A quantitative analysis method for seismic data quality indicators

Abstract

The present invention relates to the technical field of seismic data analysis, and discloses a method for quantitatively analyzing seismic data quality indicators. The method comprises the following steps: obtaining geological characteristic parameters of an area to which a target station belongs; obtaining seismic waveform data of the area to which the target station belongs, performing spike detection processing on the seismic waveform data, and obtaining a power spectrum density and a spike event list corresponding to the seismic waveform data; performing coupling feature deep fusion processing on a site fundamental frequency, a fault distance, a power spectrum, and a spike event list, and respectively determining a geological resonance-related noise indicator, a fault zone spike interference indicator, and a coupled nonlinear interaction indicator; performing correlation analysis processing on the geological resonance-related noise indicator, the fault zone spike interference indicator, and the coupled nonlinear interaction indicator, and obtaining a joint anomaly score; and comparing the joint anomaly score with a preset anomaly warning value to obtain seismic data quality.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data analysis, and more particularly to a method for quantitatively analyzing seismic data quality indicators. Background Art

[0002] Quantitative analysis of seismic data quality indicators is the core technology to ensure the reliability of earthquake early warning systems. The evaluation method and device for seismic acquisition data is disclosed with the publication number CN102012522B. By obtaining the shot-detection relationship data including shot point and detection point data and the seismic network data including beam data; determining the shot energy, signal-to-noise ratio and resolution based on the shot-detection relationship data; generating a single-beam plane layout diagram and / or a multi-beam plane layout diagram based on the shot energy, signal-to-noise ratio and resolution and the seismic network data; obtaining a satellite remote sensing map including geomorphological feature information of the work area; evaluating seismic acquisition data based on the single-beam plane layout diagram and / or the multi-beam plane layout diagram and the satellite remote sensing map. Through this invention, the accuracy and efficiency of evaluating seismic acquisition data can be improved.

[0003] However, the above scheme lacks deep coupling between geological characteristics and signal characteristics, and has the following problems:

[0004] Thick sedimentary areas are prone to low-frequency resonant noise due to geological structures, and traditional indicators cannot accurately distinguish resonant noise from effective seismic waves, resulting in a high false alarm rate. Stations near fault zones are affected by the electrochemical effects of lithologic interfaces, often experiencing high-frequency spike interference. However, existing methods only count spike frequency without associating it with fault distance, resulting in a high false alarm rate.

[0005] The early warning network uses a unified threshold standard and does not dynamically adjust station geological parameters (such as sediment thickness and distance to faults). For example, the noise characteristics of mountain stations differ significantly from those of plain stations, but the existing system cannot automatically adapt, resulting in threshold adjustments relying on manual experience.

[0006] In summary, the existing seismic data quality assessment methods are not reliable enough in complex geological scenarios. It is urgent to introduce geological environment parameters and build a quantitative analysis system that couples geology and signals to improve the accuracy of anomaly identification and regional adaptability. Summary of the Invention

[0007] The present invention provides a method for quantitative analysis of seismic data quality indicators, which solves the technical problems raised in the background technology.

[0008] The present invention provides a method for quantitative analysis of seismic data quality indicators, comprising:

[0009] Step 1: Obtain geological characteristic parameters of the area to which the target station belongs; wherein the geological characteristic parameters include: sediment thickness, site average shear wave velocity, location coordinates, and active fault coordinates; determine the site fundamental frequency of the target station based on the sediment thickness and site average shear wave velocity, and determine the fault distance of the target station based on the location coordinates and active fault coordinates;

[0010] Step 2: Obtain seismic waveform data of the target station's region, perform spike detection on the seismic waveform data, and obtain the power spectrum density and spike event list corresponding to the seismic waveform data;

[0011] Step 3: The site fundamental frequency, fault distance, power spectrum, and spike event list are deeply fused to perform coupling feature processing to determine the geological resonance-related noise index, fault zone spike interference index, and coupled nonlinear interaction index respectively;

[0012] Step 4: perform correlation analysis on the geological resonance-related noise index, the fault zone spike interference index, and the coupled nonlinear interaction index to obtain a joint anomaly score;

[0013] Step 5: Compare the combined anomaly score with the preset anomaly warning value to obtain the seismic data quality.

[0014] Furthermore, the fundamental frequency of the target station is determined based on the sediment thickness and the average shear wave velocity of the site, including:

[0015] Based on the theory of sedimentary layer seismic response, the fundamental frequency of the target station site is determined as follows:

[0016]

[0017] in, represents the site fundamental frequency of the target station, represents the average shear wave velocity at the site, Indicates the thickness of the sediment layer.

[0018] Furthermore, the fault distance of the target station is determined based on the position coordinates and the active fault coordinates, including:

[0019] The Euclidean distance between the location coordinates and the active fault coordinates is taken as the fault distance of the target station.

