Method for joint detection and identification of tsunami resonance oscillation mode

Abstract

The invention relates to a method for jointly detecting and identifying a tsunami resonance oscillation mode, which comprises the following steps of: acquiring a first tsunami wave signal in a specified tsunami event in a target area and background noise under a conventional sea condition, and acquiring a second tsunami wave signal and amplitude field information for simulating the tsunami event by adopting a tsunami numerical simulation mode; when the accuracy verification of the tsunami numerical simulation is passed; performing spectral analysis on the first tsunami wave signal to obtain a spectral analysis result, performing spectral analysis on the wave amplitude field information, and determining a high-energy oscillation mode of the region; in combination with the multi-scale water depth topographic data of the target area, performing analysis to obtain modal information of intrinsic oscillation; determining a tsunami resonance mode based on the high-energy oscillation mode and the high-energy frequency component of the background noise; and determining the spatial distribution characteristics of the tsunami co-amplitude values by combining the intrinsic model spatial spectrum amplitude structure obtained by analysis. According to the method, the problem that the analysis precision cannot meet the requirement due to the fact that spatial structure characteristics are not considered in an existing scheme is solved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for jointly detecting and identifying tsunami resonance oscillation modes. Background Art

[0002] Although tsunamis rarely occur, they are extremely deadly. Therefore, tsunamis are recognized as one of the most extensive and destructive natural disasters in the world. Why tsunamis have such a strong ability to cause disasters and losses is closely related to the physical characteristics and disaster-causing mechanisms of tsunamis themselves. Tsunamis are usually triggered by factors such as submarine earthquakes and landslides, and are a series of water waves with ultra-long wavelengths that spread rapidly in the ocean. When tsunamis propagate in the deep ocean, they are almost unnoticeable and have very small amplitudes. However, when tsunamis reach nearshore areas, the water depth becomes shallower, the wave speed becomes smaller, and the wavelength shortens, resulting in a decrease in the volume of water per unit wavelength, a relative increase in energy flux, and a faster flow rate and higher amplitude waves. After rushing onto land, they evolve into disasters with super destructive power. The danger of tsunamis also lies in the fact that tsunamis are a series of waves. The first huge waves that arrive may not be the most dangerous, and higher tsunamis will appear one after another to hit the coast. Under the influence of local terrain features (such as ports, bays, continental shelves, etc.), such impacts can last for a few hours or even dozens of hours, further exacerbating the impact and losses of tsunami disasters.

[0003] The tsunami wave period is usually in the range of a few minutes to two hours, which is very close to the typical intrinsic oscillation period of the local terrain near the coast. Therefore, tsunamis can easily excite multi-scale resonant oscillations near the coast. Tsunami resonant oscillations in ports and bays can also cause sudden rise and fall of water levels and strong currents, leading to the flooding of docks, threatening the navigation and mooring safety of ships, and in severe cases may lead to catastrophic accidents. Therefore, identifying and determining the tsunami resonant oscillation modes and understanding the characteristics of tsunami resonant modes are of great significance for delineating tsunami risk areas and enhancing the resilience of coastal communities.

[0004] The most common historical tsunami data analysis method among existing methods can only be used to analyze past events, and due to the spatial sparsity of observation sites, it is unable to characterize the spatial characteristics of tsunami resonance modes. Theoretical analytical methods are mainly aimed at estimating the resonance oscillation characteristics of tsunamis under simple ideal terrain conditions, but have great difficulties in accurately determining tsunami resonance modes under complex terrain conditions. Summary of the invention

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method for jointly detecting and identifying tsunami resonant oscillation modes, which solves the problem that the solutions of the prior art do not take into account the spatial structure characteristics, resulting in the analysis accuracy failing to meet the requirements.

[0006] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0007] In a first aspect, an embodiment of the present invention provides a method for jointly detecting and identifying a tsunami resonant oscillation mode, comprising:

[0008] S100, for a target area, obtaining a first tsunami wave signal of a designated tsunami event and a noise signal under normal sea conditions outside a time period of a non-tsunami event, wherein the first tsunami wave signal and the noise signal are both obtained by removing tide trend from water level data of multiple observation stations in the target area;

[0009] S200, according to the tsunami parameters of the tsunami event specified in the target area, using a tsunami numerical simulation method to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event;

[0010] The second tsunami wave signal is obtained by using simulated water level data output by a virtual observation station simulating a tsunami event;

[0011] S300, using the first tsunami wave signal to verify the accuracy of the second tsunami wave signal, and when the accuracy verification is passed, performing spectrum analysis on the first tsunami wave signal to obtain a first spectrum analysis result, performing spectrum analysis on the amplitude field information to obtain a high-energy tsunami oscillation mode; performing spectrum analysis on the noise signal to obtain a high-energy frequency feature of the background noise;

[0012] S400, analyzing and obtaining modal information of intrinsic oscillation according to multi-scale water depth topography data of the target area;

[0013] S500, determining the tsunami resonance oscillation mode and the resonance amplitude spatial distribution characteristics according to the high-energy frequency characteristics of the background noise, the first spectrum analysis result, the high-energy tsunami oscillation mode, and the modal information of the intrinsic oscillation.

[0014] Optionally, before S100, the method further includes:

[0015] Acquire multi-scale bathymetric topographic data of the target area;

[0016] Specifically, the water depth database of the target area is constructed by fusion of ETOPO, GEBCO, and SRTM data with different resolutions;

[0017] Use bathymetric data to supplement the water depth topography within a specified range of the nearshore and offshore areas and optimize the water depth database;

[0018] Satellite images or DEM data are used to extract and confirm local irregular coastlines, and based on the established irregular terrain coastlines and the optimized water depth database, multi-scale unstructured grid data of the target area are constructed as multi-scale water depth terrain data.

[0019] Optionally, the S100 includes:

[0020] The original water level data under the specified tsunami event and normal sea conditions are obtained respectively, the sampling interval of the original water level data is 1 minute, and the water level data of multiple observation stations are included;

[0021] The water level data of each observation station in the tsunami-affected area and under normal sea conditions are processed to remove tidal trends and obtain the first tsunami wave signal and noise signal.

[0022] Optionally, the S100 further includes:

[0023] Calculating the mean square error of the first tsunami wave signal by means of a moving root mean square operation method to obtain the tsunami data length corresponding to the first tsunami wave signal;

[0024] Accordingly, the S200 includes:

[0025] According to the tsunami parameters of the tsunami event specified in the target area and the tsunami data length, a tsunami numerical simulation method is used to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event.

[0026] Optionally, the S200 includes:

[0027] According to the earthquake focal mechanism solution, the earthquake epicenter position, focal depth, dip angle and strike angle in the tsunami parameters are obtained;

[0028] The empirical formula is used to obtain the fault length, fault rupture area, fracture width and slip amount in the tsunami parameters;

[0029] According to the values ​​of each parameter in the tsunami parameters, a tsunami numerical model is used to simulate the tsunami propagation process, and the seabed deformation field of the earthquake fault is input into the tsunami numerical model as an initial condition to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event; the tsunami data length of the second tsunami wave signal meets the tsunami data length of the first tsunami wave signal.

