Method for monitoring infrastructure, venue and human health and safety using seismic sensors

Abstract

The invention discloses a system, a method and a workflow for real-time monitoring of modern city infrastructures. A computer-implemented method; a computer software product; and a machine learning model based on the seismic data. The infrastructure comprises bridges, highway tunnels, airports, buildings and wind power towers. Monitoring is not limited to recorded seismic data. The disclosure also emphasizes the importance of monitoring various key infrastructures using seismic sensors, including security sites and manufacturing facilities, to enhance security and security capabilities, detect infrastructure health, and prevent unauthorized intrusion. In addition, the seismic sensor can also be used for indoor human activity monitoring. Bridge monitoring relates to continuous evaluation to ensure structural integrity and prolong service life; highway tunnel monitoring focuses on optimizing traffic conditions and monitoring structure images. The airport monitoring is beneficial to improving the runway safety, ensuring the optimal performance of the aviation infrastructure, reducing the potential risk to the greatest extent, and improving the overall safety and efficiency of aviation travel. By deploying the seismic sensor to monitor the infrastructure of the building, the potential risk of the building structure can be detected and positioned. Monitoring of a wind power generation tower is crucial to optimization of green energy production and guarantee of structural health of a fan. The security and manufacturing place monitoring aims to guarantee the security and security of facilities and prevent unauthorized invasion. By monitoring and analyzing seismic data of indoor human activities, different types of activities can be identified, and events such as walking and falling down can be detected. By using seismic sensors and processing seismic data in real time, these monitoring systems jointly improve the security, security, toughness and reliability of key infrastructures in different fields.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to the field of data processing, and in particular, to a method, system, electronic device, non-transitory computer-readable storage medium storing instructions, and a computer program product for monitoring infrastructure, site facilities, and human activities using time series data, such as seismic data generated by seismic waves. BACKGROUND

[0002] Seismic sensors, also known as seismometers, are important instruments for monitoring and understanding the dynamic behavior of the Earth. These devices are capable of detecting and recording ground motion caused by seismic waves, providing critical data for seismic research, earthquake detection, and tectonic studies. Seismic sensor data plays a vital role in accurately locating epicenters, determining earthquake magnitudes, developing earthquake warning systems, making seismic predictions, and inferring the internal structure of the Earth.

[0003] In addition to their use in seismic research, seismic sensors are widely used in the exploration of Earth's resources, such as searching for valuable resources such as underground minerals, coal, water resources, and hydrocarbons. In mineral exploration, seismic exploration can identify geological structures and strata to indicate potential economically viable mineral deposits. These surveys can provide detailed information about the underground structure, helping geologists understand the composition and potential yield of resources. In oil and gas exploration, seismic surveys use one or more controlled sources and receivers to collect phase and amplitude information of seismic waves. The sources and receivers are arranged at regular or irregular locations on the surface. By analyzing seismic waves, geophysicists can image underground structures and medium velocities, identify potential hydrocarbon reservoirs, and provide guidance for drilling site selection.

[0004] In recent years, seismic sensors have been deployed alongside roads to monitor ground motion caused by passing vehicles as passive sources. By analyzing seismic data collected during vehicle passage, traffic flow information such as vehicle speed, weight, number of axles, position, lane position, and driving patterns can be obtained. In addition, seismic data can also be used to monitor periodic changes in road surface conditions and underground structures for risk assessment.

[0005] Seismic sensors can also be installed as a network to monitor changes in infrastructure or sites such as bridges, tunnels, airports, buildings, wind turbines, security sites, production facilities, environmental sites, and human activities. By analyzing seismic data in real-time monitoring systems, potential dangers, accidents, or emergencies can be detected early, allowing timely intervention and reducing the likelihood of serious accidents to infrastructure and personnel.

[0006] Bridge monitoring involves the use of various technologies and methods to assess and ensure the structural health, safety, and performance of bridges. Bridge monitoring is crucial for early detection of potential problems, preventing structural failures, and extending the service life of critical infrastructure. Commonly used sensors and instruments for bridge monitoring include accelerometers, strain gauges, inclinometers, and temperature sensors, which can help collect data on the response of bridges to external factors.

[0007] Installing seismic sensors on bridges can collect traffic vibration data, allowing for the monitoring of traffic information, including vehicle speed, weight, lane position, and more, and helping to identify unusual situations, such as a vehicle suddenly stopping, a pedestrian, or a traffic accident. The same data can also be used to monitor the vibrations of the bridge caused by passing vehicles. By analyzing the unusual vibration response of vehicles, it is possible to detect cracks in the bridge, deformation of the substructure, or other structural issues.

[0008] Signal processing of the data can extract surface and coda waves, which can be used to generate images of the velocity variations in the medium of the bridge, allowing for real-time health diagnosis of the bridge. The extracted surface and coda waves can also be used to generate images of the subsurface structures surrounding the bridge. By removing the Green's function, it is also possible to extract the vehicle source response, quantifying the friction conditions between the tires and the bridge deck. Implementing a real-time monitoring system based on seismic data using the above capabilities can allow management to receive immediate alerts in the event of an anomaly or critical situation, allowing for timely intervention and preventing dangerous situations.

[0009] Highway tunnels are an essential part of modern transportation infrastructure, allowing vehicles to pass through complex environments. In addition to traffic violations, highway tunnels present challenges such as limited visibility, potential congestion, and the risk of accidents or emergencies. Therefore, monitoring systems are needed to identify and mitigate potential dangers, ensuring the safety of drivers and passengers. Existing tunnel monitoring systems typically include surveillance cameras, traffic and vehicle monitoring sensors, environmental monitoring systems, fire detection and extinguishing devices, and more.

[0010] In addition to existing visual equipment, integrating seismic sensors into tunnel monitoring systems can further enhance their ability to detect and respond to abnormal events, providing a safer and more efficient experience for all tunnel users. Seismic systems should be able to reflect the traffic flow in real-time within the tunnel, identifying vehicle violations, unusual driving, pedestrians, falling objects, and road conditions. In addition, seismic data can be used to generate images of the structures below the road and behind the tunnel walls, as well as to monitor changes in the medium velocity of the tunnel structure. Real-time monitoring can allow for early detection of potential dangers, accidents, or emergencies, allowing for timely intervention and reducing the likelihood of serious incidents.

[0011] Airport monitoring is critical for ensuring flight safety and operational efficiency during key phases such as aircraft taxi, takeoff, and landing. Radar and camera systems continuously track aircraft positions, ensuring safe separation and optimal traffic flow. Foreign object debris (FOD) detection is another critical aspect of airport safety. FOD refers to any object, substance, or debris that is present in an inappropriate location on the airport runway, taxiway, or apron. FOD detection is crucial for preventing aircraft damage and ensuring safe takeoff and landing. Common detection and removal methods include manual visual inspection and cleanup, bird and wildlife detection systems, and automated FOD detection systems.

[0012] Bird and wildlife detection systems use radar, acoustic sensors, and cameras to monitor birds and wildlife near the runway. Automated FOD detection systems use advanced technologies such as radar, infrared sensors, cameras, and laser scanners to identify and locate debris on the runway.

[0013] Installing seismic sensors and analyzing the collected data can assist in airport monitoring in the following areas: (1) mapping and tracking aircraft positions on the ground at any time, regardless of weather and lighting conditions; (2) quantifying aircraft landing and takeoff speeds and their impact on the ground; (3) monitoring objects dropped during aircraft takeoff; (4) monitoring and mapping airport ground vehicle positions; (5) detecting and locating airport ground human activity; (6) detecting human or animal intrusions on the airport ground; and (7) monitoring long-term changes in the airport ground and shallow subsurface.

[0014] Monitoring changes in subsurface geophysical conditions can enable early risk detection, helping to prevent potential accidents. Combining traditional FOD detection systems with seismic sensors can provide real-time alerts, rapid debris removal, optimized maintenance schedules, and reduced accident risk.

[0015] Building monitoring plays a critical role in ensuring safety and security in various environments such as residential, commercial complexes, and industrial facilities. Building collapse incidents have occurred. Effective monitoring systems aim to identify potential risks, reduce hazards, and respond quickly to emergencies. With technological advancements, traditional building monitoring systems have integrated various devices, including surveillance cameras, smoke detectors, fire alarms, motion sensors, and environmental sensors for personnel safety.

