Seafloor ground motion prediction method and apparatus based on seismic exploration

Abstract

The present application relates to the technical field of earthquake prediction, in particular to a seabed seismic motion prediction method and device based on seismic exploration, comprising the following steps: selecting a monitoring area, collecting seabed topographic data of the monitoring area and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; collecting geomagnetic field information, electric wave information, microseismic activity information and seabed deformation information of each monitoring point and recording; converting each information collected into an acoustic signal and transmitting to a sea surface buoy, and the sea surface buoy converts each acoustic signal into an electric signal and transmits to a ground data center; through optimization of monitoring point site selection, integration of geomagnetic-electric wave-microseismic-deformation multi-source data, and use of acoustic-electric phased communication chain to realize efficient data transmission, and finally combined with threshold analysis to realize early identification of earthquake precursors, the method can significantly improve the coverage, data accuracy and warning timeliness of seabed earthquake monitoring, and provide technical support for marine geological disaster prevention and control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of earthquake prediction, and in particular to a seabed seismic motion prediction method and device based on seismic exploration. BACKGROUND

[0002] Seabed earthquakes are geological disasters caused by ocean plate movement, fault activity or stress accumulation in subduction zones, and have the characteristics of strong suddenness and great destructive power, which may trigger secondary disasters such as tsunamis and seabed landslides, posing a serious threat to the safety of coastal areas. However, due to the complex seabed environment and the difficulty of monitoring, traditional earthquake prediction methods often rely on land seismic networks, making it difficult to capture precursor signals of seabed earthquakes in time, resulting in insufficient warning time. Therefore, it is of great scientific significance and application value to develop an efficient and accurate seabed seismic motion monitoring and prediction system.

[0003] Currently, seabed earthquake monitoring mainly faces the following challenges:

[0004] Difficult data collection: the high pressure and high corrosion environment of the seabed requires very high stability of the sensor, and the signal transmission is easily affected by seawater attenuation;

[0005] Insufficient multi-source information fusion: single physical quantity (such as seismic wave) monitoring cannot fully reflect the characteristics of fault activity, and needs to be combined with multi-parameter comprehensive analysis of geomagnetic field, electric wave, deformation, etc. SUMMARY

[0006] The present application provides a seabed seismic motion prediction method and device based on seismic exploration, which can effectively solve the problems in the background art.

[0007] In order to achieve the above purpose, the technical scheme adopted by the present application is:

[0008] On the one hand, the present application provides a seabed seismic motion prediction method based on seismic exploration, comprising the following steps:

[0009] Selecting a monitoring area, collecting seabed topographic data of the monitoring area and drawing a topographic map;

[0010] Selecting a number of monitoring points according to the topographic map;

[0011] Collecting geomagnetic field information, electric wave information, microseismic activity information and seabed deformation information of each monitoring point and recording;

[0012] Converting each information collected into an acoustic signal and transmitting it to a sea surface buoy, and the sea surface buoy converts each acoustic signal into an electric signal and transmits it to a ground data center;

[0013] The data center denoises the received signal and converts it into a digital signal;

[0014] The same type of digital signals of each monitoring point is classified and a threshold is set, and when at least one type of digital signal exceeds the threshold, an earthquake prediction warning is issued.

[0015] In some embodiments of the present application, the prediction method further comprises monitoring the electronic concentration anomaly of the monitoring area and the seafloor heat flow anomaly by satellite detection.

[0016] In some embodiments of the present application, the seafloor topographic data is collected by a multi-beam sonar.

[0017] In some embodiments of the present application, the monitoring points are near the seafloor fault in the topographic map, and the ground of the monitoring points is flat.

[0018] In some embodiments of the present application, the time of the information collected by each monitoring point is synchronized.

[0019] In some embodiments of the present application, the acoustic signal transmission has at least one of the following functions: acoustic modulation protocol, relay node deployment, or adaptive power control.

[0020] In some embodiments of the present application, the denoising process includes eliminating ocean current movement noise, biological activity noise, solar wind interference, lightning interference, and tidal interference.

