A method and system for background noise seismic frequency imaging based on autocorrelation

Abstract

The application belongs to the technical field of geophysical exploration, and particularly relates to a background noise seismic frequency imaging method and system based on autocorrelation, aiming to solve the problem of insufficient resolution of the existing background noise exploration method. The method comprises the following steps: obtaining background noise data of multiple acquisition points in a target area and segmenting, to obtain horizontal component signals in different directions; performing autocorrelation calculation on each horizontal component signal, respectively, to obtain multiple autocorrelation sequences, then performing frequency domain conversion, respectively, and stacking according to the direction, to obtain a north component characteristic frequency spectrum and an east component characteristic frequency spectrum; merging the east component characteristic frequency spectrum and the north component characteristic frequency spectrum, to map the underground abnormal body depth, to generate a frequency imaging result. After autocorrelation processing, the passive source body wave signal propagation is used in the frequency domain to reflect on the ground interference for imaging through the underground geological interface, the exploration cost is low, the applicability is strong, and the imaging resolution of karst and other low-velocity abnormal bodies is high.

Description

Technical Field

[0001] This application belongs to the field of geophysical exploration technology, specifically involving a background noise seismic frequency imaging method and system based on autocorrelation. Background Technology

[0002] In the fields of engineering inspection and underground exploration, accurate detection of internal structural defects and underground strata is a core requirement for ensuring project quality. Detection technologies for different scenarios vary in resolution and depth due to differences in principles and targets, and also face bottlenecks, driving the exploration of cross-disciplinary technological integration.

[0003] In the field of engineering flaw detection, the "impact-echo" technique, which emerged in the 1990s, is an important method for detecting structural defects. Its principle is as follows: a transient impact is applied to the free surface of the structure to excite a P-wave. When the wavefront contacts the defect, it is reflected due to the difference in wave impedance, resulting in multiple reflections between the free surface and the defect. The resonance effect exhibits a characteristic peak in the frequency domain, and the peak frequency corresponds to the defect depth, allowing for precise defect location. Currently, this technology has been successfully applied to engineering flaw detection and quality inspection, effectively achieving high-resolution imaging in the frequency domain using low-frequency wavelet excitation, thus solving the problem of difficult identification of structural defects.

[0004] In the field of active-source seismic exploration, traditional time-domain methods (surface wave, reflected wave, and refracted wave exploration) have long been the mainstream approach. Relying on the travel time characteristics of seismic waves for imaging, they have shown significant effectiveness in characterizing deep strata, providing crucial support for mineral exploration and geological research. However, this type of technology has limitations in shallow (0-50 meters) exploration: high-frequency energy decays rapidly, wavelet wavelengths are relatively long, and the resolution of fine shallow structures is insufficient, making shallow exploration a technical challenge. To address this problem, international research has attempted to introduce the characteristic frequency effect of "impact-echo" into active-source seismic exploration, but early experiments failed due to limitations in source performance and signal processing methods. Only in recent years, research has shifted to the field of pile logging, achieving a breakthrough. In shallow concrete, by optimizing the offset and analysis time window, high-resolution frequency characteristics corresponding to concrete layers have been successfully obtained, with imaging results approaching those of traditional time-domain methods, validating the shallow exploration potential of this technology.

[0005] However, current technology still faces bottlenecks: the excitation energy of active seismic sources is insufficient, making it difficult to stably excite low-frequency signals. This limits the detection depth to within 5 meters. Beyond 5 meters, the weak low-frequency signals and short propagation distance result in a sharp drop in imaging quality, failing to meet the detection needs of shallow to intermediate layers (5-50 meters). Therefore, overcoming the limitations of seismic source energy and stably exciting low-frequency signals to expand the detection depth of "impact-echo" technology and improve the imaging quality of shallow to intermediate layers is the core issue for its application in active source seismic exploration. Summary of the Invention

[0006] To address the aforementioned problems in the prior art, namely the difficulty of traditional active-source seismic exploration techniques in meeting the needs of shallow and intermediate-depth exploration, the first aspect of this application proposes a background noise seismic frequency imaging method based on autocorrelation, the method comprising the following steps:

[0007] Acquire background noise data from multiple sampling points in the target area, wherein the background noise data includes vertical component data, north component data, and east component data;

[0008] The north component data and the east component data are segmented based on a preset time window to obtain horizontal component signals in different directions.

[0009] Autocorrelation processing is performed on the horizontal component signal for each time period to generate a virtual self-excited and self-receiving shot set record, thereby obtaining an autocorrelation sequence.

[0010] Frequency domain transformation is performed on the relevant sequences to obtain multiple single-segment spectra. Based on the time window, the single-segment spectra are superimposed according to the direction to generate eastward characteristic frequency spectrum and northward characteristic frequency spectrum.

[0011] The eastward and northward characteristic frequency spectra are merged to obtain the horizontal component characteristic frequency spectrum;

[0012] The horizontal component characteristic frequency spectrum is mapped to the depth of the underground anomaly to obtain the depth characteristic spectrum of each acquisition point. The depth characteristic spectrum is then arranged and the elevation is corrected in combination with the location of the acquisition point to generate frequency imaging results.

