Building structure seismic wave velocity change extraction method, device and equipment

Abstract

The invention provides a building structure seismic wave velocity change extraction method, device, equipment and program product. The method comprises the following steps: deploying seismometers at a plurality of floors of a target building to continuously collect a microseismic time sequence of each of the plurality of floors in a specified direction; and based on the micro-seismic time sequence of each adjacent floor, calculating an interference result corresponding to each adjacent floor, and performing inverse Fourier transform through the interference result to obtain an impulse response function corresponding to each adjacent floor. And determining travel time offset of the pulse response function corresponding to each adjacent floor and a reference pulse response function, and performing linear fitting on the travel time offset to obtain seismic wave velocity change of each adjacent floor.

Description

Technical Field

[0001] The present application relates to the technical field of structural health monitoring, and in particular to a method, device and equipment for extracting seismic wave velocity changes in building structures. Background Art

[0002] Seismic background noise is a continuous vibration signal generated by natural factors (such as wind and ocean activity) and human activities (such as traffic and machinery). Its propagation path is affected by the properties of the medium, and the recorded signal comprehensively reflects the noise source, propagation path, and instrument response characteristics. Assuming a uniform distribution of noise sources and a stable instrument response, the spatiotemporal variations of this noise can reveal changes in the physical and mechanical parameters of the medium. This has been applied to structural health monitoring, indirectly inferring parameters such as stiffness and wave velocity from passive noise data.

[0003] However, existing research on structural monitoring of high-rise buildings has focused on the time-varying characteristics of overall modal frequencies, making it difficult to analyze local dynamic changes and damage location within vertical floors. While some researchers have attempted to correlate floor response differences using polarization information, they still lack accurate methods for quantifying inter-story wave velocity variations. Summary of the Invention

[0004] To solve the above problems, this application provides a state prediction method, device and equipment, and the technical solutions are as follows: In a first aspect, a method for extracting earthquake wave velocity changes in a building structure is provided, comprising: deploying seismometers on a plurality of floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the plurality of floors; Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the inverse Fourier transform is performed on the interference result to obtain the impulse response function corresponding to each adjacent floor; The travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function is determined, and a linear fit is performed on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0005] In a second aspect, a device for changing the seismic wave velocity of a building structure is provided, comprising: an acquisition module, configured to deploy seismometers on multiple floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the multiple floors; A first calculation module is configured to calculate an interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, and perform an inverse Fourier transform on the interference result to obtain an impulse response function corresponding to each adjacent floor; The second calculation module is used to determine the travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function, and perform linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor; and a memory configured to store computer-executable instructions, wherein the computer-executable instructions, when executed, cause the processor to execute the method described in the first aspect.

[0007] The present embodiment continuously collects microseismic data by deploying seismometers on each floor, calculates interference results using data from adjacent floors, and extracts an impulse response function that characterizes the propagation path of structural waves through an inverse Fourier transform. Furthermore, by comparing the travel-time offset of this function with a reference function and performing a linear fit, the relative change in seismic wave velocity between adjacent floors is quantified, achieving non-invasive continuous monitoring of wave velocity changes between building floors. This method directly correlates noise data with interlayer dynamic characteristics, capturing subtle structural responses by leveraging the sensitivity of wave velocity to medium changes. This method breaks through reliance on overall structural response and provides a basis for independent assessment of different vertical sections, forming a closed-loop technology chain from data acquisition to wave velocity change output. Compared to existing technologies that rely on overall modal frequency or polarization information analysis, this invention accurately analyzes the dynamic changes between vertical layers of a building structure by directly extracting the travel-time offset of the interlayer impulse response function, solving the problem of difficulty in quantifying interlayer wave velocity changes and providing a reliable means for locating local damage in high-rise buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0009] Figure 1 This is a schematic diagram of the first flow chart of the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application.

[0010] Figure 2 Schematic diagram of a target building in the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application.