[0021] In some embodiments of the present application, the threshold is a dynamically adjusted threshold, and the dynamically adjusted threshold includes signal strength, duration, and spatial range.

[0022] In some embodiments of the present application, the data center is also used to store digital signals and construct a prediction model based on digital signals and historical earthquake event data, and the prediction model is used to predict the occurrence time and intensity of seafloor earthquakes.

[0023] In another aspect, the present application also provides a seafloor earthquake motion prediction device based on earthquake exploration, which is executed according to the seafloor earthquake motion prediction method based on earthquake exploration, and the device comprises:

[0024] An environmental monitoring unit for selecting a monitoring area, collecting seafloor topographic data of the monitoring area and drawing a topographic map;

[0025] A positioning unit for selecting a plurality of monitoring points according to the topographic map;

[0026] An acquisition unit for collecting geomagnetic field information, radio wave information, microseismic activity information, and seafloor deformation information of each monitoring point and recording;

[0027] A transmission unit for converting the collected information into acoustic signals and transmitting them to a sea surface buoy, and the sea surface buoy converts each acoustic signal into an electrical signal and transmits it to a ground data center;

[0028] A data center is used for de-noising and converting the received signals into digital signals.

[0029] An analysis unit classifies the same type of digital signals of each monitoring point and sets a threshold value, and when at least one type of digital signal exceeds the threshold value, an earthquake prediction warning is issued.

[0030] The technical scheme of the present application can achieve the following technical effects:

[0031] By optimizing the site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, and using acoustic-electric phased communication chain to realize efficient data transmission, and finally combining threshold analysis to realize early identification of earthquake precursors, this method can significantly improve the coverage, data accuracy and warning timeliness of submarine earthquake monitoring, and provide technical support for marine geological disaster prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a flowchart of the present application; DETAILED DESCRIPTION

[0033] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, device, electronic device and computer readable storage medium. Therefore, the present application can be specifically implemented as the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), hardware and software combined form. In addition, in some embodiments, the present application can also be implemented as a computer program product in one or more computer readable storage media, which contains computer program code.

[0034] The above computer readable storage medium can adopt any combination of one or more computer readable storage media. The computer readable storage medium includes: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination thereof. More specific examples of computer readable storage medium include: portable computer disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, optical disk read-only memory, optical storage device, magnetic storage device or any combination thereof. In this application, the computer readable storage medium can be any tangible medium containing or storing programs, which can be used or combined with instruction execution systems, devices, devices.

[0035] The acquisition, storage, use, processing, etc. of data in the technical scheme of the present application comply with the relevant provisions of national laws.

[0036] The method, device and electronic equipment provided by the present application are described by flowchart and / or block diagram.

[0037] It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer readable program instructions. These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0038] These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0039] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable data processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0040] The application is described in detail below with reference to the accompanying drawings. Embodiment

[0041] As shown in the figure, the seabed ground motion prediction method based on seismic exploration of the application comprises the following steps: Figure 1

[0042] S1, selecting a monitoring area, collecting seabed topographic data of the monitoring area and drawing a topographic map;

[0043] This step is the first step of seabed earthquake prediction, and the core goal is to determine the potential high-risk area of earthquake and provide accurate seabed geological structure basis for subsequent monitoring point layout; the selected monitoring area is the thrust fault that may trigger tsunami or seabed earthquake according to the actual engineering needs or the sea area of densely populated area;

[0044] The seabed topographic data can be collected in the following ways:

[0045] Multi-beam echo sounding system (MBES): mounted on a research ship or AUV, emitting a fan-shaped acoustic beam (frequency 12-50 kHz), calculating the water depth through echo time and angle, generating a topographic grid with a resolution of 1 m x 1 m, this method has the advantages of full coverage, high precision, and can identify fault scarps, submarine landslide bodies, etc.;

[0046] ​Side scan sonar (SSS): Provides seabed surface texture information (such as rock outcrops, sediment distribution), assists in judging fault activity traces (such as linear faulted landforms);

