A high-precision magnetic data processing method for aeromagnetic measurement

Abstract

The present invention belongs to the field of data processing, and more particularly, relates to a high-precision magnetic data processing method for aeromagnetic surveys. This method comprises the following steps: first, adaptively suppressing noise on collected aeromagnetic data using a convolutional neural network through an adaptive noise suppression network; second, combining enhanced wavelet packet transform with a particle swarm optimization algorithm for denoising; and finally, dynamically correcting the magnetic data using a deep reinforcement learning method. This method utilizes multiple technical means to effectively remove environmental noise and equipment interference noise, improving denoising and signal quality. It can also adjust correction parameters based on real-time data feedback, increasing correction accuracy and ultimately ensuring high-precision magnetic data.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and in particular relates to a high-precision magnetic survey data processing method for aeromagnetic measurement. Background Art

[0002] With the increasing demand for geological exploration and resource surveys, aeromagnetic surveying has become a widely used geophysical exploration method. By recording the spatial distribution of the Earth's magnetic field, aeromagnetic surveys provide important data on geological structure, mineral resource distribution, and other aspects. However, because aeromagnetic surveys are often conducted in complex terrain and electromagnetic environments, data are subject to multiple factors, including environmental noise, electromagnetic interference, and equipment errors. This can reduce measurement accuracy and increase the difficulty of post-processing. Existing data processing methods primarily rely on signal filtering and noise suppression techniques, such as low-pass filtering and wavelet decomposition. However, these methods often struggle to achieve optimal results in complex and variable noise environments. In particular, when measurement noise and equipment errors overlap nonlinearly, existing methods lack error correction and noise suppression capabilities, resulting in measurement accuracy that fails to meet high-precision requirements. Furthermore, traditional methods lack effective dynamic adjustment mechanisms and are unable to adapt to noise characteristics and equipment deviations under varying environmental conditions, limiting the reliability and stability of aeromagnetic data processing. Summary of the Invention

[0003] In response to the technical problems existing in the above-mentioned background technology, the present invention proposes a new high-precision magnetic measurement data processing method to cope with the noise and error problems in complex environments, and further improve the accuracy and efficiency of data processing through adaptive and intelligent means.

[0004] In order to achieve the above object, the technical solution adopted by the present invention includes the following steps:

[0005] S1. The first step is data collection and preprocessing. Through the adaptive noise suppression network, the convolutional neural network is used to perform adaptive noise suppression on the collected aeromagnetic measurement data to remove noise caused by environmental noise, sensor error and electromagnetic interference. The adaptive denoising formula is: where f θ (X) is the output of the convolutional neural network, is the denoised signal, X is the original input data;

[0006] S2, then the enhanced wavelet packet transform and particle swarm optimization algorithm are used to jointly remove noise. The specific formula is:

[0007] Y k =f(EWT(S k ,θ PSO )), where S kis the kth input signal, EWT(S k ,θ PSO ) represents the signal S transformed by enhanced wavelet packet transform k Perform wavelet packet decomposition, f function is the signal reconstruction function, Y k is the final signal;

[0008] S3. Finally, the magnetic survey data is dynamically corrected by a deep reinforcement learning method to adjust the deviation in data acquisition. The deep reinforcement learning method dynamically corrects by designing a reward and punishment mechanism and automatically adjusts the correction parameters to maximize the measurement accuracy.

[0009] The processing steps of the enhanced wavelet packet transform in step S2 are:

[0010] S21, first use enhanced wavelet packet transform to denoise the signal Perform multi-scale decomposition to obtain sub-signals in several frequency bands: Where W i Represents the denoised signal Decomposition result on the i-th frequency subband, n is the number of decomposed frequency bands;

[0011] S22. In each subband, introduce an adaptive threshold τ i , and use the particle swarm algorithm to optimize the threshold selection. For the i-th subband signal W i , its adaptive denoising formula is: where β i The adaptive adjustment coefficient obtained by particle swarm optimization;

[0012] S23, the particle swarm algorithm searches for the best combination of threshold and adjustment coefficient. The fitness function is defined as: Among them S i , are the target signal and denoised signal of the i-th sample, is the j-th frequency band amplitude of the denoised signal and the target signal in the frequency domain;

[0013] S24. After finding the optimal parameters, reconstruct each denoised subband to restore the denoised signal Y k :

[0014] Preferably, step S1 is implemented by first using a convolutional neural network to use a large amount of noisy data and corresponding clean data in the training phase, and ensuring that the network can minimize the error between the output and the target signal through the loss function; then performing adaptive denoising, the adaptive denoising formula is: where f θ (X) is the output of the convolutional neural network, which represents the noise component predicted by the network. is the denoised signal and X is the original input data.

