Geological disaster early warning device based on seismic waveform recognition

Abstract

The invention relates to the technical field of earthquake monitoring, in particular to a geological disaster early warning device based on earthquake waveform recognition, which comprises an earthquake data receiving module, an earthquake wave type analysis module, a disaster mode recognition module, an early warning signal construction module, a risk judgment module and an early warning information issuing module. According to the invention, through identifying and analyzing the seismic waveform data, the characteristics of the earthquake and the type of the caused disaster, such as landslide, rock collapse, debris flow, ground fissure, surface collapse and the like, are rapidly judged, and through comprehensively evaluating the emergency and the influence range of the earthquake event, the targeted early warning information is generated and issued in time. The geological disaster response speed and efficiency can be remarkably improved, earthquake monitoring is not limited to recording of earthquake activities, disaster risks can be evaluated in real time, more accurate disaster prediction and early warning can be provided, for areas where geological disasters frequently occur, the early warning accuracy and timeliness can be remarkably improved, casualties and property losses can be reduced, and the economic benefit is improved. And the method has extremely high practical value and social significance.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake monitoring, and in particular to a geological disaster early warning device based on earthquake waveform recognition. Background Art

[0002] Earthquake monitoring is an important branch of geophysics that focuses on detecting and recording the movement of the Earth's interior, especially seismic activity, through various technologies and equipment. Seismographs and sensors are mainly used to capture seismic waves, and waveform data can help scientists assess the intensity, location, and impact of earthquakes. The purpose of earthquake monitoring is not only to record seismic activity, but also to assess and warn of earthquake risks and reduce losses caused by disasters. With the advancement of technology, earthquake monitoring has gradually included the use of artificial intelligence and machine learning algorithms to improve the accuracy and timeliness of predictions.

[0003] Among them, the geological disaster early warning device of earthquake waveform recognition refers to the use of advanced sensors and data analysis technology to identify earthquake activities that lead to serious geological disasters in advance. This device analyzes the earthquake waveform to quickly determine the characteristics of the earthquake and the type of disaster caused, such as landslides, rock collapses, mudslides, ground fissures, ground collapse, etc. The purpose of using the early warning device is to give sufficient warnings before the disaster occurs so that appropriate disaster prevention measures can be taken to protect people's lives and property. Especially for areas where geological disasters occur frequently, it has extremely high practical value and social significance.

[0004] Existing earthquake monitoring technology mainly relies on traditional seismographs and sensor technology to capture and analyze earthquake activities. It can only provide data after an earthquake occurs, making it difficult to achieve instant prediction of specific disaster types caused by earthquakes. This technology limits the emergency response capabilities of earthquake early warning devices before disasters occur, so there is a response delay in emergency evacuation and response measures deployment after an earthquake. Existing devices fail to fully utilize advanced data analysis technologies, such as artificial intelligence and machine learning, in the analysis and processing of earthquake data, resulting in a lack of accuracy and timeliness in the identification of earthquake activities and the determination of disaster types. In actual operations, the shortcomings of these technologies lead to dual challenges in the response time and accuracy of disaster warnings, especially in preventing disaster losses, which limits the overall effectiveness of earthquake monitoring devices. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geological disaster early warning device based on seismic waveform recognition.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a geological disaster early warning device based on seismic waveform recognition comprises:

[0007] The seismic data receiving module collects the original seismic data from the seismic monitoring site, including the frequency, amplitude and velocity change information, marks the time and location of the earthquake, and generates the original waveform data set;

[0008] The seismic wave type analysis module uses the original waveform data set to identify the types of differentiated seismic waves, including body waves and surface waves, analyze the energy, frequency and velocity characteristics of the seismic waves, and obtain seismic waveform feature records;

[0009] The disaster pattern recognition module uses the earthquake waveform feature records to compare the waveform spectra of differentiated geological disasters, including tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes, to identify the disaster types associated with real-time earthquake activities and generate disaster type diagnosis results;

[0010] The early warning signal construction module evaluates the urgency and impact range of the earthquake event according to the disaster type diagnosis result, and obtains disaster early warning information in combination with the earthquake intensity and potential harm degree;

[0011] The risk determination module analyzes the disaster warning information, determines the risk level of the affected area, calculates the expected impact and potential losses, and obtains the risk level analysis results;

[0012] The warning information release module uses the risk level analysis results to send customized warning information to regions and institutions, including emergency response measures and resource allocation guidance, to obtain a disaster warning log.

[0013] As a further solution of the present invention, the original waveform data set includes the timestamp, location coordinates, frequency change sequence, amplitude change sequence and velocity change information of the earthquake; the earthquake waveform feature record includes the identification of differentiated types of seismic waves, key frequency characteristics of seismic waves, amplitude range and propagation speed; the disaster type diagnosis results include tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes; the disaster warning information includes the emergency level of the earthquake event, the expected impact range and the potential degree of harm; the risk level analysis results include the risk level of the affected area, the expected degree of impact and the estimate of potential losses; the disaster warning log includes the content of the warning information sent, the list of receiving regions and institutions, emergency response measures and resource allocation guidance.

