A microseismic event detection method and system based on multi-scale feature fusion
Through the multi-scale feature fusion microseismic event detection method, a convolutional neural network is used to extract and integrate the multi-scale features of microseismic signals, which solves the problem of ineffective feature fusion in existing technologies and improves the accuracy and precision of microseismic signal processing.
Patent Information
- Application Number
- CN202411508681.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-09-19
- Estimated Expiration
- 2044-10-28
AI Technical Summary
Existing microseismic signal processing methods fail to effectively integrate features at different levels, affecting the accuracy of classification and recognition.
A microseismic event detection method based on multi-scale feature fusion is adopted. Multi-scale features are extracted from the spectrum graph through a convolutional neural network and fused. The upsampling module and feature fusion module are used to integrate local and global features.
The classification accuracy and phase picking accuracy of microseismic data are improved, and the feature processing capability is optimized.
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Figure CN119335603B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of microseismic event detection, and in particular relates to a microseismic event detection method and system based on multi-scale feature fusion. Background Art
[0002] Hydraulic fracturing technology is widely used in oilfield production stimulation and shale gas production monitoring. High-pressure pumps inject fluid into the formation at the bottom of the well, creating fractures and releasing oil and gas resources in the reservoir. This process generates numerous microseismic events, the signals of which can be collected and recorded by seismic monitoring equipment. By analyzing and processing these microseismic signals, valuable information can be extracted to help evaluate and optimize hydraulic fracturing operations.
[0003] In practical applications, microseismic signals often require complex analysis and processing to extract features and classify events. Traditional signal processing methods, including time-frequency analysis and wavelet transforms, often struggle to extract stable and effective features from complex and variable microseismic signals. In recent years, deep learning technology, due to its powerful feature extraction and learning capabilities, has gradually demonstrated superior performance in the field of microseismic signal processing. However, some current studies focus only on a single feature extraction level and do not fully consider the integration of features at different levels. This can lead to the omission of some important information, affecting the accuracy of classification and recognition.
[0004] Multi-scale feature fusion technology has been widely used in the field of computer vision and has achieved remarkable results. This technology uses neural networks to extract features at different levels and fuses these features to fully utilize the feature information at each level. By fusing these multi-scale features, it can effectively avoid important information that may be missed by single-scale features, thereby improving the classification accuracy of microseismic data. Summary of the Invention
[0005] To address the problem of existing microseismic signal processing research failing to effectively integrate features at different levels, this paper proposes a microseismic event detection method and system based on multi-scale feature fusion. This method uses a convolutional neural network to extract multi-scale features from the spectrum, capturing information at different levels in the microseismic signal. These features are then fused to effectively integrate global and local features. This method not only optimizes the processing capabilities of seismic data features but also improves the accuracy of microseismic phase picking.
[0006] To achieve the above object, the present invention provides the following solutions:
[0007] A microseismic event detection method based on multi-scale feature fusion includes the following steps:
[0008] Acquiring time series data by collecting microseismic data, converting the time series data into frequency domain data, and saving the data in the form of a spectrum graph;
[0009] Constructing a multi-scale feature extraction module based on a convolutional neural network, and using the multi-scale feature extraction module to extract features from the spectrum graph to obtain features at different levels in turn;
[0010] The features at different levels are fused, and the fusion results are input into the multi-layer perceptron to obtain the final seismic phase picking results.
[0011] Preferably, the method of extracting features from the spectrum using the multi-scale feature extraction module to sequentially obtain features at different levels includes:
[0012] Inputting the spectrum graph into a convolutional neural network to obtain features at different levels in sequence, wherein the convolutional neural network includes four convolutional layers connected in sequence;
[0013] Specifically: the spectrogram is input into the first convolutional layer for preliminary feature extraction, wherein the first convolutional layer uses 8 9x9 convolution kernels with a stride of 2;
[0014] The second convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 7x7 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the first convolution layer is added to the output of the second convolution layer to form a residual connection. The local features in the microseismic signal are obtained and recorded as F L ;
[0015] The third convolutional layer uses 16 5x5 convolution kernels with a step size of 2 to further extract and enhance the local complex features in the microseismic signal, denoted as F M , as the input of the fourth convolutional layer;
[0016] The fourth convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 3x3 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the third convolution layer is added to the output of the fourth convolution layer to form a residual connection. The global feature of the microseismic signal is obtained and recorded as F H .