[0047] Laser radar (LIDAR): Through blue-green laser penetration of water, it obtains centimeter-level terrain details, which is mainly suitable for shallow sea;

[0048] Topographic mapping, importing collected data into GIS platform (such as QGIS or ArcGIS), performing coordinate unification and gridding interpolation, generating digital elevation model (DEM) and three-dimensional seabed topographic map, calculating slope, slope direction, curvature and other derived parameters, marking steep slope (> 30°) and flat area (< 5°), detecting linear structure through automatic fault recognition algorithm (such as Hough transform or machine learning), verifying fault extension and activity combined with seismic profile data (such as single-channel seismic), then outputting topographic map with terrain contour, fault line, historical epicenter superimposed layers and other comprehensive information, and dividing high, medium and low risk areas according to terrain complexity and fault density;

[0049] Of course, in some embodiments, seabed topographic data can also be collected by multi-beam sonar, and the multi-beam sonar system mainly includes a transmitting transducer array, a receiving transducer array, a motion sensor, a GNSS positioning system, and a sound velocity profiler. The functions of each component are as follows:

[0050] Transmitting transducer array: Transmit high-frequency fan-shaped sound beams (such as 30 kHz, beam angle 120°), covering the strip area perpendicular to the heading;

[0051] Receiving transducer array: Receive the echo signals reflected by the seabed, record the arrival time and angle of each beam;

[0052] Motion sensor (MRU): Real-time monitoring of ship body roll (Roll), pitch (Pitch), heave (Heave), dynamic compensation of sound wave transmission / reception geometry deviation;

[0053] Sound velocity profile data: Combined with real-time sound velocity profile, correct the bending of sound wave propagation path (sound ray tracing algorithm), ensure the accuracy of water depth calculation;

[0054] GNSS positioning system: Synchronously records the transmission position of each beam, providing spatial coordinates for subsequent data splicing.

[0055] In actual use, data collection can be carried out according to the following steps:

[0056] Before sailing: GNSS demarcates 10 parallel lines (spacing 6 km), SVP measures the sound velocity profile;

[0057] Acquisition: Transmit 30 kHz acoustic waves (cover width 7 km), MRU real-time compensation for 5° roll of the ship;

[0058] Processing: Software removes edge beams (opening angle > 60°), generates 30 m grid DTM;

[0059] Results: The topographic map shows that the fault scarp has a height difference of 200 m, guiding the layout of monitoring points.

[0060] Through the cooperation of multiple components (acoustics, positioning, attitude, and sound velocity) and hierarchical data processing, the multi-beam sonar realizes the accurate acquisition of seabed topography. Each component plays a specific role in the process, and finally outputs a high-resolution topographic model, providing basic spatial data support for the seismic monitoring system.

[0061] S2, select several monitoring points according to the topographic map;

[0062] In this method, the core principle of monitoring point selection is the priority of tectonic activity, i.e. near-field layout of faults. Monitoring points need to be deployed within 1-3 times the fault width range (e.g. 10-30 km for a 10 km wide fault) on both sides of the fault trace. This is because fault zones are areas of stress concentration, and seismic precursors such as microseisms and geomagnetic anomalies are more pronounced in these areas. Plate boundary type faults (such as subduction zones) need to be symmetrically deployed on the upper and lower plates to capture differential deformation.

[0063] At the same time, the seabed surface needs to be flat, i.e. selecting areas with a seabed slope < 5° (verified by slope matrix calculation using multi-beam topographic data). This ensures stable contact of monitoring equipment and avoids slipping (the risk of equipment overturning increases by 80% when the inclination is > 10°), reducing the shielding effect of terrain on acoustic signal transmission (steep slopes cause multi-path interference of sound waves).

[0064] The specific selection process is as follows:

[0065] Fault spatial analysis: Use GIS tools to extract fault lines from the topographic map (such as through curvature analysis or manual interpretation), generate fault buffer zones (Buffer Zone), and the buffer zone width = fault slip rate (mm / year) x coefficient (such as 1 km / mm / yr).

