Seismic phase and weak signal pickup method and device based on multi-band cross-correlation

Abstract

The invention relates to a multi-band cross-correlation-based seismic phase and weak signal pickup method and device, and the method comprises the steps: obtaining the theoretical first arrival time of a reference seismic phase and target seismic phases, and intercepting the target waveform data of the reference seismic phase and each target seismic phase or the time period before and after a weak signal in waveform data through the theoretical first arrival time; calculating a cross-correlation function of the reference seismic phase and the target seismic phase based on the target waveform data, obtaining a multiband cross-correlation probability density through the cross-correlation function, and determining final cross-correlation first arrival time; and picking up the observation first arrival time of the target seismic phase by using the theoretical first arrival time and the final cross-correlation first arrival time. According to the invention, high-precision detection of the seismic phase and the weak signal can be efficiently realized, and reliable technical support is provided for earthquake monitoring and early warning and research on the internal structure of the earth.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic monitoring and signal processing, and particularly to a method and device for picking seismic phases and weak signals based on multi-band cross-correlation. Background Art

[0002] Seismic phase information is important basic data for geophysical research. Its accurate picking is not only crucial for seismic monitoring and early warning, but also widely applied in fields such as earthquake location, focal mechanism analysis, and travel-time tomography of the Earth's interior medium. Seismic phase picking refers to the process of precisely detecting and extracting the arrival times of seismic phases such as P-waves and S-waves from the data recorded by seismic stations.

[0003] Currently, seismic phase picking methods are mainly divided into manual picking, traditional methods, and artificial intelligence methods. Although manual picking has high accuracy, it is limited by the shortage of professional personnel, large workload, low efficiency, and strong dependence on experience; traditional methods such as the short-term average / long-term average (STA / LTA) method and the autoregressive Akaike information criterion (AIC), etc., although simple in calculation, are sensitive to noise, strongly dependent on parameters, and difficult to adapt to complex signal environments; artificial intelligence methods are outstanding in automatically processing massive data and adapting to complex signal and noise environments, significantly improving the accuracy of seismic phase picking, but still have problems such as dependence on a large amount of labeled data, high consumption of computing resources, poor model interpretability, and limited generalization ability.

[0004] Existing traditional methods rely on single-band signal processing, and have problems such as high computational complexity, poor real-time performance, and insufficient weak signal detection ability. Manual picking has high accuracy but low efficiency and is difficult to process massive data. Combining the two can improve the ability to process complex seismic data, but faces technical challenges such as the collaborative optimization of manual and automatic algorithms, the design of real-time interaction mechanisms, and the balance of real-time performance and detection accuracy in complex noise environments. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method and device for picking seismic phases and weak signals based on multi-band cross-correlation, which can improve the ability to pick the arrival times of seismic phases and detect weak signals, combine the collaborative mechanism of manual and automatic processing, take into account both high efficiency and accuracy, and solve the problem of accurately picking seismic phases in complex noise environments.

[0006] To achieve the above purpose, the present invention provides the following solutions:

[0007] A method for picking seismic phases and weak signals based on multi-band cross-correlation, comprising:

[0008] Obtaining the theoretical first arrival times of the reference seismic phase and the target seismic phase, and using the theoretical first arrival times to intercept the reference seismic phase and the target waveform data of the time periods before and after each target seismic phase or weak signal in the waveform data;

[0009] Based on the target waveform data, calculate the cross-correlation function between the reference seismic phase and the target seismic phase. Through the cross-correlation function, obtain the multi-band cross-correlation probability density, and determine the final cross-correlation arrival time.

[0010] Utilize the theoretical arrival time and the final cross-correlation arrival time to pick up the observed arrival time of the target seismic phase.

[0011] Optionally, obtaining the theoretical arrival times of the reference seismic phase and the target seismic phase includes:

[0012] Set up a one-dimensional Earth velocity reference model, and utilize the one-dimensional Earth velocity reference model to calculate the theoretical arrival times of the reference seismic phase and the target seismic phase.

[0013] Optionally, obtaining the waveform data includes:

[0014] Obtain the original seismic data, where the original seismic data includes: the longitude and latitude of the seismic event, the focal depth, and the longitude and latitude of the station.

[0015] Preprocess the original seismic data, where the preprocessing includes: removing the instrument response, removing the mean, removing the trend, and resampling to obtain the waveform data.

[0016] Optionally, intercepting the target waveform data includes:

[0017] According to the theoretical arrival time, set up a signal analysis time window containing the target seismic phase, and utilize the signal analysis time window to intercept the target waveform data of the reference seismic phase and each target seismic phase or the time periods before and after the weak signal.