[0011] Figure 3 This is a schematic diagram of a first type of earthquake event record collected by the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application; Figure 4This is a schematic diagram of a second earthquake event record collected by the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application; Figure 5 Schematic diagram of the impulse response function of the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application. Figure 6 Schematic diagram of clock drift between adjacent floors in the method for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application.

[0012] Figure 7 This is a schematic diagram of the seismic wave velocity changes mentioned in the method for extracting seismic wave velocity changes in building structures in an embodiment of the present application.

[0013] Figure 8 This is a structural schematic diagram of a device for extracting earthquake wave velocity changes in building structures according to an embodiment of the present application.

[0014] Figure 9 This is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0016] An embodiment of the present application provides a method for extracting earthquake wave velocity changes in a building structure. Figure 1 The figure is a flow chart of the method for extracting the seismic wave velocity variation of the building structure, including: S101 , deploying seismographs on multiple floors of a target building to continuously collect microseismic time series of each of the multiple floors in a specified direction.

[0017] This embodiment employs non-invasive monitoring. Non-invasive monitoring refers to a technical method that uses external sensing to obtain status information of the object being measured without interfering with its original structure, function, or normal operation. Specifically, this embodiment can attach a three-component seismometer to a floor or wall to collect microseismic time series in the vertical, north-south, and east-west directions. Simultaneously, natural seismic background noise (such as wind and human activity) is used as a vibration source to achieve microseismic monitoring of building structures.

[0018] S102: Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the interference result is subjected to inverse Fourier transform to obtain the impulse response function corresponding to each adjacent floor.

[0019] This example performs deconvolution interferometry on microseismic time series from adjacent floors in the frequency domain, using the Wiener filter formula to eliminate the influence of the earthquake source, thereby extracting the interferometry results (Green's function) between floors. Subsequently, the frequency domain interferometry results are converted into a time-domain impulse response function through an inverse Fourier transform. The symmetry between the causal and non-causal branches reflects the reciprocity of the wave field and directly characterizes the structural wave propagation characteristics between adjacent floors.

[0020] As an example, assume that the adjacent floor is Layer and layer, you can Microseismic time series of the layer Hedi Microseismic time series of the layer Perform Fourier transform respectively to obtain the microseismic time series The corresponding frequency domain signal and microseismic time series The corresponding frequency domain signal ;in, Represents time, Represents frequency.

[0021] Deconvolution is usually performed in the frequency domain because convolution becomes multiplication in the frequency domain. Correspondingly, the background noise signal can be expressed as the convolution of the source function and the Green's function of the medium: ; ; in, is a frequency domain signal of a random source (usually assumed to be white noise or approximately white noise), and is from a random source To the receiving floor or floor Frequency domain result of the path Green's function.

[0022] right Perform deconvolution to remove the influence of the source: ; Assume that the random source Far enough (or the background noise has an isotropic distribution), it can be approximated that , Represents the time domain Green's function between adjacent floors. In the frequency domain, convolution becomes multiplication: ; Substituting into the deconvolution formula we can get: ; Therefore, by calculating , the interference results can be directly obtained , represents the Green’s function of the ray path between adjacent floors.

[0023] In the actual data processing process, Wiener filtering or regularization methods are needed to avoid the problem of division by zero or noise amplification, namely: ; in, is a small regularization parameter that prevents the denominator from being zero and is usually set to 1% of the mean amplitude spectrum. Represents the response of layer i The complex conjugate of .

[0024] Afterwards, the calculation is Then perform inverse Fourier transform to obtain :

[0025] in, Stands for inverse Fourier transform.

[0026] In practical applications, the microseismic time series recorded by three-component seismographs in the vertical, north-south, and east-west directions may interfere with the analysis results due to factors such as instrument response, occasional seismic events, and intermittent operation of equipment such as elevators in the building. Therefore, before calculating the interference results, the microseismic time series of each floor can be preprocessed, for example, by performing at least one of detrending, removing instrument response, and bandpass filtering (the frequency range of the bandpass filter is 0.2 to 5 Hz).