[0020] Furthermore, spike detection processing is performed on the seismic waveform data to obtain the power spectrum density and spike event list corresponding to the seismic waveform data, including:

[0021] The power spectral density can be obtained by:

[0022] The seismic waveform data is segmented to obtain data sub-segments, and the power spectrum density is determined based on the Welch power spectrum estimation method as follows:

[0023]

[0024] in, represents the power spectral density, Indicates the number of data segments, represents the Hamming window of the k-th segment of data, represents the kth data segment, represents Fourier transform;

[0025] The methods for obtaining the spike event list include:

[0026] Determine the seismic waveform data at any two adjacent sampling moments in the seismic waveform data, and perform first-order difference to obtain the corresponding waveform amplitude;

[0027] Calculate the median absolute deviation based on the waveform amplitude as follows:

[0028]

[0029] in, represents the median absolute deviation, express function, represents the waveform amplitude at the i-th adjacent sampling moment, Represents the median of the waveform amplitudes at all adjacent sampling moments;

[0030] like ,but As a spike event;

[0031] The waveform amplitude at each adjacent sampling moment is determined to form a spike event list.

[0032] Furthermore, the geological resonance-related noise indicators include:

[0033] Determine the site fundamental frequency The g-order harmonic frequency band, , select the resonance half bandwidth of each harmonic frequency band , ;

[0034] Integrate the power spectrum density to obtain the energy of the g-order harmonic frequency band ;

[0035] Energy for each harmonic frequency band Perform weighted summation and calculate the integral of the power spectrum density of all harmonic frequency bands to obtain the geological resonance related noise index;

[0036] The calculation formula for determining the geological resonance-related noise index based on the sedimentary layer seismic resonance theory is as follows:

[0037]

[0038]

[0039]

[0040] in, represents the geological resonance related noise index, represents the harmonic weight, and Indicates the upper and lower limits of the effective frequency range, represents the integration variable.

[0041] Furthermore, the fault zone spike interference indicators are as follows:

[0042]

[0043]

[0044]

[0045]

[0046] in, It represents the fault zone spike interference index, represents the amplitude weight, represents the event weight, represents the spatial weight, represents a list of spike events, represents an index into the list of spike events, Indicates the The waveform amplitude of a spike event, Indicates the The median absolute deviation of spike events, Indicates the current moment, Indicates the The moment corresponding to the spike event, represents the time decay constant, represents the fault distance of the target station, represents the spatial attenuation constant.

[0047] Furthermore, coupled nonlinear interaction indicators include:

[0048] The geological resonance-related noise index and the fault zone spike interference index are obtained synchronously at fixed time intervals to obtain the geological resonance-related noise sequence. and fault zone spike interference sequence ;

[0049] Calculating geological resonance-related noise sequences and fault zone spike interference sequence Pearson correlation coefficient , , represents a correlated noise sequence and fault zone spike interference sequence The covariance of represents a correlated noise sequence The standard deviation of Indicates the fault zone spike interference sequence The standard deviation of

[0050] Geo-resonance-related noise sequence and fault zone spike interference sequence Normalize and divide them into 20 equal-width histograms, and calculate the KL divergence as follows:

[0051]

[0052] in, represents the KL divergence, Represents the bins interval of the histogram, Represents the geological resonance-related noise sequence The corresponding histogram probability distribution, Indicates the fault zone spike interference sequence The corresponding histogram probability distribution;

[0053] Based on Pearson correlation coefficient and KL divergence Calculate the coupled nonlinear interaction index as follows:

[0054]

[0055] in, represents the coupled nonlinear interaction index.

[0056] Furthermore, correlation analysis is performed on the geological resonance-related noise index, fault zone spike interference index, and coupled nonlinear interaction index to obtain a joint anomaly score, including:

[0057] Geo-resonance related noise index , fault zone spike interference index and coupled nonlinear interaction indices Normalization is performed separately as follows:

[0058]

[0059]

[0060]

[0061] in, Represents the geological resonance related noise index The normalization parameter of Indicates the fault zone spike interference index The normalization parameter of Denotes the coupled nonlinear interaction index Normalization parameter of ; and Represents the geological resonance-related noise sequence The mean and standard deviation of and Indicates the fault zone spike interference sequence The mean and standard deviation of and Representing a coupled nonlinear interaction sequence The mean and standard deviation of

[0062] Among them, the coupled nonlinear interaction sequence is the geological resonance-related noise sequence and fault zone spike interference sequence The corresponding sequence is formed, Represents the extraction of geological resonance related noise sequence and fault zone spike interference sequence The size of the sliding time window;

[0063] Calculate the joint anomaly score as follows:

[0064]

[0065]

[0066] in, represents the joint abnormality score, represents the indicator vector, express 、 and The covariance matrix of represents the inverse of the covariance matrix.