[0013] In some preferred embodiments, background noise data from multiple sampling points in the target area is acquired using the following method:

[0014] Multiple seismic detectors are deployed at equal intervals along a pre-set straight survey line, and the distance between adjacent detectors is no greater than half of the minimum lateral dimension of the geological anomaly to be detected.

[0015] In some preferred embodiments, the horizontal component signal is divided into N time windows according to a preset time window length, where N is an integer greater than 100; before combining the north component data and the east component data, a step of preprocessing the horizontal component signals in different directions is also included.

[0016] The preprocessing steps include mean reduction and abnormal amplitude noise suppression.

[0017] Specifically, the abnormal amplitude noise suppression process includes:

[0018] Calculate the root mean square (RMS) value of the amplitude of the horizontal component signal for each time period;

[0019] In addition, amplitude suppression is applied to the portion of the amplitude that exceeds a preset multiple of the RMS value within each time period.

[0020] In some preferred embodiments, autocorrelation processing is performed on the horizontal component signal for each preprocessed time period to obtain an autocorrelation sequence, the method being as follows:

[0021] The autocorrelation processing is a one-sided autocorrelation processing, which truncates the virtual self-excited and self-receiving shot set records, selects and retains the one-sided record segment from the zero-delay point to its positive-delay end in the symmetrical virtual self-excited and self-receiving shot set records as the autocorrelation sequence, and discards the record segment on the other side.

[0022] In some preferred embodiments, the frequency domain transformation is a Fourier transform, which involves superimposing each single-segment spectrum according to direction based on a time window. The method is as follows:

[0023] The characteristic frequencies of all single-segment spectra in the north and east directions are extracted by superimposing them within the same frequency range to obtain the east-west characteristic frequency spectrum and the north-west characteristic frequency spectrum of the current acquisition point.

[0024] In some preferred embodiments, the eastward characteristic frequency spectrum and the northward characteristic frequency spectrum are merged, and the method is as follows:

[0025] The eastward and northward characteristic frequency spectra are combined horizontally using vector synthesis, specifically including:

[0026] At each frequency, the sum of squares of the eastward and northward characteristic frequency spectrum values ​​is calculated; the arithmetic square root of the sum of squares is taken as the horizontal component characteristic frequency spectrum of the current acquisition point.

[0027] In some preferred embodiments, the frequency imaging results are generated by means of:

[0028] Based on the preset velocity model, the horizontal component characteristic frequency spectrum of each acquisition point is transformed into depth, and the frequency of the horizontal axis is converted into depth to obtain the depth characteristic spectrum of each acquisition point.

[0029] The depth feature spectrum is arranged along the survey line according to the location of the acquisition point, and the depth feature spectrum of each acquisition point is corrected for elevation using actual elevation information;

[0030] The corrected depth feature spectra are subjected to two-dimensional linear interpolation to generate frequency imaging results.

[0031] In some preferred embodiments, the frequency of the horizontal axis is converted into depth based on a preset velocity model, and the method is as follows:

[0032] By dividing the preset underground medium velocity by twice the frequency, the quotient is the depth value corresponding to that frequency, thus accurately mapping the spectrum abscissa from the frequency axis to the depth axis.

[0033] In some preferred embodiments, an instrument conformance test is further included before data acquisition, the test step including:

[0034] Multiple seismic detectors are arranged at equal intervals along a pre-set rectangular array at the test site.

[0035] The background noise was continuously observed in the rectangular array for more than 30 minutes; and

[0036] Confirm that all detectors used to collect background noise data of the target area meet the following conditions: waveform amplitude deviation is less than or equal to the first threshold, the time difference of recording the same impact event is less than the second threshold, and the spectral difference between each component is less than or equal to the third threshold.

[0037] A second aspect of this application proposes a background noise seismic frequency imaging system based on autocorrelation, the system comprising:

[0038] The data acquisition module is configured to acquire background noise data from multiple acquisition points in the target area using a three-component seismic detector. The background noise data includes vertical component data, north component data, and east component data.

[0039] The preprocessing module is configured to segment the north component data and the east component data based on a preset time window to obtain horizontal component signals in different directions, and to perform abnormal amplitude noise suppression and mean reduction processing.

[0040] The virtual record generation module is configured to perform one-sided autocorrelation processing on the horizontal component signal of each time period to generate a virtual self-excited and self-receiving shot collection record containing underground interface reflection information, and obtain an autocorrelation sequence.

[0041] The feature frequency extraction module is configured to perform frequency domain transformation on each autocorrelation sequence to obtain multiple single-segment spectra; based on a time window, the single-segment spectra are superimposed according to direction to generate eastward feature frequency spectrum and northward feature frequency spectrum.

[0042] The data processing module is configured to merge the eastward and northward characteristic frequency spectra to obtain the horizontal component characteristic frequency spectrum.

[0043] The depth conversion and imaging module is configured to map the horizontal component characteristic frequency spectrum to the depth of the underground anomaly, obtain the depth characteristic spectrum of each acquisition point, arrange the depth characteristic spectrum in combination with the acquisition point location and correct the elevation to generate frequency imaging results.

