A geophysical exploration system based on a subsea crawler

Abstract

The present invention provides a geophysical exploration system based on a subsea crawler, belonging to the technical field of geophysics, including: a survey ship, a subsea crawler, a gravimeter, a shallow layer profiler, a seismic source, a seismic receiver array, and a control chip. Among them, the survey ship is connected to the subsea crawler through a towing rope, and a drag cable is arranged at the tail of the subsea crawler, and related instruments are sequentially and fixedly arranged on the drag cable; the control chip is electrically connected to the drive unit of the subsea crawler and other instruments and performs data interaction; a geophysical exploration control module is arranged in the control chip, which is used to set the parameters of the seismic source, and preprocess the data collected by the receiver, and finally obtain geophysical exploration data and use a data transmission device to send the geophysical exploration data to the survey ship. The present invention solves the technical problem of low accuracy existing in the existing subsea geophysical exploration methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysics, and more particularly, relates to a geophysical exploration system based on a subsea crawler. Background Art

[0002] In recent years, with the continuous increase in marine resource exploration and development activities, the application demand for subsea geophysical exploration technology has also been increasing day by day. Through a comprehensive survey of the subsea geological structure and mineral resources, it can provide important geological basis for marine oil and gas exploitation, deep-sea mining, etc. Traditional subsea geophysical exploration mainly relies on ship platforms, using towed detection equipment, such as gravimeters, magnetometers, and multi-channel seismic survey systems, etc., to conduct comprehensive exploration of the seabed. This method has the advantages of wide detection range and rich information, but there are also some problems at the same time.

[0003] Firstly, ship platforms are limited by water depth and navigation conditions and it is difficult to enter some sea areas with complex terrains, such as ridge, canyon and other areas, thus causing detection blind spots. Secondly, towed detection equipment is easily affected by ocean currents, surges, etc., and it is very difficult to ensure the quality and accuracy of detection data. Moreover, traditional detection means often require large ships and professional operators, and the operation cost is relatively high. In addition, due to the complex and changeable subsea geological structure, it is very difficult for a single detection means to comprehensively depict the detailed information of the subsea strata.

[0004] In order to overcome the above problems, in recent years, subsea geophysical exploration systems based on underwater robots have emerged. Such systems usually carry detection equipment by an autonomous underwater vehicle (AUV) or a remotely operated vehicle (ROV), directly contact the seabed for detection, can penetrate into complex terrain areas, and improve the quality and accuracy of data. At the same time, underwater robots are flexible in operation, programmable for autonomous operation, and greatly reduce the labor cost. In addition, by integrating multiple detection means, such as gravimeters, magnetometers, sonar sounding, etc., more rich and comprehensive geological information can be obtained.

[0005] However, the existing detection systems based on underwater robots also have some defects. For shipborne geophysical exploration and near-surface towed geophysical exploration, due to the influence of sea surface wind, waves and currents, the noise is relatively large and the detection accuracy is relatively low; moreover, the extremely thick seawater layer will weaken or even completely cover the high-frequency information of the geophysical field, which also leads to a reduction in detection accuracy. For near-surface towed geophysical exploration, geophysical equipment such as magnetometers will be interfered by the tow body itself, resulting in relatively low accuracy. Summary of the Invention

[0006] In view of this, the present invention provides a geophysical exploration system based on a subsea crawler, which can solve the technical problem of relatively low accuracy existing in the existing subsea geophysical exploration methods.

[0007] The present invention is implemented as follows:

[0008] The present invention provides a geophysical exploration system based on a subsea crawler, which includes: a survey ship, a subsea crawler, a gravimeter, a shallow layer profiler, a seismic source, a seismic receiver array, and a control chip. The survey ship is connected to the subsea crawler by a towing rope. A drag cable is arranged at the tail of the subsea crawler. The gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array are sequentially and fixedly arranged on the drag cable. An electronic cabin is arranged inside the subsea crawler, and the data control chip is arranged in the electronic cabin. The control chip is electrically connected to the drive unit of the subsea crawler, the gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array and conducts data interaction. A geophysical exploration control module is arranged inside the control chip, which is used to set the parameters of the seismic source and preprocess the data collected by the receiver, and finally obtain geophysical exploration data. A data transmission device is also arranged in the electronic cabin. The control chip is electrically connected to the data transmission device, and the data transmission device is used to send the geophysical exploration data to the survey ship. Additionally, the gravimeter can also be directly and fixedly arranged on the subsea crawler.

[0009] Based on the above technical solution, a geophysical exploration system based on a subsea crawler of the present invention can also be improved as follows:

[0010] It further includes a self - contained geomagnetic diurnal variation station, which is carried by the crawler and deployed to the seabed, and recovered by the crawler after the operation is completed.

[0011] Furthermore, the survey ship is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

[0012] Furthermore, the subsea crawler adopts a tracked subsea crawling robot or a subsea ore collector, a subsea mining vehicle.

[0013] Furthermore, the vibration surface of the emission array of the seismic source contacts the seabed sediment.

[0014] Furthermore, it further includes a positioning beacon, which is fixed on the drag cable.

[0015] Furthermore, the data transmission device sends the geophysical exploration data to the survey ship through a data cable or a wireless channel.

[0016] Furthermore, a sensor module is also arranged on the subsea crawler, which at least includes a depth gauge, an altimeter, a gyroscope, and an accelerometer.

[0017] Further, it further includes a magnetometer, an electromagnetic instrument excitation source, and an electromagnetic instrument receiving array that are fixedly arranged on the drag cable in sequence.

[0018] Among them, the gravimeter, magnetometer, electromagnetic instrument excitation source, electromagnetic instrument receiving array, shallow layer profiler, seismic source, and seismic receiving array can each include zero, one, or more. Among them, the seismic source can be a percussion drill, air gun, heavy hammer striking device, etc., which are seismic sources with adjustable frequencies.

[0019] Among them, the geophysical exploration control module is used to perform the following steps:

[0020] S10. Control the subsea crawler to crawl in a predetermined detection area, obtain the pose information of the subsea crawler, and at the same time control the seismic source to continuously emit seismic waves and receive the reflected waves through the seismic receiving array;

[0021] S20. Preprocess the received reflected wave data, including denoising, filtering, and gain control;

[0022] S30. Use the sliding time window method to segment and analyze the preprocessed reflected wave data, and extract the characteristics of the reflected waves within each time window, including frequency, amplitude, and phase characteristics;

[0023] S40. According to a preset threshold, identify the abnormal points in the reflected wave characteristics, including amplitude abnormality, frequency abnormality, and phase abnormality;

[0024] S50. Align the identified abnormal points with the emitted seismic wave sequence in time, and establish a corresponding matrix between the abnormal points and specific seismic waves;

[0025] S60. Perform singular value decomposition on the corresponding matrix to obtain a basic matrix and a variation matrix;

[0026] S70. Input the variation matrix into a pre-trained seismic source parameter model to obtain corresponding optimization parameters, including source intensity and emission frequency; and control the seismic source to re-emit seismic waves according to the optimization parameters, and receive new reflected wave data through the seismic receiving array;

[0027] S80. Process the newly received reflected wave data, including dynamic correction, spectral analysis, and amplitude recovery, to improve the data quality;

[0028] S90. Fuse and analyze the processed reflected wave data with the data collected by the gravimeter and shallow layer profiler to generate comprehensive geophysical exploration data; further, the geophysical exploration data also fuses gravitational field, magnetic field, electromagnetic, and shallow profile data.