[0018] Optionally, after intercepting the target waveform data includes:

[0019] Calculate the mean value of the seismic phases within the signal analysis time window:

[0020]

[0021] where, x i is the waveform data point within the signal time window, and N is the total number of data points;

[0022] According to the theoretical arrival time, set up a noise time window for obtaining the background noise time period before the theoretical arrival time;

[0023] Based on the noise time window and the signal analysis time window, calculate the signal-to-noise ratio of the reference seismic phase and the target seismic phase or the weak signal:

[0024]

[0025] Among them, E signal and E noise are the energies of the signal time window and the noise time window, respectively.

[0026] Optionally, the energies of the signal time window and the noise time window include:

[0027]

[0028] Among them, x i and y i are the waveform data points within the signal time window and the noise time window, respectively, N s and N n are the total numbers of data points of the signal time window and the noise time window, respectively.

[0029] Optionally, calculating the cross-correlation function between the reference seismic phase and the target seismic phase includes:

[0030] Using multiple signal frequency bands covering the main frequency components of the target seismic phase as a multi-band filter bank for decomposing the target waveform data into multiple band signals;

[0031] Using the multiple band signals to calculate the cross-correlation function between the reference seismic phase and the target seismic phase:

[0032]

[0033] Among them, τ is the time delay parameter indicating the offset of the target seismic phase relative to the reference seismic phase, l is the length of the cross-correlation time window, is the cross-correlation function of the i-th frequency band.

[0034] Optionally, determining the final cross-correlation first arrival time includes:

[0035] Determining the cross-correlation first arrival time:

[0036] T cc = τ·δ

[0037] Among them, δ is the signal sampling interval;

[0038] By weighted fusion of the cross-correlation first arrival times of different frequency bands, constructing a multi-band cross-correlation probability density:

[0039]

[0040] Among them, M is the total number of frequency bands, w i is the weight of the i-th frequency band, and the weight w i is dynamically adjusted according to the frequency characteristics of T cc ;

[0041] Extract the time corresponding to the peak of the probability density in the multi-band cross-correlation probability density as the final cross-correlation first arrival time, and comprehensively screen the reliability of the result based on the signal quality evaluation parameter. Finally, optimize the accuracy of the first arrival time through manual verification.

[0042] To achieve the above object, the present invention also provides a picking device for a seismic phase and weak signal picking method based on multi-band cross-correlation, including:

[0043] A theoretical calculation module, configured to obtain the theoretical first arrival times of the reference seismic phase and the target seismic phase, and use the theoretical first arrival times to intercept the reference seismic phase in the waveform data and the target waveform data of the time periods before and after each target seismic phase or weak signal;

[0044] A cross-correlation analysis module, configured to calculate the cross-correlation function between the reference seismic phase and the target seismic phase based on the target waveform data, obtain the multi-band cross-correlation probability density through the cross-correlation function, and determine the final cross-correlation first arrival time;

[0045] An artificial picking module, configured to pick the observed first arrival time of the target seismic phase by using the theoretical first arrival time and the final cross-correlation first arrival time.

[0046] The beneficial effects of the present invention are as follows:

[0047] 1. High-precision seismic phase picking: By adopting the multi-band cross-correlation technology, the present invention makes full use of the multi-band characteristics of seismic signals, significantly improves the accuracy of seismic phase picking, and performs excellently especially in weak signal and complex noise environments.

[0048] 2. High efficiency and user-friendliness: By optimizing the algorithm design and modular processing, the present invention can improve the data processing efficiency while ensuring high precision, meeting the real-time requirements of seismic monitoring and early warning. In addition, an intuitive user-friendly interface is provided, supporting real-time monitoring, parameter adjustment and manual intervention, significantly improving the user experience and operation convenience.

[0049] 3. Provide reliable technical support for related fields: The present invention provides reliable technical support with high precision and high efficiency for fields such as seismic monitoring, early warning, focal mechanism analysis and earth interior structure research. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 Flow chart of a method for picking seismic phases and weak signals based on multi-band cross-correlation according to an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of the ray paths of three seismic phases according to an embodiment of the present invention;

[0053] Figure 3 Under the condition of high signal-to-noise ratio, the P, PcP, and PKiKP seismic phase first arrivals picked according to the theoretical first arrival time T pre Schematic diagram of the first arrival picking of seismic phases;

[0054] Figure 4 According to the actually picked first arrival time T obs Schematic diagram of the first arrival picking of seismic phases after alignment;