[0030] Optionally, the S300 includes:

[0031] The following formula is used to verify the accuracy of the second tsunami wave signal obtained from the tsunami numerical simulation;

[0032]

[0033] Where N is the number of observation stations, H o and H s are the tsunami amplitude in the first tsunami wave signal and the tsunami amplitude in the second tsunami wave signal respectively, and K is the set geometric mean rate index;

[0034] If the verification fails, the step of obtaining a second tsunami wave signal by using a tsunami numerical simulation method in S200 is executed again until the verification passes.

[0035] Optionally, the S300 further includes:

[0036] S301, performing Fourier analysis on the first tsunami wave signal to obtain a tsunami observation spectrum, and picking up a tsunami observation peak period;

[0037] and, performing Fourier analysis on the second tsunami wave signal to obtain a simulated tsunami spectrum and pick up the tsunami peak period;

[0038] S302, verifying the consistency of the amplitude and spectrum of the tsunami observation spectrum and the amplitude and spectrum of the simulated tsunami spectrum in terms of waveform, spectrum shape and peak period. If there is any inconsistency, re-execute S200;

[0039] S303. After verification, perform spectrum analysis on the simulated amplitude field information to obtain the integral spectrum of the entire field, i.e., the high-energy tsunami oscillation mode.

[0040] Optionally, the S303 includes:

[0041] By spatially integrating the spectrum data, the high-energy oscillation modes are detected. The integral formula is as follows:

[0042]

[0043] in, and S(x, y, f) represent the spectrum of the entire integration region and the spectrum at each grid point in the integration region, respectively.

[0044] Optionally, the range of the tsunami observation peak period picking in S301 is an energy distribution interval of more than 5% of the spectrum amplitude peak of the tsunami observation spectrum;

[0045] The range of the tsunami peak period picked up in S301 is the energy distribution interval of more than 5% of the peak value of the spectrum amplitude of the simulated tsunami spectrum;

[0046] The threshold value for passing the consistency check of the peak period accuracy in S302 is 90%;

[0047] Alternatively, the S400 includes:

[0048] The multi-scale water depth topography data of the target area is discretized in the network, and the finite element method is used to discretize the eigenvalues ​​of the homogeneous linear shallow water equation to obtain the eigenmode solution of the target area, that is, the modal information of the eigenoscillation.

[0049] The eigenmode solution includes: the spatial distribution characteristics of the eigenmode frequency and the energy corresponding to the mode, the period of the eigenmode, and the amplitude distribution / oscillation type corresponding to the eigenmode;

[0050] Alternatively, the S400 includes:

[0051] S401, performing network discretization on the multi-scale water depth topographic data of the target area;

[0052] S402, obtaining an eigenmode solution based on the following formula;

[0053] Using the separation of variables method, we can obtain η(x, y, t) = α(x, y)β(t);

[0054] A total reflection boundary condition with no flux exchange is assumed at the land boundary, and a radiation boundary is adopted at the offshore boundary. The shape of the radiation boundary is defined as a semicircle with a radius of R, and the center of the circle is located at the location of the scattered radiation wave source. The boundary condition is:

[0055]

[0056] Where η is the free surface displacement, g is the gravitational acceleration, h and t are the water depth and time respectively; α and β represent the amount of spatial and temporal changes respectively; is the normal vector pointing outside the definition domain; C w is the shallow water wave velocity; assuming that the time variation is the standard attenuation model, that is, β(t) = e -(ζ+iω)t , ζ is the attenuation factor.

[0057] Optionally, the S500 includes:

[0058] S501, stacking the integral spectrum corresponding to the amplitude field information, the tsunami observation spectrum, and the simulated tsunami spectrum together, and screening out the high-energy mode that appears the most times in all the spectra or exists in all the spectra and has a specified correlation with the tsunami resonance mode, that is, the tsunami oscillation mode;

[0059] S502, based on the high-energy frequency characteristics of the background noise, analyzing the modal information of the intrinsic oscillation to eliminate the false numerical oscillation introduced in the modal analysis process and confirming the excited nearshore terrain resonance mode;

[0060] S503, comparing and analyzing the tsunami oscillation mode with the nearshore terrain resonance mode, selecting the oscillation mode with similar frequency, and confirming the tsunami resonance oscillation mode according to the amplification ratio of the tsunami observation and the background spectrum;

[0061] S504. Based on the resonant oscillation mode of the tsunami, compare the spatial distribution characteristics of the amplitude distribution in the eigenmode solution with the amplitude of the resonant oscillation mode, and jointly determine the influence of the source on the spatial distribution characteristics of the resonant amplitude.

[0062] The method of the present invention utilizes a high-resolution, high-precision tsunami numerical model to make up for the current situation of insufficient historical tsunami data records in the nearshore area, simulates and obtains a nearshore tsunami process data set with high-density spatial distribution, and analyzes its spectral response characteristics. A modal analysis model based on finite element discretization is used to solve the multi-scale intrinsic oscillation modal information of the nearshore area. The solution and confirmation of the tsunami oscillation mode under the conditions of complex nearshore areas and limited historical tsunami observation data are realized through modal analysis and numerical simulation methods. In particular, while being able to detect and identify the tsunami resonance period, the spatial characteristics of the mode can also be determined from the numerical simulation and modal analysis results. It can effectively help identify high-risk areas for tsunamis in the nearshore area, and take response and disaster reduction measures in advance, thereby reducing the impact of tsunami disasters.

[0063] In the past, a single technical method was used to analyze and identify tsunami resonance modes, mostly for qualitative judgment of possible tsunami resonance modes, and it was difficult to accurately identify and determine specific resonance modes and their spatial characteristics. The present invention uses spectral analysis and modal analysis of tsunami observation data, background noise data, and numerical simulation tsunami wave field data to jointly identify and determine the nearshore multi-scale tsunami resonance oscillation period and spatial characteristics, further improving the accuracy of tsunami resonance oscillation mode identification, and providing solutions and tool reserves for the improvement of tsunami warning and disaster reduction systems and risk management of marine engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of a process for jointly detecting and identifying tsunami resonance oscillation modes provided by an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of multi-scale gridded water depth data for an irregular region provided by an embodiment of the present invention;

[0066] Figure 3 A schematic flow chart of a method for jointly detecting and identifying tsunami resonance oscillation modes provided in accordance with another embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0068] At present, the determination and identification of tsunami resonant oscillation modes mainly refer to the technical solutions for the harbor oscillation problem, and combined with the actual situation, the spectral analysis of historical tsunami observation data, the theoretical analysis of ideal working terrain, physical model experiments and numerical simulation methods are used to determine the tsunami resonant oscillation modes, so as to predict the dangerous characteristics of the tsunami and take necessary disaster reduction measures before the tsunami arrives to reduce the losses and casualties caused by the tsunami.

[0069] In coastal areas, the impact range of tsunamis is much larger than that of wind waves and swells. Therefore, tsunamis usually have cross-scale characteristics. For example, when propagating on the continental shelf, it may stimulate shelf resonance, in the bay it may stimulate bay-scale resonance, and when reaching the harbor, it may stimulate harbor resonance. This poses new challenges to identifying and determining the multi-scale resonant oscillation modes of actual tsunamis, and the original single technical method is difficult to meet the requirements.