[0029] The seismic source parameter model of the seismograph adopts an adversarial neural network model. The generator is specifically a multi-layer perceptron, which contains 3 hidden layers, with 128, 64, and 32 neurons in each layer respectively. The ReLU activation function is used, and the tanh activation function is used in the output layer; the discriminator is specifically a convolutional neural network, which contains 3 convolutional layers and 2 fully connected layers. The LeakyReLU activation function is used in the convolutional layer, the ReLU activation function is used in the fully connected layer, and the sigmoid activation function is used in the output layer; the initial input of the generator is specifically a variation matrix and a 100-dimensional random noise vector; the initial input of the discriminator is specifically the initially set seismic source parameters and the corresponding reflected wave characteristics.

[0030] The loss function of the seismic source parameter model of the seismograph adopts the Wasserstein loss function.

[0031] The generator is specifically:

[0032] 1. Input layer: Receive two inputs, one is a vector containing 100 random noise values sampled from the standard normal distribution; the other is a variation matrix (assuming the dimension is m×n);

[0033] 2. Matrix processing layer: Use 1×1 convolution to reduce the dimension of the variation matrix and compress it into a k-dimensional vector (k < m×n); Concatenate the compressed k-dimensional vector with the 100-dimensional random noise vector to obtain a (100 + k)-dimensional vector;

[0034] 3. Fully connected layer: Include the first hidden layer: 256 neurons, using the LeakyReLU activation function; the second hidden layer: 128 neurons, using the LeakyReLU activation function; the third hidden layer: 64 neurons, using the LeakyReLU activation function;

[0035] 4. Output layer: Use the tanh activation function, and the output dimension is the same as the number of required seismic source parameters.

[0036] The steps for establishing the training data set of the seismic source parameter model of the seismograph:

[0037] 1. Data collection: Collect a large amount of historical seismic detection data, including seismic source parameters and corresponding reflected wave data under different seabed environments, ensuring that the data covers various seabed geological conditions and detection equipment parameters.

[0038] 2. Data preprocessing: Denoise the original data to eliminate environmental noise and equipment noise, standardize the reflected wave data to make data of different scales comparable, and normalize the seismic source parameters to ensure that all parameters are within the range of [-1, 1].

[0039] 3. Feature Extraction: Perform time-frequency analysis on the reflected wave data, extract frequency, amplitude, and phase features, calculate the statistical features of the reflected wave data such as mean, variance, skewness, and kurtosis, and extract the correlation index between the source parameters and the reflected wave features.

[0040] 4. Data Segmentation: Randomly divide the processed dataset into a training set (70%), a validation set (15%), and a test set (15%).

[0041] 5. Data Augmentation: Perform data augmentation on the training set, including operations such as adding random noise, small-scale scaling, and translation, generate synthetic data, and expand the dataset through interpolation and extrapolation of existing data.

[0042] 6. Variation Matrix Generation: For each group of training data, calculate the change trend of the reflected wave features, generate the corresponding variation matrix, and pair the variation matrix with the original source parameters and reflected wave features to form complete training samples.

[0043] The training steps of the seismic source parameter model of the seismograph are as follows:

[0044] 1. Model Initialization:

[0045] a. Initialize the generator and discriminator according to the provided structure.

[0046] b. Initialize the Adam optimizer for training the generator and discriminator respectively.

[0047] 2. The training loop is described in computer pseudocode as follows:

[0048]

[0049] 3. Model Fine-tuning:

[0050] Analyze the loss curve and performance metrics during the training process.

[0051] Adjust the learning rate, batch size, or model structure as needed.

[0052] If overfitting occurs, consider using regularization techniques or early stopping.

[0053] 4. Final Evaluation: Evaluate the performance of the final model on the test set and calculate key metrics such as mean square error, correlation coefficient, etc.

[0054] This training process makes full use of the information of the variation matrix and stabilizes the training process through the framework of Wasserstein GAN. Through this method, the model should be able to learn a more accurate source parameter generation strategy.

[0055] Among them, the specific steps of step S10 include: controlling the subsea crawler to crawl along a predetermined detection area, and obtaining its position and attitude information through the positioning sensors on the crawler; at the same time, controlling the seismic source to continuously emit seismic waves to the seabed with preset parameters (such as source intensity, emission frequency, etc.), and the reflected seismic wave signals are received by the seismic receiver array. The position and attitude information of the crawler, together with the emission time of the seismic waves and the reception time of the receiver array, can calculate the seabed position corresponding to the reflected wave signal, laying a foundation for subsequent formation analysis.

[0056] Among them, the specific steps of step S20 include: first, performing denoising processing on the received reflected wave signals to eliminate environmental noise and noise interference from the measurement equipment itself. Commonly used denoising methods include time-domain filtering, frequency-domain filtering, etc.; then, performing filtering processing to mainly remove some useless high-frequency or low-frequency components and improve the signal-to-noise ratio. Butterworth filters, Chebyshev filters, etc. can be used; finally, performing adaptive gain control to amplify weak signals and suppress strong signals to keep the overall dynamic range at an appropriate level. An adaptive algorithm based on LMSE can be used. Through the above preprocessing, the quality of the reflected wave signals can be significantly improved, laying a foundation for subsequent time-frequency feature extraction.

[0057] Among them, the specific steps of step S30 include: using the sliding time window method to perform segmented analysis on the preprocessed reflected wave data, dividing the entire reflected wave signal into several time windows, and each window contains a certain number of sampling points; for each time window, extracting its frequency, amplitude, and phase characteristics. The frequency characteristics can be obtained through short-time Fourier transform or wavelet transform, the amplitude characteristics can be calculated by the root mean square value of the signal, and the phase characteristics can directly extract the phase angle of the signal. In this way, the feature description of the reflected wave in the two dimensions of time and frequency is obtained, providing a basis for subsequent anomaly point identification.

[0058] Among them, the specific steps of step S40 include: identifying the anomaly points in the reflected wave characteristics according to the preset threshold. For the frequency characteristics, if the main frequency in a certain time window differs from that of the adjacent window by more than 10%, it is determined as a frequency anomaly; for the amplitude characteristics, if the root mean square value of a certain window exceeds 2 times the average value, it is determined as an amplitude anomaly; for the phase characteristics, if the phase change rate of a certain window exceeds 30 degrees / ms, it is determined as a phase anomaly. Through the above threshold judgment, the anomaly points in the reflected wave characteristics can be preliminarily screened out, providing important clues for subsequent geological analysis.

[0059] Among them, the specific steps of step S50 include: according to the time position of the abnormal points, align them with the emitted seismic wave sequence in time, and establish the corresponding relationship between the abnormal points and specific seismic waves. First, according to the seismic wave emission time and the receiving array receiving time, calculate the seabed positions corresponding to each reflected wave signal, and then map the abnormal points identified in step S40 to these positions to determine the seismic wave emission sequence they correspond to. Through this time alignment, a corresponding matrix between abnormal points and specific seismic waves can be established, providing important information for subsequent optimization of the seismic source parameters.

[0060] Among them, the specific steps of step S60 include: using the singular value decomposition (SVD) algorithm to decompose the abnormal point - seismic wave corresponding matrix, obtaining three matrices U, Σ, and V. Among them, U and V are regarded as the basic matrices, containing the basic characteristics of the seabed formation structure; Σ is regarded as the variable matrix, reflecting the dynamic changes of the formation structure. In this way, the original corresponding relationship between abnormal points and seismic waves is decomposed into a basic part and a variable part, providing more detailed input for subsequent optimization of the seismic source parameters.

