中；
    S4、判断数据文件接收到采样数据的时间长度是否小于预先设置的子时间长度L，若否则执行步骤S5；
    S5、读取该子时间长度L内的采样数据，并根据读取的采样数据计算该子时间长度L内采样数据的功率谱；
    S6、重复执行上述步骤S3~ S5，并将至少两次功率谱累加求平均值，得到微动采集设备所对应的功率谱平均值；
    S7、将各微动采集设备所对应的功率谱平均值以曲线图形式进行显示，并根据功率谱平均值对所述微动采集设备的采集质量进行评估。
  8. 如权利要求7所述的数据质量监控方法，其特征在于，还包括：
    将所述微动采集设备两两组成台站对；
    判断所述各数据文件接收到采样数据的时间长度所重叠的时间段是否大于或等于设定的时间段M；
    若是，则计算各台站对在时间段M内的空间自相关系数；
    将用户自定义的微动信号有效频率范围和相速度范围进行网格化，得到有效频率网格数据和相速度网格数据；
    利用零阶标准贝塞尔函数，对所述台站对中两微动采集设备之间的距离、有效频率网格数据和相速度网格数据进行处理，得到网格理论空间自相关系数；
    将所述空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差；
    将网格误差进行颜色映射，得到频散谱伪色彩图；
    根据频散谱伪色彩图对所述微动采集设备采集的数据质量进行评价。
  9. 如权利要求8所述的数据质量监控方法，其特征在于，在所述计算各台站对在时间段M内的空间自相关系数之后，还包括：
    根据预先设置的各微动探勘设备的位置坐标，计算各台站对间的距离；
    在所有台站对中，将间距相同的台站对的空间自相关系数归类，并计算其平均值，得到不同间距的实测空间自相关系数平均值；
    相应地，所述将空间自相关系数与网格理论空间自相关系数进行比较，得到网格误差，具体为：
    将所述实测空间自相关系数平均值与网格理论空间自相关系数进行比较，得网格误差。
  10. 如权利要求8或9所述的数据质量监控方法，其特征在于，还包括：
    将各时间段M所对应的网格误差求平均处理，得到平均误差值；
    将平均误差值进行颜色映射，以实时更新频散谱伪色彩图。
  11. 如权利要求7所述的数据质量监控方法，其特征在于，还包括：
    所述客户端接收经所述服务器转发的所述微动采集设备的状态信息，该设备的状态信息包括微动采集设备的温度、电量、存储空间以及所述采样参数；
    所述客户端以图表形式对所述微动采集设备的状态信息进行显示；
    所述客户端按照所述时间戳和所述微动采集设备的编号，将所述采样数据进行动态显示。
  12. 如权利要求7所述的数据质量监控方法，其特征在于，还包括：
    所述客户端根据所述时间戳判断所述数据包在传输过程中是否出现丢包情况；
    若是，则所述客户端发送丢包指令至所述服务器，该丢包指令包括丢包时间戳和微动采集设备编号信息；
    所述服务器对所述丢包查找指令进行解析，并将丢包时间戳和微动采集设备编号发送至对应的微动采集设备，以供微动采集设备按照时间戳读取对应的数据包并重新发送至所述服务器。
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