[0070] The scheme of the present invention can effectively identify and determine the resonant oscillation characteristics of tsunamis, which includes not only determining the peak resonance period of tsunamis, but also obtaining the spatial distribution characteristics of the resonant modes of tsunamis in nearshore or continental shelf areas. Accurately identifying, determining and understanding the resonant oscillation characteristics of tsunamis can provide a comprehensive and in-depth understanding of the disaster-causing mechanism of tsunamis in nearshore areas, and can also help identify dangerous areas of tsunamis, providing technical support for coastal planning and maritime emergency management. Scientific disaster reduction response measures can also be given for existing projects, thereby minimizing human and property losses.

[0071] To achieve the above objectives, the present invention proposes a method for jointly detecting and identifying tsunami resonant oscillation modes, focusing on irregular areas, and relying on sparse tide observation stations to detect, identify and determine tsunami resonant oscillation modes. This method can improve the efficiency and accuracy of tsunami multi-scale resonant oscillation mode identification, and provide solutions and tool reserves for the improvement of tsunami warning and disaster reduction systems and risk management of marine engineering.

[0072] Example 1

[0073] Figure 1 This is a flow chart of a method for jointly detecting and identifying tsunami resonance oscillation modes provided in Example 1 of the present invention. The method of this embodiment includes the following steps.

[0074] S100. For a target area, obtain a first tsunami wave signal of a specified tsunami event and a noise signal under normal sea conditions outside a time period of a non-tsunami event, wherein the first tsunami wave signal and the noise signal are obtained by removing tide trend from water level data of multiple observation stations in the target area.

[0075] The water level data under normal sea conditions in this embodiment refers to the water level data of the observation point when there is no tsunami or storm activity.

[0076] S200, according to the tsunami parameters of the tsunami event specified in the target area, using a tsunami numerical simulation method to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event;

[0077] The second tsunami wave signal is obtained by using simulated water level data output by a virtual observation station simulating a tsunami event.

[0078] In this embodiment, according to the tsunami parameters of the tsunami event specified in the target area (such as the epicenter position of the earthquake, the focal depth, the inclination angle, the strike angle, the fault length, the fault rupture area, the width of the fracture surface, the slip amount, etc.) and the tsunami data length (obtained by calculating the mean square error of the first tsunami wave signal with the help of a moving root mean square operation), a tsunami numerical simulation method is used to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event.

[0079] S300, using the first tsunami wave signal to verify the accuracy of the second tsunami wave signal, when the accuracy verification is passed, performing spectral analysis on the first tsunami wave signal to obtain a first spectral analysis result, performing spectral analysis on the amplitude field information to obtain a high-energy tsunami oscillation mode; performing spectral analysis on the noise signal to obtain a high-energy frequency characteristic of the background noise.

[0080] In this embodiment, the following formula is used to verify the accuracy of the second tsunami wave signal obtained by tsunami numerical simulation:

[0081]

[0082] Where N is the number of observation stations, H o and H s are the tsunami amplitude in the first tsunami wave signal and the tsunami amplitude in the second tsunami wave signal respectively, and K is the set geometric mean rate index;

[0083] If the verification fails, the step of obtaining a second tsunami wave signal by using a tsunami numerical simulation method in S200 is executed again until the verification passes.

[0084] S400, analyzing the multi-scale water depth topography data of the target area to obtain the modal information of the intrinsic oscillation, that is, the nearshore topography resonance mode.

[0085] It is understandable that before executing the steps of this embodiment, multi-scale water depth topographic data of the target area can be obtained in advance, such as using data fusion of ETOPO, GEBCO, and SRTM of different resolutions to construct a water depth database of the target area; using bathymetric data to supplement the water depth topography within a specified range of the nearshore and offshore, and optimizing the water depth database; using satellite images or DEM data to extract and confirm local irregular coastlines, and based on the established irregular terrain coastlines, combined with the optimized water depth database, construct multi-scale unstructured grid data of the target area as multi-scale water depth topographic data.

[0086] S500, determining the tsunami resonance oscillation mode and the resonance amplitude spatial distribution characteristics according to the high-energy frequency characteristics of the background noise, the first spectrum analysis result, the high-energy tsunami oscillation mode, and the modal information of the intrinsic oscillation.

[0087] The method of this embodiment utilizes a high-resolution, high-precision tsunami numerical model to make up for the current situation of insufficient historical tsunami data records in the nearshore area, simulates and obtains a nearshore tsunami process data set with high-density spatial distribution, and analyzes its spectral response characteristics. A modal analysis model based on finite element discretization is used to solve the multi-scale intrinsic oscillation modal information of the nearshore area. The solution and confirmation of the tsunami oscillation mode under the conditions of complex nearshore areas and limited historical tsunami observation data are realized through modal analysis and numerical simulation methods. In particular, while being able to detect and identify the tsunami resonance period, the spatial characteristics of the mode can also be determined from the numerical simulation and modal analysis results. It can effectively help identify high-risk areas for tsunamis in the nearshore area, and make response and disaster reduction measures in advance, thereby reducing the impact of tsunami disasters.

[0088] In a possible implementation, the above step S300 in the method of this embodiment may include:

[0089] S301, performing Fourier analysis on the first tsunami wave signal to obtain a tsunami observation spectrum, and picking up a tsunami observation peak period;

[0090] And, Fourier analysis is performed on the second tsunami wave signal to obtain a simulated tsunami spectrum and pick up the tsunami peak period.

[0091] In practice, the range of the tsunami observation peak period picking is the energy distribution interval of more than 5% of the spectral amplitude peak of the tsunami observation spectrum, and the range of the tsunami peak period picking is the energy distribution interval of more than 5% of the spectral amplitude peak of the simulated tsunami spectrum.

[0092] S302, verifying the consistency of the amplitude and spectrum of the observed tsunami spectrum and the amplitude and spectrum of the simulated tsunami spectrum in terms of waveform, spectrum shape and peak period. If there is any inconsistency, re-execute S200.

[0093] In this embodiment, the threshold value for passing the consistency check of the peak period accuracy is 90%.

[0094] S303. After verification, perform spectrum analysis on the simulated amplitude field information to obtain the integral spectrum of the entire field, i.e., the high-energy tsunami oscillation mode.

[0095] For example, by spatially integrating the spectrum data, the high-energy oscillation mode can be detected. The integral formula is as follows:

[0096]

[0097] in, and S(x, y, f) represent the spectrum of the entire integration region and the spectrum at each grid point in the integration region, respectively.

[0098] In one embodiment, step S400 may include:

[0099] S401, performing network discretization on the multi-scale water depth topographic data of the target area;

[0100] S402, obtaining an eigenmode solution based on the following formula; it should be noted that the eigenmode solution to be solved includes at least: the period of the eigenmode (abbreviated as eigenmode) and the amplitude distribution corresponding to the mode (also called oscillation type or vibration mode).