[0014] As a further solution of the present invention, the seismic data receiving module includes:

[0015] The data collection submodule captures the frequency change, amplitude change and speed change during earthquake through the sensor interface of the seismic monitoring station, performs initialization filtering and amplification of the signal through the real-time data stream of the data interface, and obtains the original seismic signal data;

[0016] The time-stamping submodule uses the raw seismic signal data to timestamp the data, encodes it according to the time and location of the earthquake, records the source and time of each data packet, and generates labeled seismic data;

[0017] The data synchronization submodule uses the marked earthquake data to construct an earthquake waveform curve, integrates and synchronizes the waveform data, and obtains an original waveform data set.

[0018] As a further solution of the present invention, the seismic wave type analysis module includes:

[0019] The propagation rate identification submodule uses the original waveform data set to identify body waves and surface waves in the waveform through frequency characteristic analysis, classifies the waveform based on the amplitude, and determines the wave propagation rate to obtain seismic wave type data;

[0020] The feature analysis submodule determines the energy peak of the differentiated type of seismic wave based on the seismic wave type data, evaluates the frequency characteristics of the waveform by identifying the frequency distribution, quantifies the velocity characteristics, and obtains a seismic wave feature distribution diagram;

[0021] The data integration submodule uses the seismic wave characteristic distribution map to perform data integration on the analysis results, and obtains seismic waveform characteristic records by integrating and synchronizing multiple characteristic parameters.

[0022] As a further solution of the present invention, the disaster pattern recognition module includes:

[0023] The signal filtering and analysis submodule uses the earthquake waveform characteristic record to perform signal filtering, remove noise interference, perform time and frequency analysis, extract the amplitude, frequency and duration of the earthquake, and obtain the earthquake wave signal analysis result;

[0024] The disaster type comparison submodule adopts the seismic wave signal analysis results and the convolutional neural network technology to compare the waveform spectra of known tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes, perform feature comparison and image matching of seismic waveforms, and obtain waveform difference analysis results;

[0025] Based on the waveform difference analysis results, the type identification submodule iteratively analyzes the signs and characteristic indicators of the earthquake waveform, distinguishes the disaster types associated with real-time earthquake activities, identifies the occurrence categories of disasters, and obtains disaster type diagnosis results.

[0026] As a further solution of the present invention, the formula of the convolutional neural network technology is as follows:

[0027]

[0028] Among them, Sim(A,B) is the fitting score of the tectonic earthquake and the comparative earthquake waveforms, represents the ith eigenvalue of the tectonic earthquake waveform, represents the ith eigenvalue of the comparative earthquake waveform (including tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes), w i represents the weight of the i-th feature, v i is the adjustment coefficient for normalizing the waveform data, and n is the total number of features.

[0029] As a further solution of the present invention, the warning signal construction module includes:

[0030] The urgency assessment submodule adopts the disaster type diagnosis result, analyzes the type and intensity of earthquake activity, assesses the urgency of the earthquake event, determines the time of issuing the alarm, and obtains the urgency rating data;

[0031] The geographic scope analysis submodule analyzes and predicts the geographic scope of earthquake impact based on the urgency rating data, applies geographic information system and population density data, and obtains the analysis results of the impact area by referring to the potential degree of hazard and the geographic characteristics of the impact area;

[0032] The early warning signal issuing submodule uses the analysis results of the impact area, combines the earthquake intensity and the predicted degree of harm, checks the consistency of the information, issues a disaster early warning signal, and obtains disaster early warning information.

[0033] As a further solution of the present invention, the risk determination module includes:

[0034] The demand analysis submodule uses the disaster warning information and a multi-criteria decision-making method to assess the risk level of the affected area, analyze the impact of the earthquake event, and obtain the initial risk level data;

[0035] The impact degree calculation submodule calculates the expected impact degree based on the initialized risk level data, including potential casualties, property losses and economic impacts, conducts quantitative assessment of the risks, and obtains potential loss assessment results;

[0036] The risk measures construction submodule utilizes the potential loss assessment results, integrates the risk level and loss assessment, provides emergency response measures for decision makers and emergency management departments, and obtains risk level analysis results.

[0037] As a further solution of the present invention, the formula of the multi-criteria decision-making method is as follows:

[0038]

[0039] Among them, R z is the risk level data value, Sz Represents the earthquake intensity, P z represents population density, E z represents the intensity of economic activity, W z Represents early warning response capability.