[0055] Figure 5 Schematic diagram of the first arrival picking of seismic phases under the condition of low signal-to-noise ratio and high noise environment according to an embodiment of the present invention;

[0056] Figure 6 Under the condition of high noise environment, according to the actually picked first arrival time T obs Schematic diagram of the picking effect of seismic phases after alignment. Specific implementation mode

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] This embodiment discloses a method for picking seismic phases and weak signals based on multi-band cross-correlation, including: obtaining the theoretical first arrival times of the reference seismic phase and the target seismic phase, and using the theoretical first arrival times to intercept the reference seismic phase and the target waveform data of the front and rear time periods of each target seismic phase or weak signal in the waveform data; based on the target waveform data, calculating the cross-correlation function between the reference seismic phase and the target seismic phase, obtaining the multi-band cross-correlation probability density through the cross-correlation function, and determining the final cross-correlation first arrival time; using the theoretical first arrival time and the final cross-correlation first arrival time to pick the observed first arrival time of the target seismic phase.

[0059] Specifically, this embodiment discloses a method for picking seismic phases and weak signals based on multi-band cross-correlation, including:

[0060] Step S1: Obtain seismic data and perform preprocessing to obtain waveform data;

[0061] Step S2: Initialize the model parameters, including: reference model, reference seismic phase, target seismic phase, noise time window, signal time window, cross-correlation time window, and multi-band filter bank;

[0062] Step S3: Calculate the theoretical arrival time T, signal-to-noise ratio, and mean value of each seismic phase according to the reference model and time window selection. pre , signal-to-noise ratio, and mean value;

[0063] Step S4: Calculate the multi-band cross-correlation probability density of the target seismic phase according to the reference seismic phase and the multi-band filter bank, and construct the arrival time prediction model T. cc ;

[0064] Step S5: Refer to the theoretical arrival time T pre and the cross-correlation arrival time T cc , manually and accurately pick the observed arrival time T of the target seismic phase obs , and store all arrival data;

[0065] Step S6: After completing the seismic phase picking of the current seismic data, iteratively process the next seismic data, repeat steps S3 to S5 to perform the seismic phase picking of the next seismic data until all seismic data are processed;

[0066] Step S7: Store the arrival time data table of the target seismic phase of multi-channel seismic data.

[0067] Furthermore, obtaining waveform data includes: obtaining the original seismic data, where the original seismic data includes: the longitude and latitude of the seismic event, the focal depth, and the longitude and latitude of the station; preprocessing the original seismic data, and the preprocessing includes: removing the instrument response, removing the mean value, removing the trend, and resampling to obtain the waveform data.

[0068] Specifically, step S1 specifically includes the following steps: obtaining the original seismic data, where the seismic data information includes basic information such as the longitude and latitude of the seismic event, the focal depth, and the longitude and latitude of the station; preprocessing the original seismic data, including removing the instrument response, removing the mean value, removing the trend, and resampling.

[0069] Furthermore, obtaining the theoretical arrival times of the reference seismic phase and the target seismic phase includes: setting a one-dimensional earth velocity reference model, and using the one-dimensional earth velocity reference model to calculate the theoretical arrival times of the reference seismic phase and the target seismic phase.

[0070] Intercepting the target waveform data includes: according to the theoretical arrival time, setting a signal analysis time window containing the target seismic phase, and using the signal analysis time window to intercept the target waveform data of the time periods before and after the reference seismic phase and each target seismic phase or weak signal.

[0071] After intercepting the target waveform data, it includes: calculating the mean value of the seismic phase within the signal analysis time window, and setting the noise time window according to the theoretical arrival time to obtain the background noise time period before the theoretical arrival time; calculating the signal-to-noise ratio of the reference seismic phase and the target seismic phase or weak signal based on the noise time window and the signal analysis time window.

[0072] Specifically, further, step S2 specifically includes the following steps:

[0073] Reference model: Set a one-dimensional earth velocity reference model for calculating the theoretical arrival times T of the reference seismic phase and the target seismic phase. pre ;

[0074] Reference seismic phase: Select the P-wave or S-wave as the reference seismic phase, specifically determined according to the type of seismic event;

[0075] Target seismic phase: Set the type of seismic phase or weak signal to be detected (such as PcP wave, ScS wave or other specific seismic phases);

[0076] Noise time window: Select the background noise time period before the theoretical arrival time of the seismic phase for calculating the signal-to-noise ratio;

[0077] Signal time window: Set the signal analysis time window containing the target seismic phase according to the theoretical arrival time of the seismic phase;

[0078] Cross-correlation time window: Set the time window for multi-band cross-correlation calculation, usually a part of the signal time window;

[0079] Multi-band filter bank: Preferably adopt multiple signal frequency bands covering the main frequency components of the target seismic phase to capture the signal characteristics in different frequency bands and avoid inaccurate arrival time picking caused by the spectral truncation of a single filter.