[0101] Using the separation of variables method, we can obtain η(x, y, t) = α(x, y)β(t);

[0102] A total reflection boundary condition with no flux exchange is assumed at the land boundary, and a radiation boundary is adopted at the offshore boundary. The shape of the radiation boundary is defined as a semicircle with a radius of R, and the center of the circle is located at the location of the scattered radiation wave source. The boundary condition is:

[0103]

[0104] Where η is the free surface displacement, g is the gravitational acceleration, h and t are the water depth and time respectively; α and β represent the amount of spatial and temporal changes respectively; is the normal vector pointing outside the definition domain; C w is the shallow water wave velocity; assuming that the time variation is the standard attenuation model, that is, β(t) = e -(ζ+iw)t , ζ is the attenuation factor.

[0105] In this embodiment, in order to better fit the changes of the complex nearshore coastline and objectively reflect the influence of the terrain shape on the eigenvalue, the finite element method is used to discretize and solve the homogeneous eigenvalue problem of the linear equation. Therefore, in this step, the multi-scale terrain data of the target area is discretized, and then the equation of the vibration response is numerically solved. In this way, the theoretical eigenmode of the target area and the spatial distribution of its corresponding amplitude are obtained.

[0106] In another embodiment, step S500 may include:

[0107] S501, the integral spectrum corresponding to the amplitude field information, the tsunami observation spectrum, and the simulated tsunami spectrum are superimposed together, and the high-energy mode, i.e., the tsunami oscillation mode, which appears the most times in all the spectra or exists in all the spectra and has a specified correlation with the tsunami resonance mode is screened out.

[0108] For example, taking the observed spectrum peak as the target spectrum, the simulated integral spectrum and the tsunami output spectrum (tsunami spectrum) as references, and taking the 10% error limit as the boundary, the three spectrum peak periods that meet the above conditions are selected as the tsunami oscillation mode.

[0109] S502, based on the high-energy frequency characteristics of the background noise, analyzing the modal information of the intrinsic oscillation to eliminate the false numerical oscillation introduced in the modal analysis process and confirming the excited nearshore terrain resonance mode;

[0110] S503, comparing and analyzing the tsunami oscillation mode with the nearshore topography resonance mode, selecting oscillation modes with similar frequencies, and confirming the tsunami resonance oscillation mode according to the amplification ratio between the tsunami observation and the background spectrum.

[0111] In this step, after obtaining the spatial distribution of the nearshore terrain resonance modes and corresponding amplitudes, comprehensive verification is performed to identify which modes are resonant.

[0112] S504. Based on the resonant oscillation mode of the tsunami, compare the spatial distribution characteristics of the amplitude distribution in the eigenmode solution with the amplitude of the resonant oscillation mode, and jointly determine the influence of the source on the spatial distribution characteristics of the resonant amplitude.

[0113] That is, after determining the resonant mode, the amplitude distribution characteristics of the mode obtained by numerical simulation amplitude field can be compared with the amplitude distribution characteristics of the intrinsic oscillation mode to analyze the spatial resonance characteristics of the specific tsunami event in the region. The whole process of this embodiment is to illustrate how to jointly identify and determine the resonant oscillation mode of the tsunami. Only by adopting this joint identification method can the resonant mode of the tsunami be truly determined.

[0114] In this embodiment, spectral analysis and modal analysis of tsunami observation data, background noise data, and numerical simulation tsunami wave field data are used to jointly identify and determine the nearshore multi-scale tsunami resonance oscillation period and spatial characteristics, thereby further improving the accuracy of tsunami resonance oscillation mode identification, and providing solutions and tool reserves for the improvement of tsunami warning and disaster reduction systems and risk management of marine engineering.

[0115] Example 2

[0116] Figure 2 A flowchart of a method for jointly detecting and identifying tsunami resonance oscillation modes is provided in accordance with another embodiment of the present invention. The method of this embodiment includes the following steps.

[0117] Step 201: construct a multi-scale bathymetric terrain dataset;

[0118] At present, the simulation effect of the multi-scale propagation process of tsunami and the analytical accuracy of the intrinsic oscillation mode are closely related to the water depth and topography data. Therefore, constructing high-precision water depth data in the target area is particularly critical for detecting and identifying tsunami resonant oscillation modes.

[0119] For example, the target area in this embodiment may be China's offshore area, which includes offshore basins, continental shelves, harbor areas, etc.

[0120] If China's offshore areas belong to open sea areas, such as the sea basin scale, shelf area scale, etc., then a multi-scale bathymetric topographic dataset can be constructed using globally available gridded datasets;

[0121] For example, publicly available gridded datasets include the ETOPO series, GEBCO series, and SRTM series, which cover more than 80% of the Earth's land and ocean areas, with spatial resolutions ranging from 3 arcsec to 5 arcmin.

[0122] If China's offshore areas belong to the nearshore and coastal waters, such as bays and harbors, high-precision bathymetric data and land elevation data can be used to establish a fused data set to construct a multi-scale bathymetric topographic data set. At this time, the spatial resolution of the constructed multi-scale bathymetric topographic data set can be 100 meters to 3 meters / 1 meter.

[0123] In this embodiment, in order to more accurately depict the coastal boundary and the shape of the coast, shoreline data may be collected to correct the measurement errors existing in the coastal boundary points in the multi-scale water depth topographic dataset constructed above.

[0124] Take the East China Sea of ​​China as an example: First, the data fusion of ETOPO, GEBCO, and SRTM (resolutions are 1 arcmin, 0.5 arcmin, and 15 arcsec, respectively) is used to construct a water depth database for the East China Sea. Experience shows that the data is relatively accurate in areas deeper than 50m. Different data sources can be selected or fused according to research needs; then, the bathymetric data is used to supplement the water depth topography of the nearshore and offshore (within 50m) for later data fusion, so as to obtain topographic data that conforms to the actual situation to optimize the water depth database; further, satellite images or DEM data are needed to extract and confirm local irregular coastlines, combined with coastline data fusion and supplement local water depth data; finally, based on the established irregular terrain coastline, combined with the optimized water depth database, multi-scale unstructured grid data of the study area is constructed. Figure 2 As shown, Figure 2 A schematic diagram showing multi-scale gridded bathymetric data in an irregular area.

[0125] It should be noted that the multi-scale water depth terrain dataset constructed in this embodiment may be a high-resolution terrain dataset, and the multi-scale refers to various different scales set according to calculation requirements.

[0126] Step 202: constructing a tsunami and historical water level observation dataset;

[0127] The topographic configuration determines the tsunami resonance oscillation mode. For the same location, the historical tsunami response spectrum and the response spectrum of the real-time tsunami observation data can better reflect the resonance oscillation characteristics of the area or location. Therefore, when there is no real-time tsunami, the historical tsunami data can be used instead of the current tsunami data to achieve the same detection and identification effect.

[0128] In this embodiment, a tsunami event that occurred in the target area in history is targeted, and a data set of observation data belonging to the tsunami event is constructed based on relevant information of the tsunami event.

[0129] The tsunami impact period mentioned below can be understood as the time period between the start and end time points of the tsunami event in the target area.

[0130] The raw water level data of the tsunami event described below may be understood to include: water level data recorded at a designated station, fluctuations of tsunamis, fluctuations of tides, etc. The station here may include multiple observation stations.

[0131] 2021. Collect relatively complete original water level data during the tsunami impact period of designated tsunami events in the target area;

[0132] In this embodiment, in order to ensure the accuracy and high resolution of the subsequent spectrum analysis, the sampling interval of the original water level data is 1 minute.