[0067] Furthermore, the combined anomaly score is compared with the preset anomaly warning value to obtain the seismic data quality, including:

[0068] Preset abnormal alert values based on expert settings;

[0069] If the combined abnormality score If the value is greater than or equal to the preset abnormal warning value, a quality warning of the seismic data quality is triggered.

[0070] The beneficial effects of the present invention are: by coupling the site fundamental frequency and fault distance depth into the power spectrum and spike event analysis, coupling indicators such as the multiharmonic geological resonance coefficient, the time-space amplitude three-weight spike density factor and the coupled nonlinear interaction index are constructed, so as to achieve accurate quantitative identification of resonance noise and fault interference under complex geological conditions, significantly improve the sensitivity and reliability of seismic data quality assessment, reduce the false alarm and missed alarm rate, and have the ability of automatic regional adaptive threshold adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flowchart of a method for quantitative analysis of seismic data quality indicators. DETAILED DESCRIPTION

[0072] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0073] like Figure 1 As shown, a method for quantitative analysis of seismic data quality indicators includes:

[0074] Step 1: Obtain geological characteristic parameters of the area to which the target station belongs; wherein the geological characteristic parameters include: sediment thickness, site average shear wave velocity, location coordinates, and active fault coordinates; determine the site fundamental frequency of the target station based on the sediment thickness and site average shear wave velocity, and determine the fault distance of the target station based on the location coordinates and active fault coordinates;

[0075] Step 2: Obtain seismic waveform data of the target station's region, perform spike detection on the seismic waveform data, and obtain the power spectrum density and spike event list corresponding to the seismic waveform data;

[0076] Step 3: The site fundamental frequency, fault distance, power spectrum, and spike event list are deeply fused to perform coupling feature processing to determine the geological resonance-related noise index, fault zone spike interference index, and coupled nonlinear interaction index respectively;

[0077] Step 4: perform correlation analysis on the geological resonance-related noise index, the fault zone spike interference index, and the coupled nonlinear interaction index to obtain a joint anomaly score;

[0078] Step 5: Compare the combined anomaly score with the preset anomaly warning value to obtain the seismic data quality.

[0079] In one embodiment of the present invention, determining the site fundamental frequency of the target station based on the sediment layer thickness and the site average shear wave velocity includes:

[0080] Based on the theory of sedimentary layer seismic response, the fundamental frequency of the target station site is determined as follows:

[0081]

[0082] in, represents the site fundamental frequency of the target station, represents the average shear wave velocity at the site, Indicates the thickness of the sediment layer.

[0083] Specifically, through the theory of sedimentary seismic response, a quantitative relationship between the physical properties of the sediment (thickness, shear wave velocity) and the site's fundamental frequency was established, providing support for subsequent analysis of sedimentary resonance noise. By clarifying the site's fundamental frequency, the frequency-selective amplification of seismic waves by the geological structure beneath the station can be further evaluated, thereby improving the accuracy of seismic data quality assessment.

[0084] The fundamental frequency of the target station site The inherent resonance frequency of the geological structure below the station is a key parameter for determining whether the sedimentary layer will induce low-frequency resonance noise. , which can easily resonate and amplify low-frequency seismic waves, leading to noise interference.

[0085] Site average shear wave velocity The elastic properties of the site medium. The higher the shear wave velocity, the faster the seismic wave propagates in the sediment layer. Under the same thickness, the site fundamental frequency The higher.

[0086] Sediment layer thickness Directly affects the fundamental frequency. The greater the thickness, the lower the fundamental frequency. The lower it is, the easier it is to induce resonance effect in the low frequency band.

[0087] In one embodiment of the present invention, determining the fault distance of a target station based on the position coordinates and the active fault coordinates includes:

[0088] The Euclidean distance between the location coordinates and the active fault coordinates is taken as the fault distance of the target station.

[0089] In detail, the position coordinates represent the position of the target station in the plane coordinate system (usually converted from longitude and latitude), which is the basic parameter for determining the spatial position of the station.

[0090] The active fault coordinates represent the position coordinates of the active faults in the region in the plane coordinate system, reflecting the spatial distribution of the faults.

[0091] Fault distance is used to measure the spatial proximity of the target station to the active fault, because geological tectonic activities near the active fault (such as lithologic changes, electrochemical effects, etc.) can interfere with the quality of the station's seismic data (spike noise).