[0080] Step S3 specifically includes the following steps:

[0081] According to the one-dimensional earth velocity reference model, calculate the theoretical arrival times T of the reference seismic phase and each target seismic phase or weak signal. pre ;

[0082] Based on the arrival time of the seismic phase and different time window parameters, respectively intercept the waveform data of the time periods before and after the reference seismic phase and each target seismic phase or weak signal;

[0083] Based on the signal time window, calculate the mean value of the seismic phase within the signal time window:

[0084]

[0085] Among them, x i is the waveform data point within the signal time window, and N is the total number of data points;

[0086] Based on the noise time window and the signal time window, calculate the signal-to-noise ratio (SNR) of the reference seismic phase and the target seismic phase, which is used to evaluate the signal quality and provide a reference for subsequent multi-band cross-correlation calculation:

[0087]

[0088] Among them, E signal and E noise are the energies of the signal time window and the noise time window respectively, and the calculation formula is:

[0089]

[0090] Among them, x i and y i are the waveform data points within the signal time window and the noise time window respectively, N s and N n are the total number of data points in the signal time window and the noise time window respectively.

[0091] Furthermore, calculating the cross-correlation function between the reference seismic phase and the target seismic phase includes: using multiple signal frequency bands covering the main frequency component of the target seismic phase as a multi-band filter bank to decompose the target waveform data into multiple band signals; calculating the cross-correlation function between the reference seismic phase and the target seismic phase using the multiple band signals.

[0092] Specifically, step S4 specifically includes the following steps:

[0093] Multi-band signal decomposition: Using a multi-band filter bank, decompose the waveform data of the reference seismic phase and the target seismic phase into multiple band signals: Each band signal is expressed as x i [n] and y i [n], where i represents the band index and n represents the time point;

[0094] Calculate the multi-band cross-correlation function between the reference seismic phase and the target seismic phase: Calculate the cross-correlation function between the reference seismic phase x i [n] and the target seismic phase y i [n] according to each band signal, and the formula is:

[0095]

[0096] Among them, τ is the time delay parameter, representing the offset of the target seismic phase relative to the reference seismic phase, l is the length of the cross-correlation time window, is the cross-correlation function of the i-th band.

[0097] Further, determining the final cross-correlation first arrival time includes: determining the cross-correlation first arrival time, constructing a multi-band cross-correlation probability density by weighted fusion of the cross-correlation first arrival times of different frequency bands, extracting the time corresponding to the peak probability density in the multi-band cross-correlation probability density as the final cross-correlation first arrival time, and comprehensively screening the reliability of the result with the signal quality evaluation parameter, and finally optimizing the accuracy of the first arrival time through manual verification.

[0098] Specifically, determine the cross-correlation first arrival time T cc :

[0099] T cc = τ·δ (6)

[0100] where δ is the signal sampling interval;

[0101] Construct a probability density model of the cross-correlation first arrival time Tcc: By weighted fusion of the cross-correlation results of each frequency band, obtain the multi-band cross-correlation probability density P[k]:

[0102]

[0103] where M is the total number of frequency bands, w i is the weight of the i-th frequency band, and the weight w i is dynamically adjusted according to the frequency characteristics of Tcc.

[0104] This embodiment also discloses a seismic phase and weak signal picking device based on multi-band cross-correlation, including: a theoretical calculation module, configured to obtain the theoretical first arrival times of the reference phase and the target phase, and use the theoretical first arrival times to intercept the reference phase and the target waveform data of the time periods before and after each target phase or weak signal in the waveform data; a cross-correlation analysis module, configured to calculate the cross-correlation function between the reference phase and the target phase based on the target waveform data, obtain the multi-band cross-correlation probability density through the cross-correlation function, and determine the final cross-correlation first arrival time; an artificial picking module, configured to pick the observed first arrival time of the target phase by using the theoretical first arrival time and the final cross-correlation first arrival time.

[0105] This embodiment also discloses a seismic phase and weak signal picking device based on multi-band cross-correlation, including: a data acquisition module, configured to execute step S1; a parameter initialization module, configured to execute step S2; a theoretical calculation module, configured to execute step S3; a cross-correlation analysis module, configured to execute step S4; an artificial picking module, configured to execute step S5; an iterative processing module, configured to execute step S6; a data storage module, configured to execute step S7.

