WO2022062508A1 - Procédé et appareil de traitement de correction statique pour données sismiques, et dispositif informatique et support de stockage - Google Patents

Procédé et appareil de traitement de correction statique pour données sismiques, et dispositif informatique et support de stockage Download PDF

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WO2022062508A1
WO2022062508A1 PCT/CN2021/102018 CN2021102018W WO2022062508A1 WO 2022062508 A1 WO2022062508 A1 WO 2022062508A1 CN 2021102018 W CN2021102018 W CN 2021102018W WO 2022062508 A1 WO2022062508 A1 WO 2022062508A1
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data
arrival
static correction
neural network
arrival time
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PCT/CN2021/102018
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Chinese (zh)
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亢永敢
魏嘉
陈金焕
朱海伟
庞锐
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中国石油化工股份有限公司
中国石油化工股份有限公司石油物探技术研究院
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Publication of WO2022062508A1 publication Critical patent/WO2022062508A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/362—Effecting static or dynamic corrections; Stacking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G01V2210/41—Arrival times, e.g. of P or S wave or first break
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/50—Corrections or adjustments related to wave propagation
    • G01V2210/53—Statics correction, e.g. weathering layer or transformation to a datum

Definitions

  • the present application relates to the technical field of seismic data processing, and in particular, to a method, apparatus, computer equipment and storage medium for static correction processing of seismic data.
  • Static correction is an important part of seismic data processing.
  • the accuracy of static correction processing is directly related to the effect of a series of subsequent processing.
  • the currently widely used static correction processing method is to use the first arrival data to perform near-surface velocity tomography processing, obtain the near-surface velocity model data, and use the velocity model to calculate the travel time difference caused by the undulating surface for static correction.
  • Accurate first-arrival data and near-surface velocity model data are required.
  • the first-arrival picking process is time-consuming and labor-intensive, the near-surface velocity modeling process is complex, and it is a difficult process to obtain an accurate near-surface velocity model.
  • the present application provides a static correction processing method, device, computer equipment and storage medium for seismic data, which realizes the automatic first-arrival picking and direct static correction calculation, and does not need to manually pick the first-arrival data, avoids the complex near-surface modeling process, and realizes high-efficiency Accurate static correction processing function.
  • the present application provides a static correction processing method for seismic data, including:
  • first-arrival pickup data including the first first-arrival time data
  • static correction processing neural network for training to obtain first-arrival pickup data and second first-arrival time data after static correction processing
  • the seismic data is corrected according to the static correction amount.
  • the present application provides a seismic data static correction processing device, comprising:
  • a target seismic data acquisition module configured to acquire target seismic data
  • a first neural network training module configured to input the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtain first-arriving picking data including first first-arrival time data;
  • the second neural network training module is configured to input the first-arrival pickup data including the first first-arrival time data into a static correction processing neural network for training, and obtain the static-corrected first-arrival pickup data and the second first arrival time data;
  • a static correction amount calculation module configured to calculate a static correction amount according to the second first arrival time data and the first first arrival time data after the static correction processing
  • a static correction module configured to correct the seismic data according to the static correction amount.
  • the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the following steps when executing the computer program:
  • first-arrival pickup data including the first first-arrival time data
  • static correction processing neural network for training to obtain first-arrival pickup data and second first-arrival time data after static correction processing
  • the seismic data is corrected according to the static correction amount.
  • the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • first-arrival pickup data including the first first-arrival time data
  • static correction processing neural network for training to obtain first-arrival pickup data and second first-arrival time data after static correction processing
  • the seismic data is corrected according to the static correction amount.
  • Seismic data static correction processing method realize automatic first arrival picking and direct static correction calculation, and do not need to manually pick first arrival data, avoid complex near-surface modeling process, and realize efficient and accurate static correction. Correction processing function.