[0016] For large and complex structures, monitoring the health of the building itself is particularly important. Traditional structural health monitoring systems typically use sensors to assess vibrations, displacements, and stresses of building components. As a passive seismic source, ambient building noise generates ground vibrations that are recorded by seismic sensors. By deploying seismic sensors on and around the building, abnormal noise, fractures, collapses, or displacements of the building structure can be monitored and located.

[0017] By correlating data from different seismic sensors, changes in building structures can be identified and potential risks reported. Combining machine learning (ML) techniques with seismology further enhances the capabilities of real-time building monitoring systems. These systems can learn and adapt, recognizing normal response patterns and quickly identifying anomalies that may indicate safety risks, allowing for a rapid response to potential threats and reducing the likelihood of incidents.

[0018] Wind turbines are integral components of green energy systems and symbols of sustainable power generation. These towering structures harness the kinetic energy of the wind and convert it into electrical energy. Due to wind forces and mechanical operation, wind turbine towers are subjected to dynamic and varying loads. Efficient and reliable monitoring of wind turbine towers is crucial for optimizing their performance, ensuring structural integrity, and promoting the sustainability of wind energy. By installing seismic sensors at multiple locations on the wind turbine tower and turbine, data correlation can be used to infer structural changes over time and identify anomalies. Monitoring the structural health of wind turbine towers is essential for ensuring their long-term viability. Continuous assessment of tower vibrations, strains, and fatigue helps identify potential weak points or stress concentration areas. The use of seismic sensors further enhances the ability to detect and address structural issues in a timely manner, contributing to the overall sustainability of renewable energy.

[0019] Secure sites are specific areas or facilities where comprehensive security measures are implemented to protect assets, personnel, and information from physical threats. Such sites can include commercial buildings, government facilities, critical infrastructure sites, and data centers. To prevent unauthorized entry, theft, and other forms of physical intrusion, surveillance cameras, motion sensors, and intrusion detection systems are commonly used. However, these traditional monitoring systems have drawbacks, such as being easily discovered by intruders and potentially having blind spots. In contrast, seismic sensors are buried underground and are invisible to intruders, allowing them to record ground vibrations caused by intruder footsteps, thereby enabling intrusion monitoring. By combining seismic sensors with traditional monitoring systems, an enhanced real-time monitoring system can be constructed, helping security personnel identify anomalies and proactively address potential threats. Additionally, the integration of artificial intelligence and data analysis techniques can further enhance the monitoring process, enabling the system to recognize patterns, detect abnormal behavior, and trigger alerts.

[0020] A manufacturing site refers to a physical location or facility where industrial processes are carried out, enabling mass production of products. Implementing robust security measures and monitoring systems at manufacturing sites is crucial for overall safety, preventing unauthorized access, and protecting critical assets. In traditional approaches, a comprehensive security network is often built by deploying surveillance cameras, access control systems, and perimeter sensors. Seismic sensors, as a low-cost solution, can be installed both inside and around the facility to record ground vibrations caused by human activities or intruders' footsteps. The collected seismic data can also be used to monitor the safety of the facility structure and changes in underground structures. By applying advanced machine learning techniques for footstep detection, any unauthorized activity can be identified in real-time, triggering a rapid response mechanism to address potential security threats. Additionally, within the manufacturing site, machine learning algorithms analyzing seismic data can help identify unsafe working environments, track employee movements, and detect abnormal activities, triggering real-time alerts or automated safety protocols. Through continuous monitoring of manufacturing sites day and night, businesses can enhance overall security, protect sensitive information and property, and maintain operational integrity.

[0021] Indoor human activity monitoring involves the use of various technological tools and systems to observe, analyze, and ensure the safety and efficiency of enclosed spaces. Common monitoring methods include surveillance cameras, motion sensors, and smart devices that track indoor personnel movements, interactions, and other behaviors. However, the use of these traditional monitoring devices, such as cameras, often raises concerns about potential violations of privacy laws. With the development of facial recognition technology, these concerns are further exacerbated, as the technology can identify and track individuals' activities without their knowledge or consent. To overcome these challenges, seismic sensors can be employed to monitor indoor human activities while respecting privacy. Seismic sensors can detect vibrations caused by human movement or activity, providing a non-intrusive monitoring method. Unlike traditional approaches, seismic sensors offer a means of protecting privacy. By analyzing seismic data related to human activity, monitoring can be achieved without infringing on individuals' privacy rights. Using machine learning to analyze seismic data, activities within a room can be identified in real-time, including fall detection. When an event occurs, the seismic sensor real-time detection system can automatically trigger an alarm, notifying caregivers, medical personnel, or relevant agencies, enabling a rapid response that can even save lives. SUMMARY

[0022] The first aspect of the present application provides a method for bridge monitoring based on seismic data, which can include:

[0023] • obtaining seismic data from a seismic recording device;

[0024] • processing the seismic data; and

[0025] • monitoring the bridge based on the seismic data.

[0026] According to the first aspect, the seismic recording devices can be arranged at one side of the bridge, at both sides of the bridge, at the middle of the bridge, or any combination thereof, with fixed or variable spacing. According to the first aspect, the processing of the seismic data can include performing signal enhancement on the seismic data. According to the first aspect, the processing of the seismic data can further include performing trace equalization on the seismic data. According to the first aspect, the monitoring of the bridge based on the seismic data can include:

[0027] • performing bridge resonance frequency detection processing; and

[0028] • calculating bridge load based on traffic flow data.

[0029] A second aspect of the present application provides a method for monitoring bridge structural changes based on seismic data, which can include:

[0030] • obtaining target seismic data and reference seismic data from seismic recording devices;

[0031] • generating target Green's functions based on the target seismic data;

[0032] • generating reference Green's functions based on the reference seismic data;

[0033] • detecting bridge structural changes based on the target Green's functions and the reference Green's functions.

[0034] According to the second aspect, the method can further include performing signal preprocessing, denoising, resampling, data drift correction, and filtering on the target seismic data and the reference seismic data. According to the second aspect, the filtering includes multi-frequency band range pass filtering, where ; and are the lower and upper limits of the frequency range of the seismic recording devices, respectively. According to the second aspect, the method can further include:

[0035] • extracting one or more of body waves (including elastic P waves, S waves, SH waves), surface waves, coda waves, Rayleigh waves, and Love waves from the target seismic data and the reference seismic data;

[0036] • generating the reference Green's functions and the target Green's functions based on the extracted waves.

[0037] According to the second aspect, the detecting of the bridge structural changes can include generating bridge relative velocity changes based on the target Green's functions and the reference Green's functions using an ambient noise imaging method.

[0038] The third aspect of the present application provides a method for detecting and locating cracks and voids in bridge structures, which can include:

[0039] • a deconvolution imaging method; and

[0040] • a time-reversal focusing imaging method.

[0041] According to the second aspect, the method can also be applied to near-surface structure change monitoring based on seismic data.

[0042] The fourth aspect of the present application provides a method for monitoring underground structures (including highway tunnels) using seismic data, which can include:

[0043] • obtaining seismic data from a seismic recording device;

[0044] • processing the seismic data;

[0045] • generating a target Green's function based on the seismic data;

[0046] • generating a reference Green's function based on the seismic data;

[0047] • generating an underground structure change based on the target and reference Green's functions.

[0048] According to the fourth aspect, the seismic recording device can be arranged on one side of the tunnel, on both sides of the tunnel, on the side wall of the tunnel, or any combination thereof, with a fixed or variable spacing. According to the fourth aspect, the method can further include performing signal preprocessing, denoising, resampling, data drift correction, filtering, multi-frequency range bandpass filtering, Fourier and inverse Fourier transform on the target and reference seismic data. According to the fourth aspect, generating an underground structure change can include:

[0049] • generating a relative velocity change behind the tunnel side wall based on the target and reference Green's functions using an ambient noise imaging method;

[0050] • generating a relative velocity change behind the tunnel side wall based on the target and reference Green's functions using an ambient noise imaging method;

[0051] According to the fourth aspect, the method can further include:

[0052] • detecting and locating near-surface cracks and voids in the tunnel using a deconvolution focusing beam method;

[0053] • detecting and locating near-surface cracks and voids in the tunnel using a time-reversal focusing method;

[0054] • detecting and locating cracks and voids in the tunnel side wall using a deconvolution focusing beam method;

[0055] • Detect and locate tunnel side wall cracks and voids using time reversal focusing method.