[0133] 2022. Filter the water level data of each station (i.e., observation point / observation station such as a buoy) during the tsunami impact period to obtain tsunami waves (the tsunami waves here are also tsunami wave sequences, tsunami signal waves, tsunami signals, tsunami signal sequences or tsunami signal wave sequences) and determine the tsunami data length.

[0134] The tsunami data length in this embodiment is the time length of the tsunami wave, and is used to obtain simulated tsunami waves with the same tsunami data length in the following tsunami simulation.

[0135] The filtering process in step 2022 may be to use a Butterworth high-pass filter to filter the water level data of the site. For example, the water level data may be filtered using formula (1).

[0136]

[0137] In formula (1), H represents the frequency response function of the filter, j is the imaginary unit, ω is the angular frequency of the signal, and ω c is the cut-off frequency, i.e. the frequency when the amplitude drops to -3dB, and n is the order of the filter, which determines the steepness of the roll-off characteristic.

[0138] In order to facilitate the subsequent spectrum analysis of the tsunami wave, the filtering process here can be understood as removing the tidal trend from the water level data, i.e., the original observation data set. In this embodiment, the low-frequency component of the original tsunami wave is maintained, and the filter algorithm is not used to remove the tidal trend.

[0139] The method of obtaining noise data, i.e., noise signal sequence wave (or background noise data set) may include: pre-collecting a historical water level data set, which is water level observation data under normal sea conditions before the arrival of the tsunami in a tsunami event. The construction of the historical water level data set should also exclude water level signals affected by typhoons, tropical cyclones, temperate cyclones, strong cold air, etc. The total duration of the water level data collected in this embodiment is not less than 30 days.

[0140] After the water level data is collected, the water level data set is divided into non-overlapping data segments, each of which is 24 hours long. Each segment of data is then processed to remove the tide trend, and a background noise data set of the historical water level data is obtained. In this embodiment, the polynomial fitting method in the non-digital filtering method can be used to remove the tide trend, that is, the original water level data is subtracted from the fitted tidal trend to obtain the required noise data. The method of obtaining noise data in this embodiment is consistent with the method of processing the tide trend of the tsunami wave.

[0141] In this embodiment, the historical water level data set before the tsunami event is obtained mainly to determine the background noise of each station area in the target area, so as to accurately determine the peak period of the background oscillation in the subsequent spectral analysis to verify the intrinsic oscillation period of the area and the resonance period of the tsunami signal.

[0142] 2023. The tsunami signal in the tsunami signal sequence is calculated using the moving root mean square calculation method described in the following formula (2), so as to obtain the final tsunami data length.

[0143]

[0144] In formula (2), t is time, M(t) is the root mean square at time t, w is the moving window length, and h(t) is the tsunami amplitude. The final tsunami data duration (i.e., tsunami data length) can be determined based on the mean square error threshold of the tsunami signal (usually, the mean square error result can be estimated to be equivalent to the background noise).

[0145] The tsunami data length in this embodiment is used to standardize the length of the tsunami wave in the simulated tsunami signal sequence. For the accuracy and comparability of subsequent spectrum analysis, the tsunami data length here is used in this embodiment to ensure a uniform length window for subsequent waves.

[0146] In order to obtain a better simulated tsunami signal sequence, in this embodiment, the tsunami signal in the tsunami signal sequence needs to be verified to determine whether the tsunami signal in the simulated tsunami signal sequence is valid. The following step 203 is a process description of the simulated tsunami signal sequence.

[0147] Step 203: numerical simulation and verification of tsunami events;

[0148] This step is mainly independent of the above-mentioned steps 201 and 202. Based on the focal mechanism solution of the joint inversion and the finite fault solution of the multi-source joint inversion, the tsunami numerical simulation method is used to obtain the propagation simulation results of the tsunami event at the fixed point and in the whole field in the target area.

[0149] The focal mechanism solution and multi-source joint inversion finite fault solution are used to initialize the tsunami numerical simulation process. The simulated tsunami datasets of each observation station are deduced with the help of the numerical results obtained by the simulation method to verify whether the simulated tsunami datasets are consistent with the actual observed tsunami datasets. If they are consistent, the subsequent steps are executed.

[0150] If the data are inconsistent, the focal mechanism solution and the multi-source joint inversion finite fault solution are re-optimized, and the simulated water level data set of each observation station is re-derived until the data meets the verification criteria.

[0151] If the data are consistent, the tsunami simulated amplitude field data based on the focal mechanism solution and the multi-source joint inversion finite fault solution are used for the identification and characteristic analysis of the tsunami oscillation mode.

[0152] The specific instructions are as follows:

[0153] Tsunami simulation mainly involves three stages: tsunami generation, propagation, and nearshore inundation. Among the three stages, the tsunami generation stage is the most important step in tsunami numerical simulation, which directly affects the results of tsunami simulation. In other words, only when the initial conditions are relatively accurate can accurate simulation results be obtained. Therefore, it is crucial to select a tsunami source that can accurately describe the characteristics of fault rupture for tsunami numerical simulation.

[0154] 2031. Selection of tsunami source and determination of initial sea surface displacement.

[0155] The method usually used to generate tsunamis is the transient response method, that is, it is assumed that the changes in seafloor topography caused by the slip of the fault rupture zone are directly regarded as the vertical displacement of the water surface, and this change in the water surface is the initial tsunami condition. The deformation of the seafloor displacement can be calculated using the elastic half-space dislocation model. This model has become the main model tool for describing earthquake fault deformation. To calculate the deformation field of the fault using this model, it is also necessary to know the basic information of the earthquake and the geometric parameters of the fault rupture. For example: parameters such as the epicenter of the earthquake, the length, width, depth of the fault rupture, the amount of fault slip, the dip, and the strike angle. Among them, the epicenter, focal depth, dip, and strike angle can be obtained by solving the earthquake focal mechanism, while the length, width, and slip of the fault can be estimated by the following empirical formula:

[0156] Fault length: lgL=-3.22+0.69Mw, where L is the fault length and Mw is the earthquake magnitude.

[0157] Fault rupture area: lgA = -3.09 + 0098Mw, where A is the area of ​​the rupture surface, then the width of the rupture surface W = A / L.

[0158] Slippage: lgD = -4.8 + 0.69Mw

[0159] At present, fault focal mechanism solutions can be divided into two categories. One is the single-point source rectangular fault plane focal mechanism solution model. This type of model can obtain the corresponding geometric parameters within 20 minutes after the earthquake, and has a good characterization of thrust-type large earthquakes. The other is a finite fault solution model based on the joint inversion of multi-source observation data. This type of model can characterize the local characteristics of fault ruptures. Fault ruptures have various anisotropies and can more realistically and objectively reflect the spatial properties of fault slip distribution. It can also improve the simulation accuracy of near-field tsunamis to a certain extent. However, this type of model requires multi-source observation data from mobile phones. Affected by the spatial distribution of data, the timeliness of the model is subject to certain limitations. Therefore, the type of model can be reasonably selected according to the differences in work objectives.

[0160] 2032. Establishment of tsunami propagation model.