[0044] The beneficial effects of this application are:

[0045] (1) The method of this application innovatively combines background noise interferometry with the "impact-echo" theory, uses autocorrelation technology to process background noise data, and generates a virtual self-excited and self-receiving shot gather record; it calculates the frequency domain characteristic spectrum of a single instrument through a multi-time window statistical strategy and converts it into a depth spectrum, thereby realizing high-resolution imaging of low-frequency signals in the time domain in the frequency domain. This not only improves the detection resolution, but also enables the detection of deeper underground structures, providing a high-resolution solution for the detection of shallow underground structures.

[0046] (2) A series of preprocessing measures such as windowing, noise reduction, and mean removal were adopted, and combined with the multi-time window spectrum superposition strategy, weak but stable resonance signals can be effectively extracted from long-term background noise, random interference is suppressed, and the reliability of the final imaging results is ensured.

[0047] (3) By extracting and utilizing the "one-sided autocorrelation" results, the computational load of Fourier transform is significantly reduced, and the data processing efficiency is improved. It can directly extract the interference characteristics of seismic waves between different strata or anomalies in the frequency domain, with a wide detection depth range and greater detection depth. It makes full use of the abundant low-frequency signals in natural sources to achieve deep-depth detection. It is suitable for underground structure detection from shallow to deep layers, making this application have great application potential and market value in multiple fields such as geological exploration, environmental monitoring, and engineering construction.

[0048] (4) The method of this application belongs to non-destructive testing technology, which reduces the impact on the environment and reduces exploration costs. At the same time, the passive source detection characteristics make the method more widely applicable, even in complex terrain environments, and it is especially suitable for areas such as cities, mountains, and ecological protection areas where traditional active source methods are difficult to implement. Attached Figure Description

[0049] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0050] Figure 1 This is a flowchart of the background noise seismic frequency imaging method based on autocorrelation provided in the embodiments of this application;

[0051] Figure 2 These are comparison images of preprocessing provided in the embodiments of this application. Figure (a) shows the entire acquired seismic data segment, and Figure (b) shows the data within the nth time window after windowing. ;

[0052] Figure 3 This is the original seismic data containing anomalous amplitude noise provided in the embodiments of this application;

[0053] Figure 4 This is a schematic diagram of the hanning window function provided in an embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the data after suppressing abnormal amplitude noise based on the Hanning window function, provided in an embodiment of this application.

[0055] Figure 6 This is a schematic diagram of seismic data before and after mean reduction processing provided in the embodiments of this application;

[0056] Figure 7 This is a schematic diagram of a preprocessed time segment provided in an embodiment of this application;

[0057] Figure 8 This is a schematic diagram of the autocorrelation-processed seismic data provided in the embodiments of this application;

[0058] Figure 9 This is a schematic diagram of the Fourier transform spectrum provided in the embodiments of this application;

[0059] Figure 10 This is a schematic diagram of the depth spectrum after depth conversion provided in an embodiment of this application;

[0060] Figure 11 This is the frequency imaging result provided in the embodiments of this application;

[0061] Figure 12 This is a schematic diagram of the structure of a computer system used to implement the methods, systems, and electronic devices of this application. Detailed Implementation

[0062] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0063] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0064] This application addresses the long-standing technical contradiction in existing technologies: the active-source "impact-echo" method is limited in detection depth due to the lack of low-frequency energy at the seismic source, while traditional passive-source methods suffer from insufficient resolution, making fine imaging of shallow layers difficult. This application provides a seismic frequency imaging method based on autocorrelation-based background noise, overturning the technical bias that the "impact-echo" method must rely on an active source. For the first time, it combines background noise autocorrelation technology with frequency domain resonance analysis, breaking through the energy and frequency bottlenecks of active sources. It leverages the inherent low-frequency advantage of background noise to overcome the limitation on detection depth, while simultaneously utilizing the high-resolution characteristics of frequency domain analysis to overcome the imaging accuracy bottleneck of traditional passive-source methods in shallow layers. This produces unexpected technical effects that combine both great depth and high resolution, unattainable by a single technique. Therefore, it effectively extends the detection depth of the "impact-echo" frequency analysis method to the hundreds of meters or even kilometers, achieving high-resolution imaging of shallow and intermediate geological anomalies, thus broadening its applicability.

[0065] To more clearly explain the autocorrelation-based background noise seismic frequency imaging method of this application, the following will be combined with... Figure 1 The steps in the embodiments of this application are described in detail.

[0066] The autocorrelation-based background noise seismic frequency imaging method of the first embodiment of this application, steps S100-S600, are described in detail below:

[0067] S100. Acquire background noise data from multiple collection points in the target area, wherein the background noise data includes vertical component data, north component data, and east component data.

[0068] Preferably, the method for acquiring background noise data from multiple sampling points in the target area is as follows:

[0069] The preset survey line adopts a straight layout with the same orientation for measurement. Multiple seismic detectors are arranged at equal intervals along the preset straight survey line, and the distance between adjacent detectors is not greater than half of the minimum lateral dimension of the geological anomaly to be detected.

[0070] After the design is completed, the coordinate data of the observation system is imported into the RTK (Real Time Kinematic) equipment so that the seismic detectors can be accurately placed and calibrated during field operations.

