Real-time and fast calculation method for steady-state sensitivity of geophone based on variable frequency sampling

Abstract

The application discloses a real-time and fast calculation method of steady-state sensitivity of a detector based on variable frequency sampling, which comprises the following steps in sequence: S1. obtaining a corresponding response by using a vibration driving detector; S2. adjusting a sampling frequency according to the response state and sampling; S3. judging whether the detector reaches a steady state and obtaining an average value of the steady-state frequency response, which is the steady-state sensitivity of the detector. The method provided by the application has strong universality and is not influenced by the performance, type and parameter changes of the detector, and the frequency response characteristics of the detector are more comprehensively and truly reflected through the frequency response of the detector in different frequency bands.

Description

Technical Field

[0001] This invention belongs to the technical field of special instruments and equipment for petroleum geophysical exploration seismic testing, and relates to a method for calculating the sensitivity of geophones, specifically a method for real-time and rapid calculation of the steady-state sensitivity of geophones based on frequency conversion sampling. Background Technology

[0002] Geophones are specialized vibration sensors used in seismic exploration to receive vibration signals. They are key equipment in exploration, and their performance determines the accuracy, precision, and reliability of seismic acquisition data. Seismic waves detected in geophysical exploration are characterized by large differences in amplitude, ranging from several orders of magnitude, and frequencies from a few hertz to hundreds of hertz. Geophones, as specialized geophysical equipment, need to respond quickly and accurately to and reflect these changes in seismic signals over a wide area, especially minute changes. This requires the geophone to have a certain level of sensitivity. If a geophone cannot detect minute changes in seismic signals, its sensitivity is substandard, its consistency is poor, and its deviation is large, indicating that the instrument cannot accurately and effectively acquire actual seismic signals. Sensitivity is a key indicator of a geophone's ability to respond to dynamic vibration signals and is an important parameter for evaluating its performance. Therefore, it is essential to test and calibrate the sensitivity of geophones.

[0003] Currently, there are two commonly used methods for detector sensitivity calibration: Method one involves system modeling of the detector, establishing an ideal abstract system response model through theoretical analysis, and then applying a standard pulse response signal to this model to calculate the corresponding response excitation result to obtain the sensitivity. Because this method uses modeling techniques, the calculation results are affected by factors such as model accuracy and the quality of the excitation pulse. Furthermore, different types of detectors have different models, making calibration using a uniform method costly, difficult to test, and with unsatisfactory accuracy. Method two uses the industry-standard fixed frequency of 31.5Hz to drive the detector and obtain the sensitivity value. However, due to detector technology upgrades, increased types, and operating frequency bands ranging from a few hertz to hundreds of hertz, this single-point value is not representative. Additionally, differences in manufacturing processes and other factors among various detectors lead to discrepancies between the actual response and the theoretical value, resulting in lower reliability. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time and rapid calculation method for the steady-state sensitivity of a seismic detector based on frequency conversion sampling, so as to achieve the goal of accurately, quickly, and conveniently detecting, judging, and calculating the sensitivity of a seismic detector.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for real-time and rapid calculation of the steady-state sensitivity of a detector based on frequency conversion sampling includes the following steps performed sequentially:

[0007] S1. Obtain the corresponding response by using a vibration-driven detector;

[0008] S2. Adjust the sampling frequency according to the response status and perform sampling;

[0009] S3. Determine if the detector has reached a steady state, and calculate the average value of the steady-state frequency response, which is the steady-state sensitivity of the detector.

[0010] As a limitation, step S2 specifically includes:

[0011] S21. Sample the predicted response state using the following formula;

[0012]

[0013] In the formula, F s The sampling frequency f under different response states: l For rising state s l The sampling frequency at that time; f m For the transition state s m The sampling frequency at that time; f h For a steady state s h The sampling frequency at that time;

[0014] S22. Select the time window data to be detected according to the following formula:

[0015]

[0016] In the formula, Data wnd y is the time window data segment to be detected; x is the starting position of the data segment; n is the frequency coordinate data length; y x The frequency response corresponding to data x;

[0017] S23. Determine the response status of the time window data, and adjust the sampling frequency in step S21 according to the response status, and perform sampling.