[0061] Among them, the specific steps of step S70 include: input the variable matrix obtained in step S60 into a pre - trained seismic source parameter model of the seismograph to obtain the optimized seismic source parameters. This parameter model adopts the architecture of the generative adversarial network (GAN), including two main parts: a generator and a discriminator. The generator is responsible for outputting the optimized seismic source parameters according to the input variable matrix and random noise; the discriminator is responsible for determining whether these parameters match the real data. During the training process, the generator and the discriminator perform adversarial training with the Wasserstein loss function as the target until the convergence condition is reached.

[0062] Among them, the specific steps of step S80 include: First, according to the pose information of the crawler, perform dynamic correction on the reflected wave data to eliminate the time drift and spatial misalignment caused by the movement of the crawler; then, perform spectral analysis on the corrected reflected wave data to extract richer frequency information, which helps to identify the characteristics of different lithologic formations; at the same time, normalize the amplitude values to eliminate the attenuation caused by the propagation distance. Through the above processing, the quality of the reflected wave data can be further improved, laying a foundation for subsequent geophysical data fusion analysis.

[0063] Compared with the prior art, the beneficial effects of a geophysical exploration system based on a seabed crawler provided by the present invention are:

[0064] 1. The crawler directly contacts the seabed, enabling it to obtain more accurate position and attitude data, providing a basis for the spatial registration of detection data. Submarine geophysical exploration based on the seabed crawler can avoid the attenuation of the geophysical field by the seawater layer and improve the acquisition of high-frequency information of the geophysical field in marine geophysical exploration. By dragging a submarine geophysical sensor array behind the crawler, the interference of the electromagnetic field of the operation platform (survey ship, towed body or crawler) itself on the geophysical sensor can be avoided, and high-precision geophysical field information can be obtained.

[0065] 2. The crawler has strong working persistence and can operate on the seabed for a long time, greatly improving the continuity and integrity of data collection. It can break through terrain limitations and can penetrate into complex seabed landform areas, such as submarine canyons, ridges, etc., expanding the detection range.

[0066] 3. By adopting advanced signal processing and parameter optimization algorithms, the quality and reliability of the detection data are further improved.

[0067] In summary, the present invention solves the technical problem of the low accuracy existing in the existing submarine geophysical exploration methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the composition of a geophysical exploration system based on a seabed crawler provided by the present invention;

[0069] Figure 2 It is a flowchart of the steps executed by the geophysical exploration control module;

[0070] Figure 3 It is a schematic diagram of the system in the wired mode of Embodiment 2 of the present invention;

[0071] Figure 4 It is a schematic diagram of the system in the wireless mode of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0073] Such as Figure 1As shown in the figure, it is a schematic diagram of the composition of a geophysical exploration system based on a subsea crawler provided by the present invention, including: a survey ship, a subsea crawler, a gravimeter, a shallow layer profiler, a seismic source, a seismic receiver array, and a control chip. Among them, the survey ship is connected to the subsea crawler by a towing rope, and a drag cable is arranged at the tail of the subsea crawler. The gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array are successively fixedly arranged on the drag cable; an electronic cabin is arranged inside the subsea crawler, and the data control chip is arranged in the electronic cabin. The control chip is electrically connected to the drive unit, the gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array of the subsea crawler and conducts data interaction; a geophysical exploration control module is arranged inside the control chip, which is used to set the parameters of the seismic source and preprocess the data collected by the receiver, and finally obtain geophysical exploration data; a data transmission device is also arranged in the electronic cabin. The control chip is electrically connected to the data transmission device, and the data transmission device is used to send the geophysical exploration data to the survey ship. Further, it also includes a magnetometer, an electromagnetic source, and an electromagnetic receiver array that are successively fixedly arranged on the drag cable. Among them, the gravimeter, the magnetometer, the electromagnetic source, the electromagnetic receiver array, the shallow layer profiler, the seismic source, and the seismic receiver array can all include zero, one, or more. In addition, the gravimeter can also be directly fixedly arranged on the subsea crawler.

[0074] Among them, the seismic source can be a shock drill, an air gun, a drop hammer striking device, etc., which are sources that can adjust the frequency.

[0075] Among them, the geophysical exploration control module is used to perform the following steps:

[0076] S10. Control the subsea crawler to crawl in a predetermined exploration area, obtain the pose information of the subsea crawler, and at the same time control the seismic source to continuously emit seismic waves and receive the reflected waves through the seismic receiver array;

[0077] S20. Preprocess the received reflected wave data, including denoising, filtering, and gain control;

[0078] S30. Use the sliding time window method to segment and analyze the preprocessed reflected wave data, and extract the characteristics of the reflected waves within each time window, including frequency, amplitude, and phase characteristics;

[0079] S40. According to the preset threshold, identify the abnormal points in the reflected wave characteristics, including amplitude abnormality, frequency abnormality, and phase abnormality;

[0080] S50. Align the identified abnormal points with the emitted seismic wave sequence in time, and establish a corresponding matrix between the abnormal points and specific seismic waves;

[0081] S60. Perform singular value decomposition on the corresponding matrix to obtain a fundamental matrix and a variation matrix;

[0082] S70. Input the variation matrix into a pre-trained seismic source parameter model of a seismograph to obtain corresponding optimization parameters, including source strength and emission frequency; and control the seismic source of the seismograph to re-emit seismic waves according to the optimization parameters, and receive new reflected wave data through a seismograph receiving array;

[0083] S80. Process the newly received reflected wave data, including dynamic correction, spectral analysis, and amplitude recovery, to improve data quality;

[0084] S90. Perform fusion analysis on the processed reflected wave data and the data collected by a gravimeter and a shallow layer profiler to generate comprehensive geophysical exploration data.

[0085] The specific implementation manners of the above steps are described in detail below:

[0086] The specific implementation manner of step S10 is to first control the subsea crawler to crawl along a predetermined detection area, and at the same time obtain its position and attitude information through the positioning sensors on the crawler. These position and attitude information provide a basis for subsequent spatial registration of the detection data.

[0087] At the same time, the control chip is also responsible for coordinating the operation of the seismic source of the seismograph, making it continuously emit seismic waves to the seabed with preset parameters (such as source strength, emission frequency, etc.). These seismic waves will propagate in the seabed strata and be reflected back, and be received by the seismograph receiving array.

[0088] The position and attitude information of the crawler, plus the emission time of the seismic waves and the receiving time of the receiving array, can calculate the seabed position corresponding to the reflected wave signal, laying a foundation for subsequent strata analysis. The purpose of this step is to obtain basic detection data of the seabed environment, including the movement trajectory of the crawler and the reflection information of the seismic waves.

[0089] The specific implementation manner of step S20 is to preprocess the received reflected wave signal. First, perform denoising processing to eliminate environmental noise and noise interference of the measurement equipment itself. Commonly used denoising methods include time-domain filtering, frequency-domain filtering, etc.

[0090] Then perform filtering processing, focusing on removing some useless high-frequency or low-frequency components to improve the signal-to-noise ratio of the signal. Filtering methods can use Butterworth filters, Chebyshev filters, etc.

[0091] Finally, perform adaptive gain control to amplify weak signals and suppress strong signals, so that the overall dynamic range is maintained at an appropriate level, which is beneficial to subsequent signal analysis. The gain control can adopt an adaptive algorithm based on LMSE (least mean square error).