[0027] Furthermore, the STA / LTA algorithm can be used to identify and remove abnormal interference signals from the microseismic time series of each adjacent floor. The short-time window of the STA / LTA algorithm is set to 1 second, the long-time window is set to 30 seconds, and the STA / LTA value is set to be greater than 0.2 and less than 0.25. Specifically, a STA / LTA value greater than 2.5 indicates a sudden increase in short-term energy, such as an earthquake or elevator operation, and the data from that time period should be removed. A STA / LTA value less than 0.2 indicates a sudden decrease in short-term energy, such as an instrument failure or human interference, and the abnormal data segment should also be removed.

[0028] Furthermore, current seismometers typically rely on satellite positioning signals (e.g., GPS signals) for timing or network synchronization to maintain time consistency across sensors. However, inside high-rise buildings, GPS signals may not be properly received due to shielding effects from reinforced concrete or multipath effects, forcing the seismometer to rely on its internal crystal oscillator clock for timing. However, crystal oscillator clocks can gradually experience time deviations (clock drift) due to factors such as temperature changes, voltage fluctuations, and device aging. This can lead to inconsistent time bases for data recorded by seismometers on different floors, ultimately causing distortion in the impulse response function. To address this issue, all seismometers can be placed in an open area before instrument deployment to facilitate GPS alignment and synchronization, thus avoiding initial time errors. Subsequently, a reference instrument capable of receiving satellite positioning signals is identified, and the impulse response functions of other instruments relative to this reference instrument are calculated over a certain time range to determine the extent of clock drift and apply corrections. Specifically, by maximizing the correlation coefficient between the positive and negative branches of the impulse response function, the time difference between instruments can be determined and used to time-correct the microseismic time series. When the positive and negative branches of the impulse response function are symmetrical, that is, when the correlation coefficient of the positive and negative branches of the interferometer function is the largest, there is no time difference between the instruments. The seismometer on the top floor can directly receive GPS signals via a GPS antenna, while instruments within the building can, under appropriate conditions, connect to the internet for timing or use a GPS signal amplifier to enhance the signal.

[0029] S103 , determining the travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function, and performing linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0030] This embodiment first selects the impulse response function of the structure in a healthy state as a reference benchmark (usually taking the average value of long-term observations or the stable result over a specific period of time). The currently calculated impulse response function is then compared point by point in the time domain with the reference function. The moving window cross-correlation method (MWCS) is used to divide the coda portion into multiple time windows to calculate the maximum cross-correlation coefficient and travel-time offset dt between the impulse response function and the reference impulse response function for each adjacent floor. Assuming that the relative seismic velocity change (dv / v) is spatially uniform, the travel-time offset dt between the current impulse response function and the reference impulse response function is proportional to the travel-time t, i.e., dv / v = -dt / t. For this purpose, the slope of the linear fit (dv / v) can be used as the seismic velocity change for each adjacent floor.

[0031] The error of the above linear fitting should satisfy: ; in, is the sequence number of the time window corresponding to the impulse response function; The first The center time of the time window; is the weighted average of the center time of each time window of the impulse response function; is the error of the linear fit.

[0032] In related applications, this embodiment can import the seismic velocity variations of each adjacent floor into a preconfigured building structural analysis model, allowing the building structural analysis model to assess the location of structural damage and / or the progress of repair in the target building based on the seismic velocity variations of each adjacent floor. It should be noted that the assessment of structural damage location and repair progress is a prior art and will not be further described herein.