[0018] As a further limitation, step S23 specifically includes:

[0019] Sort the time window data:

[0020]

[0021] Calculate the maximum and minimum frequency responses:

[0022]

[0023] In the formula y max For maximum frequency response; y min Minimum frequency response;

[0024] Calculate the difference between the maximum and minimum frequency responses:

[0025] y delt =y max -y min

[0026] If Y delt If the value is greater than a given threshold, the system response state is S. l Return to step S21 to continue sampling;

[0027] If Y delt If the frequency response is less than or equal to a given threshold, calculate the expectation and variance of the frequency response within the window:

[0028]

[0029]

[0030] If the variance is greater than the set threshold, the system response state is S. m Return to step S21 to continue sampling;

[0031] If the variance is less than the set threshold, perform linear fitting within the time window:

[0032] The following relationship exists between a given frequency and its response:

[0033] y = a + bx

[0034] In the formula, a and b are parameters to be determined, representing the intercept and slope, respectively;

[0035] Estimate two undetermined parameters to minimize the weighted sum of squares of the frequency response:

[0036]

[0037] By solving for the estimated values ​​of a and b, we can obtain:

[0038]

[0039]

[0040] Simultaneously calculate the correlation coefficient r:

[0041]

[0042] In the formula,

[0043] If |r|→1, then the system response state is S. h Then return to step S21 to continue sampling.

[0044] As a second limitation, step S3 specifically includes:

[0045] Analysis of continuous time windows:

[0046]

[0047] In the formula, j represents the j-th time window; This is the estimated intercept value for the j-th time window; This is the estimated intercept value for the j-th time window; Let T be the expected frequency response for the j-th time window; b T a T μ A threshold parameter for determining the continuity of two time windows;

[0048] If the deviation results between consecutive time windows are all satisfied, the detector is considered to have reached a steady state. The average value of the steady-state amplitude-frequency response is then obtained, which is the steady-state sensitivity of the detector.

[0049] By adopting the above technical solution, the technical progress achieved by this invention compared with the prior art is as follows:

[0050] ① The present invention provides a real-time fast calculation method for the steady-state sensitivity of a detector based on frequency conversion sampling. It proposes a reliable, fast and direct sensitivity detection and calculation method based on multiple frequency bands and multiple time windows. It has strong versatility and is not affected by changes in detector performance, type, or parameters. By collecting the frequency response data of the detector for different frequency bands such as low frequency, medium frequency, and high frequency through the frequency response of the detector in different frequency bands, i.e., the sensitivity obtained by driving the detector in multiple frequency bands on the vibration table, the frequency response characteristics of the detector are more comprehensively and realistically reflected.

[0051] ② The real-time fast calculation method for steady-state sensitivity of detector based on frequency conversion sampling provided by the present invention uses the detection time window method to select all effective target data during the detection process, performs fast calculation and discrimination on these small amounts of data, eliminates obvious non-steady-state data, improves detection speed, and reduces the number of samples, greatly reducing the cost of acquisition, monitoring and analysis;

[0052] ③ The real-time fast calculation method for steady-state sensitivity of detector based on frequency conversion sampling provided by this invention, through time-series analysis of detection results, that is, through the discrimination and analysis of results of multiple continuous time windows, makes more reliable discrimination and calculation of sensitivity, reduces systematic and random errors caused by intermediate links, and is closer to the individual characteristics of the detector, making the calibration more accurate;

[0053] ④ The real-time fast calculation method for steady-state sensitivity of detector based on frequency conversion sampling provided by this invention can directly calculate the sensitivity of the detector under test without manually drawing the amplitude-frequency response curve, and the calculation result is more accurate.

[0054] The method provided by this invention is highly versatile and is not affected by changes in detector performance, type, or parameters. By observing the frequency response of detectors in different frequency bands, it more comprehensively and realistically reflects the frequency response characteristics of detectors. Attached Figure Description

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0056] Figure 1 This is a simplified flowchart of the method in an embodiment of the present invention. Detailed Implementation

[0057] The present invention will be further described in detail below through specific embodiments. It should be understood that the described embodiments are only for explaining the present invention and do not limit the present invention.