[0161] The propagation of tsunamis and the evolution and deformation process in the nearshore are typical multi-scale dynamic processes. Obtaining the multi-scale resonance mode of the tsunami requires accurate simulation of the multi-scale process of the tsunami, based on the water depth topography data set obtained in the aforementioned steps, multi-scale simulation is performed for the basin, shelf, bay or port respectively. In the tsunami numerical calculation process of the present invention, an advanced tsunami numerical model (JAGURS model) that can take into account wave dispersion, earth elasticity, and seawater density stratification is used to accurately simulate the tsunami propagation process. The model also has a multi-layer nested function to meet the multi-scale simulation.

[0162] The governing equations for this model are as follows:

[0163]

[0164]

[0165] In the above formula, H is the still water depth of the ocean, ξ is the shape of the seabed, R is the radius of the earth, R = 6,371km, t is the time, η is the tsunami amplitude relative to the still water surface, g is the gravitational acceleration, f is the Coriolis force parameter, n is the Manning roughness coefficient, ρ H and ρ ave are the seafloor and depth-averaged seawater densities, respectively; M and N represent the fluxes along longitude and latitude, respectively.

[0166] The seafloor deformation field of the earthquake fault obtained in the previous step is used as the initial condition input into the established tsunami propagation model to initialize the multi-scale tsunami numerical model, and the synthetic data required for analyzing the multi-scale tsunami resonance oscillation in the target area can be obtained. In this embodiment, the output data of the multi-scale tsunami numerical model is mainly divided into virtual observation station output and amplitude field output, and the output time interval is 1 minute.

[0167] Considering the large amount of data output, in order to facilitate the subsequent resonance mode detection, identification and analysis, the simulation data can be organized into a tsunami propagation dataset and stored in the Netcdf file format.

[0168] 2033. Validation of the tsunami model.

[0169] After completing the modeling and simulation of the multi-scale high-precision tsunami model, it is necessary to analyze the rationality of the simulation results from point to surface based on the dynamic characteristics of water wave propagation. At the same time, the performance of tsunami wave and flow simulation is quantitatively evaluated in combination with point and surface observation data to ensure that the simulation data is highly credible, thereby improving the accuracy of resonance mode identification.

[0170] In this embodiment, the performance of tsunami model simulation is evaluated by checking the geometric mean ratio of simulation results and observation results.

[0171] In this embodiment, the following evaluation method of formula (6) can be used for evaluation:

[0172]

[0173] In formula (6), N is the number of observation stations, H o and H s are the observed and simulated tsunami amplitudes, respectively, and K is a geometric mean index, and its value close to 1 represents a higher simulation accuracy. In this embodiment, it is recommended that a K value of not less than 80% can be used for the characteristic analysis of the resonance mode in the following other steps.

[0174] Of course, if it is less than 80%, step 203 is re-executed, for example, the focal mechanism solution and the multi-source joint inversion finite fault solution are re-optimized, and the simulated water level data sets of each observation station are re-derived until the data meets the inspection standard.

[0175] Step 204: Tsunami and background spectrum peak control period detection

[0176] 2041. For the tsunami signal sequence of each observation station in step 202, the Welch power density spectrum method is used to perform Fourier analysis, and the half-window overlap and Hanning window function are selected to calculate the 95% confidence interval, so as to obtain the spectrum data of the tsunami signal. The peak period can be obtained by performing spectrum display on the spectrum data. That is, the tsunami signal wave sequence constructed in step 202 is subjected to Fourier analysis to obtain the tsunami observation spectrum. The tsunami spectrum characteristics are detected, and the recorded tsunami observation peak period is picked up.

[0177] In this embodiment, the range of tsunami cycle picking is the energy distribution interval above 5% of the spectrum peak value. For example, the tsunami observation peak period can be in the range of several minutes to several hours, depending on the terrain characteristics of the research area.

[0178] 2042. For the output data of the virtual observation station simulated in step 203, the Welch power density spectrum method is used to perform Fourier analysis, and half-window overlap and Hanning window functions are selected to calculate the 95% confidence interval, thereby obtaining the spectrum data of the simulated tsunami signal. The peak period can be obtained by performing spectrum display on the spectrum data.

[0179] That is, the tsunami signal wave sequence of the tsunami simulation corresponding to the virtual site is subjected to spectrum analysis to pick up the tsunami peak period.

[0180] 2043. Compare and verify the consistency of the spectrum analysis results of the observation site with those of the virtual observation site in terms of spectrum shape and peak period.

[0181] In this embodiment, the threshold value of the peak control period accuracy test may be set as a relative error of no more than 10% for passing the test. The spectrum analysis results of each observation station that has passed the test are selected.

[0182] If the test fails (either of the two tests fails), step 203 is re-executed, for example, the focal mechanism solution and the multi-source joint inversion finite fault solution are re-optimized, and the simulated water level data sets of each observation station are re-derived until the data meets the test criteria.

[0183] 2044. Background noise spectrum peak period detection.

[0184] For the original water level data of each observation station during the period without forcing, the tidal component of the original water level was fitted using the polynomial fitting method. The background noise signal was obtained by subtracting the fitted tidal component from the original water level, and the background noise was analyzed as follows.

[0185] The spectrum of the observation station after removing the tidal trend can be regarded as a linear combination of the tsunami spectrum, the background oscillation spectrum and the instrument error spectrum, which can be expressed as follows:

[0186]

[0187] In formula (7), the subscript c represents the spectrum near the coast, the first term on the right side of the equation represents the contribution of the tsunami, the second term represents the spectrum of the background oscillation, and the third term is the instrument error spectrum. The instrument error spectrum is small compared to the other two terms and can therefore be ignored.

[0188] If there is no tsunami signal or other dynamic processes such as storms during the instrument observation period, formula (7) can be simplified to:

[0189]

[0190] This indicates that the obtained nearshore spectral peak period may be accompanied by a local resonance period, that is, after other background field noise is removed, the remaining can be understood as being affected only by the resonance of the terrain.

[0191] If a tsunami event exists, it contains both the period of the tsunami source and the local resonance response. It is also known that the observation results of tsunamis and background oscillations can be regarded as the evolution results of deep-water long waves, from which we can obtain:

[0192] S. (ω)=Y(ω)P(ω)Q(ω)S deep (ω) (9)

[0193] In formula (9), Y(ω), P(ω), and Q(ω) are the conversion functions of deep-water waves propagating in the ocean, shelf, and harbor, respectively. deep (ω) is the deep water spectrum or wave source spectrum. From this, the expressions of tsunami spectrum and background spectrum can be obtained as follows:

[0194]

[0195] In the above formula, Z(ω) is the spectrum of tsunami waves in the deep water tsunami source area, S o is the background spectrum in the deep ocean. Because the background spectrum in deep water looks very smooth and has no obvious peak, the nearshore spectrum of the background oscillation can be regarded as being completely caused by the transfer function, that is, the background spectrum actually plays the role of a filter for the tsunami source signal. At this time, it is believed that the spectrum peak period of the background noise is strongly correlated with the influence of the local environment, and can more objectively reflect the inherent resonant frequency characteristics of the local environment.