[0015] As a preference, the loss function is defined as: total =λ1Γ MSE +λ2Γ smooth +λ3Γ noise , where λ1, λ2, λ3 are the weight coefficients of each item in the loss function;

[0016] where Γ MSE is the mean square error loss, which measures the similarity between the denoised signal and the target signal. Among them S i , Represent the i-th sample value of the target signal and the denoised signal respectively, and n is the total number of samples;

[0017] where Γ smooth is the frequency domain smoothness loss, which measures the frequency domain smoothness of the denoised signal. Among them F j represents the amplitude of the denoised signal in the jth frequency band in the frequency domain, and m is the total number of frequency bands;

[0018] where Γ noise In order to constrain the residual noise in the denoised signal and reduce the residual noise component in the network output, in represents the residual noise after denoising, and p is the exponential parameter that controls the penalty for the residual noise.

[0019] Preferably, the specific implementation method of evaluating the correction strategy by designing a reward and punishment mechanism in step S3 is:

[0020] S31. First, the reward and punishment mechanism is divided into short-term rewards and long-term rewards. The short-term reward function is: r short-term =-α·|E current -E expected |, the long-term reward function is: Where α is the short-term error weight, β is the long-term error weight, E current is the current correction error, E expected is the ideal error, T is the length of the historical time window, E trend,t Indicates the trend error within the time window;

[0021] S32. Dynamically adjust the weight coefficients of short-term and long-term rewards and punishments according to the complexity of the current data, α adaptive =α·(1+γ·E complexity ), β adaptive =β·(1-γ·E complexity ), where E complexityrepresents the complexity of the data, and γ is the adjustment factor;

[0022] S33. Finally, the reward and punishment weights are designed for different error sources. The error source reward and punishment formula is: where ω k is the adaptive weight of the k-th error source, The correction error value of each error source is assigned different weights to enhance the sensitivity of the correction strategy to the key error sources.

[0023] Compared with the existing technology, the advantages and positive effects of the present invention are that, first, the present invention can effectively remove environmental noise and equipment interference noise through adaptive noise suppression network and deep learning technology, significantly improving the denoising effect and making the signal-to-noise ratio more significantly improved. Secondly, the present invention introduces a joint denoising method of enhanced wavelet packet transform and particle swarm optimization algorithm, performs multi-scale decomposition of the signal, combines adaptive threshold optimization, effectively processes noise with complex frequency components, and ensures higher signal quality. In addition, through the dynamic correction strategy of deep reinforcement learning, the present invention can adjust the correction parameters according to real-time data feedback, significantly improving the correction accuracy and achieving more accurate data correction in a multi-noise source environment. DETAILED DESCRIPTION

[0024] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described below in conjunction with the embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways than those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Embodiment: In order to achieve high-precision denoising and dynamic correction of aeromagnetic measurement data, a multi-stage optimized data processing method is proposed. Through adaptive noise suppression, enhanced wavelet packet transform and deep reinforcement learning correction strategy, it can significantly reduce complex noise, system errors and environmental interference, achieve accurate data correction, and improve the accuracy and reliability of measurement results. First, in order to reduce environmental noise and sensor errors in the data and improve the quality of the original data, the present invention first performs data acquisition and preprocessing, and processes the collected aeromagnetic measurement data through an adaptive noise suppression network. The specific steps are as follows: A convolutional neural network is used to perform adaptive denoising on the data. The network is first trained using a large amount of noisy data and corresponding noise-free data in the training phase, and the loss function is used to ensure that the network can minimize the error between the output and the target signal. The loss function includes mean square error, frequency domain smoothness loss and noise penalty loss, and the formula is as follows: Γtotal =λ1Γ MSE +λ2Γ smooth +λ3Γ noise , where λ1, λ2, λ3 are the weight coefficients of each loss function; where Γ MSE is the mean square error loss, which measures the similarity between the denoised signal and the target signal. Among them S i , Represents the i-th sample value of the target signal and the denoised signal respectively, and n is the total number of samples; where Γ smooth is the frequency domain smoothness loss, which measures the frequency domain smoothness of the denoised signal. Among them F j represents the amplitude of the jth frequency band of the denoised signal in the frequency domain, m is the total number of frequency bands; where Γ noise In order to constrain the residual noise in the denoised signal and reduce the residual noise component in the network output, in represents the residual noise after denoising, and p is the exponential parameter that controls the penalty for the residual noise.