[0056] Fifth aspect

[0057] The fifth aspect of the present application provides a method for airport monitoring using seismic data, the method comprising:

[0058] • obtaining seismic data from seismic recording devices;

[0059] • processing the seismic data; and

[0060] • monitoring the airport based on the seismic data.

[0061] According to the fifth aspect, wherein the seismic recording devices are placed at one side of the runway and taxiway, both sides of the runway and taxiway, the apron, or any combination thereof, with fixed or variable spacing.

[0062] According to the fifth aspect, wherein the processing of seismic data includes: detrending; noise removal or attenuation; gather balancing; gather normalization; filtering; bandpass filtering; Fourier transform and its inverse.

[0063] Sixth aspect

[0064] The sixth aspect of the present application also provides a method for airport monitoring, the method further comprising:

[0065] • monitoring and evaluating the health of the runway and taxiway;

[0066] • detecting and locating foreign objects (FOD);

[0067] • detecting and locating dropped luggage; and

[0068] • monitoring the takeoff and landing of aircraft.

[0069] According to the sixth aspect, wherein monitoring and evaluating the health of the runway and taxiway includes:

[0070] • obtaining target seismic data and reference seismic data from seismic recording devices;

[0071] • pre-processing the seismic data;

[0072] • generating a target Green's function based on the target seismic data;

[0073] • generating a reference Green's function based on the reference seismic data;

[0074] • Generating relative velocity changes using target and reference Green's functions to monitor changes in the health of the geology under the runway and taxiway using ambient noise imaging methods. The ambient noise imaging methods include: moving window cross-spectral method, coda wave interferometry method, ambient noise tomography method.

[0075] According to the sixth aspect, wherein detecting and locating foreign object debris (FOD) includes one or more of:

[0076] • Multi-band pass filtering;

[0077] • Determining an amplitude attribute over a specified time window at a given frequency range;

[0078] • Determining a zero-crossing attribute over a specified time window;

[0079] • Calculating a FOD attribute from the ratio of the amplitude attribute to the zero-crossing attribute;

[0080] • Determining a FOD signal when the FOD attribute exceeds a threshold value;

[0081] • Determining a FOD location using detected FOD signals from multiple sensors and a back-projection focused beam method;

[0082] • Determining a location with a maximum stack value using detected FOD signals from multiple sensors and a time-reversed stack method.

[0083] According to the sixth aspect, wherein detecting and locating dropped luggage can employ the same methods described above.

[0084] According to the sixth aspect, wherein monitoring takeoff and landing of an aircraft includes:

[0085] • Performing multi-band pass filtering, including full frequency band;

[0086] • Applying a back-projection focused beam method to data from three or more seismic sensors;

[0087] • Determining a maximum stack value to identify a takeoff or landing location.

[0088] According to the sixth aspect, wherein monitoring takeoff and landing of an aircraft further includes:

[0089] • Performing multi-band pass filtering, including full frequency band;

[0090] • Applying a time-reversed focused method to data from three or more seismic sensors;

[0091] • Determining a maximum stack value to identify a takeoff or landing location.

[0092] Seventh aspect

[0093] The seventh aspect of the present application provides a method of calculating the impact of an aircraft on a runway during take-off or landing, the method comprising:

[0094] • calculating the impact of an aircraft on a runway during take-off;

[0095] • calculating the impact of an aircraft on a runway during landing.

[0096] According to the seventh aspect, wherein calculating the impact of an aircraft on a runway during take-off comprises:

[0097] • performing multi-band band-pass filtering, including all bands;

[0098] • calculating an energy attribute for each sensor and frequency range within a given time window. The energy attribute includes: sum of square of amplitude, sum of absolute value of amplitude, sum of any exponential of amplitude, sum of any exponential of data envelope;

[0099] • calculating the energy attribute on multi-band data;

[0100] • calculating a reference energy attribute at a reference time;

[0101] • calculating the ratio of the energy attribute to the reference energy attribute.

[0102] According to the seventh aspect, wherein calculating the impact of an aircraft on a runway during landing uses the same method as for take-off.

[0103] The eighth aspect of the present application provides a method of mapping the position of an aircraft, vehicle or person on the ground of an airport, the method comprising:

[0104] • mapping the position of an aircraft on the ground or taxiway;

[0105] • mapping the position of a moving vehicle on the ground of an airport;

[0106] • mapping the position of a person on the ground of an airport.

[0107] According to the eighth aspect, wherein mapping the position of an aircraft on the ground or taxiway comprises:

[0108] • pre-processing seismic data collected by seismic sensors deployed along the runway and taxiway of an airport;

[0109] • monitoring aircraft moving within the airport;

[0110] • tracking and associating aircraft to generate real-time aircraft movement trajectories;

[0111] • locating an aircraft using seismic data collected by three or more seismic sensors and determining the position of the maximum stack value using a focused beam method;

[0112] • locating an aircraft using seismic data collected by three or more seismic sensors and determining the position of the maximum stack value using a focused beam method;

[0113] • determining the stopping position of the aircraft by identifying the discontinuity point in the motion trajectory;

[0114] • analyzing the recorded seismic data during the taxiing process of the aircraft to identify the position where the aircraft signal energy disappears;

[0115] According to the eighth aspect, the mapping of the position of moving vehicles on the ground of the airport can be done using the same method as the positioning of the aircraft.

[0116] According to the eighth aspect, the mapping of the position of people on the ground of the airport includes:

[0117] • pre-processing the collected seismic data according to the fifth aspect;

[0118] • detecting human footstep signals using traditional template matching methods and / or machine learning algorithms;

[0119] • identifying, distinguishing, and correlating the detected footstep signals using machine learning algorithms;

[0120] • using the focused beam method to back-project the footstep signals from the same person detected by three or more seismic sensors to determine the position of the individual.

[0121] Ninth aspect

[0122] The ninth aspect of the present application provides a method for monitoring and evaluating the health condition of the runway and taxiway using geophysical imaging methods, the method comprising:

[0123] • pre-processing the seismic data of the airport according to the same method as the fifth aspect;

[0124] • generating a moving seismic source for imaging underground structures; and

[0125] • generating an underground image of the airport using seismic imaging methods.

[0126] According to the ninth aspect, the generation of a moving seismic source for imaging underground structures includes:

[0127] • positioning the moving position of the aircraft during takeoff or landing according to the method of the eighth aspect;

[0128] • creating aircraft source signatures, including but not limited to, by using synthetic source wavelets or extracting Green's functions from the seismic sensor array.

[0129] According to the ninth aspect, the generation of an underground image of the airport using seismic imaging methods includes:

[0130] • obtaining the aircraft source signatures generated above;

[0131] • using pre-processed airport seismic data;

[0132] • implementing reverse-time migration imaging;

[0133] • applying full-waveform inversion;

[0134] • using seismic tomography;

[0135] • applying wave equation extrapolation.

[0136] Tenth aspect

[0137] The tenth aspect of the present application provides a method for monitoring the health condition of a building structure using seismic data, the method comprising:

[0138] • pre-processing seismic data collected from the building;

[0139] • multi-band band-pass filtering;

[0140] • generating multi-band power spectrum;

[0141] • calculating power spectrum ratio attributes between different bands;

[0142] • continuously monitoring the abnormal changes of the power spectrum ratio attributes to provide early warning for potential hazards.

[0143] According to the same method as the fourth aspect, an underground image of the building can be generated for monitoring.

[0144] Eleventh aspect

[0145] The eleventh aspect of the present application provides a method for monitoring a wind turbine tower using seismic data, the method can comprise one or more of the following steps:

[0146] • pre-processing seismic data collected from the wind turbine tower;

[0147] • detecting anomalies of the wind turbine using the method of the tenth aspect;

[0148] • detecting anomalies of the wind turbine tower using the method of the tenth aspect;

[0149] • using multiple sensors to extract coda waves confined within the body of the wind turbine tower;

[0150] • detecting anomalies of the wind turbine and its tower using the coda waves according to the tenth aspect;

[0151] • detecting cracks in the structure of the wind turbine tower;

[0152] • using wind turbine tower data collected by multiple sensors installed on the ground to obtain an underground structure image, such as the method described in the fourth aspect.