[0033] In summary, the method described in this embodiment continuously collects microseismic data by deploying seismometers on each floor. Interference results are calculated using data from adjacent floors, and an inverse Fourier transform is used to extract an impulse response function (IRF) representing the structural wave propagation path. This function is then compared with a reference function and linearly fitted to quantify the relative variation in seismic wave velocity between adjacent floors, achieving non-invasive and continuous monitoring of inter-story velocity variations. This method directly correlates noise data with inter-story dynamic characteristics, leveraging the sensitivity of wave velocity to medium variations to capture subtle structural responses. This method eliminates reliance on the overall structural response and provides a basis for independent assessment of different vertical segments, forming a closed-loop technology chain from data acquisition to velocity variation output. Compared to existing techniques that rely on analysis of global modal frequencies or polarization information, this method directly extracts the travel-time offset of the inter-story IRF function, accurately analyzing the dynamic variations between vertical layers of a building structure. This overcomes the difficulty in quantifying inter-story velocity variations and provides a reliable means for localizing damage in high-rise buildings.

[0034] The method of this embodiment is introduced below with reference to a practical application scenario.

[0035] This application scenario is Figure 2 The target building shown has a three-component seismometer deployed on each of its 21 floors and basement, installed at the center of the floor slab to ensure rigid coupling with the structure ( Figure 1 Triangle mark). Reference stations were simultaneously deployed in the parking lot and lawn outside the building to distinguish between building vibration and environmental noise. Data was continuously recorded at a sampling rate of 1000Hz in the vertical (Z), north-south (N), and east-west (E) directions of microseismic time series, and transmitted back to the cloud server in real time via the 5G network. Figure 2Description: Floor markings: F1 (1st floor) to F21 (21st floor); Zone divisions: Zone 1 (Zone 1) to Zone 7 (Zone 7); Gray bars: Equipment mezzanines (equipment installation space); Blue bars: Transverse stress zones (structural areas subject to horizontal loads); Light gray bars: Longitudinal stress zones (structural areas subject to vertical loads); Small rectangles: Elevator shafts and stairwells; Triangles: Three-component seismographs (vibration monitoring sensors).

[0036] Afterwards, the microseismic time series is preprocessed in the following way: ① Trend and instrument response correction: First, the original earthquake time series is detrended to eliminate potential linear drift; then the instrument response is removed to restore the actual ground vibration signal.

[0037] ② Abnormal Signal Removal: Bandpass filtering effectively removes high-frequency noise and low-frequency interference. STA / LTA (short window 1 s, long window 30 s, trigger threshold set between 0.2 and 2.5) is used to identify and remove non-target event interference, such as transient signals caused by earthquakes or elevator operation. ③ Data Segmentation: Continuous recordings are segmented into 30-minute segments to facilitate standardization and further feature extraction.

[0038] ③ Data segmentation processing: Cut the continuous records into 30-minute segments to facilitate standardized processing and further feature extraction.

[0039] refer to Figure 3 As shown in Figure 2, it is assumed that in the early observation period of this application scenario, the earthquake records of the building bottom layer BOT, middle layer MID and top layer TOP do not show obvious instrument time drift. Figure 4 As shown in the figure, in the record of a hypothetical earthquake event that occurred at the end of the observation period, the earthquake records of the bottom, middle and top layers show obvious instrumental time drift.

[0040] To solve the time drift problem, a 0.2-5 Hz bandpass filter is applied in the process of calculating the impulse response function to retain the maximum resonance energy of the high-rise building. The impulse response function calculated hour by hour is as follows Figure 5 As shown in a, due to the loss of GPS signal, the linear trend of clock drift is significant. Nonlinear clock drift can be accurately calculated using the symmetry of IRFs, as shown in Figure 6As shown in the figure. Since the horizontal component is the primary energy component of the building's resonance, the impulse response function results between the EE, EN, NE, and NN components are more stable, and the interference results between the four components are highly consistent. However, since the vertical component has a lower degree of resonance and is inconsistent with the motion pattern of the horizontal component, the interference results between vertical components or between vertical components and horizontal components are poor. Given the nonlinearity of clock drift, the interference results between NE components have the highest correlation and the strongest time drift stability, so the interference results of the NE components are selected for time correction.