[0092] In one embodiment of the present invention, spike detection processing is performed on seismic waveform data to obtain a power spectrum density and a spike event list corresponding to the seismic waveform data, including:

[0093] The power spectral density can be obtained by:

[0094] The seismic waveform data is segmented to obtain data sub-segments, and the power spectrum density is determined based on the Welch power spectrum estimation method as follows:

[0095]

[0096] in, represents the power spectral density, Indicates the number of data segments, represents the Hamming window of the k-th segment of data, represents the kth data segment, represents Fourier transform;

[0097] The methods for obtaining the spike event list include:

[0098] Determine the seismic waveform data at any two adjacent sampling moments in the seismic waveform data, and perform first-order difference to obtain the corresponding waveform amplitude;

[0099] Calculate the median absolute deviation based on the waveform amplitude as follows:

[0100]

[0101] in, represents the median absolute deviation, express function, represents the waveform amplitude at the i-th adjacent sampling moment, Represents the median of the waveform amplitudes at all adjacent sampling moments;

[0102] like ,but As a spike event;

[0103] The waveform amplitude at each adjacent sampling moment is determined to form a spike event list.

[0104] In detail, the seismic waveform data are segmented and the power spectrum density is calculated based on the Welch power spectrum estimation method to reduce spectrum leakage and estimation variance and more accurately analyze the energy distribution of the seismic waveform at different frequencies.

[0105] Indicates the power spectrum density, reflecting the frequency of the earthquake waveform The energy distribution at is used to identify the frequency band where noise is concentrated. Indicates the number of data sub-segments, improves the stability of power spectrum estimation by averaging multiple segments, and reduces the random influence of a single data segment. Indicates the The Hamming window of the segment data is used to perform windowing on the data sub-segments to reduce spectrum leakage (i.e., the phenomenon that signal energy spreads to adjacent frequencies in spectrum analysis) and make the power spectrum estimation more accurate.

[0106] formula This method is based on the Welch power spectrum estimation method, a classic spectral estimation technique. By segmenting, windowing, and averaging the data, it effectively reduces spectral leakage and estimation variance. It is suitable for analyzing non-stationary signals such as seismic waveforms and can more reliably obtain power spectral density.

[0107] Perform first-order differences on seismic waveform data at adjacent sampling moments to highlight sudden changes in waveform amplitude and facilitate the detection of spike events. Calculate the median absolute deviation , used to determine the threshold of spike events. This indicator is insensitive to outliers and can effectively suppress the interference of random noise. Determine spike events, identify amplitude changes that meet the conditions as spikes, and form a spike event list.

[0108] Represents the waveform amplitude difference between the i-th adjacent sampling moments, which is used to detect sudden changes in waveform amplitude. It represents the median absolute deviation, which measures the degree of discreteness of the waveform amplitude difference sequence. It serves as the threshold basis for determining spike events and is highly robust to outliers. Represents the median of the waveform amplitude difference between all adjacent sampling moments, used to calculate .

[0109] The first-order difference can directly reflect the change in waveform amplitude at adjacent moments and is the basic operation for detecting spikes (i.e., sudden changes in amplitude). Compared with the standard deviation, it is less sensitive to outliers and is more suitable for processing signals such as seismic data that may contain sudden interference (spikes). Determine spike events, where 6 times is an empirical value. In practical applications, it can effectively suppress random noise and retain real spike events (such as amplitude mutations caused by geological interference or equipment failure), thus forming a reliable spike event list.

[0110] In one embodiment of the present invention, the geological resonance-related noise indicators include:

[0111] Determine the site fundamental frequency The g-order harmonic frequency band, , select the resonance half bandwidth of each harmonic frequency band , ;

[0112] Integrate the power spectrum density to obtain the energy of the g-order harmonic frequency band ;

[0113] Energy for each harmonic frequency band Perform weighted summation and calculate the integral of the power spectrum density of all harmonic frequency bands to obtain the geological resonance related noise index;

[0114] The calculation formula for determining the geological resonance-related noise index based on the sedimentary layer seismic resonance theory is as follows:

[0115]

[0116]

[0117]

[0118] in, represents the geological resonance related noise index, represents the harmonic weight, and Indicates the upper and lower limits of the effective frequency range, represents the integration variable.

[0119] In detail, determine the harmonic frequency band and half bandwidth to focus on the site fundamental frequency The 1st to 3rd order harmonic frequency band (harmonic order, 1, 2, 3, represents the fundamental frequency and its 2nd and 3rd harmonics, low frequency harmonics have a significant effect on the resonance of thick sediment layers), by setting the resonance half bandwidth , clarify the core frequency range of sedimentary layer resonance, avoid interference from other frequency bands, and provide a target range for subsequent energy calculation. Traditional seismic data quality assessment does not pay attention to the relationship between multi-order harmonics and geological resonance. Based on the geological characteristics of thick sedimentary layers, 1st to 3rd order harmonics (this range covers the main resonance frequency bands of thick sedimentary layers) are selected and analyzed through Define the half-bandwidth (determined in combination with the frequency-selective amplification characteristics of seismic waves by geological structures, which can effectively separate the frequency bands where the resonance energy is concentrated, avoiding the blindness of frequency band division in traditional methods).