[0066] Flat area screening: Input multi-beam DEM data, calculate slope map and roughness index, extract areas with slope < 5° and roughness < 0.1, and exclude pseudo-flat areas with gravel or biological mounds on the surface based on side-scan sonar images;

[0067] Optimal point determination: Superimpose the fault buffer zone and flat area layer, take the intersection area, and confirm the basement lithology (preferably hard basement such as basalt) through AUV close-range photography.

[0068] The monitoring point site selection is a process of balancing tectonic sensitivity and engineering feasibility, which needs to comprehensively interpret geology, quantitatively analyze terrain and verify in the field. The standard can ensure that the observation equipment can effectively capture the precursor signal and work stably for a long time.

[0069] S3, collecting the geomagnetic field information, electric wave information, microseismic activity information and seabed deformation information of each monitoring point and recording;

[0070] In this step, the geomagnetic field information can be obtained by an ocean bottom magnetometer (OBM), which mainly generates a piezomagnetic effect (0.1-10 nT change) through fault friction and can reflect the stress state of rock; the electric wave information can be obtained by an ocean bottom electromagnetic instrument (OBEM), which measures the ultra-low frequency electric field (0.001-10 Hz), and the source is the electromagnetic signal (10-100 μV / km) induced by pore fluid flow, which can indicate the change of fault permeability; the microseismic activity information can be obtained by a broadband ocean bottom seismograph (OBS), which measures 0.01-50 Hz, can capture foreshocks / slow earthquakes (ML≥0.5), and can locate fault microfractures; the seabed deformation information can be obtained by a pore pressure gauge / inclinometer, which mainly measures vertical displacement and inclination angle, and seabed deformation is mainly caused by seabed uplift / subsidence through fault creep;

[0071] During collection, each monitoring point needs to be collected simultaneously, and the synchronization error is ≤1 ms (corresponding to the seismic wave propagation distance of about 5 m). The specific measurement method can use pulse test method, in which the main node transmits sound / light trigger signal, and calculates the response time dispersion of each node; or natural event method, which uses the time difference of teleseismic P wave arrival at each station to calculate the clock bias;

[0072] The technical system realizes three-dimensional observation of tectonic activity multi-parameter through multi-sensor collaborative networking and high-precision time synchronization, and the time synchronization error is controlled within milliseconds to microseconds, which meets the correlation analysis requirements of seismic precursor signals (such as electromagnetic-deformation coupling phenomenon accompanied by slow slip events), and provides evidence chain at the physical mechanism level for earthquake prediction.

[0073] S4, converting the collected information into acoustic signals and transmitting them to the sea surface buoy, and then converting the acoustic signals into electric signals and transmitting them to the ground data center;

[0074] Because electromagnetic waves, electrical signals, etc. decay very quickly in water, while acoustic waves decay relatively less, acoustic waves are the main means of long-distance underwater communication. Converting the collected information into acoustic signals can help information transmission. In the air, electrical signals are more convenient to transmit, and their attenuation is relatively small. Therefore, when the signal is transmitted to the buoy on the sea surface, the acoustic signal can be converted into an electrical signal for transmission. In this way, signal transmission functions in different paths in water and air are realized, and the signal transmission efficiency is maximized. The buoy can transmit the electrical signal to the ground station through radio (such as satellite, 4G / 5G) or optical fiber, and finally to the data center.

[0075] In the process of acoustic transmission, the following technical means can also be used to enhance signal transmission quality:

[0076] Acoustic modulation protocol, which defines how acoustic waves encode data (such as FSK, PSK, OFDM, etc.), solves underwater multipath, Doppler effect, etc. For example, adaptive modulation is used to dynamically adjust the modulation method according to the channel quality, improving reliability.

[0077] Relay node deployment, in long-distance transmission, deploy relay nodes (such as autonomous underwater vehicles, fixed relays) to relay and forward signals to avoid signal attenuation. For example, in deep sea monitoring, single-hop communication distance is limited (usually a few kilometers), and multiple-hop relaying is required.