[0153] According to the tenth aspect, wherein detecting cracks in a wind turbine tower structure comprises:

[0154] • treating the wind turbine as a seismic source and forward propagating the source signal to generate a forward wavefield;

[0155] • backward propagating the recorded seismic signals from the receivers to generate a backward wavefield;

[0156] • correlating the forward and backward wavefields to generate an image for detecting cracks in the wind turbine tower.

[0157] Twelfth aspect

[0158] The twelfth aspect of the present application provides a method of monitoring human walking, the method comprising one or more of the following steps:

[0159] • pre-processing seismic data;

[0160] • creating a library of seismic signals containing footstep signatures;

[0161] • applying a traditional template matching algorithm to identify footsteps;

[0162] • detecting footsteps using machine learning;

[0163] • establishing a real-time alert system when footstep activity is detected.

[0164] According to the twelfth aspect, the method further comprises:

[0165] • monitoring human intrusions within a secure area;

[0166] • monitoring unauthorized human intrusions within a manufacturing facility.

[0167] According to the fourth aspect, the method further comprises:

[0168] • monitoring changes in underground structures of a manufacturing facility based on seismic data;

[0169] • detecting illegal border crossings from underground tunnel excavation.

[0170] Thirteenth aspect

[0171] The thirteenth aspect of the present application provides a method of monitoring human activity indoors, the method comprising:

[0172] • pre-processing seismic data;

[0173] • creating a library of seismic signal signatures for various activities;

[0174] • applying a signal matching algorithm;

[0175] • Provide a real-time alert system when a fall activity is detected.

[0176] According to the thirteenth aspect, wherein the created library of seismic signal signatures includes: a human fall, a footstep, a jump, a moving chair or table.

[0177] According to the thirteenth aspect, wherein applying a signal matching algorithm includes:

[0178] • Using a template matching algorithm;

[0179] • Using machine learning. BRIEF DESCRIPTION OF DRAWINGS

[0180] Figure 1 schematically illustrates a schematic diagram of a system architecture for implementing infrastructure monitoring (e.g. bridges, highway tunnels, airports, buildings, security sites, wind power towers, and manufacturing facilities), and human monitoring methods, as shown in the present specification;

[0181] Figure 2 is a schematic flow chart illustrating a method of bridge monitoring according to embodiments of the present disclosure;

[0182] Figure 3 is a schematic diagram illustrating several exemplary position designs of laying seismic sensors on a bridge for bridge monitoring;

[0183] Figure 4 is a schematic flow chart illustrating a process of processing seismic data;

[0184] Figure 5 illustrates seismic data of a bridge, and the response of the bridge in time domain and frequency domain, and Green's function;

[0185] Figure 6 is a schematic flow chart illustrating a process of generating a relative velocity change of a bridge structure;

[0186] Figure 7 illustrates a comparison of the relative velocity change of a bridge structure and meteorological data;

[0187] Figure 8 is a schematic diagram illustrating several exemplary position designs of laying seismic sensors along a highway tunnel for tunnel monitoring;

[0188] Figure 9 is a schematic flow chart illustrating a process of generating a relative velocity change of a highway tunnel;

[0189] Figure 10 illustrates the relative velocity change of a highway tunnel near the ground surface and behind the sidewall;

[0190] Figure 11 is a schematic diagram illustrating several exemplary position designs of laying seismic sensors along an airport runway or taxiway for airport monitoring;

[0191] FIG. 12 is a schematic flow chart illustrating a process of processing seismic data from an airport;

[0192] FIG. 13 is a schematic flow chart illustrating a process of detecting runway foreign object debris (FOD) or fallen objects based on seismic data from an airport;

[0193] FIG. 14 illustrates seismic data of a luggage fall and its detection result;

[0194] FIG. 15 illustrates seismic data representing runway foreign object debris (FOD) and its detection result;

[0195] FIG. 16 schematically illustrates a process of determining a FOD location based on a detected FOD signal;

[0196] FIG. 17 is a schematic flow chart illustrating a process of monitoring a flight trajectory and its takeoff or landing location using seismic data from an airport;

[0197] FIG. 18 illustrates seismic data of simulated airplane takeoff and landing impacts on a runway, and results of detecting respective locations of takeoff and landing;

[0198] FIG. 19 is a schematic diagram illustrating several exemplary location designs of deploying seismic sensors on a wind power tower for wind power tower monitoring;

[0199] FIG. 20 is a schematic flow chart illustrating a method of wind power tower monitoring;

[0200] FIG. 21 illustrates seismic wave propagation simulation within a wind power tower wall, and a process of detecting cracks in the tower wall;

[0201] FIG. 22 illustrates seismic data of a human footstep;

[0202] FIG. 23 illustrates a numerical simulation for underground tunnel excavation detection;

[0203] FIG. 24 illustrates seismic data of an indoor human fall. DETAILED DESCRIPTION

[0204] Various exemplary embodiments of the present disclosure will be described herein below with reference to the drawings. In the following description, specific details are set forth (such as detailed configurations and components) in order to provide a thorough understanding of the embodiments of the present disclosure. Accordingly, those skilled in the art will understand that various modifications and changes can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of brevity and clarity, well-known functions and constructions are not described in detail herein.

[0205] The example embodiments and terminology used herein are not intended to limit the technology of the present disclosure to particular forms and are to be understood to include various modifications, equivalents, and / or alternatives to the corresponding embodiments. Similar reference numerals can be used to denote similar constituent elements when describing the drawings. The singular form can also include the plural form unless the context clearly dictates otherwise.

[0206] In addition, the various embodiments described herein are not necessarily mutually exclusive, and certain embodiments can be combined with one or more other embodiments to form new embodiments.

[0207] As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms “first,” “second,” “the first,” or “the second” and the like in the various embodiments of the present disclosure can modify different or the same components unless otherwise clear from the context. The term “and / or” between a first component and a second component can cover the relationship where a component is either present or both components are present.

[0208] The term “configured to” used in the various embodiments of the present disclosure can be used interchangeably with terms such as “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of,” depending on the context. In some cases, the term “configured to” can mean that a device can “be able to,” together with another device or component, “be capable of” carrying out a certain function. For example, a “processor configured (or adapted) to perform A, B, and C” can refer to a dedicated processor (e.g., an embedded processor) for performing the corresponding operations, or a general-purpose processor (e.g., a central processing unit (CPU) or an application processor (AP)) capable of performing the corresponding operations by executing one or more software programs stored in a memory device.

[0209] As used herein, the term “or” means “and / or” unless otherwise stated. The examples given herein are only used to help understand the implementable ways of the embodiments of the present disclosure and enable those skilled in the art to implement the embodiments. Therefore, these examples should not be interpreted in a way that limits the scope of the embodiments.

[0210] In accordance with common practice, the embodiments can be described herein with the use of function blocks. These function blocks can be understood as units, engines, managers, modules, etc. They can be physically implemented by either analogue and / or digital circuits, such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hard-wired circuits, etc. and can be driven by firmware and / or software. These circuits can be embodied in one or more semiconductor chips, or on a substrate such as a printed circuit board. The circuits implementing the function blocks can be implemented by dedicated hardware or by a processor (such as one or more programmed microprocessors and associated circuitry) or by a combination of dedicated hardware and a processor implementing the different functions of the function blocks. The function blocks of the various embodiments can also be split into two or more separate function blocks without departing from the scope of the present disclosure. Similarly, the function blocks of the various embodiments can be combined into a more complex function block without departing from the scope of the present disclosure. When using expressions such as "at least one of A, B, and C", it should generally be interpreted that "a system including at least one of A, B, and C" shall mean that the system falls into at least one of the following 7 categories: (1) a system including A alone, (2) a system including B alone, (3) a system including C alone, (4) a system including both A and B, (5) a system including both A and C, (6) a system including both B and C, and (7) a system including all of A, B, and C.

[0211] Seismic data includes a large number of seismic traces, each recorded by a sensor that can acquire a vertical component or three orthogonal components. These seismic traces record the ground motion as a function of time and are acquired by different types of receivers, including geophones, hydrophones, MEMS sensors, and DAS systems. Receivers are typically laid out at fixed intervals on the ground surface. In addition to passively monitoring ground motion, seismic surveys can also excite seismic waves by artificial sources. A source refers to the location where seismic waves are generated, and "source excitation" refers to the action of releasing energy at the source, usually by explosive blasts or shakers. Multiple source excitations can be performed at different locations and at different times to enhance data acquisition.