[0106] In a second aspect, this embodiment also provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0107] Thirdly, this embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method for picking up seismic phases and weak signals based on multi-band cross-correlation is implemented.

[0108] Fourthly, this embodiment also provides a computer program product, including a computer program, which implements the method for accurately picking up seismic phases and weak signals when executed. The program is modularly designed, supports multi-format data, provides an interactive graphical user interface, optimizes the algorithm to improve efficiency, and combines manual intervention to ensure high-precision picking. It is applicable to seismic monitoring, early warning and research, reduces labor costs, and meets the requirements of real-time processing.

[0109] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0110] As Figure 1 shown, this embodiment provides a method for picking up seismic phases and weak signals based on multi-band cross-correlation, which specifically includes the following steps:

[0111] Step S1: Obtain seismic data and perform preprocessing to obtain waveform data;

[0112] Step S2: Initialize model parameters, including: reference model, reference seismic phase, target seismic phase, noise time window, signal time window, cross-correlation time window, and multi-band filter bank;

[0113] Step S3: According to the reference model and time window selection, calculate the theoretical arrival time T pre , signal-to-noise ratio, and mean value of each seismic phase;

[0114] Step S4: According to the reference seismic phase and the multi-band filter bank, calculate the multi-band cross-correlation probability density of the target seismic phase, and construct the arrival time prediction model T cc ;

[0115] Step S5: Refer to the theoretical arrival time T pre and the cross-correlation arrival time T cc , manually and accurately pick up the observed arrival time T obs of the target seismic phase, and store all arrival data;

[0116] Step S6: After completing the picking of seismic phases of the current seismic data, iteratively process the next seismic data, repeat steps S3 to S5 to pick up the seismic phases of the next seismic data until all seismic data are processed;

[0117] Step S7: Store the arrival time data table of the target seismic phases of multi-channel seismic data.

[0118] Further, step S1 specifically includes the following steps:

[0119] Obtain the original seismic data, and the seismic data information includes basic information such as the longitude and latitude, focal depth, and station longitude and latitude of the seismic event;

[0120] Preprocess the original seismic data, including removing instrument response, removing mean, removing trend, and resampling.

[0121] Further, step S2 specifically includes the following steps:

[0122] Reference model: Set a one-dimensional Earth velocity reference model for calculating the theoretical arrival times T of the reference phase and the target phase. pre In this embodiment, the ak135 one-dimensional velocity model is selected as the reference model. Further, according to the specific application scenario, other standard models such as the PREM model and the iasp91 model can also be selected. These reference models all provide standardized Earth interior velocity structure parameters, which can ensure the consistency and comparability of the theoretical arrival time calculation. In practical applications, the most suitable reference model can be flexibly selected according to the type of target phase and the characteristics of the research area;

[0123] Reference phase: Select the P-wave or S-wave as the reference phase. In this embodiment, the P-wave is selected as the reference phase. For P-wave related target phases, the P-wave is preferably selected as the reference phase (such as P diff , PKIKP phases, etc.);

[0124] Target phase: Set the seismic phase or weak signal type to be detected. Set the seismic phase or weak signal type to be detected. In this embodiment, PcP and PKiKP are specifically selected as the target phases (up to 3 target phases can be processed simultaneously). Figure 2 It shows that the reference P-wave and the target phases PcP and PKiKP have different ray path characteristics. Among them, due to the long propagation path, large energy attenuation, and influence of the deep complex structure of the PKiKP phase, its signal-to-noise ratio is usually significantly lower than that of other phases, belonging to a typical weak signal that is difficult to detect;

[0125] Noise time window: Select the background noise time period before the theoretical arrival time of the seismic phase for calculating the signal-to-noise ratio. In this embodiment, intercept the waveform data of [-30s, -10s] of the theoretical arrival time T pre as the noise time window;

[0126] Signal time window: Set the signal analysis time window including the target phase according to the theoretical arrival time of the seismic phase. In this embodiment, intercept the waveform data of [-8s, 8s] of the theoretical arrival time T pre as the signal time window;

[0127] Cross - correlation time window: Set the time window for multi - band cross - correlation calculation, usually a part of the signal time window. In this embodiment, the waveform data in the range of [-8s, 8s] of the theoretical first arrival time T pre is intercepted as the cross - correlation time window;

[0128] Multi - band filter bank: It is preferably to use multiple signal frequency bands covering the main frequency components of the target seismic phase to capture the signal characteristics of different frequency bands and avoid inaccurate first arrival picking caused by the spectral truncation of a single filter. In this embodiment, a multi - band filter bank in the range of 0.5 - 5Hz is adopted, focusing on covering the main frequency band of 1 - 3Hz, and the filter parameters are set at intervals of 0.1Hz to extract the seismic phase characteristics of different frequency bands and improve the accuracy of first arrival picking.