[0078] Adaptive power control, dynamically adjust the transmission power according to environmental noise, distance, etc. to balance energy consumption and communication quality. Its main advantages are energy saving (very important for underwater devices with limited battery) and reducing multi-user interference.

[0079] In actual use, at least one of the above three functions (or combination) can be provided to cope with the challenges of underwater communication.

[0080] Through underwater acoustic-radio cross-medium communication, combined with modulation protocols, relay deployment, or power control, reliable transmission of underwater data to the ground center is achieved, which is an important part of ocean informationization.

[0081] S5, the data center performs denoising processing on the received signal and converts it into a digital signal.

[0082] The signal received by the data center from the sea surface buoy is an analog electrical signal converted from "underwater acoustic signal → electrical signal". This signal may contain valid data (such as temperature, salinity, sonar images collected by sensors, etc.) and environmental noise (ocean currents, biological, solar wind, lightning, tides, etc. Interference), because the underwater acoustic channel is complex, the signal may have been affected by multipath effect, Doppler shift, attenuation, etc. Therefore, denoising and digitizing processing are needed.

[0083] Different denoising methods can be used for different noise types, as follows:

[0084] Ocean current movement noise, which is caused by the scattering of sound waves due to seawater flow, is denoised by dynamically adjusting filter parameters to track changing noise and separate noise in different frequency bands.

[0085] Biological activity noise, which is caused by marine organisms such as whales and shrimps emitting sound (e.g., "shrimp singing"), is denoised by training a deep learning model (e.g., CNN) to recognize and filter out biological soundprints or suppressing in specific frequency bands (e.g., 2-20 kHz).

[0086] Solar wind interference, which is caused by electromagnetic noise coupling to the underwater acoustic channel due to solar activity, is denoised by using electromagnetic shielding to reduce coupling interference or using wavelet denoising to eliminate high-frequency impulse noise.

[0087] Lightning interference, which is caused by transient high-voltage pulses conducted through water by atmospheric lightning, is denoised by using transient noise suppression (TNR) to detect and eliminate sudden spikes or using nonlinear filtering (e.g., median filtering) to smooth out abnormal values.

[0088] Tidal interference, which is caused by periodic water level changes causing low-frequency pressure fluctuations, is denoised by using synchronous averaging to cancel out noise using the periodicity of tides or using high-pass filtering to filter out low-frequency fluctuations (<1 Hz).

[0089] Since computers generally process digital signals when processing data, the denoised signal needs to be converted to a digital signal. According to the Nyquist sampling theorem, the sampling frequency is ≥ 2 times the highest frequency of the signal (e.g., 10-50 kHz sampling rate for underwater acoustic communication), the continuous amplitude is discretized (e.g., 16-bit ADC, dynamic range up to 96 dB), and then it is converted to binary data (e.g., PCM encoding) for easy storage and analysis.

[0090] Denoising and digitization in data centers is the last link in the underwater information chain, requiring the integration of signal processing (filtering / transformation), hardware (ADC), AI (noise identification), and other technologies to cope with the extreme complexity of the marine environment. This process directly determines the usability of data and is a core technical support for marine observation, resource exploration, national defense, and other fields.

[0091] S6. Classify the same type of digital signals from each monitoring point and set a threshold. When at least one type of digital signal exceeds the threshold, an earthquake prediction warning is issued.

[0092] Specifically, the monitoring data of the same type of digital signals (such as geomagnetic field, electric wave, microseismic activity and seabed deformation) are grouped by type, for example, A type: geomagnetic field signal; B type: electric wave signal; C type: microseismic activity signal; D type: seabed deformation signal; According to the distribution of each type of signal and the monitoring point in the topographic map, a threshold value is set, which can be the threshold value of each type of signal in each monitoring point, or the overall threshold value of a single type of signal in the topographic map. When the digital signal exceeds the threshold value, an earthquake prediction warning can be sent through SMS or sound and light.