[0071] In this embodiment, the seismic detector is a three-component seismic detector. The minimum required frequency band range of the detector should be selected as 0.2Hz ~ 150Hz (not exceeding this range). The data acquisition duration T is set in layers according to the preset target depth of the geological anomaly to be detected.

[0072] More preferably, before data acquisition, the method further includes a step of performing an instrument consistency test, the test step including:

[0073] Multiple seismic detectors are arranged at equal intervals along a pre-set rectangular array at the test site.

[0074] The background noise was continuously observed in the rectangular array for more than 30 minutes; and

[0075] Confirm that all detectors used to acquire background noise data of the target area meet the following conditions: waveform amplitude deviation is less than or equal to the first threshold, the time difference of recording the same seismic event is less than the second threshold, and the spectral difference between the three components is less than or equal to the third threshold.

[0076] In this embodiment, the first threshold is The second threshold is 4 milliseconds, and the third threshold is the maximum amplitude. .

[0077] In this embodiment, it is necessary to ensure that the amplitude changes of all waveforms are consistent. If any instrument fails to meet the above standard, it will be considered unqualified and will not be used. This ensures that all instruments can maintain a high degree of consistency when recording the same background noise.

[0078] More preferably, the raw seismic data acquired by the seismic detectors is imported into a computer, the target time period is extracted, and exported as a SAC format file. Each detector generates three files (E, N, Z), corresponding to the east, north, and vertical components, respectively. The data file names are standardized according to the naming rule of "survey line number + point number + instrument number + start time" for easy traceability. Let the seismic data of the three components recorded by the i-th detector be... , .

[0079] Since the "impact-echo" method mainly utilizes body waves for calculation, and in three-component seismic data, the vertical component often records a large amount of surface wave and P-wave data. Because surface waves have strong energy and P-waves have relatively weak energy, the imaging results obtained by using the vertical component are mainly affected by the surface wave polarization effect, and cannot effectively image the information of the subsurface strata. On the other hand, the horizontal component often carries a large amount of S-wave data, which has high energy and carries a lot of subsurface information. Tests have shown that the subsurface imaging results obtained by using only the horizontal component are better. Therefore, this method only uses the seismic data of the horizontal component for calculation and processing.

[0080] S200. Based on a preset time window, the north component data and east component data are segmented to obtain horizontal component signals in different directions. The method is as follows:

[0081] Set autocorrelation time window The northern component data and the eastern component data are each divided into N segments of equal time window length, wherein... Let the nth data segment be... , , Representing the first The first time recorded by the detector Quantity, =1 indicates the northward horizontal component signal. =2 indicates the eastward horizontal component signal; where, time window The numerical selection criteria are based on the actual data acquisition time. As it is determined, it needs to meet the following requirements. ,Right now .

[0082] Preferably, before combining the north component data and the east component data, the method further includes a step of preprocessing the horizontal component signals in different directions, wherein the preprocessing method includes de-meaning processing and abnormal amplitude noise suppression processing.

[0083] More preferably, in one specific embodiment, the abnormal amplitude noise suppression processing specifically includes:

[0084] Calculate the root mean square (RMS) value of the amplitude of the horizontal component signal for each time period:

[0085]

[0086] in, It is the root mean square value;

[0087] Will As an outlier criterion, amplitude suppression is applied to the portion of the amplitude exceeding a preset multiple of the RMS value within each time period. In this embodiment, the preset multiple k is preferably not less than 4 and not greater than 7.

[0088] During data acquisition, human activities or vehicle traffic around the instrument can easily induce high-amplitude anomalous noise in seismic records, which in turn can produce false anomalies in frequency imaging. Amplitude suppression is used to suppress this anomalous amplitude noise, and de-meaning is used to reduce signal shift, thereby improving the effectiveness of subsequent processing steps (such as Fourier transform).

[0089] More preferably, in a specific embodiment, amplitude suppression is performed using a Hanning window. By interacting with the data through the Hanning window, the amplitude is suppressed to below k times the root mean square (RMS), for example, in... Within the data spanning a certain time period, significant anomalous amplitudes were identified. The first step was to calculate the time point within that time range when the amplitude reached its maximum. The maximum amplitude is E, and the Hanning window function is used. for:

[0090] ;

[0091] in, By multiplying the Hanning window function with the data in the t1-t2 time period, abnormal amplitudes can be suppressed.

[0092] More preferably, in a specific embodiment, the method for demeaning is as follows:

[0093] Calculate the average value of seismic data. Its mean The calculation is as follows:

[0094] ;

[0095] Next, subtract the calculated mean from each sample of the signal. The signal after mean reduction is obtained. :

[0096] ;

[0097] After the mean reduction process is completed, verify the processed signal. mean Is it close to zero?

[0098] ;

[0099] The data from each subsequent seismic detector were processed using the same preprocessing procedure to obtain multi-channel preprocessed seismic data.

[0100] S300. Autocorrelation processing is performed on the horizontal component signal for each time period to generate a virtual self-excited and self-receiving shot set record, thereby obtaining an autocorrelation sequence. The method is as follows:

[0101] The autocorrelation processing is a one-sided autocorrelation processing, which involves truncating the virtual self-excited and self-receiving shot set records, selecting and retaining the one-sided record segment from the zero-delay point to its positive-delay end in the symmetrical virtual self-excited and self-receiving shot set records as the autocorrelation sequence, and discarding the record segment on the other side.