[0040] As a further solution of the present invention, the warning information release module includes:

[0041] The action plan customization submodule uses the risk level analysis results to design customized warning information, including alert levels and action plans, based on the needs and risk levels of differentiated regions and institutions, and checks the pertinence and effectiveness of the information to obtain customized warning messages;

[0042] The measures guidance submodule plans emergency response measures based on the customized warning message, including evacuation routes, safety protection and resource allocation, and provides resource allocation guidance in combination with regional conditions to obtain an emergency response guidance plan;

[0043] The program implementation submodule implements the emergency response guidance program, records the issued warning information and emergency measures, conducts real-time tracking feedback and evaluates the effectiveness of the emergency plan, and obtains a disaster warning log.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by identifying and analyzing seismic waveform data, the characteristics of earthquakes and the types of disasters caused, such as landslides, rock collapses, mudslides, ground fissures, ground collapses, etc., are quickly determined. By comprehensively evaluating the urgency and impact range of earthquake events, targeted early warning information is generated and released in a timely manner, which can significantly improve the response speed and efficiency of geological disasters. Earthquake monitoring is not limited to recording earthquake activities, but can also assess disaster risks in real time, provide more accurate disaster prediction and early warning, and especially for areas where geological disasters frequently occur, can significantly improve the accuracy and timeliness of early warnings, reduce casualties and property losses, and has extremely high practical value and social significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the device of the present invention;

[0047] Figure 2 It is a schematic diagram of the device framework of the present invention;

[0048] Figure 3 It is a flow chart of the seismic data receiving module of the present invention;

[0049] Figure 4 It is a flow chart of the seismic wave type analysis module of the present invention;

[0050] Figure 5This is a flow chart of the disaster pattern recognition module of the present invention;

[0051] Figure 6 It is a flowchart of the early warning signal construction module of the present invention;

[0052] Figure 7 is a flow chart of the risk determination module of the present invention;

[0053] Figure 8 This is a flow chart of the early warning information release module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] See also Figure 1 to Figure 2 , a geological disaster early warning device based on earthquake waveform recognition includes:

[0057] The seismic data receiving module collects the original seismic data from the seismic monitoring site, including the frequency, amplitude and velocity change information, marks the time and location of the earthquake, and generates the original waveform data set;

[0058] The seismic wave type analysis module uses the original waveform data set to identify the types of differentiated seismic waves, including body waves and surface waves, analyze the energy, frequency and velocity characteristics of seismic waves, and obtain seismic waveform feature records;

[0059] The disaster pattern recognition module uses earthquake waveform feature records to compare waveform maps of differentiated geological disasters, including tectonic earthquakes, volcanic earthquakes, collapse earthquakes, and artificial earthquakes, to identify the disaster types associated with real-time earthquake activities and generate disaster type diagnosis results;

[0060] The early warning signal construction module evaluates the urgency and impact range of earthquake events based on the disaster type diagnosis results, and obtains disaster warning information based on the earthquake intensity and potential damage level;

[0061] The risk assessment module analyzes disaster warning information, determines the risk level of the affected area, calculates the expected impact and potential losses, allocates resources and response strategies according to the risk level, and obtains the risk level analysis results;

[0062] The warning information release module uses the risk level analysis results to send customized warning information to related regions and institutions, including emergency response measures and necessary resource allocation guidance, and obtains a disaster warning log.

[0063] The original waveform data set includes the timestamp, location coordinates, frequency change sequence, amplitude change sequence and velocity change information of the earthquake. The earthquake waveform feature record includes the identification of differentiated types of seismic waves, key frequency characteristics of seismic waves, amplitude range and propagation speed. The disaster type diagnosis results include tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes. The disaster warning information includes the emergency level of the earthquake event, the expected impact range and the potential degree of harm. The risk level analysis results include the risk level of the affected area, the expected degree of impact and the estimate of potential losses. The disaster warning log includes the content of the warning information sent, the list of receiving regions and institutions, the emergency response measures and the guidance of resource allocation.

[0064] See also Figure 2 , 3 , the seismic data receiving module includes:

[0065] The data collection submodule captures the frequency change, amplitude change and speed change during earthquake through the sensor interface of the seismic monitoring station, performs initialization filtering and amplification of the signal through the real-time data stream of the data interface, and obtains the execution process of the original seismic signal data as follows;

[0066] Through the sensor interface of the seismic monitoring station, the frequency changes, amplitude changes and speed changes during earthquakes are captured, and the signal is initialized, filtered and amplified through the real-time data stream of the data interface. After the sensor interface captures the earthquake-related data, the signal is initialized. The key is to remove noise and enhance the clarity of the seismic signal. The initialization filtering is to set the filter parameters, such as cutoff frequency and bandwidth, to adapt to different seismic signal characteristics. The amplification part adjusts the amplitude of the signal to ensure the availability of the data in subsequent processing. For example, the gain setting of the amplifier is used to adjust the amplitude to ensure that the signal is not distorted during the conversion process. The technical parameters involved in the whole process include the type of filter selection, the specific numerical setting of the gain, and how to adjust these parameters to match the characteristics of the seismic signal to obtain the original seismic signal data.