[0212] Seismometers installed on the ground can record vibrations generated by earthquakes or artificial sources, providing valuable information about seismic wave propagation and source characteristics through data analysis. In addition, seismic sensors can be laid along roads, bridges, and highway tunnels to passively monitor ground motion caused by passing vehicles. At the same time, they can also be installed at airports, around buildings, security and manufacturing sites, wind power towers, etc. to enhance safety and prevent intrusion. By applying specialized algorithms, collected data can be analyzed to detect potential problems in advance, thereby avoiding structural failures and extending the service life of critical infrastructure. Ultimately, infrastructure monitoring results help prevent accidents, mitigate disasters, and efficiently manage safety while maintaining relatively low costs.

[0213] The design steps of infrastructure monitoring (including bridges, highways, tunnels, buildings, airports, wind power facilities, security and manufacturing sites) using seismic data can include one or more of the following:

[0214] (1) receiving seismic data collected on site and transmitted to a computer in real time; (2) enhancing data signals through digital processing; (3) bidirectional traffic wave field separation or attenuation; (4) surface consistency amplitude correction; (5) detecting footsteps, human falls, airport foreign objects (FOD) and luggage falling using machine learning methods; (6) locating the positions of the above special events; (7) monitoring the health of various infrastructures; (8) monitoring changes in underground structures at infrastructure sites.

[0215] These application embodiments provide a method for monitoring infrastructure using seismic data, thereby reducing system deployment costs, reducing the amount of data to be processed, protecting personal privacy, and ensuring timely response.

[0216] Figure 1 is a schematic diagram of a system architecture for implementing the infrastructure monitoring method in the exemplary embodiments of the present disclosure.

[0217] It should be noted that Figure 1 is only an example of a system architecture that can be applied to the disclosed embodiments, which is used to help those skilled in the art understand the disclosed technology, but does not mean that the disclosed embodiments cannot be applied to other devices, systems, environments or scenarios.

[0218] As shown in Figure 1, according to this embodiment, the system architecture 100 can include seismic recording devices such as seismic sensors 101, 102 and 103, a network 104, and a server 105. The network 104, as a communication medium between the seismic sensors and the server, can include various connection methods, including wired, wireless communication links or fiber optic cables.

[0219] In one embodiment, seismic data can be transmitted through a 5G / 4G cellular network, Wi-Fi or a fiber optic Internet cable.

[0220] The server 105 can provide a variety of services, both as a cloud server (also known as a cloud computing server or cloud host), as a server for a distributed system, or as a server integrated with blockchain technology.

[0221] The infrastructure monitoring methods described in the present embodiments are generally performed by the server 105, which can internally have corresponding units or modules arranged therein. However, these methods can also be performed by other devices, servers or server clusters that are independent of the server 105, as long as they are capable of communicating with the server 105. In this case, the corresponding units or modules performing the infrastructure monitoring methods can be installed in these devices, servers or server clusters.

[0222] Bridge monitoring

[0223] Figure 2 For flowchart 200, a method of monitoring a bridge according to one embodiment of the present disclosure is schematically illustrated.

[0224] As Figure 2 shown, the flowchart 200 can include the following operations:

[0225] • At step S210, seismic data is acquired from seismic recording devices. In one embodiment, the seismic recording devices can be geophones, seismic sensors or ground vibration recording equipment. In one embodiment, the seismic recording devices can be arranged at fixed or variable intervals on one side of the road, on both sides of the road, in the middle of the road or any combination thereof. The specific arrangement of the seismic recording devices is described in more detail with reference to Figure 3 . The seismic vibrations generated by vehicles travelling on the bridge are recorded by the seismic sensors. The recorded seismic data is then used for real-time monitoring of the bridge, health condition detection, crack identification, structural deformation assessment and other issues.

[0226] • At step S220, the seismic data from each seismic recording device is processed to enhance the signal. The detailed steps of signal enhancement can include one or more of the following:

[0227] 1. removing or attenuating noise;

[0228] 2. removing or attenuating irregular signals not caused by vehicle vibrations;

[0229] 3. removing or attenuating secondary responses of initial vehicle vibrations;

[0230] 4. applying mathematical logarithmic operations to traffic data to normalize the amplitude;

[0231] 5. applying arbitrary exponential operations (including square root) to traffic data to normalize the amplitude;

[0232] 6. applying automatic gain control (AGC) to traffic data to normalize the amplitude;

[0233] 7. applying root mean square (RMS) processing to traffic data to normalize the amplitude;

[0234] 8. Applying median filter to traffic data to smooth data and remove outliers;

[0235] 9. Applying mean filter to traffic data to smooth data;

[0236] 10. Applying integral filter to traffic data to enhance weak signals;

[0237] 11. Applying global normalization to traffic data;

[0238] 12. Applying single-station (sensor) normalization to traffic data;

[0239] 13. Applying local window normalization to traffic data;

[0240] 14. Applying methods in (4)-(13) to signal envelope of traffic data;

[0241] 15. Applying band-pass filter to traffic data or processed data;

[0242] 16. Applying linear extrapolation correction (LMO) to data with preset vehicle speed relative to recording station;

[0243] 17. Any combination or partial combination of (1)-(16) above.

[0244] • At step S230, based on the processed seismic data, judge the health condition of the bridge structure, including crack, cavity and deformation detection.

[0245] Figure 3 Examples of seismic sensor arrangement in bridge traffic monitoring are illustrated: (a) single-side arrangement (e.g. right side); (b) single-side arrangement (e.g. left side); (c) middle arrangement (e.g. central median); (d) double-side staggered arrangement; and any combination thereof.

[0246] Figure 4 For flowchart 400, further processing of bridge vibration data is schematically shown. The steps include:

[0247] • At step S410, further signal enhancement is performed on the vibration data;

[0248] • At step S420, noise reduction, inter-channel balancing and normalization are performed;

[0249] • At step S430, band-pass filter, Fourier transform and inverse Fourier transform are applied.

[0250] For monitoring the health of a bridge structure and detecting its resonance frequency, it is necessary to identify the characteristic response (or Green's function) of the bridge.

[0251] Figure 5A shows raw data collected from a bridge, with the vertical axis representing the spatial location of the sensor and the horizontal axis representing the recording time. The Green's function of the bridge can be obtained by correlating the seismic signals of adjacent sensors within the same time window. Figure 5 B shows the Green's function of the bridge, where the reference arrow indicates the Green's function at the reference time and the target arrow indicates the Green's function at the current time. Through spectral analysis, the power spectrum can be obtained to identify the resonance frequency of the bridge, as shown in Figure 5 C.

[0252] According to one embodiment of the present disclosure, a method for monitoring the bridge structure and / or near-surface geological conditions using seismic data is provided. The method comprises:

[0253] • obtaining target seismic data and reference seismic data from a seismic recording device;

[0254] • preprocessing the target seismic data and the reference seismic data;

[0255] • generating a target Green's function based on the target seismic data;

[0256] • generating a reference Green's function based on the reference seismic data;

[0257] • generating a bridge structure relative velocity change and / or a near-surface relative velocity change based on the target Green's function and the reference Green's function to monitor the bridge structure and / or the near-surface geological conditions.

[0258] Figure 6 For flowchart 600, a method for monitoring the bridge structure using seismic data is schematically shown:

[0259] • In step 610, target seismic data and reference seismic data are obtained from a seismic recording device;

[0260] • In step 620, the target seismic data and the reference seismic data are preprocessed;

[0261] • In step 630, a target Green's function is generated based on the target seismic data;

[0262] • In step 640, a reference Green's function is generated based on the reference seismic data;

[0263] • In step 650, a bridge structure relative velocity change is generated based on the target Green's function and the reference Green's function to monitor the bridge health condition.