  • FIG. 1 is a schematic diagram of an application scenario of a static correction processing method for seismic data in one embodiment
  • FIG. 2 is a schematic flowchart of a method for static correction processing of seismic data in one embodiment
  • FIG. 3 is a structural block diagram of an apparatus for static correction processing of seismic data in one embodiment
  • Fig. 4 is the internal structure diagram of the computer device in one embodiment
  • FIG. 5 is a schematic diagram of an implementation process of a static correction processing method for seismic data in one embodiment
  • FIG. 7A is a schematic diagram of first-arrival pickup before static correction processing in one embodiment
  • FIG. 7B is a schematic diagram of first-arrival pickup after static correction processing in one embodiment.
  • the seismic data static correction processing method provided by this application can be configured in the application environment as shown in FIG. 1 .
  • the computer 102 communicates with the server 104 through the network through the network.
  • the terminal 102 can be, but is not limited to, various personal computers, servers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.
  • the user sends the target seismic data to the server 104 through the terminal 102, and the server 104 obtains the target seismic data; the target seismic data is input into the pre-trained first arrival picking neural network model for training, and the first arrival time data including the first arrival time data is obtained.
  • a static correction processing method for seismic data which includes:
  • Step 210 acquiring target seismic data.
  • Step 220 Input the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtain first-arrival picking data including first first-arrival time data.
  • the data volume of the sample seismic data is extracted from the target seismic data, and the file header description information of the sample seismic data and the trace header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the acquired seismic data volume is extracted one data at a time, and the amplitude value of each sampling point of one channel is input as input data into each node of the first-arrival picking neural network model, and the corresponding input channel contains the first-arrival time.
  • the track data is used as the output inspection data.
  • the seismic data is not input in sequence, but input a data at a certain distance, and loops in turn, and finally completes the training of all input data.
  • a seismic trace data volume is extracted from the seismic data that needs first-arrival picking, and is input into the first-arrival picking neural network model according to the track sequence.
  • the sampling position of the seismic trace corresponding to the node whose node value of the output layer is 1 is taken as the first arrival time of the target trace. All the seismic data that needs to be picked up by the first arrival are input into the neural network in turn, and finally the first arrival time of all the data is obtained.
  • the output layer By inputting a piece of seismic data to the first-arrival picking neural network model, and after calculating through the first-arrival picking neural network model, the output layer outputs a piece of data that is consistent with the input seismic trace data samples, and the value of each sample point in the data is 1 or 0. 1 indicates that the sample point is the first arrival time point, and 0 indicates that the sample point is not the first arrival time point.
  • the first arrival time point corresponding to the sampling point is the first first arrival time data
  • the first first arrival time data is the first arrival time obtained by picking up the actual elevation.
  • Step 230 Input the first-arrival pickup data including the first first-arrival time data into a static correction processing neural network for training, and obtain the first-arrival pickup data and the second first-arrival time number after static correction processing.
  • the goal of the static correction processing neural network is to establish the relationship between the surface elevation and the time of first arrival.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the shot coordinates and elevations (Sx, Sy, Sz) and the receiver coordinates and elevations (Rx, Ry, Rz) of each track of the seismic data are extracted, and the extracted six parameters are input into the input layer of the network.
  • the first arrival time of the input channel is used as the verification data of the output layer.
  • Neural network training is performed on each track of the target seismic data in turn.
  • Step 240 Calculate a static correction amount according to the second first arrival time data and the first first arrival time data after the static correction processing.
  • the step of calculating the static correction amount according to the second first arrival time data and the first first arrival time data after static correction processing includes: calculating the first arrival time after static correction processing. The difference between the second first arrival time data and the first first arrival time data is to obtain the static correction amount.
  • the neural network establishes the relationship between the elevation data of each track and the first arrival time, resets the static correction elevation for the target seismic data that needs static correction processing, inputs it into the neural network, and calculates the output value through the neural network.
  • the first arrival time of the target elevation is subtracted from the first arrival time picked up by the actual elevation, and the difference obtained is the static correction amount of the target elevation.
  • Step 250 Correct the seismic data according to the static correction amount.
  • first-arrival automatic pick-up and direct static correction calculation are realized.
  • the method does not need to manually pick up the first arrival data, avoids the complex near-surface modeling process, and realizes an efficient and accurate static correction processing function.