[0067] The time-stamping submodule uses the original seismic signal data, timestamps the data, encodes it according to the time and location of the earthquake, records the source and time of each data packet, and generates the execution flow of the labeled seismic data as follows;

[0068] Use the original seismic signal data, mark the data with a timestamp, and use the formula T d =T0

[0069] +ΔT×(n d -1), calculate the timestamp of each data packet. d represents the timestamp of the data packet, T0 represents the timestamp of the start of observation, ΔT represents the time interval between data packets, and n d Represents the sequence number of the data packet. Detailed explanation of the formula and the derivation process of the formula calculation: Considering that each data packet must have an accurate time stamp to ensure the integrity and traceability of the seismic data, this formula is used to calculate the exact time of each data packet in the sequence. If the seismic monitoring starts at midnight (T0 = 0 hours), and the recording interval of each data packet is 1 second (ΔT = 1 second), the timestamp of the 100th data packet will be based on T d = 0 + 1 × (100-1) = 99 seconds of calculation. This ensures that each data packet has a unique and continuous time reference, which is crucial for the encoding and subsequent processing of seismic data.

[0070] The data synchronization submodule uses the marked earthquake data to construct the earthquake waveform curve, integrates and synchronizes the waveform data, and obtains the execution process of the original waveform data set as follows;

[0071] Use labeled seismic data to construct earthquake waveform curves, and integrate and synchronize waveform data. During the integration and synchronization process, the waveforms of multiple data sources are aligned based on the collected time-tagged seismic data to ensure the consistency of all waveform data on the time axis. The main technologies involved in the synchronization operation are data alignment and time correction. By calculating the time difference between different data packets and adjusting them, the consistency and continuity of the data are ensured. The waveform data also needs to be normalized to eliminate deviations between devices, for example, adjusting the sensitivity differences of different sensors to output the original waveform data set.

[0072] See also Figure 2 , 4 , the seismic wave type analysis module includes:

[0073] The propagation rate identification submodule uses the original waveform data set to identify body waves and surface waves in the waveform through frequency characteristic analysis, classifies the waveform based on the amplitude, and determines the wave propagation rate to obtain the seismic wave type data. The execution process is as follows;

[0074] Using the original waveform data set, the body waves and surface waves in the waveform are identified through frequency characteristic analysis. According to the formula Calculate the wave propagation velocity. Where, v b represents the wave propagation speed, λ b represents the wavelength, T b Represents period. Detailed explanation of the formula and the process of formula calculation and derivation: In earthquake monitoring, wavelength and period are basic physical quantities determined by earthquake signal analysis. For example, if through data analysis, the period of a body wave is T b The measurement is 0.5 seconds, and the wavelength λ is measured on the seismic recorder b is 800 m, then the propagation speed of the wave is calculated to be Such calculations help determine the speeds at which different types of seismic waves travel, which is crucial to understanding the propagation properties of seismic waves in underground structures.

[0075] The feature analysis submodule determines the energy peak of the differentiated type of seismic waves based on the seismic wave type data, evaluates the frequency characteristics of the waveform by identifying the frequency distribution, quantifies the velocity characteristics, and obtains the seismic wave feature distribution map. The execution process is as follows;

[0076] Based on the seismic wave type data, the energy peak of differentiated types of seismic waves is determined, and the frequency characteristics of the waveform are evaluated by identifying the frequency distribution. The key to the process is to accurately identify and quantify the frequency characteristics of different types of seismic waves. By analyzing the seismic wave type data collected by the equipment, the frequency characteristics of different seismic waves are identified using spectrum analysis technology. The determination of energy peaks involves signal processing technology, such as Fourier transform, which can convert seismic signals in the time domain into frequency domain signals to reveal the energy size of different frequency components. For the quantification of velocity characteristics, it is necessary to combine the velocity and frequency data of the seismic waves and describe the relationship through mathematical modeling. It not only provides a basic description of seismic waves, but also reveals the propagation characteristics of seismic waves in different geological structures, and obtains a seismic wave characteristic distribution map.

[0077] The data integration submodule uses the seismic wave characteristic distribution map to perform data integration on the analysis results. By integrating and synchronizing multiple characteristic parameters, the execution process of obtaining the seismic waveform characteristic record is as follows;

[0078] The analysis results are integrated using the seismic wave characteristic distribution map. During the process, data from different seismic monitoring stations are checked and compared to ensure data consistency and accuracy, and various seismic wave characteristic parameters are integrated, including amplitude, frequency, speed, etc. The techniques used in the data integration process include statistical analysis and pattern recognition methods, which can effectively extract and reorganize information from multi-source data, not only reflecting the characteristics of a single earthquake event in detail, but also providing a scientific basis for future earthquake predictions. The integrated data is an indispensable foundation for earthquake analysis and research, and a record of seismic waveform characteristics is obtained.