[0027] In order to further remove the residual noise and improve the stability of the signal, the enhanced wavelet packet transform and particle swarm optimization algorithm are used to jointly denoise. This step can refine the noise suppression in the frequency domain. The steps are as follows: First, the enhanced wavelet packet transform is used to denoise the denoised signal. Perform multi-scale decomposition to obtain sub-signals in several frequency bands: Where W i Represents the denoised signal The decomposition result on the i-th frequency subband, n is the number of decomposed frequency bands; in each subband, an adaptive threshold τ is introduced i , and use the particle swarm algorithm to optimize the threshold selection. For the i-th subband signal W i , its adaptive denoising formula is: where β i The adaptive adjustment coefficient is obtained by particle swarm optimization. The particle swarm algorithm searches for the best combination of threshold and adjustment coefficient. The adaptability function is defined as: Among them S i , are the target signal and denoised signal of the i-th sample, is the jth frequency band amplitude of the denoised signal and the target signal in the frequency domain; finally, after finding the optimal parameters, the denoised sub-bands are reconstructed to restore the denoised signal Y k :

[0028] Finally, in order to further improve the accuracy of data correction, a deep reinforcement learning method is used to dynamically correct the magnetic measurement data. This method evaluates the correction strategy by designing a reward and punishment mechanism, automatically adjusts the correction parameters, and ensures the optimization of measurement accuracy. The steps are as follows: First, the reward and punishment mechanism is divided into short-term rewards and long-term rewards. The short-term reward function is: r short-term =-α·|E current -E expected |, the long-term reward function is: Where α is the short-term error weight, β is the long-term error weight, E current is the current correction error, E expected is the ideal error, T is the length of the historical time window, E trend,t Represents the trend error within the time window; then dynamically adjusts the weight coefficients of short-term and long-term rewards and penalties according to the complexity of the current data, α adaptive =α·(1+γ·E complexity ), β adaptive =β·(1-γ·E complexity ), where E complexity represents the complexity of the data, γ is the adjustment factor; finally, the reward and punishment weights are designed for different error sources, and the error source reward and punishment formula is: where ω k is the adaptive weight of the k-th error source, The correction error value of each error source is assigned different weights to enhance the sensitivity of the correction strategy to the key error sources.

[0029] In order to verify the effectiveness of the method of the present invention, a series of comparative experiments were designed to evaluate the performance of the method of the present invention in terms of denoising effect and correction accuracy. The experiment set reasonable noise levels and correction accuracy indicators. Table 1 is the evaluation of the denoising effect. In terms of denoising effect, the method of the present invention is significantly better than the traditional method. Compared with the low-pass filtering method and the wavelet decomposition denoising method, the signal-to-noise ratio of the method of the present invention is improved by 6.0dB, and the mean square error is significantly reduced to 0.07, indicating that the method has obvious advantages in suppressing noise and improving the signal-to-noise ratio.

[0030] Table 1: Denoising effect evaluation table

[0031]

[0032] The correction effect was also evaluated by comparing the mean error before and after correction and the percentage improvement in correction accuracy. Table 2 below presents the experimental results, demonstrating that the method of the present invention also outperforms traditional methods in terms of correction accuracy. Using the method of the present invention, the mean error was reduced from 0.40 mT to 0.125 mT, and correction accuracy was improved by 62.5%, significantly exceeding the low-pass filtering and adaptive filtering methods, demonstrating the high accuracy of this method in data correction.