[0092] Through the above preprocessing, the quality of the reflected wave signal can be significantly improved, laying a foundation for subsequent time-frequency feature extraction. The purpose of this step is to perform preliminary noise elimination and energy adjustment on the original reflected wave data, creating favorable conditions for subsequent signal analysis.

[0093] The specific implementation of step S30 is to perform segmented analysis on the preprocessed reflected wave data using the sliding time window method. First, the entire reflected wave signal is divided into several time windows, and each window contains a certain number of sampling points.

[0094] For each time window, its frequency, amplitude, and phase characteristics are extracted. The frequency characteristics can be obtained through short-time Fourier transform or wavelet transform; the amplitude characteristics can be calculated by the root mean square value of the signal; the phase characteristics can be directly extracted from the phase angle of the signal.

[0095] In this way, the feature description of the reflected wave in both time and frequency dimensions is obtained. These features can reflect the geological information of different depth strata, providing a basis for subsequent abnormal point identification.

[0096] The purpose of this step is to perform detailed time-frequency feature extraction on the reflected wave signal, laying a foundation for the judgment of abnormal points and the optimization of seismic source parameters. Through segmented analysis, the variation laws of the reflected wave in the time and frequency domains can be better captured.

[0097] The specific implementation of step S40 is to identify abnormal points in the reflected wave features according to a preset threshold. First, reasonable thresholds are set for the three types of features of frequency, amplitude, and phase respectively.

[0098] For the frequency feature, if the main frequency in a certain time window differs from that of the adjacent window by more than 10%, it is determined as a frequency anomaly. For the amplitude feature, if the root mean square value of a certain window exceeds twice the average value, it is determined as an amplitude anomaly. For the phase feature, if the phase change rate of a certain window exceeds 30 degrees / ms, it is determined as a phase anomaly.

[0099] Through the above threshold judgment, the abnormal points in the reflected wave features can be preliminarily screened out. These abnormal points often correspond to the discontinuity surfaces or lithology changes of the seabed strata, providing important clues for subsequent geological analysis.

[0100] The purpose of this step is to extract geological abnormal information from the time-frequency features of the reflected wave, providing a basis for the optimization of seismic source parameters in the next step. Reasonable setting of the threshold is crucial for accurate identification of anomalies.

[0101] The specific implementation of step S50 is to align the anomaly points with the emitted seismic wave sequence according to their time positions, and establish the corresponding relationship between the anomaly points and specific seismic waves.

[0102] First, calculate the sea floor positions corresponding to each reflected wave signal based on the seismic wave emission time and the receiving time of the receiving array. Then, map the anomaly points identified in step S40 to these positions to determine the corresponding seismic wave emission sequence.

[0103] Through this time alignment, a corresponding matrix between the anomaly points and specific seismic waves can be established. This matrix describes the distribution characteristics of the anomaly points in time and space, providing important information for subsequent optimization of the seismic source parameters.

[0104] The purpose of this step is to establish the corresponding relationship between the identified anomaly points and the seismic wave emission process, laying the foundation for the next array decomposition. This time alignment work needs to make full use of the positioning information of the crawler and the propagation characteristics of seismic waves.

[0105] The specific implementation of step S60 is to perform array decomposition on the anomaly point - seismic wave corresponding matrix established in step S50 to obtain the basic matrix and the variation matrix.

[0106] First, use the singular value decomposition (SVD) algorithm to decompose the anomaly point - seismic wave corresponding matrix to obtain three matrices:

[0107] A = UΣV T ;

[0108] where A is the original matrix, U and V are orthogonal matrices, and Σ is a diagonal matrix containing the singular values of matrix A.

[0109] Next, regard U and V as the basic matrix, which contains the basic characteristics of the sea floor formation structure; regard Σ as the variation matrix, which reflects the dynamic changes of the formation structure.

[0110] In this way, the original corresponding relationship between the anomaly points and the seismic waves is decomposed into a basic part and a variation part, providing more detailed input for subsequent optimization of the seismic source parameters.

[0111] The purpose of this step is to extract the basic characteristics and dynamic changes of the formation structure from the corresponding relationship between the anomaly points and the seismic waves through matrix decomposition, providing more valuable information for the next parameter optimization.

[0112] The specific implementation of step S70 is to input the variation matrix obtained in step S60 into the pre - trained seismic source parameter model of the seismograph to obtain the optimized seismic source parameters.

[0113] This parametric model adopts the architecture of a generative adversarial network (GAN), including two main parts: a generator and a discriminator. The generator is responsible for outputting optimized seismic source parameters based on the input variation matrix and random noise, while the discriminator is responsible for determining whether these parameters match the real data.

[0114] The specific structure of the generator is as follows:

[0115] 1. Input layer: Receives the variation matrix and a 100-dimensional random noise vector

[0116] 2. Matrix processing layer:

[0117] a. Uses 1x1 convolution to reduce the dimension of the variation matrix and compress it into a k-dimensional vector

[0118] b. Concatenates the k-dimensional vector with the 100-dimensional noise vector to obtain a (100 + k)-dimensional vector

[0119] 3. Fully connected layer:

[0120] a. First hidden layer: 256 neurons, using the LeakyReLU activation function

[0121] b. Second hidden layer: 128 neurons, using the LeakyReLU activation function

[0122] c. Third hidden layer: 64 neurons, using the LeakyReLU activation function

[0123] 4. Output layer: Uses the tanh activation function and outputs with the same dimension as the number of seismic source parameters

[0124] The discriminator adopts the structure of a convolutional neural network, including 3 convolutional layers and 2 fully connected layers.

[0125] During the training process, the generator and the discriminator conduct adversarial training with the Wasserstein loss function as the objective until the convergence condition is reached. After training is completed, the variation matrix is input into the generator to obtain the optimized seismic source parameters.

[0126] The purpose of this step is to automatically optimize the parameters of the seismograph source according to the abnormal characteristics of the reflected waves by using the method of deep learning, creating conditions for the next data fusion analysis.

[0127] The specific implementation of step S80 is to further process the newly received reflected wave data, including dynamic correction, spectral analysis, amplitude recovery, etc.

[0128] First, according to the pose information of the crawler, dynamic correction is performed on the reflected wave data to eliminate the time drift and spatial misalignment caused by the movement of the crawler. This step ensures the consistency of the reflected wave data in time and space.

[0129] Next, perform spectral analysis on the corrected reflected wave data to extract richer frequency information. These frequency characteristics help to identify the characteristics of different lithologic strata. At the same time, normalize the amplitude values to eliminate the attenuation caused by the propagation distance.

[0130] Through the above processing, the quality of the reflected wave data can be further improved, laying a foundation for subsequent geophysical data fusion analysis. The purpose of this step is to conduct in-depth analysis on the optimized reflected wave data to extract more abundant and reliable geological information.

[0131] The specific implementation manner of step S90 is to perform fusion analysis on the processed reflected wave data and the data collected by a gravimeter and a shallow stratum profiler to generate comprehensive geophysical exploration data. Further, the geophysical exploration data also incorporates gravity field, magnetic field, electromagnetic, and shallow profile data.

[0132] Specifically, the principle of the present invention is as follows:

[0133] First, use a subsea crawler as a mobile platform. Compared with the traditional ship towing method, it can penetrate into complex subsea terrain areas to obtain more accurate position and attitude data. A variety of detection devices such as a gravimeter, a shallow stratum profiler, and a seismic measurement system are integrated on the crawler, which can comprehensively survey subsea geological information. At the same time, the crawler has strong working persistence and can operate on the seabed for a long time, making up for the shortcoming of the short operation time of underwater vehicles.