[0079] See also Figure 2 , 5 , the disaster pattern recognition module includes:

[0080] The signal filtering and analysis submodule uses the earthquake waveform feature record to perform signal filtering, remove noise interference, perform time and frequency analysis, extract the amplitude, frequency and duration of the earthquake, and obtain the execution process of the earthquake wave signal analysis results as follows;

[0081] Seismic waveform feature records are used to filter signals and remove noise interference. The primary task in the signal filtering process is to effectively remove noise, which is achieved by setting filter parameters, such as selecting a suitable cutoff frequency to suppress the frequency band of non-seismic signals. Time and frequency analysis is performed using advanced signal processing techniques, such as fast Fourier transform (FFT), which is used to convert seismic signals from the time domain to the frequency domain, so that the frequency components of the signal can be more clearly observed. After performing these analyses, key parameters of the earthquake such as amplitude, frequency and duration can be extracted. These data are crucial for assessing the strength and potential destructiveness of an earthquake. The entire process involves not only the application of signal theory, but also strict requirements for data accuracy and availability to obtain seismic wave signal analysis results.

[0082] The disaster type comparison submodule uses the seismic wave signal analysis results and the convolutional neural network technology to compare the waveform spectra of known tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes, and performs feature comparison and image matching of seismic waveforms to obtain the waveform difference analysis results. The execution process is as follows;

[0083] The formula for the convolutional neural network technique is as follows:

[0084]

[0085] Among them, Sim(A,B) is the fitting score of the tectonic earthquake and the comparative earthquake waveforms, represents the ith eigenvalue of the tectonic earthquake waveform, represents the ith eigenvalue of the comparative earthquake waveform (including tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes), w i represents the weight of the i-th feature, v i is the adjustment coefficient for normalizing the waveform data, and n is the total number of features.

[0086] Consider the following eigenvalue and weight data, obtained based on real-world testing:

[0087] The characteristic values ​​of tectonic earthquakes, such as magnitude, frequency and amplitude, were measured as: 3.0, 15Hz, 200m / s.

[0088] The characteristic values ​​of volcanic earthquakes are measured as: 2.8, 20Hz, 180m / s.

[0089] w i : The weight of each feature is set by the expert team according to the importance of the feature in earthquake identification, and the values ​​are: 0.5, 0.3, 0.2.

[0090] v i : Adjustment coefficient, determined by previous experiments, used to control the impact of differences in the magnitude of eigenvalues, with values ​​of 1.0, 0.8, and 0.6.

[0091] An example of calculating goodness of fit analysis is as follows:

[0092] 1. Feature difference calculation:

[0093]

[0094] 2. Weighted Feature Differences:

[0095]

[0096] 3. Adjustment of the sum of squares of eigenvalues:

[0097]

[0098] 4. Goodness of fit calculation:

[0099]

[0100] The results show that tectonic earthquakes and volcanic earthquakes have a low degree of fit in the selected features. The numerical results show that the two types of earthquakes differ significantly in frequency, amplitude and magnitude, which further verifies the accuracy and necessity of earthquake type identification.

[0101] Based on the waveform difference analysis results, the type identification submodule iteratively analyzes the signs and characteristic indicators of the earthquake waveform, distinguishes the disaster types associated with real-time earthquake activities, identifies the occurrence categories of disasters, and obtains the disaster type diagnosis results. The execution process is as follows;

[0102] Based on the results of waveform difference analysis, the signs and characteristic indicators of earthquake waveforms are iteratively analyzed. The type identification process involves complex data analysis and pattern recognition technology, in which each sign and characteristic indicator of the earthquake waveform needs to be accurately identified and classified. The use of machine learning algorithms, such as support vector machines or decision trees, can effectively distinguish different types of earthquake activities and associated disasters. This analysis not only relies on direct observation of earthquake waveforms, but also includes data evaluation methods including statistics and probability theory. Through these methods, potentially destructive types of earthquake activities can be identified, providing a scientific basis for the formulation of disaster management and response plans, and having important practical significance for the optimization of disaster warning devices and emergency response measures, and obtaining disaster type diagnosis results.

[0103] See also Figure 2 , 6 , the early warning signal building blocks include:

[0104] The urgency assessment submodule uses the disaster type diagnosis results to analyze the type and intensity of earthquake activity, assess the urgency of earthquake events, determine the time of alarm issuance, and obtain the urgency rating data. The execution process is as follows;

[0105] Using the results of disaster type diagnosis, by analyzing the type and intensity of earthquake activity, the analysis mainly relies on the accurate identification of earthquake types and the assessment of the intensity of the impact caused. The formulation of the urgency rating involves many considerations, including the scale and depth of the earthquake and its performance in the prediction data. The time to issue the alarm is determined based on the urgency and potential impact of the earthquake. By using earthquake event data models and real-time monitoring systems, the urgency of earthquake events can be quickly assessed and an effective alarm response time can be formulated. The entire process requires a high degree of data accuracy and fast processing capabilities to ensure a rapid response after an earthquake occurs and obtain emergency rating data.