[0129] Further, step S3 specifically includes the following steps:

[0130] According to the one - dimensional earth velocity reference model, calculate the theoretical first arrival time T of the reference seismic phase and each target seismic phase or weak signal pre . In this embodiment, the ray - tracing method is used to calculate the theoretical first arrival time of seismic phases.

[0131] Based on the first arrival time of the seismic phase and different time window parameters, intercept the waveform data of the front and rear time window segments of the reference seismic phase and each target seismic phase or weak signal respectively. In this embodiment, the waveform data interception operation for each seismic phase is as follows: Taking the theoretically calculated first arrival time Tpre as the reference point, intercept the complete waveform data within the corresponding time window range before and after it (such as the signal time window [-8s, 8s]). It should be noted that for the following two main reasons: 1) There are systematic differences in different one - dimensional earth velocity reference models themselves; 2) Seismic waves are affected by the inhomogeneity and attenuation effects of the earth's internal medium during actual propagation, resulting in a significant deviation between the theoretically calculated first arrival time Tpre and the actually observed first arrival time Tobs. Figure 3 Shows the seismic phase waveforms of P, PcP, and PKiKP phases based on T alignment under high signal - to - noise ratio conditions in the embodiment of the present invention pre , clearly presenting the first arrival characteristics, amplitude attenuation, and waveform changes of each seismic phase. Figure 3 and Figure 5 Comparatively shows the waveform characteristics of P, PcP, and PKiKP phases based on T alignment under different signal - to - noise ratio conditions in the embodiment of the present invention pre . In a high signal - to - noise ratio environment ( Figure 3 ), each seismic phase shows clear waveform characteristics: the P - wave first arrival is sharp, the PcP - wave has a moderate amplitude, and although the PKiKP - wave has weak energy, it can still be identified. While in low signal - to - noise ratio conditions ( Figure 5)Under such circumstances, the background noise is significantly enhanced, the characteristics of the P-wave arrival are still visible, and it is not easy to pick up the arrival times of the PcP and PKiKP phases. This comparison intuitively presents the energy attenuation law of seismic waves under different propagation paths, especially the vulnerability of deep phases such as PKiKP to noise, providing an important basis for subsequent signal processing in terms of feature analysis.

[0132] Based on the signal time window, calculate the mean value of the phase within the signal time window; based on the noise time window and the signal time window, calculate the signal-to-noise ratio (SNR) of the reference phase and the target phase. In this embodiment, the mean value of the waveform within the signal time window is calculated to characterize the energy characteristics of the phase, and at the same time, the signal-to-noise ratio (SNR) is calculated based on the energy ratio between the noise time window and the signal time window. Among them, the mean value reflects the overall energy level of the phase, and the SNR is used to evaluate the signal quality. These two parameters provide important reference bases for subsequent multi-band cross-correlation analysis: the mean value can be used for energy comparison of different phases, and the SNR serves as a criterion for judging signal quality, guiding the parameter selection and result evaluation of the subsequent analysis process, thereby improving the overall reliability of phase picking. Figure 3 In [the example], the mean value of the reference phase P-wave is 0.1, and the signal-to-noise ratio SNR is 34.05. The P-wave with a high signal-to-noise ratio has a sharp arrival phase.

[0133] Further, step S4 specifically includes the following steps:

[0134] Multi-band signal decomposition: Use a multi-band filter bank to decompose the waveform data of the reference phase and the target phase into multiple band signals. In this embodiment, a pre-set multi-band filter bank (0.5 - 5 Hz, with an interval of 0.1 Hz and a main frequency band of 1 - 3 Hz) is used to perform band decomposition on the waveform data of the reference phase P-wave and the target phases (such as PcP, PKiKP, etc.) respectively. During specific implementation, each filter is designed using a zero-phase 4-pole Butterworth filter to ensure that there is no phase distortion in each band signal. This step can effectively separate the phase characteristics of different frequency bands. Especially for weak phases such as PKiKP, their high-frequency components often contain important features and are avoided from being affected by filter truncation.