[0058] Example: Real-time Fast Calculation Method for Steady-State Sensitivity of Detector Based on Frequency Conversion Sampling

[0059] This embodiment includes the following steps performed sequentially:

[0060] S1. Mount the detector under test on the vibration table and drive the detector at a specified frequency to obtain a response;

[0061] S2. Adjust the sampling frequency according to the response status.

[0062] S21. Frequency response data is acquired using different sampling frequencies. The acquired data is used to determine the system response state, and the sampling frequency is dynamically updated based on the determined system response state. Different sampling functions are used depending on different system states. The sampling frequency is adjusted according to the following formula:

[0063]

[0064] F s The sampling frequency f under different response states: l For rising state s l The sampling frequency at that time; f m For the transition state s m The sampling frequency at that time; f h For a steady state s h The sampling frequency at that time;

[0065] Based on the system response development stage, set the initial sampling frequency F. s Data is collected based on this frequency; for example, if the initial stage is the rising period, the sampling frequency is set relatively high.

[0066] S22. Collect frequency response data over a period of time, and analyze and process it segment by segment. The following formula describes the data to be detected, i.e., the time window data:

[0067]

[0068] In the formula, Data wnd y is the time window data segment to be detected; x is the starting position of the data segment; n is the frequency coordinate data length; y x This is the frequency response result corresponding to data x;

[0069] S23. Calculate the collected time window data to determine the current processing state. Different discrimination methods are used according to the system response stage. For example, a fast prediction method is used during the rise and transition periods, and a fast discrimination method based on frequency response consistency is used during the transition and steady-state transition periods. Adjust the system stage and the acquisition frequency according to the judgment result, that is, enter the initial processing of S21:

[0070] ① Rapid determination of steady state

[0071] Within the time window, find the maximum and minimum frequency response values ​​within this data segment, and make a quick prediction by calculating the difference between the frequency response values:

[0072] Sort the time window data:

[0073]

[0074] Calculate the maximum and minimum frequency responses:

[0075]

[0076] In the formula y max For maximum frequency response; y min Minimum frequency response;

[0077] Calculate the difference between the maximum and minimum frequency responses:

[0078] y delt =y max -y min

[0079] If Y delt If the value exceeds a given threshold, it is considered to be definitely not in a steady state, and the system response state is S. l Return to step S21 to adjust the sampling frequency and continue sampling and monitoring;

[0080] By making rapid predictions, the timeliness of detection is improved, and the need for in-depth processing of non-steady-state data is reduced;

[0081] ② Fast discrimination based on frequency response consistency

[0082] If Y delt If the frequency response is less than or equal to a given threshold, then calculate the expectation and variance of the frequency response within the window:

[0083]

[0084]

[0085] If the variance is greater than a given threshold, it indicates that the frequency response data is scattered, the results fluctuate within a small range, and have not reached stability. The system response state is S. m Return to step S21 to adjust the sampling frequency and continue sampling and monitoring;

[0086] If the variance is less than a given threshold, it indicates that the data is relatively concentrated and the state response is stable. Further analysis of the detection data can be performed to calculate the relevant parameters under stable conditions.

[0087] ③ Determining if the system reaches steady state

[0088] After two preliminary screenings, it is indicated that the system may be approaching a steady state. When the data reaches a steady state, the response curve can be considered as a straight line. Therefore, it is possible to perform linear fitting within a time window and use relevant parameters to determine the state.

[0089] The following relationship exists between frequency and its response:

[0090] y = a + bx

[0091] Where a and b are undetermined parameters, representing the intercept and slope, respectively;

[0092] By estimating two undetermined parameters, the weighted sum of squares of the observed frequency response is minimized:

[0093]

[0094] The best estimates of a and b can be obtained as follows:

[0095]

[0096]

[0097] Simultaneously calculate the correlation coefficient r:

[0098]

[0099] in,

[0100] If |r|→1, the data is considered to exhibit a good linear relationship. If the state judgment in this step is valid, the system is considered to be entering a steady state, and the system sampling is adjusted to the steady-state sampling frequency.