[0041] In the specific process of clock correction, it is possible to choose not to perform time and frequency domain normalization on the raw data. However, the impact of preprocessing on the time correction of instruments in high-rise buildings is unknown. Therefore, the relative clock drift of the top and middle-level stations under different preprocessing procedures was compared ( Figure 5 c). Figure 5 The color of the scattered points in c represents the correlation coefficient between the positive and negative branches of the hourly impulse response function, which is related to the reliability of the clock drift calculation. The time domain normalization method used in the calculation is the sliding absolute average method. The research results show that the resonant energy of the building is the main component of the impulse response function. Any normalization in the time or frequency domain will weaken the resonant signal, resulting in a smaller correlation coefficient ( Figure 5 c1), which reduces the reliability of the calculation results. Therefore, when calculating relative clock drift based on the ambient noise of high-rise buildings, it is not recommended to perform normalization in the time domain or frequency domain. In addition, the digital counts recorded by the seismometer can be converted into physical displacement, velocity or acceleration within a certain frequency band by removing the instrument response step, thereby minimizing the measurement error between seismometers and facilitating a more accurate calculation of relative clock drift. In this experiment, Figure 5 c5 and Figure 5 The correlation coefficient histogram in C6 shows that after instrument de-escalation, the number of low-quality results with correlation coefficients below 0.2 decreased, indicating the necessity of the instrument de-escalation step.

[0042] in, Figure 5 Illustration: (a) Impulse response function calculated between adjacent floors during the observation period. The vertical axis represents the observation time, and the horizontal axis represents the causal and non-causal time of the impulse response function. (b) The impulse response function after instrument clock drift correction. (c) Relative clock drift calculated using different preprocessing methods for the EE component: (c1) using normalization in the time domain (RAMN) and frequency domain; (c2) using instrument response removal without normalization in the time and frequency domains; and (c3) without instrument response removal or normalization in the time and frequency domains. Histograms of the correlation coefficients for (c1), (c2), and (c3) are shown in (c4), (c5), and (c6), respectively. Figure 6Caption: Relative clock drift between stations on adjacent floors. The first letter in each subplot label indicates the component for the station on the upper floor, and the second letter indicates the component for the station on the lower floor. The color bar indicates the correlation coefficient between the positive and negative branches of the IRF used to calculate the relative clock drift.

[0043] The safety and suitability of high-rise buildings under wind loads can be effectively assessed by studying the changes in seismic wave velocity before and after the strongest wind speed during the observation period to estimate the degree of impact on different floors and confirm the capability of the method. Figure 7 As shown in the figure, the location of the most severe damage was determined by calculating the change in seismic velocity between each floor of the building before and after the strongest wind speed during the observation period. Here, the structure's two main resonant energy bands, f1 and f3, were selected to investigate the impact of strong winds on the building's seismic velocity. To mitigate the effects of environmental factors such as temperature, humidity, and human activity on the building's resonant frequency, the change in average seismic velocity over the four days before and after the strong wind was used to represent the extent of structural damage.

[0044] in, Figure 7 Explanation: (a) The change of seismic wave velocity between each floor in the f1 band and the difference of structural seismic wave velocity before and after the monsoon; (b) The change of seismic wave velocity between each floor in the f3 band and the difference of structural seismic wave velocity before and after the monsoon. Among them, the seismic wave velocity results of all adjacent layers in the f1 band show that ( Figure 7 a) The location where the earthquake wave velocity changes the most is between the 6th and 7th floors. The earthquake wave velocity decreases by about 2% before and after the strong wind. Monitoring results of earthquake wave velocity and resonance frequency in the f3 band ( Figure 7 b) shows that during strong winds, significant damage occurred between floors 9 and 10, 13 and 14, and 18 and 19. The seismic velocity dropped by nearly 2% between floors. Damage to the resonant frequency was observed, decreasing by approximately 1.5% between floors 9 and 10, 0.5% between floors 13 and 14, and 2% between floors 18 and 19.