[0135] Calculate the multi-band cross-correlation function between the reference phase and the target phase, and determine the cross-correlation arrival time T cc , construct the cross-correlation arrival time T cc probability density model. In the embodiment of the present invention, the multi-band cross-correlation technology is used to accurately provide the reference time for the arrival of the phase. The specific implementation process is as follows: First, perform multi-band (0.5 - 5 Hz) cross-correlation analysis on the reference phase and the target phase, calculate the cross-correlation function under each frequency band (such as Figure 3 the right column of cross-correlation waveforms in [the example]), and obtain the probability density distribution of the multi-band cross-correlation result, as shown in Figure 3As shown, the shaded area above the target seismic phase waveform intuitively shows the probability density distribution of the multi-band cross-correlation results, where the color depth represents the reliability of the first arrival time prediction under different frequency bands. The dotted line in the figure marks the most accurate T within the main frequency band (1 - 3 Hz). cc predicted arrival time. The T cc probability density model constructed by the kernel density estimation method selects the time corresponding to the peak of the probability density by integrating the results of each frequency band as the final cross-correlation first arrival time. This multi-band fusion method significantly improves the detection reliability of weak seismic phase signals (such as PKiKP). The visual presentation of the probability density distribution not only facilitates manual verification and result evaluation, but also ensures that the main frequency band contributes a greater weight to the final result.

[0136] Furthermore, step S5 specifically includes the following steps:

[0137] In the embodiment of the present invention, by integrating the theoretical first arrival time T pre and the multi-band cross-correlation time T cc , the final observed first arrival time T obs is manually determined. To evaluate the influence of signal quality on T cc , the SNR and mean parameters are used as key indicators: when the SNR is below 1, the signal quality significantly deteriorates, resulting in a reduced reliability of the first arrival time picking; if the SNR further drops below -10, it indicates that there is a lack of recognizable first arrival information in the signal, and at this time the signal is close to the noise level and the error of T cc significantly increases, and the signal data should be reasonably selected or discarded. In addition, the mean parameter of the signal should usually approach 0. If there is a large deviation, although it reflects the abnormality of the overall energy of the signal, it has a small impact on the accuracy of T cc .

[0138] As Figure 3 shown in the middle column, under high signal-to-noise ratio conditions, the middle column of the P, PcP, and PKiKP seismic phase waveforms aligned according to T pre shows the comparison results of the normalized reference seismic phase and the target seismic phase. It can be clearly observed that the waveform consistency between the seismic phases where the first arrival time is not accurately picked is poor. Figure 4 Shows the waveform effect after alignment according to the actually picked first arrival time T obs . Compared with Figure 3 , after accurate alignment, the matching degree of each seismic phase waveform with the reference waveform is significantly improved, and the first arrival characteristics of the seismic phases are more consistent. Figure 4 The right dotted line marks the first arrival time T cc predicted by multi-band cross-correlation. Its position is close to the zero point of the time axis, indicating that the actually picked observed first arrival time T obs is close to the predicted value T ccThe high degree of coincidence strongly validates the reliability of the multi-band cross-correlation method in predicting the arrival times of seismic phases. Especially for weak seismic phases such as PcP and PKiKP, the cc prediction results are obs in general less than 0.1 s different from the manually professional judgment, fully demonstrating the accuracy of this algorithm in practical applications.

[0139] Figure 5 It shows the waveform characteristics of uncorrected seismic phases under low signal-to-noise ratio conditions. Among them, the P seismic phase, due to its strong energy, still maintains a high signal-to-noise ratio and clear arrival characteristics; while the PcP and PKiKP seismic phases are significantly interfered by the coda waves of subsequent seismic phases, resulting in a reduced recognition rate of their arrival characteristics. Figure 6 It shows the processing results after obs correction and alignment. Through the signal enhancement processing of the multi-band cross-correlation technique, the arrival characteristics of the PcP and PKiKP seismic phases are effectively extracted. It is particularly worth noting that although the PKiKP seismic phase is still interfered by the coda waves, its key arrival characteristics can already be reliably identified. Figure 6 The T cc prediction time marked by the dashed line in obs maintains good consistency (average deviation < 0.1 s) with the manually determined T, which confirms that this method can still maintain stable prediction performance under complex waveform interference conditions.

[0140] The program product developed based on this method provides complete interactive functions, including: free zooming in the waveform display area, double-clicking to precisely adjust the picking time, displaying the relative arrival time of seismic phases (ΔT = T obs -T pre ), operation reset (reset), automatic correction of waveform alignment according to T obs (correct), and core functions such as last / next data switching. The program interface intuitively displays the multi-band cross-correlation probability density distribution (such as Figure 3 the shaded area) and the optimal prediction time (marked by the dashed line), supporting manual full-process operations from arrival prediction to final confirmation through visual interaction. Experiments show that this human-machine collaborative working mode can increase the seismic phase picking efficiency by more than 40% while maintaining expert-level picking accuracy, especially suitable for the precise identification of weak seismic phases such as PcP and PKiKP. All picking results are automatically stored in a standardized data format, including T pre , T cc , T obs and other complete parameters, facilitating subsequent geophysical research.