[0196] Therefore, in this embodiment, the background noise data is crucial for confirming the frequency of the tsunami eigenmode and the period of the tsunami resonance mode. At this point, the background signal of the corresponding observation station sorted in step 202 is subjected to Fourier analysis to obtain the significant peak period of the background oscillation, which is used for the subsequent comparison and verification of the resonance mode and the eigenmode.

[0197] Step 205: Modal analysis of intrinsic oscillation.

[0198] The multi-scale water depth topography data of the target area are discretized in a network, and the finite element method is used to discretely solve the eigenvalues ​​of the homogeneous linear shallow water equation to obtain the eigenmode solution of the target area, that is, the modal information of the eigenoscillation, that is, the result of the modal analysis is obtained by solving the eigenvalues ​​of the homogeneous linear shallow water equation to obtain the eigenmode solution of the target area.

[0199] The eigenmode solution includes: the spatial distribution characteristics of the eigenmode frequency and the energy corresponding to the mode, the period of the eigenmode, and the amplitude distribution / oscillation type corresponding to the eigenmode;

[0200] This method does not require setting the solution of the tsunami source initialization equation, and only requires the multi-scale water depth topography data sorted and fused in step 201.

[0201] That is, by using the multi-scale terrain database established earlier, what is solved is the intrinsic mode (including modal frequency and spatial spectrum amplitude distribution / amplitude distribution) determined for the input area terrain.

[0202] The numerical solution process of modal analysis is as follows:

[0203]

[0204] In formula (12), η is the free surface displacement, g is the gravitational acceleration, h and t are the water depth and time, respectively.

[0205] The variable substitution to solve formula (12) transforms the time domain equation into the frequency space domain. The solution is the eigenvalue of the intrinsic oscillation (eigenperiod and amplitude distribution).

[0206] The method of separation of variables is used to obtain the following solution.

[0207] η(x,y,t)=α(x,y)β(t) (13)

[0208] Here α and β represent the amount of spatial and temporal variation, respectively.

[0209] A total reflection boundary condition with no flux exchange is assumed at the land boundary, and a radiation boundary is adopted at the offshore boundary, and the shape of the radiation boundary is defined as a semicircle with a radius of R, and the center of the circle is located at the location of the scattered radiation wave source. The above two boundary conditions are:

[0210]

[0211] in, is the normal vector pointing outside the domain of definition. w is the shallow water wave speed.

[0212] Substituting equations (13), (14) and (15) into equation (12), and assuming that the time-varying quantity is a standard decay model, that is, β(t) = e -(ζ+iω)t , ζ is the attenuation factor.

[0213] Therefore, the original wave equation can be transformed into a differential equation of the structural dynamic response system, that is, solving the quadratic eigenvalue problem. In this way, the frequency of the eigenmode and the corresponding spatial distribution of the amplitude can be obtained.

[0214] In the above process, the frequency (period) characteristics of the high-energy oscillation modes of the tsunami spectrum and background spectrum at the observation site have been calculated and determined, and the multi-scale intrinsic oscillation frequency and amplitude spatial distribution types in the theoretical target area have also been determined.

[0215] Then, for actual tsunami events, it is also necessary to combine high-resolution and high-precision tsunami numerical simulation results to determine the tsunami oscillation modes and spatial characteristics of the modes in the target area, as follows.

[0216] Step 206: Identification and confirmation of tsunami resonance oscillation modes.

[0217] Based on the numerical calculation results of the target areas of different scales formed in step 203, the tsunami spectrum peak period analysis and detection method in step 204 is used to perform spectrum analysis operations on the tsunami simulation results of each scale area, thereby obtaining a spectrum spatial distribution field data set for each area.

[0218] First, the most relevant oscillation modes are monitored and identified from the spectrum data of each scale region (i.e., the spectrum data after amplitude field spectrum analysis). The high-energy oscillation modes are detected by spatially integrating the spectrum data. The integral formula is as follows:

[0219]

[0220] here and S(x, y, f) represent the spectrum of the entire integration region and the spectrum at each grid point in the integration region, respectively.

[0221] Next, the tsunami resonant oscillation modes can be screened, identified and determined through comprehensive analysis. The specific scheme is as follows:

[0222] 2061. First, the integral spectrum of the tsunami process simulation, the tsunami observation spectrum, and the synthetic spectrum of the tsunami virtual station are superimposed together to screen out the high-energy modes that appear most frequently in the above spectra or exist in all three spectra. These high-energy modes may be highly correlated with the tsunami resonance modes.

[0223] 2062. The multi-scale nearshore area inherent oscillation modes obtained by modal analysis are confirmed by detecting and identifying the background spectrum high-energy modes, and the false numerical oscillations that may be introduced in the modal analysis process are eliminated to confirm the nearshore terrain resonance modes that may be excited.

[0224] 2063. Compare and analyze the tsunami oscillation mode with the identified intrinsic oscillation mode, screen out the oscillation modes with similar frequencies, and confirm the resonant oscillation mode of the tsunami based on the amplification ratio of the tsunami observation and the background spectrum.

[0225] 2064. After determining the resonance mode, by comparing the spatial distribution characteristics of the corresponding eigenmode vibration type with the tsunami resonance mode amplitude, the influence of the source on the spatial distribution characteristics of the resonance amplitude can be jointly determined.

[0226] In order to detect and identify which oscillation modes among the high-energy oscillation frequencies are resonant modes, the oscillation mode results obtained above are compared with the results of the eigenmode analysis.

[0227] In this embodiment, since the resonance mode solution obtained by the modal analysis method may contain numerical false solutions, the background noise peak spectrum and the tsunami signal peak spectrum obtained in step 204 need to be used to further confirm the eigenoscillation solution. After the three parties reach a consensus, the tsunami resonance mode can be determined by combining the high-energy modal components of the integral spectrum; after the resonance mode is identified, the amplitude distribution corresponding to each resonance mode can also be compared and confirmed by the spectrum amplitude field obtained by the eigenmode solution and the numerical simulation result analysis.

[0228] For example, some peak periods in the observation point results will appear in the intrinsic oscillation results, and some peak periods in the virtual station spectrum analysis results will appear in the intrinsic oscillation mode center. After analysis, the high-energy modes that appear in the background spectrum, observation site spectrum, simulation spectrum, and intrinsic oscillation spectrum are confirmed to be resonant oscillation modes. The analysis results here represent the surface and the distribution of the regional characteristics controlled by this mode.

[0229] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0230] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0231] In the present invention, unless otherwise clearly specified and limited, when a first feature is “on” or “below” a second feature, it may be that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, when a first feature is “above”, “above” or “above” a second feature, it may be that the first feature is directly above or obliquely above the second feature, or it may simply mean that the first feature is higher in level than the second feature. When a first feature is “below”, “below” or “below” a second feature, it may be that the first feature is directly below or obliquely below the second feature, or it may simply mean that the first feature is lower in level than the second feature.