[0102] This record is physically equivalent to the response obtained by applying a pulse source at the detector location and receiving it at the same location.

[0103] Preferably, autocorrelation processing is performed on the horizontal component signal for each time period:

[0104] ;

[0105] in, It is the amount of time shift. Indicates the signal at The conjugate of the time interval; this application only processes real signals, and the conjugate of a real signal is the signal itself, thus obtaining the autocorrelation sequence within that time interval. , Represents a time period. , Representing the first The first time recorded by the detector Quantity.

[0106] S400. Perform frequency domain transformation on each of the autocorrelation sequences to obtain multiple single-segment spectra; based on the time window, superimpose each single-segment spectrum according to the direction to generate an eastward characteristic frequency spectrum and a northward characteristic frequency spectrum.

[0107] Preferably, the frequency domain transformation is a Fourier transform, and the individual frequency segments are superimposed according to their directions based on a time window. The method is as follows:

[0108] The characteristic frequencies of all single-segment spectra in the north and east directions are extracted by superimposing them within the same frequency range to obtain the east-west characteristic frequency spectrum and the north-west characteristic frequency spectrum of the current acquisition point.

[0109] More preferably, this embodiment employs a multi-time-window statistical strategy, first analyzing the autocorrelation sequence of a single time window. Perform a Fourier transform to obtain the frequency domain spectrum. And plot the characteristic spectrum of the data within that time window;

[0110] By statistically superimposing the characteristic spectra of all time windows, a characteristic frequency spectrum of a single detector is generated:

[0111] .

[0112] Because the strata interface has a positive reflection coefficient, seismic waves continuously reflect between strata, generating resonance. In the frequency domain, this manifests as enhanced interference, and the amplitude of the interference energy is proportional to the reflection coefficient (i.e., the difference in wave impedance). Subsequently, through superposition, the characteristic energy gradually becomes prominent, while random noise and incoherent interference are suppressed. Ultimately, the superimposed spectral energy directly maps to the comprehensive response of wave impedance, effectively characterizing the subsurface interface.

[0113] S500. Map the horizontal component characteristic frequency spectrum to the depth of the subsurface anomaly to obtain the depth characteristic spectrum of each acquisition point. The method is as follows:

[0114] The eastward and northward characteristic frequency spectra are combined horizontally using vector synthesis, specifically including:

[0115] At each frequency, the sum of squares of the eastward and northward characteristic frequency spectrum values ​​is calculated; the arithmetic square root of the sum of squares is taken as the horizontal component characteristic frequency spectrum of the current acquisition point.

[0116] Preferably, to fully utilize and accurately reconstruct the characteristic frequency information of the S-wave, the sum of squares of the two horizontal components is calculated as the actual characteristic frequency spectrum of the S-wave for analysis; the first... individual detectors The characteristic frequency spectrum of the component (north component) is denoted as , will the individual detectors The characteristic frequency spectrum of the component (east component) is denoted as After merging, the characteristic frequency spectrum of the horizontal component is obtained as follows:

[0117] ;

[0118] After merging, the characteristic frequency spectrum of the horizontal component is denoted as... subscript Indicates the detector number.

[0119] S600. Map the horizontal component characteristic frequency spectrum to the depth of the subsurface anomaly to obtain the depth characteristic spectrum of each acquisition point. Arrange the depth characteristic spectrum according to the acquisition location and correct the elevation to generate frequency imaging results. The method is as follows:

[0120] Based on the preset velocity model, the horizontal component characteristic frequency spectrum of each acquisition point is transformed into depth, and the frequency of the horizontal axis is converted into depth to obtain the depth characteristic spectrum of each acquisition point.

[0121] The depth feature spectrum is arranged along the survey line according to the location of the acquisition point, and the depth feature spectrum of each acquisition location is corrected for elevation using actual elevation information.

[0122] The corrected depth feature spectra are subjected to two-dimensional linear interpolation to generate frequency imaging results.

[0123] Preferably, the frequency of the horizontal axis is converted into depth based on a preset velocity model, and the method is as follows:

[0124] By dividing the preset underground medium velocity by twice the frequency, the quotient is the depth value corresponding to that frequency, thus accurately mapping the spectrum abscissa from the frequency axis to the depth axis.

[0125] More preferably, the characteristic frequency spectrum of the horizontal component... Perform a depth transformation, converting the frequency of the horizontal axis to depth, specifically:

[0126] Let the depth of the underground anomaly be The underground speed is The time it takes for seismic waves to travel from the ground and be reflected back to the surface after encountering an anomalous body is... (Also known as multiple reflection time delay); therefore, the following relationship can be obtained:

[0127] ;

[0128] Due to delay Corresponding frequency The depth conversion formula can be obtained as follows:

[0129] ;

[0130] Mapping the horizontal axis of the frequency characteristic spectrum from frequency f to depth H yields the characteristic spectrum that varies with depth. This intuitively reflects the difference in wave impedance at different depths.