[0134] Secondly, the present invention adopts advanced algorithms in signal processing and parameter optimization. By extracting time-frequency domain characteristics and identifying abnormal points, the geological anomaly information contained in the reflected wave signal can be discovered, providing a basis for subsequent seismic source parameter optimization. Adopting the framework of a generative adversarial network (GAN), through the adversarial training of the generator and the discriminator, the working parameters of the seismic source of the seismograph can be adaptively adjusted to make it more in line with the characteristics of the current subsea environment, thereby obtaining more accurate seismic detection data.

[0135] Finally, the present invention uses Bayesian data fusion technology to integrate the information obtained by different detection means, such as gravity data, acoustic profile data, and seismic data, into a unified probability framework for comprehensive analysis. This method of multi-source information fusion can give full play to the advantages of various detection means and obtain more comprehensive and reliable geological information.

[0136] Generally speaking, the technical solution of the present invention fully considers the complexity of the seabed environment and the requirements of the detection task. By innovatively integrating various detection means, optimizing algorithm control, and data fusion and other measures, it effectively overcomes the deficiencies of the existing technology and greatly improves the efficiency and data quality of seabed geophysical exploration.

[0137] To better understand and implement the present invention, a specific embodiment 1 of the geophysical detection control module in the present invention is provided below. The steps of this embodiment 1 are specifically described as follows:

[0138] The specific implementation manner of step S10 is as follows:

[0139] First, control the seabed crawler to crawl along the predetermined detection area. The crawler is equipped with a positioning sensor, which can obtain its real-time position coordinates P t = [x t , y t , z t T and the attitude angle θ t = [θ x , θ y , θ z T , where t represents the timestamp. These pose information will provide a basis for subsequent spatial registration of the detection data.

[0140] At the same time, the control chip is also responsible for coordinating the work of the seismic source. The seismic source is set to continuously emit seismic waves to the seabed with predetermined parameters, such as the source intensity S, the emission frequency f, etc. These seismic waves propagate in the seabed formation and are received by the seismic receiver array to receive the reflected wave signal r(t). According to the emission time t s of the seismic wave and the reception time t r of the reception array, the seabed position P r corresponding to the reflected wave signal can be calculated. The specific calculation formula is:

[0141]

[0142] where c represents the propagation speed of sound waves in water.

[0143] Through the pose information of the crawler and the propagation characteristics of seismic waves, the corresponding relationship between the reflected wave signal and the seabed formation position can be established. This lays a foundation for subsequent formation analysis.

[0144] The specific implementation manner of step S20 is as follows:

[0145] First, perform denoising processing on the received reflected wave signal r(t) to eliminate environmental noise and noise interference of the measurement equipment itself. Commonly used denoising methods include time-domain filtering and frequency-domain filtering.

[0146] Time-domain filtering can use a Wiener filter, and its transfer function is:

[0147]

[0148] where S s (f) and S n (f) represent the power spectral densities of the signal and the noise respectively. By adaptively estimating these two spectral densities, an optimal Wiener filter can be constructed.

[0149] Frequency-domain filtering can use a Butterworth filter, and its amplitude-frequency characteristic is:

[0150]

[0151] where f c is the cut-off frequency and n is the order of the filter. Reasonable selection of f c and n can effectively remove useless high-frequency or low-frequency components.

[0152] Next, perform adaptive gain control on the denoised signal to amplify weak signals and suppress strong signals, so that the overall dynamic range is maintained at an appropriate level. The gain control can use an adaptive algorithm based on LMSE (Least Mean Square Error), and its recurrence formula is:

[0153] g(n) = g(n - 1) + μe(n)r(n - 1);

[0154] where g(n) is the gain value at the nth sampling point, μ is the step size parameter, and e(n) is the error at the nth sampling point, defined as the difference between the expected output and the actual output.

[0155] Through the above preprocessing, the quality of the reflected wave signal can be significantly improved, laying a foundation for subsequent time-frequency feature extraction.

[0156] The specific implementation manner of step S30 is as follows:

[0157] First, use the sliding time window method to perform segmented analysis on the preprocessed reflected wave data r(t). The specific method is to divide the entire reflected wave signal into N time windows, each window having a length of T w , and there is a certain overlap between adjacent windows. Denote the reflected wave signal within the ith time window as r i (t), where i = 1, 2,..., N.

[0158] For each time window r i (t), extract its frequency, amplitude, and phase features. The frequency feature can be obtained through the Short-Time Fourier Transform (STFT):

[0159]

[0160] Among them, R i (f) is the spectrum of the i-th time window, and f is the frequency variable. From R i (f), the main frequency f i max and other features can be extracted.

[0161] The amplitude feature can calculate the root mean square (RMS) value of the signal:

[0162]

[0163] The phase feature can directly extract the phase angle φ i .

[0164] Through the above processing, the feature description of the reflected wave in the two dimensions of time and frequency is obtained providing a basis for subsequent identification of abnormal points.

[0165] The specific implementation manner of step S40 is as follows:

[0166] According to the preset threshold, identify the abnormal points in the reflected wave features. Specifically, for the three types of features of frequency, amplitude, and phase, reasonable thresholds are set respectively:

[0167] Frequency anomaly threshold:

[0168] Amplitude anomaly threshold:

[0169] Phase anomaly threshold: |φ i -φ i-1 | > 30° / ms;

[0170] Among them, represents the global average amplitude value.

[0171] If the feature value within a certain time window exceeds any of the above thresholds, it is determined that there is an abnormal point in that window. These abnormal points often correspond to the discontinuity surface or lithology change of the seabed formation, providing important clues for subsequent geological analysis.

[0172] Through the above threshold judgment, geological anomaly information can be preliminarily screened from the time-frequency characteristics of the reflected wave, providing a basis for the optimization of the seismic source parameters in the next step.

[0173] The specific implementation manner of step S50 is as follows:

[0174] According to the time position of the abnormal points, align them with the emitted seismic wave sequence in time to establish the corresponding relationship between the abnormal points and specific seismic waves.

[0175] First, according to the seismic wave emission time t s and the reception time t of the receiving array r , calculate the seabed position P corresponding to each reflected wave signal r , as shown in step S10. Then, map the abnormal points identified in step S40 to these positions to determine the corresponding seismic wave emission sequence.

[0176] Specifically, for the abnormal points within the i-th time window, the corresponding seismic wave emission time can be calculated as:

[0177]

[0178] Through this time alignment, an M×N abnormal point - seismic wave correspondence matrix A can be established, where M is the number of abnormal points and N is the number of seismic wave emissions. Matrix A describes the distribution characteristics of abnormal points in time and space, laying a foundation for subsequent array decomposition.

[0179] The specific implementation manner of step S60 is as follows:

[0180] Perform singular value decomposition (SVD) on the abnormal point - seismic wave correspondence matrix A established in step S50 to obtain:

[0181] A = UΣV T ;

[0182] where U and V are orthogonal matrices, and Σ is a diagonal matrix containing the singular values of matrix A.

[0183] Next, regard U and V as the basis matrices, which contain the basic characteristics of the seabed formation structure; regard Σ as the variation matrix, which reflects the dynamic changes of the formation structure.

[0184] Specifically, the basis matrices U and V describe the inherent relationship between abnormal points and seismic waves, while the variation matrix Σ reflects the dynamic change law of this relationship.