[0093] In actual use, the threshold value is not a fixed value, but is dynamically adjusted based on signal strength, duration, and spatial range. For example, if the A type signal in a certain area exceeds 3 times the historical mean standard deviation in intensity, lasts for 120 seconds, and 5 monitoring points are triggered simultaneously, it is determined as a precursor to an earthquake.

[0094] The adjustment process of the dynamic threshold value includes:

[0095] In the basic data preparation stage, the system continuously stores monitoring data for many years, classifies and archives them according to different geological regions, and uploads the latest readings from each monitoring device every second to form a continuous data stream. It also synchronously acquires background information such as ocean weather and ocean current activity.

[0096] The threshold value dynamic updating process continuously monitors the environmental noise level. When the background interference increases (such as during a storm), the intensity threshold value is automatically increased. During calm periods, the threshold value is lowered to improve sensitivity.

[0097] Multi-dimensional collaborative judgment: intensity dimension, compare the deviation of the current signal from the dynamic baseline; time dimension, track the duration of abnormal signals; spatial dimension, monitor the geographical spread of abnormal signals.

[0098] Threshold value application: a new set of threshold parameters is generated every 5 minutes, while 3 sets of historical threshold values are retained as verification references. When the signal exceeds the threshold value, a review mechanism is triggered, and after confirmation, it is upgraded to a warning event.

[0099] This dynamic adjustment mechanism continuously adapts to the environment and is verified by multiple parties, ensuring the timeliness of the warning while effectively reducing the false alarm rate.

[0100] When at least one type of digital signal exceeds the threshold value, an earthquake prediction warning is issued, which can achieve a multi-condition joint determination method to avoid missed reports (such as tsunami warning requiring high sensitivity) and false alarms. At the same time, the warning level can be graded according to the number of signal types that exceed the threshold value, such as: yellow warning, single signal exceeding threshold value, corresponding measures are to strengthen monitoring and manual review; Orange warning, two signals exceeding threshold value, lasting 5 minutes, response measures are to notify coastal emergency departments; Red warning, multiple signals exceeding threshold value + large-scale synchronous anomaly + lasting 10 minutes, corresponding measures are to issue public warnings and initiate evacuation.

[0101] The system realizes efficient earthquake prediction in complex marine environment through dynamic multi-dimensional threshold criterion and multi-source signal fusion, and the core is:

[0102] Adaptive threshold: avoid failure of fixed threshold in variable environment;

[0103] Space-time-intensity joint analysis: extract real precursor signals from noise;

[0104] Hierarchical response mechanism: balance warning sensitivity and false alarm cost.

[0105] In some embodiments of the present application, the prediction method further comprises monitoring the abnormality of the electron concentration in the region and the abnormality of the submarine heat flow by satellite detection;

[0106] The crust stress accumulation in the earthquake gestation stage will cause the increase of the surface escaped radon, the release of alpha particles by the decay of radioactive elements, the ionization of atmospheric molecules, the abnormal increase of the local ionosphere electron density, the sharp increase of the electron concentration in the 300km height above the epicenter 6-72 hours before the earthquake, and the typical increase amplitude of 20-35%, and the coverage range of up to 500km around the epicenter, the electron concentration is inversed by the delay amount (TEC value) of the signal passing through the ionosphere of the GPS / Beidou satellite navigation system, or the electron concentration is detected by a special satellite;

[0107] The reason of the abnormality of the submarine heat flow is that the fissure expansion caused by the earthquake precursor slow slip leads to the upwelling of deep hydrothermal fluid, and the increase of the heat conduction efficiency of the rock microfracture;

[0108] The technical system marks the paradigm shift of the earthquake prediction from "reporting earthquake by earthquake" to "multi-physical field precursor identification", effectively prolongs the prediction time, and improves the prediction accuracy.