[0017] Preferably, the method of fusing features at different levels to obtain the final seismic phase picking result includes:
[0018] F H After processing by the upsampling module, the upsampled F H With FM Input them into the feature fusion module together for fusion, and the initial fusion feature is recorded as F P ;
[0019] The initial fusion feature F P After processing by the upsampling module, the initial fusion feature F P With F L They are input into the feature fusion module for fusion, and the final fusion feature is recorded as F out ;
[0020] The final fusion feature F out After upsampling, the final fusion feature F out , input into the multi-layer perceptron, and mapped into a probability sequence associated with the P wave and S wave of the seismic signal at each time point, that is, the final seismic phase picking result.
[0021] Preferably, the upsampling module processing method includes:
[0022] Reshape the C channel values at each position of the input feature map into a 1×C vector;
[0023] Perform a linear transformation on the 1×C vector to obtain a 1×12 vector;
[0024] The 1×12 vector is reshaped into a 2×2×3 tensor to obtain a new feature map.
[0025] Preferably, the upsampled F H With F M Input them into the feature fusion module together for fusion, and the initial fusion feature is recorded as F P The methods include:
[0026] The upsampled F H Input weight calculation unit to convert into a set of weights W;
[0027] The weight W and F M Multiplying, the expression is: Get the enhanced F M ;
[0028] The enhanced F M That is F M 'With F H Add and fuse to obtain the initial fusion feature F P .
[0029] Preferably, the initial fusion feature F after upsampling P With F L They are input into the feature fusion module for fusion, and the final fusion feature is recorded as Fout The methods include:
[0030] The upsampled preliminary fusion feature F P Input weight calculation unit to convert into a set of weights W';
[0031] The weight W' and F L Multiplying, the expression is: Get the enhanced F L ;
[0032] The enhanced F L That is F L 'With F P Add and fuse to obtain the final fusion feature F out .
[0033] Preferably, the method in which the weight calculation unit converts the input features into a set of weights includes:
[0034] Assume that the input feature map F∈R C×H×W , where C, H, and W represent the number of channels, length, and width, respectively;
[0035] Through the global average pooling operation, the information of each channel is compressed into a C-dimensional vector, which is recorded as the channel descriptor U;
[0036] Input the channel descriptor U into the fully connected layer to obtain the global channel information, and the output vector is recorded as U g ;
[0037] Input the channel descriptor U into the one-dimensional convolution layer to obtain the local channel information, and the output vector is recorded as U l ;
[0038] The interaction between global channel information and local channel information is performed through two parallel branches; one branch is U g with U l The transpose multiplication of is expressed as: Another branch is U l with U g The transpose multiplication of , the formula is:
[0039] Sum each column of M1 to get a one-dimensional vector U gw As the weight vector of global channel information, the expression is:
[0040] Sum each column of M2 to get a one-dimensional vector U lw As the weight vector of local channel information, the expression is:
[0041] The one-dimensional vector U gw and the one-dimensional vector U lw The result of addition is activated by the activation function to obtain U w , the expression is: U w =σ(U gw +U lw ), where σ represents the sigmoid activation function, which realizes the conversion from features to weight vectors.