[0131] More preferably, in a specific embodiment, the depth feature spectrum is arranged based on the spatial location information of the acquisition location on the survey line:

[0132] ;

[0133] in, The total number of detectors for the survey line is given, and the depth characteristic spectrum of each detector's data (acquisition location) is corrected for elevation using the actual elevation information. The results of all detectors arranged according to the survey line are then subjected to two-dimensional linear interpolation to obtain the final frequency imaging result.

[0134] The method of this application is illustrated below using a specific application scenario as an example. The detection is performed using a background noise seismic frequency imaging method based on autocorrelation, and the specific steps are as follows:

[0135] 1) Design of the observation system: Before conducting field data acquisition, a site survey must be carried out, and an observation system must be designed based on the specific geological conditions and the scope of the work area. The actual survey line locations and the specific layout of the nodal geophones must be designed. After the design is completed, the coordinate data of the observation system is imported into the RTK equipment so that the seismic geophones can be accurately placed and calibrated during field operation. In this embodiment, a three-component seismic geophone is selected for data acquisition, and the geophone's frequency band range is [missing information]. .

[0136] Before formal data collection and before conducting consistency testing, all instruments need to be checked and powered on to confirm that they are functioning properly and have sufficient power. This involves standardizing the sampling rate and gain settings of all instruments. Additionally, preparations are needed. A spare instrument is provided to replace any malfunctioning equipment during the data collection process.

[0137] 2) Instrument setup: The RTK equipment is used to accurately locate the preset coordinates of the observation system and seismic detectors, place the three-component seismic detectors, and adjust the instrument level and direction to north. The sequence and instrument number of each detector on the survey line are recorded. At the same time, the RTK is used to mark the actual placement of the instruments again to facilitate subsequent position and elevation correction.

[0138] 3) Data Acquisition: After the seismic detectors are deployed and all instruments are confirmed to be functioning properly, data acquisition officially begins. Given that this method requires acquiring background noise signals over a long period, the acquisition time needs to be set according to the actual imaging depth and the quietness of the environment. .

[0139] In this embodiment, for a detection depth of less than 100 meters, the data acquisition time is... Set to 30 minutes; target depth reached Meters, collection time Set to 1 hour; target depth reached Meters, collection time Set to 2 hours; target depth is within Meters, collection time Set to 3 hours. During the actual data collection process, the start and end times of data collection must be recorded in detail.

[0140] 4) Data preprocessing, specifically including:

[0141] Data Import and Windowing: Set autocorrelation time windows, with a total length of... The northern and eastern components (E and N components) of the data are divided into: Data with equal time windows were processed, discarding the vertical component (Z component); and the north and east component data for each time period were merged to obtain horizontal component signals for multiple time periods. The choice of τ must satisfy... ,Right now Let the first... The first detector The nth segment of the component is ,like Figure 2 As shown, where, Figure 2 (a) shows the entire earthquake data. Figure 2 (b) shows the data within the nth time window after windowing. .

[0142] Anomalous amplitude noise suppression: Anomalous amplitude noise suppression and de-meaning are performed on the segmented raw seismic data for each detector. The RMS value of the data for each time window is calculated, and a threshold multiple k is set (usually 4 to 7 depending on the noise intensity). If the amplitude of data at any point within the window exceeds k*RMS, it is considered anomalous noise. Subsequently, a Hanning window function is used, such as... Figure 3-5 As shown, the effect is applied to this abnormal amplitude range, smoothly suppressing its amplitude to within the threshold. Figure 3 The original data contains anomalous noise. Figure 4 For the Hanning window function, Figure 5 This is the compressed data.

[0143] De-meaning: To eliminate the influence of potential DC components in the data on the low-frequency part of the subsequent Fourier transform, de-meaning is performed on the data for each time window. This involves calculating the average value of all data points within the window and subtracting this average value from each data point. Figure 6 This shows a comparison of the data before and after mean reduction. The data at the top is the original data, and the data at the bottom is the data after mean reduction.

[0144] The data from each seismic detector were processed using the same preprocessing procedure described above.

[0145] 5) Frequency imaging: The preprocessed horizontal component signals are merged, autocorrelation is calculated, and characteristic frequencies are extracted to obtain single-channel characteristic frequency spectra. Then, all single-channel characteristic frequency spectra are mapped to depth and arranged in actual order for frequency imaging.

[0146] The horizontal component signals for each time period of the north and east components. (like Figure 7 Autocorrelation calculations are performed on the image to generate a virtual self-excited and self-receiving shot set record, as shown. Figure 8 As shown. Considering the conjugate symmetry of the autocorrelation function, its positive semi-axis ( ) and negative half axis ( They carry exactly the same information. Therefore, in subsequent processing, only their one-sided portions (i.e., This approach (of which the computational cost of the subsequent Fourier transform is halved without losing any information, significantly improving processing efficiency) can reduce the computational cost of the subsequent Fourier transform by half.

[0147] A multi-time-window statistical strategy is adopted, first analyzing the autocorrelation sequence of a single time period. Perform a Fourier transform to obtain the frequency domain spectrum. The characteristic spectrum of the time data segment within the time window is plotted; then, the characteristic spectra of all time data segments are statistically superimposed to generate the characteristic frequency spectrum of a single detector; through superposition, the coherent resonance signal generated by the underground stable wave impedance interface is strengthened, while random environmental noise is canceled out, ultimately resulting in a total characteristic frequency spectrum with a very high signal-to-noise ratio that reflects the characteristics of the medium beneath the detector, such as... Figure 9 As shown.