[0109] In some embodiments of the present application, the data center is also used for storing digital signals and constructing a prediction model according to the digital signals and historical earthquake event data, and the prediction model is used to predict the occurrence time and intensity of the submarine earthquake;

[0110] In the present application, the data center not only undertakes the data storage function, but also integrates multi-source monitoring data (such as geomagnetic field, electric wave, microseismic activity, seafloor deformation, etc. digital signal) and historical earthquake event data to construct a prediction model, so as to predict the occurrence time and intensity of the submarine earthquake;

[0111] The digital signals stored in the data center include:

[0112] Real-time monitoring data: Geomagnetic field signals (e.g., piezomagnetic effect induced by fault friction), radio wave signals (e.g., electromagnetic changes induced by pore fluid flow), microseismic activity signals (e.g., magnitude, frequency of foreshocks or slow earthquakes), seafloor deformation signals (e.g., vertical displacement, tilt angle changes);

[0113] Historical seismic event data: Magnitude, epicenter location, time of occurrence, focal mechanism of historical earthquakes, historical earthquake precursor signals (e.g., geomagnetic anomalies, spatial and temporal characteristics of radio wave changes);

[0114] Data storage in standardized formats (e.g., CSV, HDF5, or database tables) ensures the integrity of timestamps (accurate to milliseconds) and spatial coordinates (e.g., latitude, longitude, depth), for example, using time series databases (e.g., InfluxDB) or distributed storage systems (e.g., Hadoop) to handle massive amounts of data;

[0115] The prediction model construction process includes:

[0116] Data preprocessing, noise removal (e.g., ocean current interference, biological soundprint, tidal fluctuations), use of wavelet denoising, median filtering or deep learning models (e.g., CNN) to identify and eliminate outliers, synchronize time series data to ensure time consistency of multi-source signals (error ≤1ms); feature extraction, extract statistical features (e.g., mean, variance, slope, abrupt change point) from time series, extract frequency domain features (e.g., spectral energy after Fourier transform), spatial features (e.g., deformation gradient in fault activity area, spatial correlation of multiple parameters);

[0117] Model selection and training, select time prediction model (e.g., ARIMA, for predicting earthquake occurrence) or intensity prediction model (e.g., divide magnitude into several levels) according to prediction target; training strategy is to divide historical data into training set (70%), validation set (15%) and test set (15%), use time series cross-validation (e.g., rolling window method) to avoid future data leakage, adjust model parameters (e.g., learning rate, tree depth, neural network layer number) through grid search or Bayesian optimization;

[0118] Model evaluation and optimization, use mean square error (MSE) to evaluate the model, for example, time prediction model can measure the accuracy of prediction time, intensity prediction model can evaluate the accuracy of prediction intensity; optimization methods can be ensemble learning: combine multiple models (e.g., LSTM + random forest) to improve robustness; online learning: real-time update model, adapt to data distribution changes (e.g., background noise fluctuations); transfer learning: use earthquake data from other regions to pretrain the model, reduce the demand for labeled data in new regions. Embodiment

[0119] The seabed seismic motion prediction device based on seismic exploration of the present application is executed according to the seabed seismic motion prediction method based on seismic exploration, and comprises:

[0120] An environmental monitoring unit is configured to select a monitoring area, collect seabed topographic data of the monitoring area, and draw a topographic map.

[0121] A positioning unit is configured to select a plurality of monitoring points according to the topographic map.

[0122] An acquisition unit is configured to collect geomagnetic field information, electric wave information, microseismic activity information, and seabed deformation information of each monitoring point and record the information.

[0123] A transmission unit is configured to convert the collected information into acoustic signals and transmit the acoustic signals to a sea surface buoy, and the sea surface buoy is configured to convert the acoustic signals into electric signals and transmit the electric signals to a ground data center.

[0124] The data center is configured to perform denoising processing on the received signals and convert the signals into digital signals.

[0125] An analysis unit is configured to classify the same type of digital signals of each monitoring point and set a threshold value, and when at least one type of digital signal exceeds the threshold value, a seismic prediction warning is issued.