[0042] The present invention also provides a microseismic event detection system based on multi-scale feature fusion, comprising: a data conversion module, a feature extraction module and a feature fusion module;
[0043] The data conversion module is used to acquire time series data by collecting microseismic data, convert the time series data into frequency domain data, and save it in the form of a spectrum diagram;
[0044] The feature extraction module is used to construct a multi-scale feature extraction module based on a convolutional neural network, and use the multi-scale feature extraction module to extract features from the spectrum graph to obtain features at different levels in sequence;
[0045] The feature fusion module is used to fuse features at different levels, and the fusion result is input into a multi-layer perceptron to obtain the final seismic phase picking result.
[0046] Compared with the prior art, the present invention has the following beneficial effects:
[0047] The present invention first extracts features from seismic signals using a convolutional neural network, extracting features at different levels. Next, through an upsampling module and a feature fusion module, the features at different levels are fused in stages, effectively integrating local and global features.
[0048] The weight calculation unit in the feature fusion module can convert the feature map into a set of weights, which can effectively assign the channel weights of the feature map, thereby more accurately representing the importance of each channel.
[0049] The method of the present invention makes full use of the features extracted in each stage, which not only optimizes the processing capability of microseismic data features, but also improves the classification accuracy of microseismic data. BRIEF DESCRIPTION OF THE DRAWINGS
[0050] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0051] Figure 1 1 is a schematic flow chart of the steps of a microseismic event detection method based on multi-scale feature fusion in an embodiment of the present invention;
[0052] Figure 2 is a diagram of an upsampling module in an embodiment of the present invention;
[0053] Figure 3 is a feature fusion module diagram in an embodiment of the present invention;
[0054] Figure 4 Schematic diagram of a weight calculation unit in an embodiment of the present invention. DETAILED DESCRIPTION
[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.
[0057] Example 1
[0058] like Figure 1 As shown, this embodiment provides a microseismic event detection method based on multi-scale feature fusion, including the following steps:
[0059] Acquiring time series data by collecting microseismic data, converting the time series data into frequency domain data, and saving the data in the form of a spectrum graph;
[0060] Constructing a multi-scale feature extraction module based on a convolutional neural network, and using the multi-scale feature extraction module to extract features from the spectrum graph to obtain features at different levels in turn;
[0061] The features at different levels are fused, and the fusion results are input into the multi-layer perceptron to obtain the final seismic phase picking results.
[0062] In this embodiment, the method of extracting features from the spectrum using the multi-scale feature extraction module to sequentially obtain features at different levels includes:
[0063] The spectrum graph is input into the convolutional neural network to obtain the features F at different levels in turn. L ,F M ,F H, wherein the convolutional neural network comprises four convolutional layers, which are connected in sequence;
[0064] Specifically: the spectrogram is input into the first convolutional layer for preliminary feature extraction, wherein the first convolutional layer uses 8 9x9 convolution kernels with a stride of 2;
[0065] The second convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 7x7 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the first convolution layer is added to the output of the second convolution layer to form a residual connection. The local features in the microseismic signal are obtained and recorded as F L , where F L =Conv2(x), where x is the spectrum. In microseismic signal processing, local features are usually related to the detailed information of the waveform.
[0066] The third convolutional layer uses 16 5x5 convolution kernels with a step size of 2 to further extract and enhance the local complex features in the microseismic signal, denoted as F M , where F M =Conv3(F L ), as the input of the fourth convolutional layer;
[0067] The fourth convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 3x3 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the third convolution layer is added to the output of the fourth convolution layer to form a residual connection. The global feature of the microseismic signal is obtained and recorded as F H , where F H =Conv4(F M ), reflecting the overall pattern and structural relationship of the signal.
[0068] In this embodiment, the method of fusing features at different levels to obtain the final seismic phase picking result includes:
[0069] F H After processing by the upsampling module, the upsampled F H With F M Input them into the feature fusion module together for fusion, and the initial fusion feature is recorded as F P ;
[0070] The initial fusion feature F P After processing by the upsampling module, the initial fusion feature F P With F LThey are input into the feature fusion module for fusion, and the final fusion feature is recorded as F out ;
[0071] The final fusion feature F after upsampling out The input is processed by the multi-layer perceptron and mapped into a probability sequence related to the P wave and S wave in the seismic signal at each time point, denoted as Ppred and Spred, respectively, which is the final seismic phase picking result. The expression is:
[0072] Ppred=MLP1(F out )
[0073] Spred=MLP2(F out )
[0074] Among them, MLP is a multi-layer perceptron.