[0264] The preprocessing procedure in step 620 includes:

[0265] • noise reduction;

[0266] • resampling;

[0267] • Data drift correction;

[0268] • Multi-band Band-pass filtering, where ; and are the lower and upper limits of the frequency range of the seismic recording device, respectively;

[0269] • One-bit filtering;

[0270] • Median filtering;

[0271] • RMS filtering;

[0272] • Smoothing filtering;

[0273] • Fourier transform of the traffic vibration data;

[0274] • Conjugation of the transformed data;

[0275] • Inverse Fourier transform;

[0276] • Inverse Fourier transform of the conjugated data;

[0277] • Window tapering of the data in the time domain, frequency domain, or both.

[0278] In steps 630 and 640, the method for generating the target and reference Green’s functions includes:

[0279] • Cross-correlating the pre-processed data of the sensor pair in the time or frequency domain to generate the reference Green’s function;

[0280] • Cross-correlating the pre-processed data of the sensor pair in the time or frequency domain to generate the target Green’s function;

[0281] • Weighted stacking of the cross-correlation results.

[0282] In step 650, the relative velocity variation of the bridge structure can be generated using ambient noise imaging methods, including:

[0283] • Moving window cross-spectral method;

[0284] • Coda wave interferometry method;

[0285] • Ambient noise tomography method.

[0286] Through the processing flow shown in Figure 6 , the result of the relative velocity variation of the bridge structure over time can be generated, as shown in Figure 7 B. When comparing the variation of the relative velocity of the bridge structure with meteorological data, such as temperature ( Figure 7 A, dark gray curve) and humidity ( Figure 7A (light gray curve in A), a significant correlation can be observed.

[0287] Road tunnel monitoring

[0288] In one embodiment of road tunnel monitoring, seismic recorders are arranged at fixed or variable intervals on one side, both sides, the side walls of the tunnel, or any combination thereof.

[0289] Figure 8 Several sensor arrangement schemes for road tunnel traffic monitoring are illustrated, including: (a) single side arrangement (right side); (b) single side arrangement (left side); (c) double side staggered arrangement; (d) tunnel side wall arrangement; and any combination thereof.

[0290] Figure 9 For flowchart 900, a method of monitoring a road tunnel using seismic data is schematically illustrated. The steps are the same as in Figure 6

[0291] • At step 910, target seismic data and reference seismic data are acquired from seismic recorders;

[0292] • At step 920, the target seismic data and reference seismic data are pre-processed;

[0293] • At step 930, a target Green's function is generated based on the target seismic data;

[0294] • At step 940, a reference Green's function is generated based on the reference seismic data;

[0295] • At step 950, relative velocity changes in the near-surface and behind the side walls of the road tunnel are generated based on both.

[0296] Through the processing flow shown in Figure 9 , the relative velocity changes in the near-surface and behind the side walls of the road tunnel over time can be obtained, as shown in Figure 10 A and Figure 10 B.

[0297] Airport monitoring

[0298] In one embodiment of airport monitoring, seismic recorders are arranged on one or both sides of the airport runway and / or taxiway, at fixed or variable intervals.

[0299] Figure 11 shows several example designs for illustrating the arrangement of seismic sensors on the runway or taxiway.

[0300] ​As shown in FIG. 11, example arrangements of seismic sensors include: single-side arrangement (e.g., shown in FIG. 11(a) or (b), located on the right or left side of the runway or taxiway); double-side staggered arrangement (e.g., shown in FIG. 11(c), located on both sides of the runway or taxiway).

[0301] FIG. 12 is a schematic flowchart 1200 illustrating an airport monitoring method according to example embodiments of the present disclosure. As shown in FIG. 12, flowchart 1200 can include the following operations:

[0302] • At step S1210, seismic data is acquired from seismic recorders.

[0303] • At step S1220, seismic data is processed to enhance the signal of each seismic recorder.

[0304] • Details of step S1220 (signal enhancement) can be the same as the operations in S220.

[0305] • At step S1230, airport near-surface relative velocity changes are generated from the processed seismic data. Analyzing the relative velocity changes helps to assess the safety of the runway, taxiway, and apron.

[0306] FIG. 13 is a schematic flowchart 1300 illustrating a method of detecting and locating FOD (Foreign Object Debris) or drop-in on an airport according to example embodiments of the present disclosure:

[0307] • At step S1310, seismic data is acquired from seismic recorders.

[0308] • At step S1320, seismic data is processed to detect FOD or drop-in. Methods of detecting FOD or drop-in through amplitude and phase analysis of seismic data include:

[0309] a. Preprocessing seismic data as described in S220; b. Multi-band bandpass filtering, including full band; c. Determining amplitude attributes within a given frequency range and specified time window; d. Determining zero-crossing attributes within a specified time window; e. Determining attributes by calculating the ratio of (c) to (d); f. Determining FOD or drop-in signal when the attributes in (e) exceed a threshold value.

[0310] • At step S1330, the location of FOD or drop-in is determined. Methods of locating include:

[0311] a. using FOD signals detected by multiple sensors and employing a back-projection focused beam method to determine the location; or b. using FOD or drop signals detected by multiple sensors and employing a time-reversal focusing method to determine the location of the maximum superposition value.

[0312] Figure 14 shows the detection of dropped luggage by an airport baggage cart. Figure 14A shows the seismic signal, with black dots indicating the time of luggage drop. Figure 14B shows the FOD attribute, with black dots representing instances of luggage drop.

[0313] Figure 15 gives an example of the detection of small FOD. Figure 15A shows the seismic signal, with black dots representing detected FOD; Figure 15B shows the FOD attribute calculated using step S1320, with black dots representing detected FOD.

[0314] According to step S1330, Figure 16 shows the localization of FOD or dropped luggage. Using three detected FOD or luggage drop signals, localization is performed using the locations of the three sensors (shown as a triangle). The location is determined using a back-projection focused beam method or a time-reversal focusing method. The three back-projection wavefronts or time-reversal propagation wavefronts in Figure 16 converge at the black star location, which is the location of the FOD or dropped luggage.

[0315] Figure 17 is a schematic flowchart 1700 illustrating a method of monitoring the path of a vehicle and an aircraft, determining the location of the vehicle and the aircraft, and calculating the impact of the aircraft on the runway during takeoff and landing, according to an example embodiment of the present disclosure.

[0316] In step S1710, seismic data is obtained from the seismic recording device.

[0317] In step S1720, the seismic data is processed to monitor the path of the vehicle or the aircraft, the monitoring step comprising:

[0318] a. pre-processing seismic data collected on the airside of the airport as described in S220; b. monitoring an aircraft or a vehicle moving along a designated route of the airport; c. tracking and correlating detected moving aircraft or vehicles to generate their real-time location and path.

[0319] In step S1730, the location of the aircraft or the vehicle is determined. The method comprises:

[0320] a. using signals detected by three or more seismic sensors, employing a back-projection or time-reversal focusing method, using focused beam or propagation methods for localization, and determining the location of the aircraft or the vehicle based on the maximum superposition value.

[0321] In step S1730, the impact of the aircraft on the runway during takeoff and landing is calculated. The method comprises:

[0322] a. Multi-band bandpass filtering, including all frequency bands; b. Calculate energy attributes for each sensor and frequency range within a given time window, including but not limited to:

[0323] i. Sum of square amplitudes; ii. Sum of absolute amplitudes; iii. Sum of arbitrary exponent amplitudes; iv. Sum of arbitrary exponent envelopes;

[0324] c. Calculate energy attributes at a reference time; d. Use the ratio of the energy attributes from (b) and (c) to the energy attributes at the reference time as the result of the calculation.

[0325] Figure 18 shows numerical simulation of aircraft takeoff and landing. Figure 18A is simulated seismic data during takeoff and landing; Figure 18 B is the aircraft position determined by applying the time-reversed focusing method described in S1730.

[0326] Wind turbine monitoring

[0327] In one embodiment of wind turbine monitoring, seismic recording devices are placed inside and / or outside the wind turbine tower.

[0328] Figure 19 shows several example designs of seismic sensor placement in wind turbine monitoring.

[0329] As shown in Figure 19, the placement methods include:

[0330] • Single sensor mounted on the tower floor inside (Figure 19A);

[0331] • Multiple sensors mounted on the tower floor inside along the tower wall (Figure 19B);

[0332] • Multiple sensors mounted on the tower floor outside along the tower wall (Figure 19C);

[0333] • Multiple sensors placed in a straight line outside the tower (Figure 19D);

[0334] • Single sensor mounted on the middle of the tower wall (Figure 19E);

[0335] • Single sensor mounted on the tower top structure (Figure 19F);

[0336] • Two sensors, one on the tower top and one on the tower floor inside (Figure 19G). Figure 19 G).