[0232] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0233] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may alter, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for jointly detecting and identifying tsunami resonant oscillation modes, characterized in that: include: S100, for a target area, obtaining a first tsunami wave signal of a designated tsunami event and a noise signal under normal sea conditions outside a time period of a non-tsunami event, wherein the first tsunami wave signal and the noise signal are both obtained by removing tide trend from water level data of multiple observation stations in the target area; S200, according to the tsunami parameters of the tsunami event specified in the target area, using a tsunami numerical simulation method to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event; The second tsunami wave signal is obtained by using simulated water level data output by a virtual observation station simulating a tsunami event; S300, using the first tsunami wave signal to verify the accuracy of the second tsunami wave signal, and when the accuracy verification passes, performing spectrum analysis on the first tsunami wave signal to obtain a first spectrum analysis result, performing spectrum analysis on the amplitude field information, and obtaining a high-energy tsunami oscillation mode; Perform spectrum analysis on the noise signal to obtain the high-energy frequency characteristics of the background noise; S400, analyzing multi-scale water depth topography data of the target area to obtain modal information of intrinsic oscillation; S500, determining the tsunami resonance oscillation mode and the resonance amplitude spatial distribution characteristics according to the high-energy frequency characteristics of the background noise, the first spectrum analysis result, the high-energy tsunami oscillation mode, and the modal information of the intrinsic oscillation.

2. The method according to claim 1, characterized in that: Before S100, the method further includes: Acquire multi-scale bathymetric topographic data of the target area; Specifically, the water depth database of the target area is constructed by fusion of ETOPO, GEBCO, and SRTM data with different resolutions; Use bathymetric data to supplement the water depth topography within a specified range of the nearshore and offshore areas and optimize the water depth database; Satellite images or DEM data are used to extract and confirm local irregular coastlines, and based on the established irregular terrain coastlines and the optimized water depth database, multi-scale unstructured grid data of the target area are constructed as multi-scale water depth terrain data.

3. The method according to claim 1, characterized in that The S100 includes: The original water level data under the specified tsunami event and normal sea conditions are obtained respectively, the sampling interval of the original water level data is 1 minute, and the water level data of multiple observation stations are included; The water level data of each observation station in the tsunami-affected area and under normal sea conditions are processed to remove tidal trends and obtain the first tsunami wave signal and noise signal.

4. The method according to claim 3, characterized in that The S100 further includes: Calculating the mean square error of the first tsunami wave signal by means of a moving root mean square operation method to obtain the tsunami data length corresponding to the first tsunami wave signal; Accordingly, the S200 includes: According to the tsunami parameters of the tsunami event specified in the target area and the tsunami data length, a tsunami numerical simulation method is used to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event.

5. The method according to claim 4, characterized in that The S200 includes: According to the earthquake focal mechanism solution, the earthquake epicenter position, focal depth, dip angle and strike angle in the tsunami parameters are obtained; The empirical formula is used to obtain the fault length, fault rupture area, fracture width and slip amount in the tsunami parameters; According to the values ​​of each parameter in the tsunami parameters, a tsunami numerical model is used to simulate the tsunami propagation process, and the seabed deformation field of the earthquake fault is input into the tsunami numerical model as an initial condition to obtain a second tsunami wave signal and amplitude field information simulating the tsunami event; the tsunami data length of the second tsunami wave signal meets the tsunami data length of the first tsunami wave signal.

6. The method according to claim 1, characterized in that The S300 includes: The following formula is used to verify the accuracy of the second tsunami wave signal obtained from the tsunami numerical simulation; Where N is the number of observation stations, H o and H s are the tsunami amplitude in the first tsunami wave signal and the tsunami amplitude in the second tsunami wave signal respectively, and K is the set geometric mean rate index; If the verification fails, the step of obtaining a second tsunami wave signal by using a tsunami numerical simulation method in S200 is executed again until the verification passes.

7. The method according to claim 1, characterized in that The S300 further includes: S301, performing Fourier analysis on the first tsunami wave signal to obtain a tsunami observation spectrum, and picking up a tsunami observation peak period; and, performing Fourier analysis on the second tsunami wave signal to obtain a simulated tsunami spectrum and pick up the tsunami peak period; S302, verifying the consistency of the amplitude and spectrum of the tsunami observation spectrum and the amplitude and spectrum of the simulated tsunami spectrum in terms of waveform, spectrum shape and peak period. If there is any inconsistency, re-execute S200; S303. After verification, perform spectrum analysis on the simulated amplitude field information to obtain the integral spectrum of the entire field, i.e., the high-energy tsunami oscillation mode.

8. The method according to claim 7, characterized in that The S303 includes: By spatially integrating the spectrum data, the high-energy oscillation modes are detected. The integral formula is as follows: in, and S(x, y, f) represent the spectrum of the entire integration region and the spectrum at each grid point in the integration region, respectively.

9. The method according to claim 7, characterized in that: The range of the tsunami observation peak period picking in S301 is the energy distribution interval of more than 5% of the spectrum peak of the tsunami observation spectrum; The range of the tsunami peak period picked up in S301 is the energy distribution interval of more than 5% of the peak value of the spectrum amplitude of the simulated tsunami spectrum; The threshold value for passing the consistency check of the peak period accuracy in S302 is 90%; Alternatively, the S400 includes: The multi-scale water depth topography data of the target area is discretized in the network, and the finite element method is used to discretize the eigenvalues ​​of the homogeneous linear shallow water equation to obtain the eigenmode solution of the target area, that is, the modal information of the eigenoscillation. The eigenmode solution includes: the spatial distribution characteristics of the eigenmode frequency and the energy corresponding to the mode, the period of the eigenmode, and the amplitude distribution / oscillation type corresponding to the eigenmode; Alternatively, the S400 includes: S401, performing network discretization on the multi-scale water depth topographic data of the target area; S402, obtaining an eigenmode solution based on the following formula; Using the separation of variables method, we can obtain η(x, y, t) = α(x, y)β(t); A total reflection boundary condition with no flux exchange is assumed at the land boundary, and a radiation boundary is adopted at the offshore boundary. The shape of the radiation boundary is defined as a semicircle with a radius of R, and the center of the circle is located at the location of the scattered radiation wave source. The boundary condition is: Where η is the free surface displacement, g is the gravitational acceleration, h and t are the water depth and time respectively; α and β represent the amount of spatial and temporal changes respectively; is the normal vector pointing outside the definition domain; C w is the shallow water wave velocity; assuming that the time variation is the standard attenuation model, that is, β(t) = e -(ζ+iω)t , ζ is the attenuation factor.

10. The method according to claim 7, characterized in that The S500 includes: S501, stacking the integral spectrum corresponding to the amplitude field information, the tsunami observation spectrum, and the simulated tsunami spectrum together, and screening out the high-energy mode that appears the most times in all the spectra or exists in all the spectra and has a specified correlation with the tsunami resonance mode, that is, the tsunami oscillation mode; S502, based on the high-energy frequency characteristics of the background noise, analyzing the modal information of the intrinsic oscillation to eliminate the false numerical oscillation introduced in the modal analysis process and confirming the excited nearshore terrain resonance mode; S503, comparing and analyzing the tsunami oscillation mode with the nearshore terrain resonance mode, selecting the oscillation mode with similar frequency, and confirming the tsunami resonance oscillation mode according to the amplification ratio of the tsunami observation and the background spectrum; S504. Based on the resonant oscillation mode of the tsunami, compare the spatial distribution characteristics of the amplitude distribution in the eigenmode solution with the amplitude of the resonant oscillation mode, and jointly determine the influence of the source on the spatial distribution characteristics of the resonant amplitude.

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