[0045] In addition, corresponding to Figure 1 In addition to the method shown in FIG. 1 , another embodiment of this embodiment further provides a device for extracting earthquake wave velocity changes in building structures. Figure 8 Schematic diagram of the structure of the device 800 for extracting earthquake wave velocity changes in building structures, comprising: The acquisition module 810 is configured to deploy seismometers on multiple floors of a target building to continuously acquire a microseismic time series in a specified direction on each of the multiple floors.

[0046] The first calculation module 820 is used to calculate the interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, and perform inverse Fourier transform on the interference result to obtain the impulse response function corresponding to each adjacent floor.

[0047] The second calculation module 830 is used to determine the travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function, and perform linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0048] Optionally, before calculating the interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, the acquisition module 810 is also used to: identify and eliminate abnormal interference signals in the microseismic time series of each adjacent floor through the STA / LTA algorithm; wherein the short time window of the STA / LTA algorithm is set to 1 second, the long time window of the STA / LTA algorithm is set to 30 seconds, and the STA / LTA value of the STA / LTA algorithm is set to be greater than 0.2 and less than 0.25.

[0049] Optionally, before calculating the interference results corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, the acquisition module 810 is also used to: preprocess the microseismic time series of each floor; wherein the preprocessing includes at least one of detrending, removing instrument response and bandpass filtering, and the frequency range of the bandpass filtering is 0.2 to 5 Hz.

[0050] Optionally, there are multiple seismographs, and at least some of the seismographs receive satellite positioning signals; before comparing the travel time offset of the impulse response function with the reference impulse response function and performing linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor, the second calculation module 830 is also used to: select a seismograph that can receive satellite positioning signals as a reference instrument; for the seismograph corresponding to each adjacent floor, calculate the clock deviation relative to the reference instrument by maximizing the correlation coefficient of the positive and negative branches of the corresponding impulse response function, and perform time correction on the microseismic time series corresponding to each adjacent floor according to the calculated clock deviation.

[0051] Optionally, the first calculation module 820 calculates the interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, and performs inverse Fourier transform on the interference result to obtain the impulse response function corresponding to each adjacent floor, including: Microseismic time series of the layer Hedi Microseismic time series of the layer Perform Fourier transform respectively to obtain the microseismic time series The corresponding frequency domain signal and microseismic time series The corresponding frequency domain signal ;in, Represents time, Representative frequency; calculate the interference results corresponding to each adjacent floor according to the regularized deconvolution algorithm ;in, 1% of the average amplitude spectrum; is the complex conjugate; for the interference result Perform inverse Fourier transform and get Layer and The impulse response function corresponding to the layer .

[0052] Optionally, the second calculation module 830 determines the travel time offset between the impulse response function corresponding to each adjacent floor and a reference impulse response function, and performs linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor, including: dividing a plurality of time windows by a moving window cross-correlation method, and calculating the maximum cross-correlation coefficient and travel time offset between the impulse response function and the reference impulse response function for each adjacent floor; For each adjacent floor, a linear fit is performed with the center time of each time window as the horizontal coordinate and the travel time offset as the vertical coordinate, and the slope of the linear fit is used as the seismic wave velocity change of each adjacent floor.

[0053] Optionally, the error of the linear fitting satisfies: ;in, is the sequence number of the time window corresponding to the impulse response function; The first The center time of the time window; is the weighted average of the center time of each time window of the impulse response function; is the error of the linear fit.

[0054] Optionally, the device of this embodiment further includes: The application module is used to import the seismic wave velocity changes of each adjacent floor into a pre-configured building structure analysis model after obtaining the seismic wave velocity changes of each adjacent floor, so that the building structure analysis model can evaluate the structural damage location and / or repair progress of the target building based on the seismic wave velocity changes of each adjacent floor.