[0337] Figure 20 is a schematic flowchart 2000 illustrating a method of wind turbine tower monitoring according to an exemplary embodiment of the present disclosure.

[0338] In step S2010, seismic data is acquired from seismic recording devices.

[0339] At step S2020, source signature from the wind turbine is acquired, which can be seismic or vibration signals collected by sensors installed on or near the tower.

[0340] At step S2030, for monitoring the health of the wind turbine tower and detecting structural cracks, the method comprises:

[0341] a. Using the wind turbine as a source, forward propagating the source signature; b. Backward propagating the recorded seismic or vibration signals from the receivers; c. Correlating the wavefield obtained from (a) and (b), generating an image for detecting cracks in the wind turbine tower.

[0342] At step S2030, for monitoring the health of the wind turbine tower, the seismic data collected by multiple sensors installed on the ground can be used to generate an image of the underground structure, the steps are the same as flowcharts 600 or 900.

[0343] Figure 21 shows the numerical simulation of seismic wave propagation in a wind turbine tower. Figure 21A is a velocity model of the wind turbine tower, the gray star at the top of the tower is the position of the wind turbine source, the gray triangle at the bottom is the position of the receiver, and the cracks in the tower wall are marked by arrows. Figure 21B shows that high-frequency guided waves are confined to propagate in the tower wall, while Figure 21C shows that low-frequency waves escape the tower wall.

[0344] Figure 21D shows the results of crack detection using S2030, with arrows indicating the location of the cracks. Since only one source signal and one receiver are used to record the signal, only the vertical position of the cracks can be detected.

[0345] Figure 22 shows an example of human walking detection. The curve in Figure 22A is the seismic signal, and the black dots represent the detected human walking; Figure 22B shows the walking gait attributes, and the black dots are instances of human walking. The calculation method of gait attributes is the same as S1320.

[0346] In one embodiment, to illustrate the detection of underground excavation through a tunnel, a numerical model simulation is used, as shown in Figure 23 . Before the underground excavation, it is assumed that the underground velocity model is composed of two layers of homogeneous layers, as shown in Figure 23 A. After the underground excavation occurs, a low-velocity velocity anomaly is generated, as shown in Figure 23 B and marked by arrows. By comparing the surface wave differences between the two velocity models containing the velocity anomaly and not containing the velocity anomaly, as shown in Figure 23 C, underground imaging can be generated in step 600, as shown in Figure 23 D, thereby achieving detection of underground excavation.

[0347] Figure 24 shows a series of indoor human fall seismic signals. Since the fall signals are large in amplitude and isolated, the detection process is relatively simple.

Claims

1. A method of monitoring a bridge using seismic data, the method comprising: Comprising: • acquiring seismic data from seismic recording devices; • processing the seismic data; and • monitoring the bridge based on the seismic data.

2. The method of claim 1, wherein the seismic recording devices are deployed on one side, both sides, middle of the bridge, or any combination thereof, and the spacing can be fixed or variable.

3. The method of claim 1, wherein the seismic data processing comprises: • signal enhancement of the seismic data.

4. The method of claim 1, wherein the seismic data processing comprises: • trace equalization of the seismic data.

5. The method of claim 1, wherein the monitoring of the bridge based on the seismic data comprises: • detecting the bridge resonance frequency; and • calculating the bridge load from traffic flow data.

6. A method of monitoring changes in a bridge structure based on seismic data, characterized by, The method comprises: • acquiring target seismic data and reference seismic data from seismic recording devices; • generating a target Green's function based on the target seismic data; • generating a reference Green's function based on the reference seismic data; • detecting bridge structural changes based on the target and reference Green's functions.

7. The method of claim 6, wherein the method further comprises: • signal pre-processing, noise removal, resampling, data time shift correction, and filtering of the target and reference seismic data.

8. The method of claim 7, wherein the filtering comprises: • Multiband Bandpass filtering, where , and are the lower and upper limits of the frequency range of the seismic recording device, respectively.

9. The method of claim 7, wherein the method further comprises: • extracting one or more body waves, including P-waves, S-waves, SH-waves, surface waves, coda waves, Rayleigh waves, and Love waves, from the target and reference seismic data; • generating the reference and target Green's functions based on the extracted waves.

10. The method of claim 6, wherein detecting bridge structural changes comprises: • generating bridge relative velocity changes based on the target and reference Green's functions using ambient noise imaging methods.

11. A method of detecting and locating cracks in a bridge structure, characterized by, Comprising: • a back-projection focused beam imaging method; or • a time-reversal focused imaging method.

12. A method of detecting and locating voids in a bridge structure, characterized by, Comprising: • a back-projection focused beam imaging method; or • a time-reversal focused imaging method.

13. A method of monitoring changes in near-surface structure based on seismic data, characterized by, Comprising: • acquiring target seismic data and reference seismic data from seismic recording devices; • generating a target Green's function based on the target seismic data; • generating a reference Green's function based on the reference seismic data; • generating near-surface structural changes based on the target and reference Green's functions.

14. The method of claim 13, wherein the seismic recording devices are deployed along one side, both sides, or sidewalls of the highway tunnel, or any combination thereof, and the spacing can be fixed or variable.

15. The method of claim 13, wherein the method further comprises: • signal pre-processing, noise removal, resampling, data time shift correction, filtering, multi-frequency band-pass filtering, Fourier transform, and inverse Fourier transform of the target and reference seismic data.

16. The method of claim 13, wherein the generating near-surface structural changes comprises: • generating relative velocity changes behind the tunnel sidewall using ambient noise imaging methods based on the target and reference Green's functions.

17. The method of claim 13, wherein the method further comprises: • generating relative velocity changes behind the tunnel sidewall using ambient noise imaging methods based on the target and reference Green's functions.

18. The method of claim 13, wherein the method further comprises: • detecting and locating tunnel near-surface voids using the back-projection focused beam method; • detecting and locating tunnel near-surface voids using the time-reversal focusing method; • detecting and locating tunnel near-surface voids using the back-projection focused beam method; • detecting and locating tunnel near-surface voids using the time-reversal focusing method.

19. The method of claim 13, wherein the method further comprises: • detecting and locating tunnel sidewall voids using the back-projection focused beam method; • detecting and locating tunnel sidewall voids using the time-reversal focusing method; • detecting and locating tunnel sidewall voids using the back-projection focused beam method; • detecting and locating tunnel sidewall voids using the time-reversal focusing method.

20. A method for airport monitoring using seismic data, the method comprising: • acquiring seismic data from seismic recording devices; • processing the seismic data; • monitoring the airport based on the seismic data.

21. The method of claim 20, wherein, The seismic recording devices are arranged on one or more sides of the runway and taxiway, on the apron, or any combination thereof, and the spacing can be fixed or variable.

22. The method of claim 20, wherein, Processing the seismic data includes: • detrending; • noise removal or attenuation; • intertrace balancing; • trace normalization; • filtering; • band-pass filtering; • Fourier transform and inverse Fourier transform.

23. The method of claim 20, wherein, The method of monitoring the airport based on the seismic data further comprises: • monitoring and assessing the health of the runway and taxiway; • detecting and locating foreign object debris (FOD); • detecting and locating dropped luggage; • monitoring aircraft takeoff and landing.

24. The method of claim 23, wherein, Monitoring and assessing the health of the runway and taxiway includes: • acquiring target and reference seismic data from seismic recording devices; • pre-processing the seismic data according to claim 22; • generating a target Green's function based on the target seismic data; • generating a reference Green's function based on the reference seismic data; • generating relative velocity changes using ambient noise imaging methods based on the target and reference Green's functions to monitor changes in the geological health beneath the runway and taxiway.

25. The method of claim 24, wherein, The ambient noise imaging methods include: • moving window cross-spectral method; • coda wave interferometry; • ambient noise tomography.