[0185] In this way, the original abnormal point - seismic wave correspondence relationship is decomposed into a basic part and a variation part, providing more detailed input for subsequent optimization of seismic source parameters.

[0186] The specific implementation manner of step S70 is as follows:

[0187] Input the variation matrix Σ obtained in step S60 into the pre-trained seismic source parameter model of the seismograph to obtain optimized seismic source parameters.

[0188] This parametric model adopts the architecture of a generative adversarial network (GAN), including two main parts: a generator G and a discriminator D. The generator is responsible for outputting the optimized seismic source parameters S based on the input covariance matrix Σ and random noise z; the discriminator is responsible for determining whether these parameters match the real data.

[0189] The specific structure of the generator is as follows:

[0190] 1. Input layer: Receives two inputs

[0191] a. A vector z containing 100 random noise values, sampled from the standard normal distribution;

[0192] b. The covariance matrix Σ;

[0193] 2. Matrix processing layer:

[0194] a. Use a 1×1 convolution to reduce the dimensionality of the covariance matrix Σ and compress it into a k-dimensional vector;

[0195] b. Concatenate the compressed k-dimensional vector with the 100-dimensional random noise vector z to obtain a (100 + k)-dimensional vector;

[0196] 3. Fully connected layer:

[0197] a. The first hidden layer: 256 neurons, using the LeakyReLU activation function;

[0198] b. The second hidden layer: 128 neurons, using the LeakyReLU activation function;

[0199] c. The third hidden layer: 64 neurons, using the LeakyReLU activation function;

[0200] 4. Output layer: Using the tanh activation function, the output dimension is the same as the number of seismic source parameters.

[0201] The discriminator adopts the structure of a convolutional neural network, including 3 convolutional layers and 2 fully connected layers.

[0202] During the training process, the generator and the discriminator perform adversarial training with the Wasserstein loss function as the objective until the convergence condition is reached. After training is completed, input the covariance matrix Σ into the generator to obtain the optimized seismic source parameters S. This method makes full use of the information of the covariance matrix and can learn a more accurate seismic source parameter generation strategy.

[0203] The specific implementation of step S80 is as follows:

[0204] First, according to the pose information P of the crawler t and θ t, Perform dynamic correction on the reflected wave data r(t) to eliminate the time drift and spatial misalignment caused by the movement of the crawler.

[0205] Specifically, the coordinate transformation of the reflected wave signal can be performed through the following formula:

[0206]

[0207] where r′(t) is the corrected reflected wave signal. This ensures the consistency of the reflected wave data in time and space.

[0208] Next, perform spectral analysis on the corrected reflected wave data r′(t) to extract richer frequency information R′(f) = STFT(r′(t)). These frequency characteristics help to identify the characteristics of different lithologic strata.

[0209] At the same time, normalize the amplitude values of the reflected waves to eliminate the attenuation caused by the propagation distance. The specific formula is:

[0210]

[0211] Through the above processing, the quality of the reflected wave data can be further improved, laying a foundation for subsequent geophysical data fusion analysis.

[0212] The specific implementation manner of step S90 is as follows:

[0213] The reflected wave data processed in step S80 and R′(f) are fused and analyzed with the data of the gravimeter and the shallow stratum profiler obtained in step S10 to generate comprehensive geophysical exploration data.

[0214] Specifically, the Bayesian data fusion method can be used to integrate the data obtained by different detection devices into a unified probability framework. Assume that the reflected wave data and R′(f) follow a Gaussian distribution, N(0, C r ), where the covariance matrix C r can be estimated through the signal preprocessing steps. At the same time, the gravity data g and the shallow stratum profile data v also follow Gaussian distributions, N(0, C g ) and N(0, C v ).

[0215] According to Bayesian theory, the optimal estimate of data fusion can be obtained as:

[0216]

[0217] where, is the covariance matrix of the fused data.

[0218] In this way, the comprehensive geophysical exploration data x is obtained. fused , which includes multi-source information such as reflected waves, gravity, and shallow stratum profiles. This data fusion method can effectively improve the reliability and accuracy of the exploration results, providing a richer and more credible basis for subsequent geological interpretation.

[0219] To better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: As Figures 3 - 4 shown, this Example 2 describes a subsea geophysical exploration operation carried out in the northern slope area of the South China Sea. The water depth in this area ranges from 500 to 1500 meters, the seabed topography is complex, and there are multiple canyons and submarine landslides. The objective of this exploration is to conduct a detailed geophysical exploration of this area to understand the subsea geological structure, gravity anomaly distribution, and shallow sediment characteristics.

[0220] 1. Detection system configuration

[0221] The subsea geophysical exploration system based on a subsea crawler used in this exploration includes the following components:

[0222] 1.1 The survey ship used, with a total length of 85 meters, a width of 16 meters, and a displacement of 3500 tons. The following equipment is equipped on the ship:

[0223] Satellite navigation system: Adopting a Beidou / GPS dual-mode positioning system, with a positioning accuracy better than 0.5 meters;

[0224] Underwater positioning system: Using an ultra-short baseline (USBL) positioning system, with a positioning accuracy better than 1.5 meters at a water depth of 1000 meters;

[0225] Power supply unit: Providing 380V alternating current, with a maximum output power of 200kW;

[0226] 1.2 The subsea crawler adopts a tracked subsea crawling robot, model "Hailong I", with the main parameters as follows:

[0227] Dimensions: 3.5 meters in length, 2.2 meters in width, and 1.8 meters in height;

[0228] Weight: 3 tons in water;

[0229] Maximum working depth: 3000 meters;

[0230] Maximum crawling speed: 2 km / h;

[0231] Endurance time: 48 hours (fully battery-powered mode);

[0232] 1.3 Towing cable

[0233] Length: 200 meters;

[0234] Diameter: 32 mm;

[0235] Material: Steel wire rope core polyurethane sheath;

[0236] Load capacity: 5 tons;

[0237] 1.4 The gravimeter uses a seafloor gravimeter, model "Gravity Detection 100", and the main parameters are as follows:

[0238] Measurement range: 0 - 2000 mGal;

[0239] Accuracy: 0.01 mGal;

[0240] Drift: < 0.1 mGal / month;

[0241] Sampling frequency: 1 Hz;

[0242] 1.5 The shallow stratum profiler uses a parameterized shallow stratum profiler, model "Seafloor SBP2000", and the main parameters are as follows:

[0243] Operating frequency: 27 kHz;

[0244] Resolution: 10 cm;

[0245] Penetration depth: Maximum 100 m (depending on sediment type);

[0246] Transmission power: 2 kW;

[0247] 1.6 The seismic source of the seismograph uses an air gun source, model "Ocean Seismic Source 500", and the main parameters are as follows:

[0248] Capacity: 500 cubic inches;

[0249] Operating pressure: 2000 psi;

[0250] Main frequency: 10 - 150 Hz;

[0251] Peak sound pressure level: 220 dB re 1 μPa@1 m;

[0252] 1.7 The receiving array of the seismograph uses a streamer receiving array, model "Ocean Receiver 96", and the main parameters are as follows:

[0253] Number of channels: 96 channels;

[0254] Channel spacing: 12.5 m;

[0255] Sensitivity: 170 dB re 1 V / μPa;

[0256] Frequency response: 5 - 1000 Hz;

[0257] 1.8 The control chip uses a high-performance embedded processor, model "Ocean Control 3000", and its main parameters are as follows:

[0258] CPU: Quad-core ARM CortexA72, with a main frequency of 2.0 GHz;

[0259] RAM: 16 GB;

[0260] Storage: 512 GB SSD;

[0261] Interfaces: Gigabit Ethernet, optical fiber, RS232 / 485;

[0262] 1.9 The data transmission device uses an optical and electrical composite cable transmission system, and its main parameters are as follows:

[0263] Transmission bandwidth: 10 Gbps;

[0264] Maximum transmission distance: 50 km;

[0265] Number of optical fiber cores: 4 cores;

[0266] Power transmission: 1000 V DC, with a maximum power of 100 kW;

[0267] 1.10 The self-contained geomagnetic diurnal variation station uses a three-component magnetometer, model "Ocean Magnetic Field 3C", and its main parameters are as follows:

[0268] Measurement range: ±100000 nT;

[0269] Resolution: 0.01 nT;

[0270] Sampling frequency: 1 Hz;

[0271] Battery life: 30 days;

[0272] 1.11 The positioning beacon uses an acoustic release positioning beacon, model "Ocean Positioning AR", and its main parameters are as follows:

[0273] Operating frequency: 816 kHz;

[0274] Sound source level: 190 dB re 1μPa@1m;

[0275] Battery life: 18 months;

[0276] Maximum operating depth: 6000 m;

[0277] 2. Detection operation process

[0278] 2.1 Preparation work

[0279] (1) Equipment inspection and installation

[0280] Before the detection operation, technicians conduct a comprehensive inspection of all equipment. The gravimeter, sub-bottom profiler, seismic source, and seismic receiver array are successively fixed on the towing cable at an interval of 15 meters. The self-contained geomagnetic diurnal variation station is installed in the special card slot of the seafloor crawler.

[0281] (2) System testing

[0282] Conduct a comprehensive test of the entire system on the deck of the survey ship to ensure that each component works properly and data transmission is unobstructed. The test contents include: testing the driving system of the seafloor crawler; testing the functions of each detection device; testing the data transmission system; testing the control system;

[0283] (3) Parameter setting

[0284] According to the characteristics of the detection area and the detection target, set the following parameters:

[0285] Seafloor crawler crawling speed: 1.5 km / h;

[0286] Gravimeter sampling interval: 1 s;

[0287] Sub-bottom profiler emission interval: 0.5 s;

[0288] Seismic source emission interval: 10 s;

[0289] Seismic receiver array sampling rate: 1000 Hz;

[0290] 2.2 Equipment deployment

[0291] (1) The survey ship sails to the starting point of the predetermined detection area (116°30'E, 20°15'N) and decelerates to 2 knots.

[0292] (2) Technicians start to deploy the towing cable and the detection equipment fixed on it. First, lower the seismic receiver array into the water, and then successively the seismic source, sub-bottom profiler, and gravimeter.

[0293] (3) When the towing cable is deployed to 180 meters, use the deck crane to slowly lower the seafloor crawler into the water.

[0294] (4) According to the water depth (the water depth here is about 1000 meters), continue to pay out the cable until the seafloor crawler is about 50 meters from the seabed.

[0295] (5) Control the seafloor crawler to slowly descend and land. Real-time monitor the position of the crawler through the depth gauge and altimeter to ensure a safe landing.

[0296] 2.3 Detection operation

[0297] (1) After the subsea crawler lands, it starts to crawl along a preset path. The path is set as a series of parallel survey lines with a line spacing of 500 meters and a total coverage area of 10 square kilometers.

[0298] (2) The geophysical exploration control module performs the following steps:

[0299] S10: Control the subsea crawler to crawl in the predetermined exploration area to obtain the pose information of the subsea crawler. At the same time, control the seismic source to emit seismic waves every 10 seconds, with a peak sound pressure level of 215 dB re 1μPa@1m and a main frequency set to 50 Hz. The seismic receiver array continuously receives the reflected waves.

[0300] S20: Preprocess the received reflected wave data, including:

[0301] Denoising: Adopt the wavelet transform method with a threshold set to 3 times the standard deviation;

[0302] Filtering: Use a band-pass filter with a passband of 10 - 200 Hz;

[0303] Gain control: Adopt the programmable gain method with a gain curve of g(t) = 1 + 0.5t, where t is the two-way travel time (unit: second);

[0304] S30: Use the sliding time window method to perform segmented analysis on the preprocessed reflected wave data. The time window length is set to 100 ms and the overlap rate is 50%. Extract the reflected wave features within each time window, including:

[0305] Frequency features: Main frequency, bandwidth;

[0306] Amplitude features: Root mean square amplitude, peak value;

[0307] Phase features: Instantaneous phase, phase consistency;

[0308] S40: Identify the abnormal points in the reflected wave features according to the preset threshold:

[0309] Amplitude anomaly: Exceeding the mean ± 3 times the standard deviation;

[0310] Frequency anomaly: The main frequency deviates from the expected value by more than 20%;

[0311] Phase anomaly: The phase consistency is less than 0.6;

[0312] S50: Align the identified abnormal points with the emitted seismic wave sequence to establish a corresponding matrix between the abnormal points and specific seismic waves. The matrix dimension is m×n, where m is the number of abnormal points and n is the number of seismic wave sequences.

[0313] S60: Perform singular value decomposition (SVD) on the corresponding matrix to obtain the fundamental matrix and the variation matrix.

[0314] S70: Input the variation matrix into the pre-trained seismic source parameter model of the seismograph to obtain the optimized parameters:

[0315] Seismic source intensity: The adjustment range is 210 - 220 dB re 1μPa@1m;

[0316] Emission frequency: The adjustment range is 30 - 70 Hz;

[0317] Control the seismic source of the seismograph to re-emit seismic waves according to the optimized parameters, and receive new reflected wave data through the seismograph receiving array.

[0318] S80: Process the newly received reflected wave data:

[0319] Dynamic correction: Use the parabolic scanning method, and the velocity analysis interval is 500 meters;

[0320] Spectrum analysis: Adopt the short-time Fourier transform, and the time window length is 50 ms;

[0321] Amplitude recovery: Use spherical spreading and absorption compensation, and the absorption coefficient is set to 0.5 dB / km / Hz;

[0322] S90: Perform fusion analysis on the processed reflected wave data with the data collected by the gravimeter and the shallow layer profiler to generate comprehensive geophysical exploration data.

[0323] (3) At the predetermined position (116°32'E, 20°17'N), control the subsea crawler to stop, and use the manipulator to place the self-contained geomagnetic diurnal variation station on the seabed. After the placement is completed, the subsea crawler continues to crawl along the predetermined path.

[0324] (4) The gravimeter continuously collects gravity data, and the sampling interval is 1 second. The data is preliminarily processed through the control chip, including tidal correction and instrument drift correction.

[0325] (5) The shallow layer profiler emits acoustic waves once every 0.5 seconds, the frequency is set to 5 kHz, and the transmission power is 1.5 kW. The received echo signal generates a shallow seabed profile after envelope detection and time-depth conversion.

[0326] (6) The seismograph receiving array continuously receives seismic waves, and the sampling rate is 1000 Hz. The original data generates a seabed seismic profile after the aforementioned processing steps.

[0327] 2.4 Data transmission

[0328] (1) The control chip sends the processed geophysical exploration data to the research vessel in real time through the data transmission device. The data transmission rate is 5 Gbps.

[0329] (2) The data receiving system on the research vessel continuously receives and stores the exploration data, while performing real-time display and preliminary analysis.