[0075] In this embodiment, if Figure 2 As shown, the upsampling module enlarges the input feature map. The expression of the upsampling module is: x'=reshape2(((linear(reshape1(x))), where reshape1 represents the first reshaping operation, linear represents the linear transformation, reshape2 represents the second reshaping operation, and x is the input feature map. Specifically, the upsampling module first reshapes the C channel values at each position of the feature map into a 1×C vector, and then performs a linear transformation on this vector to obtain a 1×12 vector. Next, this 1×12 vector is reshaped into a 2×2×3 tensor. By performing the above operations on each position in the feature map, a new feature map can be obtained, thereby achieving the scale of the original feature map being enlarged.
[0076] In this embodiment, if Figure 3 As shown in Figure 2, the feature fusion module is designed to effectively fuse the input features. The expression of the feature fusion module is: Among them, F1 and F2 represent the two features to be fused, and F represents the fused feature. The features extracted by the multi-scale feature extraction module are fused to improve the accuracy and robustness of earthquake event detection. This module contains a weight calculation unit, such as Figure 4 As shown in Figure 2, this unit can convert the input features into a set of weights. The feature fusion module processes the features in two different stages to ensure full fusion and effective utilization of the features. The first time, the module converts the upsampled F H and F M The specific steps are as follows: First, the upsampled F H Input weight calculation unit to convert into a set of weights W. Then, this set of weights W and F MMultiplying, the expression is: This process is achieved by adjusting F M The importance of each channel is used to enhance useful features and suppress useless features, thereby achieving F M The enhanced F M (i.e. F M ') and F H Add and fuse to obtain the initial fusion feature F P , the expression is: F P =F M '+F H .
[0077] The second time is to combine the upsampled preliminary fusion features F P and F L The specific steps are as follows: First, the upsampled preliminary fusion features are input into the weight calculation unit to convert into a set of weights W'. Then, this set of weights W' is combined with F L Multiplying, the expression is: This process is achieved by adjusting F L The weight of each channel is used to enhance useful features and suppress useless features, thereby achieving F L The enhanced F L (i.e. F L ') and F P Add and fuse to obtain the final fusion feature F out , the expression is: F out＝ F L '+F p .
[0078] The conversion of the above feature map into a set of weights is achieved through the weight calculation unit. The specific operations are as follows:
[0079] U=GAP(F)
[0080]
[0081] Where GAP is the global average pooling operation, U is the channel descriptor, FC represents the fully connected layer, Conv1D represents the one-dimensional convolutional layer, σ represents the sigmoid activation function, and W represents the weight of the final output.
[0082] Assume that the input feature map F∈R C×H×W, where C, H, and W represent the number of channels, length, and width, respectively. First, through the global average pooling operation, the information of each channel is compressed into a C-dimensional vector, denoted as the channel descriptor U. Each value of the channel descriptor can effectively represent the channel. The channel descriptor is input into the fully connected layer. The fully connected layer takes into account the information of all input channels, and it can capture the global dependencies between all channels. This global feature extraction capability enables the fully connected layer to integrate and process global information, so inputting the channel descriptor into the fully connected layer can obtain global channel information. The output vector is denoted as U g .
[0083] At the same time, the channel descriptor is input into the one-dimensional convolution layer. Each output node in the one-dimensional convolution layer is only connected to a local area of the input channel. Through the one-dimensional convolution operation, local channel interaction can be achieved, and the local dependency between channels can be captured, so that local channel information can be obtained. The output vector is recorded as U l .