26. The method of claim 23, wherein, Detecting and locating foreign object debris (FOD) includes one or more of: • multi-band band-pass filtering; • determining an amplitude attribute within a specified time window and given frequency band; • determining a zero-crossing attribute within the time window; • calculating a FOD attribute from the ratio of the amplitude attribute and the zero-crossing attribute; • determining a FOD signal when the FOD attribute exceeds a threshold value; • determining the FOD location using the detected FOD signal from multiple sensors and the back-projection focused beam method; • Determine the maximum stack value position using the detected FOD signals from multiple sensors and a time reverse stack method.

27. The method of claim 23, wherein, Detecting and locating luggage drop can use the same method described in claim 23.

28. The method of claim 23, wherein, Monitoring aircraft takeoff and landing includes: • Performing multi-band bandpass filtering, including the full band; • Applying a deconvolution focusing beam method to data from three or more seismic sensors; • Identifying the takeoff or landing position by determining the maximum stack value.

29. The method of claim 23, wherein, Monitoring aircraft takeoff and landing includes: • Performing multi-band bandpass filtering, including the full band; • Applying a time reverse focusing method to data from three or more seismic sensors; • Identifying the takeoff or landing position by determining the maximum stack value.

30. The method of claim 23, wherein, The method further includes: • Calculating the impact of an aircraft takeoff on the runway; • Calculating the impact of an aircraft landing on the runway.

31. The method of claim 30, wherein, Calculating the impact of an aircraft takeoff on the runway includes: • Performing multi-band bandpass filtering, including the full band; • Calculating energy attributes for each sensor and band within a given time window; • Calculating energy attributes for the multi-band data; • Calculating reference energy attributes for a reference time; • Calculating the ratio of the multi-band energy attributes to the reference energy attributes.

32. The method of claim 31, wherein, Calculating energy attributes for each sensor and band within a given time window includes: • Amplitude squared sum; • Amplitude absolute sum; • Amplitude arbitrary exponent sum; • Envelope data arbitrary exponent sum.

33. The method of claim 30, wherein, Calculating the impact of an aircraft landing on the runway uses the same method described in claim 31.

34. The method of claims 28 and 29, wherein, The method further includes: • Mapping the position of an aircraft on the ground or taxiway; • Mapping the position of any moving vehicle on the ground of the airport; • Mapping the position of personnel on the ground of the airport.

35. The method of claim 34, wherein, Mapping the position of an aircraft on the ground or taxiway includes: • Preprocessing the data collected from seismic sensors deployed along the airport runway and taxiway using the method described in claim 20; • Detecting a moving aircraft on the airport runway, taxiway, or apron; • Tracking and correlating during the aircraft taxiing to generate a real-time aircraft movement path; • Locating the aircraft using a focusing beam method on seismic data from three or more seismic sensors and determining the maximum stack value position; • Determining the aircraft stop position by identifying discontinuities in the aircraft movement path; • Analyzing the recorded seismic data during the aircraft taxiing to identify the aircraft signal energy disappearance point.

36. The method of claim 34, wherein, Mapping the position of any moving vehicle on the ground of the airport includes: • Preprocessing the collected seismic data from the airport airside area using the method described in claim 20; • Detecting moving vehicles on the designated routes of the airport airside using traditional methods and / or machine learning algorithms; • Tracking and correlating the detected moving vehicles to generate real-time vehicle positions and movement paths along the designated routes; • Locating the vehicles using a deconvolution focusing beam method based on the detected vehicle signals from three or more seismic sensors; • Determining the maximum stack value position of the vehicles.

37. The method of claim 34, wherein, Mapping the position of personnel on the ground of the airport includes: • Preprocessing the collected seismic data using the method described in claim 20; • Detecting human footstep signals using traditional template matching methods and / or machine learning algorithms; • Identifying, distinguishing, and correlating the detected footstep signals using machine learning algorithms; • Using focused beam approach, the same person's footsteps signals detected from three or more seismic sensors are de-projected and the person is located.

38. The method of claim 23, wherein, Monitoring and assessing runway and taxiway health further comprises: • Pre-processing the airport seismic data as recited in claim 20; • Generating a moving source for imaging the subsurface structure; • Generating an image of the airport subsurface structure using seismic imaging methods.

39. The method of claim 38, wherein, Generating a moving source for imaging the subsurface structure comprises: • Locating the moving position of the aircraft during take-off or landing as recited in claim 34; • Creating the aircraft source signal including but not limited to: o Using a synthetic source wavelet; o Extracting the Green's function for a pair of seismic sensors.

40. The method of claim 38, wherein, Generating an image of the airport subsurface structure using seismic imaging methods comprises one or more of: • Acquiring the aircraft source signal generated according to claim 39; • Using the pre-processed airport seismic data; • Implementing reverse time migration; • Applying full waveform inversion; • Using seismic tomography; • Applying wave equation extrapolation.

41. A method for monitoring the health of a building structure using seismic data, the method comprising: • Pre-processing the seismic data of the building monitoring data according to claim 22; • Multi-band pass filtering; • Generating multi-band power spectra; • Calculating the power spectrum ratio attribute between different bands; • Establishing a continuous monitoring system to monitor the abnormal changes of the power spectrum ratio attribute and provide early warning for potential hazards.

42. A method for monitoring the surrounding environment of a building using seismic data, the method comprising: • Pre-processing the seismic data of the building monitoring data according to claim 22; • Multi-band pass filtering; • Generating an image of the surrounding subsurface structure as recited in claim 13; • Establishing a continuous monitoring system to monitor the changes of the subsurface structure and provide early warning for potential hazards.

43. A method for monitoring a wind turbine tower using seismic data, the method can comprise one or more of: • Acquiring seismic data from the seismic recording device; • Pre-processing the seismic data of the wind turbine tower as recited in claim 22; • Detecting wind turbine anomalies using the method recited in claim 41; • Detecting wind turbine tower anomalies using the method recited in claim 41; • Extracting the coda waves from the seismic data of the wind turbine tower using multiple sensors; • Detecting wind turbine and wind turbine tower anomalies using the coda waves as recited in claim 41; • Detecting wind turbine tower structural cracks; • Generating an image of the subsurface structure using the wind turbine tower data collected by sensors placed at multiple locations on the ground as recited in claim 13.

44. The method of claim 43, wherein the seismic recording devices are arranged in fixed or variable positions, which can be one or more, including: one sensor placed on the ground inside the tower, one placed in the middle of the tower, one placed on the top of the tower, or multiple sensors placed on the ground inside or outside the tower, or any combination thereof.

45. The method of claim 43, wherein detecting wind turbine tower structural cracks comprises: • Using the wind turbine as a source to forward propagate the source signal to generate a forward wavefield; • Backward propagating the seismic signals recorded by the receivers to generate a backward wavefield; • Correlating the forward wavefield and the backward wavefield to generate an image for detecting wind turbine tower cracks.

46. A method of monitoring human walking comprising one or more of the following steps: • seismic data pre-processing; • creating a library of seismic signal signatures containing footstep signatures; • applying a traditional template matching algorithm to identify footstep; • utilizing machine learning to detect footstep; • establishing a real-time alert system when footstep activity is detected.

47. The method of claim 46, further comprising monitoring for intrusions by personnel in a secure site.

48. The method of claim 46, further comprising monitoring for unauthorized personnel intrusions in a manufacturing facility.

49. The method of claim 13, further comprising: • monitoring for changes in subsurface structures of a manufacturing facility based on seismic data; • detecting illegal border crossings by tunneling.

50. The method of claim 46, further comprising detecting illegal border crossings of the ground surface.

51. A method of detecting human activity in a room comprising: • seismic data pre-processing; • creating a library of seismic signal signatures for various activities; • applying a signal matching algorithm; • providing a real-time alert system when a fall activity is detected.

52. The method of claim 51, wherein said creating a library of seismic signal tags for various activities comprises: human falling, footstep, jumping, moving a chair or table.

53. The method of claim 51, wherein the applying a signal matching algorithm comprises: • using a template matching algorithm; • using machine learning.

54. An electronic device comprising: • at least one processor; and • memory connected to the at least one processor, wherein the memory stores instructions that, when executed by the at least one processor, cause the processor to perform the method of any of claims 3-8, 15-19, 22-36, or 38-40.

55. A non-transitory computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method of any of claims 3-8, 15-19, 22-36, or 38-40.

56. A computer program product comprising a computer program containing instructions that, when executed by a computer, cause the computer to perform the method of any of claims 3-8, 15-19, 22-36, or 38-40.

Images