[0106] The geographic scope analysis submodule analyzes and predicts the geographic scope of earthquake impact based on the urgency rating data, applies geographic information system and population density data, and obtains the analysis results of the impact area by referring to the potential degree of hazard and the geographical characteristics of the affected area. The execution process is as follows;

[0107] Based on the emergency rating data, the application of geographic information system and population density data, geographic information system (GIS) provides a powerful platform for analyzing and visualizing geographic data related to earthquake impact. Combined with population density data, the specific geographical scope of earthquake impact can be effectively predicted. This analysis takes into account the geographical characteristics of the earthquake center area and the residential density of the surrounding areas. By simulating the propagation and impact of earthquake waves, the size of the affected area and the degree of population impact are assessed. Referring to the potential degree of harm and the geographical characteristics of the affected area is also an important part of the analysis. This information helps decision makers understand the areas where rescue and early warning measures need to be prioritized, and obtain the results of the affected area analysis.

[0108] The warning signal issuing submodule uses the analysis results of the impact area, combines the earthquake intensity and the predicted degree of damage, checks the consistency of the information, issues a disaster warning signal, and obtains the execution process of the disaster warning information as follows;

[0109] Using the results of the impact area analysis, combined with the earthquake intensity and the predicted degree of hazard, checking the consistency of information is the key to the process, ensuring that all analysis data support the decision to issue a warning signal. The warning device must respond quickly and be able to process and transmit key information in a short time. By comprehensively considering the intensity of the earthquake, potential impact and urgency rating, the specific content and level of the warning signal can be determined, and a disaster warning signal can be issued. The purpose of this operation is to minimize casualties and property losses caused by the earthquake, ensure that the alarm information is conveyed to all potentially affected areas in a timely and accurate manner, and obtain disaster warning information.

[0110] See also Figure 2 , 7 , the risk determination module includes:

[0111] The demand analysis submodule uses disaster warning information and a multi-criteria decision-making method to assess the risk level of the affected area, analyze the impact of the earthquake event, and obtain the execution process of the initialized risk level data as follows;

[0112] The formula of multi-criteria decision-making method is as follows:

[0113]

[0114] Among them, R z is the risk level data value, S z Represents the earthquake intensity, P z represents population density, E z represents the intensity of economic activities, W z Represents early warning response capability.

[0115] The specific parameters are obtained as follows:

[0116] Sz (Earthquake intensity) Data collected by earthquake monitoring stations, such as the Richter scale. In this example, an earthquake in a certain area has a Richter magnitude of 6.0.

[0117] P z Population density is obtained from demographic data and expressed as the number of people per square kilometer. In this example, the population density of a certain urban area is 1500 people per square kilometer.

[0118] E z (Economic intensity) is assessed by the distribution of regional GDP and major economic activities, converted into US dollars per square kilometer of economic output. In this example, the economic intensity is 20 million US dollars per square kilometer.

[0119] W z (Early Warning Response Capability) is expressed as a percentage by evaluating the coverage rate and response time of the early warning devices in the region. The efficiency of the early warning devices is set at 80%.

[0120] The parameter values ​​are obtained from the respective official data and statistical reports, ensuring the actuality and reliability of the data. The specific calculation process of the formula is as follows:

[0121] Calculate S z ×P z ×E z =6.0×1500×20000000=1800000000000.

[0122] Take the absolute value and take the square root

[0123] Divide by W z ,Right now

[0124] This result R z =5303.3 represents the risk level calculated based on the current earthquake intensity, population density, economic activity intensity, and early warning response capabilities. This high value indicates that the region faces a higher risk in the event of an earthquake due to high economic activity intensity and high population density, even with better early warning devices. Through this quantitative analysis, emergency response departments can be more specifically guided in resource allocation and action plans when disasters occur, ensuring that the impact of disasters is minimized.

[0125] The impact degree calculation submodule calculates the expected impact degree based on the initial risk level data, including potential casualties, property losses and economic impacts, and conducts a quantitative assessment of the risk. The execution process of obtaining the potential loss assessment result is as follows;

[0126] Based on the initialized risk level data, the expected impact is calculated. The calculation involves several key steps, which is a detailed analysis of the initialized risk level data, including a comprehensive assessment of the population density, building structure strength and geographical environment of the earthquake-prone area. Through these data, the potential number of casualties, property losses and the approximate range of economic impact can be predicted. Risk quantification assessment uses statistical methods and predictive models, such as probability theory and decision analysis techniques. These techniques can effectively convert earthquake risks into specific values ​​to help decision makers better understand and respond to disasters. Through a comprehensive assessment of different risk factors, it provides important data support for subsequent risk management and emergency response, and obtains potential loss assessment results.