[0084] Next, two parallel branches are used to perform the interaction between global channel information and local channel information. Specifically, one branch is U g with U l The transpose multiplication of is expressed as: Another branch is U l with U g The transpose multiplication of , the formula is:
[0085] Next, sum each column of M1 to obtain a one-dimensional vector U gw As the weight vector of global channel information, the expression is: Sum each column of M2 to get a one-dimensional vector U lw As the weight vector of local channel information, the expression is:
[0086] The weight vector U representing the global channel information gw and represents the local channel information weight vector U lw After addition and fusion, the activation function is used to activate the weight vector U that integrates the global and local channel information. w , the expression is: U w =σ(U gw +U lw ), where σ represents the sigmoid activation function. By effectively fusing global and local channel information, we can effectively assign channel weights to the feature map. Through the above operations, we achieve the conversion from features to weight vectors.
[0087] Example 2
[0088] The present invention also provides a microseismic event detection system based on multi-scale feature fusion, comprising: a data conversion module, a feature extraction module and a feature fusion module;
[0089] The data conversion module is used to acquire time series data by collecting microseismic data, convert the time series data into frequency domain data, and save it in the form of a spectrum diagram;
[0090] The feature extraction module is used to construct a multi-scale feature extraction module based on a convolutional neural network, and use the multi-scale feature extraction module to extract features from the spectrum graph to obtain features at different levels in turn;
[0091] The feature fusion module is used to fuse features at different levels, and the fusion results are input into the multi-layer perceptron to obtain the final seismic phase picking results.
[0092] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.
Claims
1. A microseismic event detection method based on multi-scale feature fusion, characterized in that: The following steps are involved: Step 1: Acquire time series data by collecting microseismic data, convert the time series data into frequency domain data, and save it in the form of a spectrum graph; Step 2: Construct a multi-scale feature extraction module based on a convolutional neural network, and use the multi-scale feature extraction module to extract features from the spectrogram to obtain features at different levels in sequence: input the spectrogram into a convolutional neural network to obtain features at different levels in sequence, wherein the convolutional neural network includes four convolutional layers, which are connected in sequence; Specifically: the spectrum graph is input into the first convolution layer for preliminary feature extraction. The second convolution layer is a two-dimensional convolution residual block. The output of the first convolution layer is added to the output of the second convolution layer to form a residual connection. The local features in the microseismic signal are obtained and recorded as F L The third convolutional layer is used to further extract and enhance the local complex features in the microseismic signal and is denoted as F M , as the input of the fourth convolutional layer; the fourth convolutional layer is a two-dimensional convolution residual block, which adds the output of the third convolutional layer to the output of the fourth convolutional layer to form a residual connection, and obtains the global feature of the microseismic signal, which is recorded as F H ; Step 3: Fuse the features at different levels and input the fusion result into the multi-layer perceptron to obtain the final seismic phase picking result, specifically: F H After processing by the upsampling module, the upsampled F H With F M Input them into the feature fusion module together for fusion, and the initial fusion feature is recorded as F P , specifically: the upsampled F H Input weight calculation unit to convert into a set of weights W; weights W and F M Multiplying, the expression is: Get the enhanced F M ; The enhanced F M That is F M 'With F H Add and fuse to obtain the initial fusion feature F P ; The initial fusion feature F P After processing by the upsampling module, the initial fusion feature F P With F L They are input into the feature fusion module for fusion, and the final fusion feature is recorded as F out ; The final fusion feature F out After upsampling, the final fusion feature F out , input into the multi-layer perceptron, and mapped into a probability sequence associated with the P wave and S wave of the seismic signal at each time point, that is, the final seismic phase picking result.