[0141] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for picking up seismic phases and weak signals based on multi-band cross-correlation, characterized in that Including: Obtain the theoretical first arrival times of the reference seismic phase and the target seismic phases, and use the theoretical first arrival times to intercept the reference seismic phase and the target waveform data of the time periods before and after each of the target seismic phases or weak signals in the waveform data; Based on the target waveform data, calculate the cross-correlation function between the reference seismic phase and the target seismic phases, and through the cross-correlation function, obtain the multi-band cross-correlation probability density and determine the final cross-correlation first arrival time; Use the theoretical first arrival time and the final cross-correlation first arrival time to pick up the observed first arrival time of the target seismic phase.

2. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 1, characterized in that Obtaining the theoretical first arrival times of the reference seismic phase and the target seismic phases includes: Set a one-dimensional earth velocity reference model, and use the one-dimensional earth velocity reference model to calculate the theoretical first arrival times of the reference seismic phase and the target seismic phases.

3. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 1, characterized in that, Obtaining the waveform data includes: Obtain the original seismic data, where the original seismic data includes: the longitude and latitude of the seismic event, the focal depth, and the longitude and latitude of the station; Preprocess the original seismic data, where the preprocessing includes: removing the instrument response, removing the mean, removing the trend, and resampling to obtain the waveform data.

4. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 1, characterized in that Intercepting the target waveform data includes: According to the theoretical first arrival time, set a signal analysis time window containing the target seismic phase, and use the signal analysis time window to intercept the reference seismic phase and the target waveform data of the time periods before and after each of the target seismic phases or the weak signal.

5. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 4, wherein, After intercepting the target waveform data includes: Calculate the mean of the seismic phases within the signal analysis time window: where x i is the waveform data point within the signal time window, and N is the total number of data points; According to the theoretical first arrival time, set a noise time window for obtaining the background noise time period before the theoretical first arrival time; Based on the noise time window and the signal analysis time window, calculate the signal-to-noise ratio of the reference seismic phase and the target seismic phases or the weak signal: Among them, E signal and E noise are the energies of the signal time window and the noise time window respectively.

6. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 5, wherein, The energies of the signal time window and the noise time window include: where x i and y i are waveform data points within the signal time window and the noise time window respectively, and N s and N n are the total numbers of data points in the signal time window and the noise time window respectively.

7. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 1, wherein Calculating the cross-correlation function between the reference seismic phase and the target seismic phases includes: Adopt multiple signal frequency bands covering the main frequency components of the target seismic phase as a multi-band filter bank for decomposing the target waveform data into multiple band signals; Use the multiple band signals to calculate the cross-correlation function between the reference seismic phase and the target seismic phases: where τ is the time delay parameter representing the offset of the target seismic phase relative to the reference seismic phase, and l is the length of the cross-correlation time window, is the cross-correlation function of the i-th frequency band.

8. The method for picking up seismic phases and weak signals based on multi-band cross-correlation according to claim 1, wherein Determining the final cross-correlation first arrival time includes: Determine the cross-correlation first arrival time: T cc = τ·δ where δ is the signal sampling interval; Construct a multi-band cross-correlation probability density by weighted fusion of the cross-correlation first arrival times of different frequency bands: where M is the total number of frequency bands, and w i is the weight of the i-th frequency band, and the weight w i is dynamically adjusted according to T cc frequency characteristics; Extract the time corresponding to the peak of the probability density in the multi-band cross-correlation probability density as the final cross-correlation first arrival time, and comprehensively screen the results according to the signal quality evaluation parameters, and finally optimize the accuracy of the first arrival time through manual verification.

9. A picking device applied to the seismic phase and weak signal picking method based on multi-band cross-correlation according to any one of claims 1-8, characterized in that, Including: A theoretical calculation module for obtaining the theoretical first arrival times of the reference seismic phase and the target seismic phases, and using the theoretical first arrival times to intercept the reference seismic phase and the target waveform data of the time periods before and after each of the target seismic phases or weak signals in the waveform data; A cross-correlation analysis module for calculating the cross-correlation function between the reference seismic phase and the target seismic phases based on the target waveform data, and through the cross-correlation function, obtaining the multi-band cross-correlation probability density and determining the final cross-correlation first arrival time; An artificial picking module for picking the observed first arrival time of the target seismic phase by using the theoretical first arrival time and the final cross-correlation first arrival time.

Images