  • the step of obtaining the first-arriving picking data including the first-arrival time includes:
  • sample data in a preset format wherein the sample data in the preset format is each trace of shot collection data, and the format of the seismic trace data includes a file header, trace header data for each trace, and a trace data body.
  • the data volume records the amplitude value at each sampling point; the sample data in the preset format is input into the first-arrival picking neural network for training, and the first-arrival picking neural network model is obtained.
  • the neural network model needs to be trained with sample data to obtain the first-arrival picking neural network model.
  • the sample data adopts the accurate first arrival time data obtained by manual picking.
  • the input sample is each track data of shot set data, and the format of seismic track data includes file header, track header data of each track and track data body.
  • the trace data body records the amplitude value at each sampling point.
  • the neural network input data only needs the trace data body, so it is necessary to reconstruct the input seismic data, strip the file header description data and the trace header data of each trace, and retain the data of each trace.
  • the data body is the sample data that constitutes the pure data body.
  • the output sample data is constructed from the input sample data and first arrival time.
  • the value of the corresponding sampling point of each track of data in the output sample data is set to 1 to obtain the output sample data.
  • the training process of the neural network is as follows: the generated input sample data is input in the order of the channels, and each time the data is input to the input layer of the neural network, each sampling point of the input channel data corresponds to a node of the input layer.
  • the output data is the output sample data of the corresponding channel. The sampling point of each output sample corresponds to a node of the output layer.
  • the step of acquiring the sample data in the preset format further includes:
  • a first-arrival picking neural network including a first input layer, a first intermediate layer and a first output layer, wherein the first input layer is configured to input a piece of seismic data, and after calculation by the first intermediate layer, the The first output layer outputs a piece of data that is consistent with the sample number of the input seismic trace data, and each sample point value in the output data is the first sample point value or the second sample point value.
  • the first-arrival picking neural network is provided with an input layer, an intermediate layer and an output layer.
  • the number of nodes in the input layer is the same as the number of sampling points of the seismic data one, and is configured to input one seismic data.
  • the middle layer consists of two layers, the number of nodes in each layer is the same as that of the input layer, and the number of nodes in the output layer is the same as the number of nodes in the input layer.
  • the network is a fully connected network.
  • a piece of seismic data is input to the input layer.
  • the output layer outputs a piece of data that is consistent with the number of sample points of the input seismic trace data.
  • the value of each sample point in the data is 1 or 0. 1 means that the sample point is the first arrival time point. 0 indicates that the sample point is not the first arrival time point.
  • the first-arrival picking neural network structure is set.
  • the neural network structure is designed according to one input layer, two middle layers, and one output layer.
  • the number of nodes in the input layer is the same as the number of seismic data sampling points that need to be picked up first, and the number of nodes in the middle two layers and the number of nodes in the output layer are the same as the number of nodes in the input layer. Consistent.
  • training sample data is generated. Part of the seismic data that needs to be picked for the first arrival is selected as the training sample data.
  • the first arrival of the training sample seismic data is manually picked to obtain the accurate first arrival time.
  • the data body of the sample seismic data is extracted, and the file header description information of the sample seismic data and the track header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the training parameters are determined.
  • the training parameters of the neural network are the key factors to determine the training effect. Considering the calculation amount and accuracy of the training, the training of the neural network is controlled by two parameters: the number of cycles and the amount of error.
  • the training error determines the accuracy of the training and prevents overfitting.
  • the number of loops controls the calculation amount of the training, preventing it from falling into multiple loops and failing to end normally.
  • the step of inputting the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtaining the first-arriving picking data including the first first-arrival time data includes:
  • the target seismic data is input into a pre-trained first-arrival picking neural network model for training, and the first-arrival picking neural network model outputs the first-arrival picking data including sample point values, wherein the sample point values include A sample point value and a second sample point value; extract the first arrival pick-up data whose sample point value is the first sample point value through the first arrival picking neural network model, and obtain the first sample point value of the first first arrival time data of the first arrival pickup data corresponding to the value.
  • the trained neural network model is used to generate the input data of the target seismic data that needs to be processed for the first-time pick-up according to the requirements of the input samples, and input them into the trained neural network. Data, extract the sample point with the sampling value of 1 in the output channel, and obtain the position time of the sample point with the value of 1 as the first arrival picking result of this channel.