[0055] This embodiment of the device continuously collects microseismic data by deploying seismometers on each floor. Interference results are calculated using data from adjacent floors, and an inverse Fourier transform is used to extract an impulse response function (IRF) representing the structural wave propagation path. This function is then compared with a reference function and linearly fitted to quantify the relative variation in seismic wave velocity between adjacent floors, achieving non-invasive and continuous monitoring of inter-story velocity variations. This method directly correlates noise data with inter-story dynamic characteristics, leveraging the sensitivity of wave velocity to medium variations to capture subtle structural responses. This method eliminates reliance on overall structural response and provides a basis for independent assessment of different vertical segments, forming a closed-loop technology chain from data acquisition to velocity variation output. Compared to existing technologies that rely on analysis of global modal frequencies or polarization information, this method directly extracts the travel-time offset of the inter-story IRF function, accurately analyzing the dynamic variations between vertical layers of a building structure. This overcomes the difficulty in quantifying inter-story velocity variations and provides a reliable means for localizing damage in high-rise buildings.

[0056] It should be noted that the building structure earthquake wave velocity change extraction device of this embodiment can be used as Figure 1 The execution subject of the method shown can thus realize Figure 1 The steps and functions in the method shown.

[0057] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 9 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0058] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 9 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0059] The memory is used to store the computer program. Specifically, the computer program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides the computer program to the processor.

[0060] Among them, the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming the above-mentioned Figure 8 The device for extracting earthquake wave velocity changes in building structures shown in FIG. Correspondingly, the processor executes the program stored in the memory and is specifically used to perform the following operations: Seismometers are deployed on multiple floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the multiple floors.

[0061] Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the interference result is subjected to inverse Fourier transform to obtain the impulse response function corresponding to each adjacent floor.

[0062] The travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function is determined, and a linear fit is performed on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0063] This electronic device, deployed on each floor, continuously collects microseismic data using seismometers. It then uses data from adjacent floors to calculate interference results and extracts an impulse response function (IRF) representing the structural wave propagation path through an inverse Fourier transform. This function then compares the travel-time offset of the IRF function with a reference function and performs a linear fit to quantify the relative variation in seismic wave velocity between adjacent floors, enabling non-invasive and continuous monitoring of inter-story IR variations. This method directly correlates noise data with inter-story dynamic characteristics, leveraging the sensitivity of IR to medium variations to capture subtle structural responses. This method eliminates reliance on overall structural response and provides a foundation for independent assessment of different vertical segments, forming a closed-loop technology chain from data acquisition to IR output. Compared to existing technologies that rely on analysis of global modal frequencies or polarization information, this method directly extracts the travel-time offset of the inter-story IRF function, accurately analyzing the dynamic variations between vertical layers of a building structure. This overcomes the difficulty in quantifying inter-story IR variations and provides a reliable means for localizing damage in high-rise buildings.

[0064] The above is as in this manual Figure 1The method for extracting seismic wave velocity changes in building structures disclosed in the illustrated embodiments can be applied to and implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the method described above can be performed by hardware integrated logic circuits within the processor or by software instructions. The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0065] Of course, in addition to software implementation, the electronic device in this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0066] In addition, an embodiment of the present application also proposes a computer-readable storage medium, which stores one or more computer programs, and the one or more computer programs include instructions.

[0067] When the above instructions are executed by a portable electronic device including multiple applications, the portable electronic device can execute Figure 1 The steps in the method shown include: Seismometers are deployed on multiple floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the multiple floors.

[0068] Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the interference result is subjected to inverse Fourier transform to obtain the impulse response function corresponding to each adjacent floor.

[0069] The travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function is determined, and a linear fit is performed on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

[0070] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The above are merely examples of the present invention and are not intended to limit this specification. For those skilled in the art, various modifications and variations of this specification are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification shall be included within the scope of the claims of this specification. In addition, all other embodiments obtained by those of ordinary skill in the art without creative effort shall fall within the scope of protection of this document.