[0330] 2.5 Operation Monitoring

[0331] (1) The monitoring personnel on the research vessel monitor the entire exploration process through the real-time transmitted data, including: the position, attitude and motion state of the subsea crawler, the working state and data quality of each exploration device, and the seabed topography and geological conditions;

[0332] (2) If any abnormal situation is found, the monitoring personnel can make real-time adjustments to the subsea crawler and each exploration device through the control system.

[0333] 2.6 Recovery of Self-Contained Geomagnetic Diurnal Variation Station

[0334] (1) After the exploration operation is completed, the research vessel controls the subsea crawler to return to the deployment position of the self-contained geomagnetic diurnal variation station according to the position information of the positioning beacon.

[0335] (2) After the subsea crawler reaches the designated position, the manipulator is used to fish and fix the self-contained geomagnetic diurnal variation station and its attached positioning beacon on the crawler.

[0336] 2.7 Equipment Recovery

[0337] (1) The research vessel winds in the composite cable through the cable winch to slowly raise the subsea crawler.

[0338] (2) When the subsea crawler approaches the sea surface, the deck crane is used to lift it and recover it to the deck.

[0339] (3) Continue to wind in the cable to recover the gravimeter, shallow layer profiler, seismic source and seismic receiver array in sequence.

[0340] (4) After all the equipment is recovered, the technical personnel inspect, clean and maintain the equipment.

[0341] 3. Data Processing and Analysis

[0342] 3.1 Gravity Data Processing

[0343] (1) The following processing is performed on the original gravity data:

[0344] Instrument drift correction: Use a linear drift model with a drift rate of 0.05 mGal / day;

[0345] Correction: Consider the influence of the earth's rotation and the motion of the measurement platform;

[0346] Free-air correction: Using elevation data measured by GPS;

[0347] Bouguer correction: Assuming the density of seawater is 1.03 g / cm 3 , and the density of seafloor sediments is 2.0 g / cm 3 ;

[0348] (2) Generate free-air anomaly maps and Bouguer anomaly maps with a grid spacing of 100 meters.

[0349] 3.2 Magnetic data processing

[0350] (1) Process the data collected by the self-contained geomagnetic diurnal variation station as follows: Diurnal variation correction: Using data from the nearest land magnetic observatory, IGRF correction: Removing the influence of the Earth's main magnetic field;

[0351] (2) Generate total magnetic intensity anomaly maps with a grid spacing of 100 meters.

[0352] 3.3 Shallow seismic profile data processing

[0353] (1) Process the shallow seismic profile data as follows:

[0354] Denoising: Using median filtering with a window size of 5×5;

[0355] Gain control: Applying automatic gain control (AGC) with a time window length of 20 ms;

[0356] Deconvolution: Using predictive deconvolution with an operator length of 30 ms;

[0357] Static correction: Performing static correction based on seafloor topography data;

[0358] (2) Generate two-dimensional shallow seismic profiles with a horizontal resolution of 1 meter and a vertical resolution of 0.1 meter.

[0359] 3.4 Seismic data processing

[0360] (1) Perform the following advanced processing on the seismic data:

[0361] Multiple suppression: Using the predictive deconvolution method with an operator length of 300 ms;

[0362] Velocity analysis: Conducting velocity analysis every 500 meters to generate a velocity model;

[0363] Pre-stack time migration: Using the Kirchhoff migration algorithm with a migration aperture of 3000 meters;

[0364] Post-stack depth conversion: Converting the time section to a depth section based on the velocity model;

[0365] (2) Generate a two-dimensional seismic profile with a horizontal resolution of 12.5 meters and a vertical resolution of 5 meters.

[0366] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A geophysical exploration system based on a subsea crawler, characterized in that, Comprising: An investigation ship, a subsea crawler, a gravimeter, a shallow layer profiler, a seismic source, a seismic receiver array, and a control chip. Among them, the investigation ship is connected to the subsea crawler by a towing rope. A drag cable is arranged at the tail of the subsea crawler. The gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array are sequentially and fixedly arranged on the drag cable. An electronic cabin is arranged inside the subsea crawler, and the control chip is arranged in the electronic cabin. The control chip is electrically connected to the drive unit of the subsea crawler, the gravimeter, the shallow layer profiler, the seismic source, and the seismic receiver array and conducts data interaction. A geophysical exploration control module is arranged inside the control chip, which is used to set the parameters of the seismic source and preprocess the data collected by the receiver, and finally obtain geophysical exploration data. A data transmission device is also arranged in the electronic cabin. The control chip is electrically connected to the data transmission device, and the data transmission device is used to send the geophysical exploration data to the investigation ship. The geophysical exploration control module is used to perform the following steps: S10. Control the subsea crawler to crawl in a predetermined exploration area, obtain the pose information of the subsea crawler, and at the same time control the seismic source to continuously emit seismic waves and receive the reflected waves through the seismic receiver array; S20. Preprocess the received reflected wave data, including denoising, filtering, and gain control; S30. Use the sliding time window method to segment and analyze the preprocessed reflected wave data, and extract the reflected wave characteristics within each time window, including frequency, amplitude, and phase characteristics; S40. According to the preset threshold, identify the abnormal points in the reflected wave characteristics, including amplitude abnormality, frequency abnormality, and phase abnormality; S50. Align the identified abnormal points with the emitted seismic wave sequence in time, and establish a corresponding matrix between the abnormal points and specific seismic waves; S60. Perform singular value decomposition on the corresponding matrix to obtain a fundamental matrix and a variation matrix; S70. Input the variation matrix into a pre-trained seismic source parameter model to obtain corresponding optimization parameters, including source intensity and emission frequency; and control the seismic source to re-emit seismic waves according to the optimization parameters and receive new reflected wave data through the seismic receiver array; S80. Process the newly received reflected wave data, including dynamic correction, spectral analysis, and amplitude recovery, to improve the data quality; S90. Perform fusion analysis on the processed reflected wave data and the data collected by the gravimeter and the shallow layer profiler to generate comprehensive geophysical exploration data.

2. The geophysical exploration system based on a subsea crawler according to claim 1, wherein It also includes a self - contained geomagnetic diurnal variation station, which is carried by the crawler and deployed to the seabed, and recovered by the crawler after the operation is completed.

3. A geophysical exploration system based on a subsea crawler according to claim 2, characterized in that, The subsea crawler adopts a tracked subsea crawling robot, a multi - legged subsea crawling robot, or a subsea mining vehicle.

4. The geophysical exploration system based on a subsea crawler according to claim 3, wherein, The data transmission device sends the geophysical exploration data to the investigation ship through a data cable or a wireless channel.

5. A geophysical exploration system based on a subsea crawler according to claim 4, characterized in that, The vibration surface of the emission array of the seismic source contacts the seabed sediment.

6. The geophysical exploration system based on a subsea crawler according to claim 5, wherein It further includes a positioning beacon which is fixed on the towing cable.

7. A geophysical exploration system based on a subsea crawler according to claim 6, wherein, The survey ship is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

8. A geophysical exploration system based on a subsea crawler according to claim 7, characterized in that, The seafloor crawler is further provided with a sensor module, which at least includes a depth gauge, an altimeter, a gyroscope, and an accelerometer.

9. A geophysical exploration system based on a subsea crawler according to claim 8, wherein, It further includes a magnetometer, an electromagnetic instrument excitation source, and an electromagnetic instrument receiving array which are fixedly arranged on the towing cable in sequence.

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