[0148] To eliminate the influence of the incident azimuth angle of the natural source S-wave and to stably extract the total S-wave energy, it is necessary to merge the characteristic frequency spectra of the E and N components into the characteristic frequency spectrum of the horizontal component. .

[0149] Single-channel characteristic frequency spectrum Perform a depth transformation, converting the frequency of the horizontal axis to depth, such as... Figure 10 As shown, the feature spectrum that varies with depth is obtained. This intuitively reflects the difference in wave impedance at different depths.

[0150] Arrange the depth feature spectra of all detector data along the entire survey line in actual order, i.e. Where I is the total number of geophones in the survey line; then, the elevation of each geophone's data is corrected based on the actual elevation information provided by RTK; and two-dimensional linear interpolation is performed on all the elevation-corrected and sequentially arranged depth spectrum data to generate a continuous and intuitive frequency imaging profile of the underground structure, such as... Figure 11 As shown in the figure, the strength of the energy clusters directly reflects the salience of the underground wave impedance interface.

[0151] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such order. They can be executed simultaneously (in parallel) or in reverse order. These simple changes are all within the protection scope of this application.

[0152] A second embodiment of this application provides a background noise seismic frequency imaging system based on autocorrelation, the system comprising:

[0153] The data acquisition module is configured to acquire background noise data from multiple acquisition points in the target area using a three-component seismic detector. The background noise data includes vertical component data, north component data, and east component data.

[0154] The preprocessing module is configured to segment the north component data and the east component data based on a preset time window to obtain horizontal component signals in different directions, and to perform abnormal amplitude noise suppression and mean reduction processing.

[0155] The virtual record generation module is configured to perform one-sided autocorrelation processing on the horizontal component signal of each time period to generate a virtual self-excited and self-receiving shot collection record containing underground interface reflection information, and obtain an autocorrelation sequence.

[0156] The feature frequency extraction module is configured to perform frequency domain transformation on each autocorrelation sequence to obtain multiple single-segment spectra; based on a time window, the single-segment spectra are superimposed according to direction to generate eastward feature frequency spectrum and northward feature frequency spectrum.

[0157] The data processing module is configured to merge the eastward and northward characteristic frequency spectra to obtain the horizontal component characteristic frequency spectrum.

[0158] The depth conversion and imaging module is configured to map the horizontal component characteristic frequency spectrum to the depth of the underground anomaly, obtain the depth characteristic spectrum of each acquisition point, arrange the depth characteristic spectrum in combination with the acquisition point location and correct the elevation to generate frequency imaging results.

[0159] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0160] It should be noted that the autocorrelation-based background noise seismic frequency imaging method and system provided in the above embodiments are only illustrative examples of the above functional module division. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of this application can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of this application are only for distinguishing each module or step and are not considered as an improper limitation of this application.

[0161] A device according to a third embodiment of this application includes:

[0162] At least one processor;

[0163] and a memory communicatively connected to at least one of the processors;

[0164] The memory stores instructions that can be executed by the processor to implement the autocorrelation-based background noise seismic frequency imaging method described above.

[0165] A computer-readable storage medium according to a fourth embodiment of this application stores computer instructions that are executed by the computer to implement the above-described autocorrelation-based background noise seismic frequency imaging method.

[0166] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0167] The following is for reference. Figure 12 It shows a schematic diagram of the structure of a computer system for implementing embodiments of the systems, methods, and electronic devices of this application. Figure 12 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0168] like Figure 12 As shown, the computer system includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 1202 or programs loaded from storage section 1208 into Random Access Memory (RAM) 1203. The RAM 1203 also stores various programs and data required for system operation. The CPU 1201, ROM 1202, and RAM 1203 are interconnected via a bus 1204. An Input / Output (I / O) interface 1205 is also connected to the bus 1204.

[0169] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0170] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by central processing unit (CPU) 1201, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0171] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0172] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0173] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0174] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0175] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A seismic frequency imaging method based on autocorrelation background noise, characterized in that, Includes the following steps: Acquire background noise data from multiple sampling points in the target area, wherein the background noise data includes vertical component data, north component data, and east component data; The north component data and the east component data are segmented based on a preset time window to obtain horizontal component signals in different directions. Autocorrelation processing is performed on the horizontal component signal for each time period to generate a virtual self-excited and self-receiving shot set record, thereby obtaining an autocorrelation sequence. Frequency domain transformation is performed on the relevant sequences to obtain multiple single-segment spectra. Based on the time window, the single-segment spectra are superimposed according to the direction to generate eastward characteristic frequency spectrum and northward characteristic frequency spectrum. The eastward and northward characteristic frequency spectra are merged to obtain the horizontal component characteristic frequency spectrum; The horizontal component characteristic frequency spectrum is mapped to the depth of the underground anomaly to obtain the depth characteristic spectrum of each acquisition point. The depth characteristic spectrum is then arranged and the elevation is corrected in combination with the location of the acquisition point to generate frequency imaging results.