  • the first-arrival pickup data including the first first-arrival time data is input into a static correction processing neural network for training, and the static-corrected first-arrival pickup data and the second first arrival are obtained
  • the steps for temporal data include:
  • the first-arrival pickup data including the first first-arrival time data is input into the static correction processing neural network for training; the static correction processing neural network is used to reset the static correction for the target seismic data that needs static correction processing. Elevation, output the first arrival time of the target elevation.
  • the shot coordinates and elevations (Sx, Sy, Sz) of each track, and the receiver coordinates and elevations (Rx, Ry, Rz) are extracted from the seismic data for which the first arrivals have been picked up.
  • the obtained six data of each track are input to the six nodes of the input layer of the neural network respectively, and the output verification data is the first arrival time of the input track. All first-arrival seismic traces are picked up according to the above process to extract training samples to train the neural network.
  • the first-arrival pickup data including the first first-arrival time data is input into a static correction processing neural network for training, and the static-corrected first-arrival pickup data and the second first arrival are obtained
  • the steps for time data also include:
  • a static correction processing neural network is constructed including a second input layer, a second intermediate layer, and a second output layer.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the deep neural network is used to automatically pick up the first arrivals of the seismic data, and after the first arrivals data are obtained, the deep neural networks are used to process the first arrivals data to obtain the relationship between the first arrivals time and the surface elevation. Using the relationship between the first arrival time and the surface elevation, by setting different target surface elevations, the first arrival time of the corresponding elevation is calculated, and the direct static correction processing is realized.
  • the present application uses a deep neural network to automatically pick up the first arrivals of the seismic data, and after acquiring the first arrivals data, the deep neural network is used to process the first arrivals data to obtain the relationship between the first arrivals time and the surface elevation.
  • the direct static correction processing process is: set a unified surface elevation, use the deep neural network to calculate the first arrival time of the unified elevation, subtract the first arrival time of the unified elevation from the actual picked first arrival time, obtain the time correction amount of the target elevation, use The time correction amount is directly processed for static correction.
  • the first-arrival picking neural network is provided with an input layer, an intermediate layer and an output layer.
  • the number of nodes in the input layer is the same as the number of sampling points of the seismic data one, and is configured to input one seismic data.
  • the middle layer consists of two layers, the number of nodes in each layer is the same as that of the input layer, and the number of nodes in the output layer is the same as the number of nodes in the input layer.
  • the network is a fully connected network.
  • a piece of seismic data is input to the input layer.
  • the output layer outputs a piece of data that is consistent with the number of sample points of the input seismic trace data.
  • the value of each sample point in the data is 1 or 0. 1 means that the sample point is the first arrival time point. 0 indicates that the sample point is not the first arrival time point.
  • the neural network model needs to be trained with sample data.
  • the sample data adopts the accurate first arrival time data obtained by manual picking.
  • the input sample is each track data of shot set data, and the format of seismic track data includes file header, track header data of each track and track data body.
  • the trace data body records the amplitude value at each sampling point.
  • the neural network input data only needs the trace data body, so it is necessary to reconstruct the input seismic data, strip the file header description data and the trace header data of each trace, and retain the data of each trace.
  • the data body is the sample data that constitutes the pure data body.
  • the output sample data is constructed from the input sample data and first arrival time.
  • the value of the corresponding sampling point of each data in the output sample data is set to 1 to obtain the output sample data.
  • the training process of the neural network is: input the generated input sample data in the order of the channels, input one data at a time, and input it to the output layer of the neural network, and each sampling point of the input data corresponds to a node of the input layer.
  • the output data is the output sample data of the corresponding channel.
  • the sampling point of each output sample corresponds to a node of the output layer.
  • the target seismic data that needs to be first picked up is generated according to the requirements of the input sample, and the input data is input into the trained neural network.
  • the position time of the sample point with the value of 1 is obtained as the first arrival picking result of this track.