[0120] Power spectral density in each harmonic frequency band Integrate and calculate the total energy of the frequency band , quantify the energy distribution of the resonance frequency band. Combining the geological resonance theory with the integral operation in signal processing, Calculating the energy of specific harmonic frequency bands rather than simply analyzing the energy of the entire frequency band. This targeted energy calculation method fully considers the resonant amplification effect of geological structures on specific frequencies and is an innovative application of traditional signal energy analysis methods. It is a basic application of integration. By integrating the power spectrum density in the harmonic frequency band, the energy of the frequency band is accumulated, which meets the physical principle of energy calculation.

[0121] Calculate the geological resonance related noise index, so as to analyze the energy of each harmonic frequency band. Weighted sum (weight ), and then divided by the integral of the full-band power spectrum density , the geological resonance-related noise index is obtained, the contribution of the low-frequency main resonance is highlighted, and the proportion of resonance noise in the total energy is quantified.

[0122] By introducing As harmonic weights, based on the characteristics of the physical mechanism of sedimentary layer resonance, low-frequency harmonics (such as the fundamental frequency) play a dominant role in the resonance of thick sedimentary layers, and the contribution of higher-order harmonics decreases rapidly with the increase of g. Traditional methods do not differentiate the weighting of harmonic contributions.

[0123] formula The geological resonance energy (weighted harmonic energy) is combined with the full-band energy to form an indicator that can reflect the proportion of geological resonance noise, realizing the quantitative correlation between geological resonance characteristics and signal energy.

[0124] In one embodiment of the present invention, the fault zone spike interference indicator is as follows:

[0125]

[0126]

[0127]

[0128]

[0129] in, It represents the fault zone spike interference index, represents the amplitude weight, represents the event weight, represents the spatial weight, represents a list of spike events, represents an index into the list of spike events, Indicates the The waveform amplitude of a spike event, Indicates the The median absolute deviation of spike events, Indicates the current moment, Indicates the The moment corresponding to the spike event, represents the time decay constant, represents the fault distance of the target station, represents the spatial attenuation constant.

[0130] Detailed, through The amplitude of the spike event Normalization processing is performed so that spikes with larger amplitudes account for a higher proportion in the index, highlighting the impact of large spikes on data quality.

[0131] In traditional seismic data quality assessment, only the number of spike events is often considered, while the amplitude difference is ignored. Normalization is performed on the indicator (a discreteness index that is insensitive to outliers) so that large spikes (such as strong interference caused by drastic lithologic changes in fault zones) have a higher weight in the indicator, reflecting the actual degree of harm caused by spike interference more accurately.

[0132] Indicates the The waveform amplitude of a spike event directly reflects the intensity of the spike. It represents the median absolute deviation of the s-th spike event, which is used to measure the degree of dispersion of the data sequence in which the spike event occurs. By normalizing the amplitude Related to the degree of dispersion of the data series.

[0133] pass Time weighting is calculated to give more weight to recent spike events, reflecting the timeliness of the disturbance (recent spikes better reflect the current state of the disturbance at the station). Traditional methods typically treat all historical spike events equally, failing to account for the effects of time decay. By introducing an exponential decay model, we simulate the decay of the disturbance's impact over time (for example, spikes caused by short-term electrochemical activity in a fault zone, more recent ones, better reflect current geological activity), making the indicator more accurate to actual dynamics.

[0134] Indicates the current moment, which serves as the time reference. Indicates the time corresponding to the s-th spike event, which is used to calculate the time difference. It represents the time decay constant (preferably 12h), which controls the decay speed of the time weight. It is determined through experiments and experience so that the time weight decays neither too quickly (missing recent important interference) nor too slowly (retaining the influence of too long historical interference).

[0135] Exponential decay formula It is widely used in physics and engineering to simulate phenomena that attenuate over time. Here, it is used for time weight calculation, which conforms to the law that the influence of interference weakens over time. It is also easy to calculate and has flexible parameter adjustment.

[0136] pass Spatial weights are calculated so that spike events are weighted higher for stations closer to the fault, reflecting the spatial correlation of fault zone interference on stations (stations near faults are more susceptible to interference from lithologic and electrochemical effects). Traditional seismic data quality assessments underutilize the spatial distance between stations and faults. By incorporating spatial factors into the indicator, based on the geological understanding that proximity to the fault increases the likelihood of interference, an exponential decay model is used to quantify the spatial impact, giving the indicator spatial resolution. It represents the spatial attenuation constant (preferably 10 km), which controls the attenuation rate of the spatial weight. It is determined through analysis of different geological regions so that the spatial weight can reasonably reflect the spatial influence range of the fault interference. It is the application of the exponential decay model in the calculation of spatial weights, which satisfies the requirement that the closer to the fault, the higher the possibility of interference.