2. The method for seismic frequency imaging based on autocorrelation background noise according to claim 1, characterized in that, The method for obtaining background noise data from multiple sampling points in the target area is as follows: Multiple seismic detectors are deployed at equal intervals along a pre-set straight survey line, and the distance between adjacent detectors is no greater than half of the minimum lateral dimension of the geological anomaly to be detected.

3. The method for seismic frequency imaging based on autocorrelation background noise according to claim 1, characterized in that, The horizontal component signal is divided into N time windows according to a preset time window length, where N is an integer greater than 100; before performing autocorrelation processing on the horizontal component signal in each time period, the method further includes a step of performing preprocessing on the horizontal component signal in different directions. The preprocessing steps include mean reduction and abnormal amplitude noise suppression. Specifically, the abnormal amplitude noise suppression process includes: Calculate the root mean square (RMS) value of the amplitude of the horizontal component signal for each time period; In addition, amplitude suppression is applied to the portion of the amplitude that exceeds a preset multiple of the RMS value within each time period.

4. The method for seismic frequency imaging based on autocorrelation background noise according to claim 3, characterized in that, Autocorrelation processing is performed on the horizontal component signal for each preprocessed time period to obtain an autocorrelation sequence. The method is as follows: The autocorrelation processing is a one-sided autocorrelation processing, which truncates the virtual self-excited and self-receiving shot set records, selects and retains the one-sided record segment from the zero-delay point to its positive-delay end in the symmetrical virtual self-excited and self-receiving shot set records as the autocorrelation sequence, and discards the record segment on the other side.

5. The method for seismic frequency imaging based on autocorrelation background noise according to claim 4, characterized in that, The frequency domain transformation is a Fourier transform, which involves superimposing each single-segment spectrum according to its direction based on a time window. The method is as follows: The characteristic frequencies of all single-segment spectra in the north and east directions are extracted by superimposing them within the same frequency range to obtain the east-west characteristic frequency spectrum and the north-west characteristic frequency spectrum of the current acquisition point.

6. The method for seismic frequency imaging based on autocorrelation background noise according to claim 5, characterized in that, The method for merging the eastward and northward characteristic frequency spectra is as follows: The eastward and northward characteristic frequency spectra are combined horizontally using vector synthesis, specifically including: At each frequency, the sum of squares of the eastward and northward characteristic frequency spectrum values ​​is calculated; the arithmetic square root of the sum of squares is taken as the horizontal component characteristic frequency spectrum of the current acquisition point.

7. The method for seismic frequency imaging based on autocorrelation background noise according to claim 1, characterized in that, The method for generating frequency imaging results is as follows: Based on the preset velocity model, the characteristic frequency spectrum of each horizontal component is transformed into depth, and the frequency of the horizontal axis is converted into depth to obtain the depth characteristic spectrum of each acquisition point. The depth feature spectrum is arranged along the survey line according to the location of the acquisition point, and the depth feature spectrum of each acquisition point is corrected for elevation using actual elevation information; The corrected depth feature spectra are subjected to two-dimensional linear interpolation to generate frequency imaging results.

8. The method for seismic frequency imaging based on autocorrelation background noise according to claim 7, characterized in that, The method for converting the frequency of the horizontal axis into depth based on a preset velocity model is as follows: By dividing the preset underground medium velocity by twice the frequency, the quotient is the depth value corresponding to that frequency, thus accurately mapping the spectrum abscissa from the frequency axis to the depth axis.

9. The method for seismic frequency imaging based on autocorrelation background noise according to claim 1, characterized in that, Before data acquisition, an instrument consistency test is also performed, which includes: Multiple seismic detectors are arranged at equal intervals along a pre-set rectangular array at the test site. The background noise was continuously observed in the rectangular array for more than 30 minutes; and Confirm that all detectors used to acquire background noise data of the target area meet the following conditions: waveform amplitude deviation is less than or equal to the first threshold, the time difference of recording the same seismic event is less than the second threshold, and the spectral difference between each component is less than or equal to the third threshold.

10. A seismic frequency imaging system based on autocorrelation background noise, characterized in that, The system includes: The data acquisition module is configured to acquire background noise data from multiple acquisition points in the target area using a three-component seismic detector. The background noise data includes vertical component data, north component data, and east component data. The preprocessing module is configured to segment the north component data and the east component data based on a preset time window to obtain horizontal component signals in different directions, and to perform abnormal amplitude noise suppression and mean reduction processing. The virtual record generation module is configured to perform one-sided autocorrelation processing on the horizontal component signal of each time period to generate a virtual self-excited and self-receiving shot collection record containing underground interface reflection information, and obtain an autocorrelation sequence. The feature frequency extraction module is configured to perform frequency domain transformation on each autocorrelation sequence to obtain multiple single-segment spectra; based on a time window, the single-segment spectra are superimposed according to direction to generate eastward feature frequency spectrum and northward feature frequency spectrum. The data processing module is configured to merge the eastward and northward characteristic frequency spectra to obtain the horizontal component characteristic frequency spectrum. The depth conversion and imaging module is configured to map the horizontal component characteristic frequency spectrum to the depth of the underground anomaly, obtain the depth characteristic spectrum of each acquisition point, arrange the depth characteristic spectrum in combination with the acquisition point location and correct the elevation to generate frequency imaging results.

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