[0126] In addition, the present application also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. The computer program is executed by the processor to implement each process of the method for controlling output data, and the same technical effects can be achieved. To avoid repetition, the above will not be described here.

[0127] The above only describes the preferred embodiments of the present application. It should be noted that for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and modifications can be made, which should also be considered as the protection scope of the present application.

Claims

1. A method for predicting submarine ground motion based on seismic exploration, characterized in that, Includes the following steps: Select a monitoring area, collect seabed topographic data for the monitoring area, and draw a topographic map; Several monitoring points were selected based on the topographic map; Collect and record geomagnetic field information, radio wave information, microseismic activity information, and seabed deformation information at each monitoring point; The collected information is converted into acoustic signals and transmitted to a sea surface buoy. The sea surface buoy then converts the acoustic signals into electrical signals and transmits them to a ground data center. The data center denoises the received signals and converts them into digital signals; The same type of digital signals from each monitoring point are classified and a threshold is set. When at least one type of digital signal exceeds the threshold, an earthquake prediction alarm is issued. The topographic mapping method includes importing the collected seabed topographic data of the monitoring area into a GIS platform, performing coordinate unification and grid interpolation, generating a digital elevation model and a three-dimensional seabed topographic map, calculating slope, aspect and curvature derived parameters, marking steep slopes and flat areas, detecting linear structures through an automatic fault identification algorithm, verifying fault extension and activity by combining seismic profile data, and outputting a topographic map with comprehensive information including topographic contour lines, fault lines and historical epicenter overlay layers. At the same time, high, medium and low risk areas are divided according to topographic complexity and fault density. Methods for selecting monitoring points include: Fault spatial analysis involves using GIS tools to extract fault lines from topographic maps and generating fault buffers. The width of the buffer is equal to the fault slip rate multiplied by a coefficient. Flat area screening: Input multibeam digital elevation model data, calculate slope map and roughness index, extract areas with slope <5° and roughness <0.1, and combine with side scan sonar images to exclude pseudo-flat areas with gravel or biomass on the surface. Once the optimal location is determined, the fault buffer zone and the flat zone layer are overlaid to obtain the intersection area, and the basement lithology in the intersection area is confirmed by imaging.

2. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The prediction method also includes using satellite detection to monitor regional electron concentration anomalies and seabed heat flow anomalies.

3. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The seabed topographic data was acquired using multibeam sonar.

4. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The monitoring point is located near a seabed fault in the topographic map, and the ground around the monitoring point is flat.

5. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The information collected by each monitoring point is synchronized in time.

6. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The acoustic signal transmission has at least one of the following functions: acoustic modulation protocol, relay node deployment, or adaptive power control.

7. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The noise reduction process includes eliminating noise from ocean currents, biological activity, solar wind, lightning, and tides.

8. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The threshold is a dynamically adjusted threshold, which includes signal strength, duration, and spatial range.

9. The method for predicting submarine ground motion based on seismic exploration according to claim 1, characterized in that, The data center is also used to store digital signals and build prediction models based on the digital signals and historical earthquake event data, and to use the prediction models to predict the timing and intensity of submarine earthquakes.

10. A submarine ground motion prediction device based on seismic exploration, characterized in that, The device performs the submarine ground motion prediction method based on seismic exploration as described in any one of claims 1-9, and the device comprises: The environmental monitoring unit is used to select a monitoring area, collect seabed topographic data of the monitoring area, and draw a topographic map. The positioning unit is used to select several monitoring points based on the topographic map; The acquisition unit is used to collect and record geomagnetic field information, radio wave information, microseismic activity information, and seabed deformation information from each monitoring point; The transmission unit is used to convert the collected information into acoustic signals and transmit them to the sea surface buoy. The sea surface buoy then converts the acoustic signals into electrical signals and transmits them to the ground data center. Data centers are used to denoise received signals and convert them into digital signals; The analysis unit categorizes the same type of digital signals from each monitoring point and sets a threshold. When at least one type of digital signal exceeds the threshold, an earthquake prediction alarm is issued.

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