Claims

1. A method for extracting earthquake wave velocity changes in building structures, characterized in that: include: deploying seismometers on a plurality of floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the plurality of floors; Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the inverse Fourier transform is performed on the interference result to obtain the impulse response function corresponding to each adjacent floor; The travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function is determined, and a linear fit is performed on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

2. The method according to claim 1, characterized in that Before calculating the interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, the method further includes: The STA / LTA algorithm is used to identify and eliminate abnormal interference signals in the microseismic time series of each adjacent floor; wherein, the short-time window of the STA / LTA algorithm is set to 1 second, the long-time window of the STA / LTA algorithm is set to 30 seconds, and the STA / LTA value of the STA / LTA algorithm is set to be greater than 0.2 and less than 0.

25.

3. The method according to claim 1, characterized in that Before calculating the interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, the method further includes: The microseismic time series of each floor is preprocessed; wherein the preprocessing includes detrending, removing instrument response and band-pass filtering, and the frequency range of the band-pass filtering is 0.2 to 5 Hz.

4. The method according to claim 1, wherein There are multiple seismographs, and at least some of the seismographs receive satellite positioning and timing signals; Before comparing the travel time offsets of the impulse response function and the reference impulse response function and performing linear fitting on the travel time offsets to obtain the seismic wave velocity change of each adjacent floor, the method further includes: Select a seismograph that can receive satellite positioning and timing signals as a reference instrument; For the seismograph corresponding to each adjacent floor, the clock deviation relative to the reference instrument is calculated by maximizing the correlation coefficient of the positive and negative branches of the corresponding impulse response function, and the microseismic time series corresponding to each adjacent floor is time-corrected based on the calculated clock deviation.

5. The method according to claim 1, wherein Based on the microseismic time series of each adjacent floor, the interference result corresponding to each adjacent floor is calculated, and the interference result is subjected to inverse Fourier transform to obtain the impulse response function corresponding to each adjacent floor, including: For the first Microseismic time series of the layer Hedi Microseismic time series of the layer Perform Fourier transform respectively to obtain the microseismic time series The corresponding frequency domain signal and microseismic time series The corresponding frequency domain signal ;in, Represents time, represents frequency; Calculate the interference results corresponding to each adjacent floor according to the regularized deconvolution algorithm ;in, 1% of the average amplitude spectrum; is the complex conjugate; The interference results Perform inverse Fourier transform and get Layer and The impulse response function corresponding to the layer .

6. The method according to claim 1, characterized in that Determining the travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function, and performing linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor, including: Dividing a plurality of time windows by a moving window cross-correlation method, and calculating the maximum cross-correlation coefficient and travel time offset between the impulse response function and a reference impulse response function for each adjacent floor; For each adjacent floor, a linear fit is performed with the center time of each time window as the horizontal coordinate and the travel time offset as the vertical coordinate, and the slope of the linear fit is used as the seismic wave velocity change of each adjacent floor.

7. The method according to claim 6, characterized in that The error of the linear fitting satisfies: ; in, is the sequence number of the time window corresponding to the impulse response function; The first The center time of the time window; is the weighted average of the center time of each time window of the impulse response function; is the error of the linear fit.

8. The method according to claim 1, characterized in that After obtaining the seismic wave velocity change of each adjacent floor, the method further includes: The seismic wave velocity variation of each adjacent floor is imported into a pre-configured building structural analysis model, so that the building structural analysis model evaluates the structural damage location and / or repair progress of the target building based on the seismic wave velocity variation of each adjacent floor.

9. A device for extracting earthquake wave velocity changes in building structures, characterized in that: include: an acquisition module, configured to deploy seismometers on multiple floors of a target building to continuously acquire a microseismic time series in a specified direction for each of the multiple floors; A first calculation module is configured to calculate an interference result corresponding to each adjacent floor based on the microseismic time series of each adjacent floor, and perform an inverse Fourier transform on the interference result to obtain an impulse response function corresponding to each adjacent floor; The second calculation module is used to determine the travel time offset between the impulse response function corresponding to each adjacent floor and the reference impulse response function, and perform linear fitting on the travel time offset to obtain the seismic wave velocity change of each adjacent floor.

10. An electronic device comprising: processor; and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to perform the method according to any one of claims 1 to 8.

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