[0137] pass The triple-weighted product of all spike events is summed up, and the amplitude, time, and space dimensions are integrated to fully quantify the degree of spike interference in the fault zone. 、 、 Three innovative weights are combined to form a multi-dimensional comprehensive indicator. While traditional methods assess disturbances from a single dimension (e.g., solely looking at the number of spikes or only their amplitude), this indicator, for the first time, integrates three dimensions: amplitude, time, and space. This allows for a more comprehensive and detailed characterization of fault zone spike disturbances (e.g., simultaneously capturing near-fault, recent, and large-scale high-risk spike events).

[0138] It represents the fault zone spike interference index. The larger the value, the more serious the fault zone spike interference. Indicates the total number of spike events in the spike event list, which serves as the summation upper limit to ensure that all spike events participate in the indicator calculation.

[0139] In one embodiment of the present invention, the coupled nonlinear interaction indicator includes:

[0140] The geological resonance-related noise index and the fault zone spike interference index are obtained synchronously at fixed time intervals to obtain the geological resonance-related noise sequence. and fault zone spike interference sequence ;

[0141] Calculating geological resonance-related noise sequences and fault zone spike interference sequence Pearson correlation coefficient , , represents a correlated noise sequence and fault zone spike interference sequence The covariance of represents a correlated noise sequence The standard deviation of Indicates the fault zone spike interference sequence The standard deviation of

[0142] Geo-resonance-related noise sequence and fault zone spike interference sequence Normalize and divide them into 20 equal-width histograms, and calculate the KL divergence as follows:

[0143]

[0144] in, represents the KL divergence, Represents the bins interval of the histogram, Represents the geological resonance-related noise sequence The corresponding histogram probability distribution, Indicates the fault zone spike interference sequence The corresponding histogram probability distribution;

[0145] Based on Pearson correlation coefficient and KL divergence Calculate the coupled nonlinear interaction index as follows:

[0146]

[0147] in, represents the coupled nonlinear interaction index.

[0148] In detail, the geological resonance-related noise index and the fault zone spike interference index are synchronously acquired at fixed time intervals (preferably 5 minutes) to form a time series.

[0149] pass measure and Linear synchronization. Reflecting the common trend of the two, the standard deviation and The degree of each discreteness is measured and the linear correlation degree is obtained after normalization. The linear relationship between geological resonance and fault spike interference is analyzed by introducing the Pearson correlation coefficient, which is a breakthrough in the traditional single indicator analysis. For example, at a station with thick sedimentary layer and close to the fault, if It is close to 1, indicating that the two change synchronously (e.g., when resonance is enhanced, the spike interference also increases), which provides clues to the coupling mechanism of geological signals. The larger the covariance value, the stronger the trend of change in the same direction. and They are and The standard deviation reflects the fluctuation range of the series.

[0150] Will and Normalize and divide into 20 equal width histograms, calculate , which measures the difference in the probability distribution of the two. By introducing KL divergence (an indicator for measuring distribution differences in information theory) into seismic data quality assessment, we can capture the difference between the two probability distributions. and For example, if the linear correlation coefficient is high but The sudden increase indicates that the distribution pattern is dislocated (e.g., the resonance continues but the spike distribution suddenly changes), revealing a complex interaction mechanism of geological signals.

[0151] KL divergence is based on information entropy theory. Quantifies the difference between two distributions. The larger the value, the more significant the difference. In seismic data, this method can effectively capture the subtle differences in the distribution of geological resonance and fault spike interference, providing support for nonlinear analysis.

[0152] pass Combining linear correlation and distribution difference, amplifying abnormal sensitivity in scenarios where linear synchronization and distribution difference mutation occur. Combining Pearson correlation coefficient with KL divergence to form The traditional method does not consider both linear and nonlinear characteristics. It can fully characterize the complex interaction between geological resonance and fault spike interference. For example, during the earthquake activity period, if the linear correlation between the two is enhanced ( Increased) and the distribution difference suddenly changed ( Increase), It will increase significantly, providing early warning of abnormal data quality. It represents the coupled nonlinear interaction index. The larger the value, the more complex the interaction between geological resonance and fault spike interference, and the higher the possibility of data quality abnormality.