2. The microseismic event detection method based on multi-scale feature fusion according to claim 1 is characterized in that: The method of extracting features from the spectrum graph using the multi-scale feature extraction module to sequentially obtain features at different levels includes: Inputting the spectrum graph into a convolutional neural network to obtain features at different levels in sequence, wherein the convolutional neural network includes four convolutional layers connected in sequence; Specifically: the spectrogram is input into the first convolutional layer for preliminary feature extraction, wherein the first convolutional layer uses 8 9x9 convolution kernels with a stride of 2; The second convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 7x7 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the first convolution layer is added to the output of the second convolution layer to form a residual connection. The local features in the microseismic signal are obtained and recorded as F L ; The third convolutional layer uses 16 5x5 convolution kernels with a step size of 2 to further extract and enhance the local complex features in the microseismic signal, denoted as F M , as the input of the fourth convolutional layer; The fourth convolutional layer is a two-dimensional convolution residual block, which contains three repeated modules. Each module is first batch normalized, and then convolution is performed using 16 3x3 convolution kernels with a step size of 1. The three modules are connected in series in sequence, and the output of the third convolution layer is added to the output of the fourth convolution layer to form a residual connection. The global feature of the microseismic signal is obtained and recorded as F H .
3. The microseismic event detection method based on multi-scale feature fusion according to claim 1 is characterized in that: The upsampling module processes the following methods: Reshape the C channel values at each position of the input feature map into a 1×C vector; Perform a linear transformation on the 1×C vector to obtain a 1×12 vector; The 1×12 vector is reshaped into a 2×2×3 tensor to obtain a new feature map.
4. The microseismic event detection method based on multi-scale feature fusion according to claim 1 is characterized in that: Initial fusion feature F after upsampling P With F L They are input into the feature fusion module for fusion, and the final fusion feature is recorded as F out The methods include: The upsampled preliminary fusion feature F P Input weight calculation unit to convert into a set of weights W'; The weight W' and F L Multiplying, the expression is: Get the enhanced F L ; The enhanced F L That is F L 'With F P Add and fuse to obtain the final fusion feature F out .
5. The microseismic event detection method based on multi-scale feature fusion according to claim 4 is characterized in that: The method by which the weight calculation unit converts the input features into a set of weights includes: Assume that the input feature map F∈R C×H×W , where C, H, and W represent the number of channels, length, and width, respectively; Through the global average pooling operation, the information of each channel is compressed into a C-dimensional vector, which is recorded as the channel descriptor U; Input the channel descriptor U into the fully connected layer to obtain the global channel information, and the output vector is recorded as U g ; Input the channel descriptor U into the one-dimensional convolution layer to obtain the local channel information, and the output vector is recorded as U l ; The interaction between global channel information and local channel information is performed through two parallel branches; one branch is U g with U l The transpose multiplication of is expressed as: Another branch is U l with U g The transpose multiplication of , the formula is: Sum each column of M1 to get a one-dimensional vector U gw As the weight vector of global channel information, the expression is: Sum each column of M2 to get a one-dimensional vector U lw As the weight vector of local channel information, the expression is: The one-dimensional vector U gw and the one-dimensional vector U lw The result of addition is activated by the activation function to obtain U w , the expression is: U w =σ(U gw +U lw ), where σ represents the sigmoid activation function, which realizes the conversion from features to weight vectors.
6. A microseismic event detection system based on multi-scale feature fusion, the system being used to implement the method according to any one of claims 1 to 5, characterized in that: include: Data conversion module, feature extraction module and feature fusion module; The data conversion module is used to acquire time series data by collecting microseismic data, convert the time series data into frequency domain data, and save it in the form of a spectrum diagram; The feature extraction module is used to construct a multi-scale feature extraction module based on a convolutional neural network, and use the multi-scale feature extraction module to extract features from the spectrum graph to obtain features at different levels in sequence; The feature fusion module is used to fuse features at different levels, and the fusion result is input into a multi-layer perceptron to obtain the final seismic phase picking result.
Citation Information
Patent Citations
Microseismic event detection method based on BiLSTM and attention mechanism
CN115220094A