[0033] Table 2: Calibration accuracy comparison table

[0034]

[0035] The above description is merely a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any person skilled in the art may utilize the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes for application in other fields. However, any simple modification, equivalent change, and modification of the above embodiments made in accordance with the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A high-precision magnetic data processing method for aeromagnetic measurement, characterized in that: The following steps are involved: S1. The first step is data collection and preprocessing. Through the adaptive noise suppression network, the convolutional neural network is used to perform adaptive noise suppression on the collected aeromagnetic measurement data to remove noise caused by environmental noise, sensor error and electromagnetic interference. The adaptive denoising formula is: where f θ (X) is the output of the convolutional neural network, is the denoised signal, X is the original input data; S2, then the enhanced wavelet packet transform and particle swarm optimization algorithm are used to jointly remove noise. The specific formula is: Y k =f(EWT(S k ,θ PSO )), where S k is the kth input signal, EWT(S k ,θ PSO ) represents the signal S transformed by enhanced wavelet packet transform k Perform wavelet packet decomposition, f function is the signal reconstruction function, Y k is the final signal; S3. Finally, the magnetic survey data is dynamically corrected by a deep reinforcement learning method to adjust the deviation in data acquisition. The deep reinforcement learning method dynamically corrects by designing a reward and punishment mechanism and automatically adjusts the correction parameters to maximize the measurement accuracy. The processing steps of the enhanced wavelet packet transform in step S2 are: S21, first use enhanced wavelet packet transform to denoise the signal Perform multi-scale decomposition to obtain sub-signals in several frequency bands: Where W i Represents the denoised signal Decomposition result on the i-th frequency subband, n is the number of decomposed frequency bands; S22. In each subband, introduce an adaptive threshold τ i , and use the particle swarm algorithm to optimize the threshold selection. For the i-th subband signal W i , its adaptive denoising formula is: where β i The adaptive adjustment coefficient obtained by particle swarm optimization; S23, the particle swarm algorithm searches for the best combination of threshold and adjustment coefficient. The fitness function is defined as: Among them S i , are the target signal and denoised signal of the i-th sample, is the j-th frequency band amplitude of the denoised signal and the target signal in the frequency domain; S24. After finding the optimal parameters, reconstruct each denoised subband to restore the denoised signal Y k : The specific implementation method of evaluating the correction strategy by designing a reward and punishment mechanism in step S3 is: S31. First, the reward and punishment mechanism is divided into short-term rewards and long-term rewards. The short-term reward function is: r short-term =-α·|E current -E expected |, the long-term reward function is: Where α is the short-term error weight, β is the long-term error weight, E current is the current correction error, E expected is the ideal error, T is the length of the historical time window, E trend,t Indicates the trend error within the time window; S32. Dynamically adjust the weight coefficients of short-term and long-term rewards and punishments according to the complexity of the current data, α adaptive =α·(1+γ·E complexity ), β adaptive =β·(1-γ·E complexity ), where E complexity represents the complexity of the data, and γ is the adjustment factor; S33. Finally, the reward and punishment weights are designed for different error sources. The error source reward and punishment formula is: where ω k is the adaptive weight of the k-th error source, The correction error value of each error source is assigned different weights to enhance the sensitivity of the correction strategy to the key error sources.

2. The high-precision magnetic data processing method for aeromagnetic measurement according to claim 1, characterized in that: The implementation of step S1 is to first use a large amount of noisy data and corresponding clean data in the training phase using a convolutional neural network, and ensure that the network can minimize the error between the output and the target signal through the loss function; then perform adaptive denoising, and the adaptive denoising formula is: where f θ (X) is the output of the convolutional neural network, which represents the noise component predicted by the network. is the denoised signal and X is the original input data.

3. The high-precision magnetic data processing method for aeromagnetic measurement according to claim 2, characterized in that: The loss function is defined as: total =λ1Γ MSE +λ2Γ smooth +λ3Γ noise , where λ1, λ2, λ3 are the weight coefficients of each item in the loss function; where Γ MSE is the mean square error loss, which measures the similarity between the denoised signal and the target signal. Among them S i , Represent the i-th sample value of the target signal and the denoised signal respectively, and n is the total number of samples; where Γ smooth is the frequency domain smoothness loss, which measures the frequency domain smoothness of the denoised signal. Among them F j represents the amplitude of the denoised signal in the jth frequency band in the frequency domain, and m is the total number of frequency bands; where Γ noise In order to constrain the residual noise in the denoised signal and reduce the residual noise component in the network output, in represents the residual noise after denoising, and p is the exponential parameter that controls the penalty for the residual noise.

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