[0153] In one embodiment of the present invention, correlation analysis is performed on the geological resonance-related noise index, the fault zone spike interference index, and the coupled nonlinear interaction index to obtain a joint anomaly score, including:

[0154] Geo-resonance related noise index , fault zone spike interference index and coupled nonlinear interaction indices Normalization is performed separately as follows:

[0155]

[0156]

[0157]

[0158] in, Represents the geological resonance related noise index The normalization parameter of Indicates the fault zone spike interference index The normalization parameter of Denotes the coupled nonlinear interaction index Normalization parameter of ; and Represents the geological resonance-related noise sequence The mean and standard deviation of and Indicates the fault zone spike interference sequence The mean and standard deviation of and Representing a coupled nonlinear interaction sequence The mean and standard deviation of

[0159] Among them, the coupled nonlinear interaction sequence is the geological resonance-related noise sequence and fault zone spike interference sequence The corresponding sequence is formed, Represents the extraction of geological resonance related noise sequence and fault zone spike interference sequence The size of the sliding time window;

[0160] Calculate the joint anomaly score as follows:

[0161]

[0162]

[0163] in, represents the joint abnormality score, represents the indicator vector, express 、 and The covariance matrix of represents the inverse of the covariance matrix.

[0164] Detailed, due to geological resonance related noise indicators , fault zone spike interference index and coupled nonlinear interaction indices The dimensions of the factors are different, so direct analysis cannot accurately reflect the comprehensive impact. Normalization is used to eliminate the dimensional differences.

[0165] pass , Calculate the joint anomaly score, use the Mahalanobis distance to comprehensively consider the covariance matrix C of each element in the indicator vector Z (three normalized indicators), quantify the degree of anomaly of the indicator vector in multidimensional space, and comprehensively reflect the comprehensive anomaly of seismic data quality. Traditional methods only simply weighted sum the indicators without considering the correlation between indicators. By introducing the Mahalanobis distance, the covariance matrix C is captured 、 、 The correlation between the geological resonance of thick sedimentary stations and fault spike interference is positively correlated.

[0166] In one embodiment of the present invention, the combined anomaly score is compared with a preset anomaly warning value to obtain the seismic data quality, including:

[0167] Preset abnormal alert values based on expert settings;

[0168] If the combined abnormality score If the value is greater than or equal to the preset abnormal warning value, a quality warning of the seismic data quality is triggered.

[0169] In detail, experts set a threshold as the benchmark for judging anomalies based on historical data, station geological conditions and seismic data quality standards, providing a clear basis for subsequent evaluation. The preset abnormal warning value is a threshold value that combines expert experience and data characteristics, reflecting the judgment standard for abnormal seismic data quality. By comparing the joint abnormality score J and the preset abnormal warning value, it is judged whether the seismic data quality is abnormal. If If the value is greater than or equal to the preset abnormal warning value, a quality warning will be triggered, indicating that the data may be interfered with by geological resonance, fault spikes, etc.

[0170] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for quantitative analysis of seismic data quality indicators, characterized in that: include: Step 1: Obtain geological characteristic parameters of the area to which the target station belongs; wherein the geological characteristic parameters include: sediment thickness, site average shear wave velocity, location coordinates, and active fault coordinates; determine the site fundamental frequency of the target station based on the sediment thickness and site average shear wave velocity, and determine the fault distance of the target station based on the location coordinates and active fault coordinates; Step 2: Obtain seismic waveform data of the target station's region, perform spike detection on the seismic waveform data, and obtain the power spectrum density and spike event list corresponding to the seismic waveform data; Step 3: The site fundamental frequency, fault distance, power spectrum, and spike event list are deeply fused to perform coupling feature processing to determine the geological resonance-related noise index, fault zone spike interference index, and coupled nonlinear interaction index respectively; Among them, geological resonance-related noise indicators include: Determine the site fundamental frequency The g-order harmonic frequency band, , select the resonance half bandwidth of each harmonic frequency band , ; Integrate the power spectrum density to obtain the energy of the g-order harmonic frequency band ; Energy for each harmonic frequency band Perform weighted summation and calculate the integral of the power spectrum density of all harmonic frequency bands to obtain the geological resonance related noise index; The calculation formula for determining the geological resonance-related noise index based on the sedimentary layer seismic resonance theory is as follows: ； ； ； in, represents the geological resonance related noise index, represents the harmonic weight, and Indicates the upper and lower limits of the effective frequency range, represents the integration variable, represents the power spectral density; Among them, the fault zone spike interference indicators are as follows: ； ； ； ； in, It represents the fault zone spike interference index, represents the amplitude weight, represents the event weight, represents the spatial weight, represents a list of spike events, represents an index into the list of spike events, Indicates the The waveform amplitude of a spike event, Indicates the The median absolute deviation of spike events, Indicates the current moment, Indicates the The moment corresponding to the spike event, represents the time decay constant, represents the fault distance of the target station, represents the spatial attenuation constant; Among them, the coupled nonlinear interaction index is as follows: The geological resonance-related noise index and the fault zone spike interference index are obtained synchronously at fixed time intervals to obtain the geological resonance-related noise sequence. and fault zone spike interference sequence ; Calculate geological resonance-related noise sequences and fault zone spike interference sequence Pearson correlation coefficient ; The geological resonance related noise sequence and fault zone spike interference sequence Normalize and divide into 20 equal width histograms, and calculate KL divergence; based on Pearson correlation coefficient and KL divergence Calculate the coupled nonlinear interaction index; Step 4: perform correlation analysis on the geological resonance-related noise index, the fault zone spike interference index, and the coupled nonlinear interaction index to obtain a joint anomaly score; Step 5: Compare the combined anomaly score with the preset anomaly warning value to obtain the seismic data quality.