[0127] The risk measures construction submodule uses the potential loss assessment results, comprehensive risk level and loss assessment, to provide emergency response measures for decision makers and emergency management departments. The execution process of obtaining the risk level analysis results is as follows;

[0128] Utilizing the potential loss assessment results, comprehensive risk level and loss assessment, the key process is to formulate specific emergency response measures based on the assessment results. By considering the severity and probability of potential losses, a series of risk control and mitigation measures are provided to decision makers and emergency management departments. These measures include emergency evacuation plans, emergency rescue material preparation and public information release strategies. Each measure is optimized for specific risk levels to ensure that it can effectively respond to earthquake disasters of varying degrees. In this way, it not only provides decision-making basis for decision makers, but also ensures that emergency management departments can take quick and effective action when an earthquake occurs, minimize the impact of the disaster and obtain risk level analysis results.

[0129] See also Figure 2 , 8 , the early warning information release module includes:

[0130] The action plan customization submodule uses the risk level analysis results to design customized warning information, including alert levels and action plans, based on the needs and risk levels of differentiated regions and institutions, and checks the pertinence and effectiveness of the information. The execution process of customized warning messages is as follows;

[0131] Using the results of risk level analysis, customized warning information is designed according to the needs and risk levels of differentiated regions and institutions. The design process involves a detailed assessment of the specific needs of each region and institution, including consideration of factors such as geographical location, population density, and infrastructure strength. Based on these data, different alert levels and specific action plans are customized to ensure the pertinence and effectiveness of warning information. This process uses advanced data analysis technology to optimize the relevance and urgency of information. The design of warning messages not only reflects the specific needs of the region, but also takes into account the worst-case scenario, so that targeted responses can be made when actual disasters occur, resulting in customized warning messages.

[0132] The measures guidance submodule plans emergency response measures based on customized warning messages, including evacuation routes, safety protection and resource allocation, and provides resource allocation guidance based on regional conditions. The execution process of the emergency response guidance plan is as follows;

[0133] Based on customized early warning messages, emergency response measures are planned. The focus of the work is to formulate corresponding emergency measures according to the specific content of the early warning messages, including the design of evacuation routes, the arrangement of safety protection measures and the allocation of necessary resources. In combination with the specific conditions of the region, resource allocation guidance is provided, such as determining which areas need to be evacuated first, the remaining key facilities need to be strengthened, and how to effectively allocate rescue personnel and materials. Geographic information systems and human resource management tools are used to ensure that the implementation of emergency response measures is both scientific and efficient, provide guidance and support for disaster response, enhance the overall coordination and execution of disaster response, and obtain emergency response guidance plans.

[0134] The plan implementation submodule implements the emergency response guidance plan, records the issued warning information and emergency measures, conducts real-time tracking feedback and evaluates the effectiveness of the emergency plan, and obtains the execution process of the disaster warning log as follows;

[0135] The key to implementing the emergency response guidance plan is to monitor and manage the release of warning information and the implementation of emergency measures in real time. By setting up a special monitoring center to track the implementation effect of emergency measures and the response of the public, real-time feedback is provided to adjust and optimize the response strategy in a timely manner. Through real-time data analysis and ground feedback, the effectiveness of implemented measures is evaluated to ensure that each measure can achieve the expected goals. All relevant disaster warning and emergency activities are recorded to provide valuable data and experience for future disaster management and improvement of warning devices, and obtain a disaster warning log.

[0136] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A geological disaster early warning device based on earthquake waveform recognition, characterized in that: The device comprises: The seismic data receiving module collects the original seismic data from the seismic monitoring site, including the frequency, amplitude and velocity change information, marks the time and location of the earthquake, and generates the original waveform data set; The seismic wave type analysis module uses the original waveform data set to identify the types of differentiated seismic waves, including body waves and surface waves, analyze the energy, frequency and velocity characteristics of the seismic waves, and obtain seismic waveform feature records; The disaster pattern recognition module uses the earthquake waveform feature records to compare the waveform spectra of differentiated geological disasters, including tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes, to identify the disaster types associated with real-time earthquake activities and generate disaster type diagnosis results; The early warning signal construction module evaluates the urgency and impact range of the earthquake event according to the disaster type diagnosis result, and obtains disaster early warning information in combination with the earthquake intensity and potential harm degree; The risk determination module analyzes the disaster warning information, determines the risk level of the affected area, calculates the expected impact and potential losses, and obtains the risk level analysis results; The warning information release module uses the risk level analysis results to send customized warning information to regions and institutions, including emergency response measures and resource allocation guidance, to obtain a disaster warning log.

2. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The original waveform data set includes the timestamp, location coordinates, frequency change sequence, amplitude change sequence and velocity change information of the earthquake; the earthquake waveform feature record includes the identification of differentiated types of seismic waves, key frequency characteristics of seismic waves, amplitude range and propagation speed; the disaster type diagnosis results include tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes; the disaster warning information includes the emergency level of the earthquake event, the expected impact range and the potential degree of harm; the risk level analysis results include the risk level of the affected area, the expected degree of impact and the estimate of potential losses; the disaster warning log includes the content of the warning information that has been sent, the list of receiving regions and institutions, emergency response measures and resource allocation guidance.

3. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The seismic data receiving module comprises: The data collection submodule captures the frequency change, amplitude change and speed change during earthquake through the sensor interface of the seismic monitoring station, performs initialization filtering and amplification of the signal through the real-time data stream of the data interface, and obtains the original seismic signal data; The time-stamping submodule uses the raw seismic signal data to timestamp the data, encodes it according to the time and location of the earthquake, records the source and time of each data packet, and generates labeled seismic data; The data synchronization submodule uses the marked earthquake data to construct an earthquake waveform curve, integrates and synchronizes the waveform data, and obtains an original waveform data set.

4. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The seismic wave type analysis module includes: The propagation rate identification submodule uses the original waveform data set to identify body waves and surface waves in the waveform through frequency characteristic analysis, classifies the waveform based on the amplitude, and determines the wave propagation rate to obtain seismic wave type data; The feature analysis submodule determines the energy peak of the differentiated type of seismic wave based on the seismic wave type data, evaluates the frequency characteristics of the waveform by identifying the frequency distribution, quantifies the velocity characteristics, and obtains a seismic wave feature distribution diagram; The data integration submodule uses the seismic wave characteristic distribution map to perform data integration on the analysis results, and obtains seismic waveform characteristic records by integrating and synchronizing multiple characteristic parameters.

5. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The disaster pattern recognition module comprises: The signal filtering and analysis submodule uses the earthquake waveform characteristic record to perform signal filtering, remove noise interference, perform time and frequency analysis, extract the amplitude, frequency and duration of the earthquake, and obtain the earthquake wave signal analysis result; The disaster type comparison submodule adopts the seismic wave signal analysis results and the convolutional neural network technology to compare the waveform spectra of known tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes, perform feature comparison and image matching of seismic waveforms, and obtain waveform difference analysis results; Based on the waveform difference analysis results, the type identification submodule iteratively analyzes the signs and characteristic indicators of the earthquake waveform, distinguishes the disaster types associated with real-time earthquake activities, identifies the occurrence categories of disasters, and obtains disaster type diagnosis results.

6. The geological disaster early warning device based on earthquake waveform recognition according to claim 5 is characterized in that: The formula of the convolutional neural network technique is as follows: Among them, Sim(A,B) is the fitting score of the tectonic earthquake and the comparative earthquake waveforms, represents the ith eigenvalue of the tectonic earthquake waveform, represents the ith eigenvalue of the comparative earthquake waveform (including tectonic earthquakes, volcanic earthquakes, collapse earthquakes and artificial earthquakes), w i represents the weight of the i-th feature, v i is the adjustment coefficient for normalizing the waveform data, and n is the total number of features.

7. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The early warning signal building module includes: The urgency assessment submodule adopts the disaster type diagnosis result, analyzes the type and intensity of earthquake activity, assesses the urgency of the earthquake event, determines the time of issuing the alarm, and obtains the urgency rating data; The geographic scope analysis submodule analyzes and predicts the geographic scope of earthquake impact based on the urgency rating data, applies geographic information system and population density data, and obtains the analysis results of the impact area by referring to the potential degree of hazard and the geographic characteristics of the impact area; The early warning signal issuing submodule uses the analysis results of the impact area, combines the earthquake intensity and the predicted degree of harm, checks the consistency of the information, issues a disaster early warning signal, and obtains disaster early warning information.

8. The geological disaster early warning device based on earthquake waveform recognition according to claim 1 is characterized in that: The risk determination module comprises: The demand analysis submodule uses the disaster warning information and a multi-criteria decision-making method to assess the risk level of the affected area, analyze the impact of the earthquake event, and obtain the initial risk level data; The impact degree calculation submodule calculates the expected impact degree based on the initialized risk level data, including potential casualties, property losses and economic impacts, conducts quantitative assessment of the risks, and obtains potential loss assessment results; The risk measures construction submodule utilizes the potential loss assessment results, integrates the risk level and loss assessment, provides emergency response measures for decision makers and emergency management departments, and obtains risk level analysis results.

9. The geological disaster early warning device based on earthquake waveform recognition according to claim 8 is characterized in that: The formula of the multi-criteria decision-making method is as follows: Among them, R z is the risk level data value, S z Represents the earthquake intensity, P z represents population density, E z represents the intensity of economic activity, W z Represents early warning response capability.

10. The geological disaster early warning device based on earthquake waveform recognition according to claim 1, characterized in that: The warning information release module includes: The action plan customization submodule uses the risk level analysis results to design customized warning information, including alert levels and action plans, based on the needs and risk levels of differentiated regions and institutions, and checks the pertinence and effectiveness of the information to obtain customized warning messages; The measures guidance submodule plans emergency response measures based on the customized warning message, including evacuation routes, safety protection and resource allocation, and provides resource allocation guidance in combination with regional conditions to obtain an emergency response guidance plan; The program implementation submodule implements the emergency response guidance program, records the issued warning information and emergency measures, conducts real-time tracking feedback and evaluates the effectiveness of the emergency plan, and obtains a disaster warning log.

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