[0101] S3. System stability assessment and parameter calculation

[0102] When the system is about to enter the steady state stage, the sampling frequency is relatively fixed, and the steady state calculation method based on the frequency response within the time window is used to calculate whether the system has reached the steady state and the sensitivity parameters.

[0103] S31. Determining if a system has reached steady state

[0104] After the system enters steady state, the data needs to remain stable for a period of time before it can be considered that the system has reached stability. This requires recording and analyzing the results of multiple consecutive time windows during the detection process for a comprehensive judgment. For example, the deviations in slope estimates, intercept estimates, and frequency response expectations between two adjacent time windows should be small. The calculation formulas are as follows:

[0105]

[0106] Where j represents the j-th time window; This is the estimated intercept value for the j-th time window; This is the estimated intercept value for the j-th time window; Let T be the expected frequency response for the j-th time window; b T a T μ A threshold parameter for determining the continuity of two time windows;

[0107] If the deviation results between consecutive time windows are all satisfied, the detector is considered to have reached a steady state.

[0108] S32. Detector steady-state sensitivity calculation

[0109] Once the system reaches stability, the steady-state sensitivity of the detector can be calculated by taking the average value of the steady-state amplitude-frequency response.

Claims

1. A method for real-time and rapid calculation of the steady-state sensitivity of a detector based on frequency conversion sampling, characterized in that, This includes the following steps performed sequentially: S1. Obtain the corresponding response by using a vibration-driven detector; S2. Adjust the sampling frequency according to the response status and perform sampling; S3. Determine if the detector has reached a steady state, and calculate the average value of the steady-state frequency response, which is the steady-state sensitivity of the detector; Step S2 specifically includes: S21. Sample the predicted response state using the following formula; In the formula, F s The sampling frequency f under different response states: l For rising state s l The sampling frequency at that time; f m For the transition state s m The sampling frequency at that time; f h For a steady state s h The sampling frequency at that time; S22. Select the time window data to be detected according to the following formula: In the formula, is the time window data segment to be detected; x is the starting position of the data segment; n is the length of the frequency coordinate data; The frequency response corresponding to data x; S23. Determine the response status of the time window data, and adjust the sampling frequency in step S21 according to the response status, and perform sampling; Step S3 specifically includes: Analysis of continuous time windows: In the formula, j represents the j-th time window; This is the estimated intercept value for the j-th time window; This is the estimated intercept value for the j-th time window; Let be the expected frequency response for the j-th time window; , , A threshold parameter for determining the continuity of two time windows; If the deviation results between consecutive time windows are all satisfied, the detector is considered to have reached a steady state. The average value of the steady-state amplitude-frequency response is then obtained, which is the steady-state sensitivity of the detector.

2. The method for real-time and rapid calculation of detector steady-state sensitivity based on frequency conversion sampling according to claim 1, characterized in that, Step S23 specifically includes: Sort the time window data: Calculate the maximum and minimum frequency responses: ； In the formula Maximum frequency response; Find the minimum frequency response; calculate the difference between the maximum and minimum frequency responses: like If the value is greater than a given threshold, the system response state is S. l Return to step S21 to continue sampling; like If the frequency response is less than or equal to a given threshold, calculate the expected value and variance of the frequency response within the window: If the variance is greater than the set threshold, the system response state is S. m Return to step S21 to continue sampling; If the variance is less than the set threshold, perform linear fitting within the time window: The following relationship exists between a given frequency and its response: In the formula, a and b are parameters to be determined, representing the intercept and slope, respectively; Estimate two undetermined parameters to minimize the weighted sum of squares of the frequency response: By solving for the estimated values ​​of a and b, we can obtain: Simultaneously calculate the correlation coefficient r: In the formula, , ; like The system response state is S. h Then return to step S21 to continue sampling.

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