2. A method for quantitative analysis of seismic data quality indicators according to claim 1, characterized in that: Determine the fundamental frequency of the target station based on the sediment thickness and the average shear wave velocity at the site, including: Based on the theory of sedimentary layer seismic response, the fundamental frequency of the target station site is determined as follows: ； in, represents the site fundamental frequency of the target station, represents the average shear wave velocity at the site, Indicates the thickness of the sediment layer.

3. A method for quantitative analysis of seismic data quality indicators according to claim 2, characterized in that: Determine the fault distance of the target station based on the location coordinates and active fault coordinates, including: The Euclidean distance between the location coordinates and the active fault coordinates is taken as the fault distance of the target station.

4. A method for quantitative analysis of seismic data quality indicators according to claim 3, characterized in that: Perform spike detection on seismic waveform data to obtain the power spectrum density and spike event list corresponding to the seismic waveform data, including: The power spectral density can be obtained by: The seismic waveform data is segmented to obtain data sub-segments, and the power spectrum density is determined based on the Welch power spectrum estimation method as follows: ； in, represents the power spectral density, Indicates the number of data segments, represents the Hamming window of the k-th segment of data, represents the kth data segment, represents Fourier transform; The methods for obtaining the spike event list include: Determine the seismic waveform data at any two adjacent sampling moments in the seismic waveform data, and perform first-order difference to obtain the corresponding waveform amplitude; Calculate the median absolute deviation based on the waveform amplitude as follows: ； in, represents the median absolute deviation, express function, represents the waveform amplitude at the i-th adjacent sampling moment, Represents the median of the waveform amplitudes at all adjacent sampling moments; like ,but As a spike event; The waveform amplitude at each adjacent sampling moment is determined to form a spike event list.

5. A method for quantitative analysis of seismic data quality indicators according to claim 4, characterized in that: Coupled nonlinear interaction indicators, including: The geological resonance-related noise index and the fault zone spike interference index are obtained synchronously at fixed time intervals to obtain the geological resonance-related noise sequence. and fault zone spike interference sequence ; Calculating geological resonance-related noise sequences and fault zone spike interference sequence Pearson correlation coefficient , , represents a correlated noise sequence and fault zone spike interference sequence The covariance of represents a correlated noise sequence The standard deviation of Indicates the fault zone spike interference sequence The standard deviation of Geo-resonance-related noise sequence and fault zone spike interference sequence Normalize and divide them into 20 equal-width histograms, and calculate the KL divergence as follows: ； in, represents the KL divergence, Represents the bins interval of the histogram, Represents the geological resonance-related noise sequence The corresponding histogram probability distribution, Indicates the fault zone spike interference sequence The corresponding histogram probability distribution; Based on Pearson correlation coefficient and KL divergence Calculate the coupled nonlinear interaction index as follows: ； in, represents the coupled nonlinear interaction index.

6. A method for quantitative analysis of seismic data quality indicators according to claim 5, characterized in that: Correlation analysis is performed on the geological resonance-related noise index, fault zone spike interference index, and coupled nonlinear interaction index to obtain a joint anomaly score, including: Geo-resonance related noise index , fault zone spike interference index and coupled nonlinear interaction indices Normalization is performed separately as follows: ； ； ； in, Represents the geological resonance related noise index The normalization parameter of Indicates the fault zone spike interference index The normalization parameter of Denotes the coupled nonlinear interaction index Normalization parameter of ; and Represents the geological resonance-related noise sequence The mean and standard deviation of and Indicates the fault zone spike interference sequence The mean and standard deviation of and Representing a coupled nonlinear interaction sequence The mean and standard deviation of Among them, the coupled nonlinear interaction sequence is the geological resonance-related noise sequence and fault zone spike interference sequence The corresponding sequence is formed, Represents the extraction of geological resonance related noise sequence and fault zone spike interference sequence The size of the sliding time window; Calculate the joint anomaly score as follows: ； ； in, represents the joint abnormality score, represents the indicator vector, express 、 and The covariance matrix of represents the inverse of the covariance matrix.

7. A method for quantitative analysis of seismic data quality indicators according to claim 6, characterized in that: Compare the joint anomaly score with the preset anomaly warning value to obtain the seismic data quality, including: Preset abnormal alert values based on expert settings; If the combined abnormality score If the value is greater than or equal to the preset abnormal warning value, a quality warning of the seismic data quality is triggered.

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