  • the goal of the static correction processing neural network is to establish the relationship between the surface elevation and the time of first arrival.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, and the distribution corresponds to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the shot coordinates and elevations (Sx, Sy, Sz) of each trace, and the receiver coordinates and elevations (Rx, Ry, Rz) are extracted from the seismic data for which the first arrivals have been picked up.
  • the obtained six data of each track are input to the six nodes of the input layer of the neural network respectively, and the output verification data is the first arrival time of the input track. All first-arrival seismic traces are picked up according to the above process to extract training samples to train the neural network.
  • the neural network establishes the relationship between the elevation data of each track and the first arrival time, resets the static correction elevation for the target seismic data that needs static correction processing, inputs it into the neural network, and calculates the output value through the neural network.
  • the first arrival time of the target elevation is subtracted from the first arrival time picked up by the actual elevation, and the difference obtained is the static correction amount of the target elevation.
  • the present application provides a static correction method for seismic data based on a deep neural network, which realizes the direct static correction processing of seismic data, avoids processing processes such as first-arrival picking and near-surface velocity modeling, meets the static correction processing requirements of complex surface seismic data, and improves the The processing effect of seismic data reduces exploration costs and improves economic benefits.
  • the neural network structure is designed according to one input layer, two middle layers, and one output layer.
  • the number of nodes in the input layer is consistent with the number of seismic data sampling points that need to be picked up first, and the number of nodes in the middle two layers and the number of nodes in the output layer are the same as those in the input layer. The numbers are the same.
  • the second step is to generate training sample data. Part of the seismic data that needs to be picked for the first arrival is selected as the training sample data. First, the first arrival of the training sample seismic data is manually picked to obtain the accurate first arrival time. According to the neural network structure, the data body of the sample seismic data is extracted, and the file header description information of the sample seismic data and the track header information of each track are removed. The acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume. The value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the third step is to determine the training parameters.
  • the training parameters of the neural network are the key factors to determine the training effect. Considering the calculation amount and accuracy of the training, the training of the neural network is controlled by two parameters: the number of cycles and the amount of error.
  • the training error determines the accuracy of the training and prevents overfitting.
  • the number of loops controls the amount of calculation for training, preventing it from falling into multiple loops and failing to end normally.
  • the fourth step is neural network training.
  • the seismic data volume is acquired in the second step, one data is extracted at a time, and the amplitude value of each sampling point of one channel is input into each node of the input layer of the neural network as input data, and the input channel corresponds to The track data containing the first arrival time is used as the output inspection data.
  • the seismic data is not input in sequence, but input a data at a certain distance, and loops in turn, and finally completes the training of all input data.
  • the fifth step first pick up.
  • the seismic data that needs to be picked up for the first time is extracted according to the requirements of the second step, and the seismic trace data volume is input into the neural network in sequence.
  • the sampling position of the seismic trace corresponding to the node whose node value is 1 in the output layer is taken as the first arrival time of the target trace. All the seismic data that needs to be picked up by the first arrival are input into the neural network in turn, and finally the first arrival time of all the data is obtained.
  • the first-arrival image after pickup is shown in Figure 6.
  • the sixth step, static correction deals with the neural network structure settings.
  • the static correction neural network structure is divided into input layer, middle layer and output layer.
  • Six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the seventh step is to statically correct the neural network training. Extract the shot coordinates and elevations (Sx, Sy, Sz) and receiver coordinates and elevations (Rx, Ry, Rz) of each trace of the seismic data, and input the extracted six parameters into the input layer of the neural network. The first arrival time is used as the validation data for the output layer. Neural network training is performed on each track of the target seismic data in turn.
  • the eighth step static correction processing. Save the trained neural network parameters.
  • determine the surface elevation replace the shot elevation and receiver elevation in the seismic data trace that needs static correction processing according to the new surface elevation, and obtain the static corrected shot coordinates (Sx, Sy, Sz), receiver point Coordinates (Rx, Ry, Rz), input the acquired six parameters of the new elevation into the neural network, and output the first arrival time data of the corresponding track through the neural network calculation.
  • the first arrival time calculated by the target elevation is subtracted from the first arrival time obtained by picking up the actual elevation, and the difference obtained is the static correction amount. Calculate all the track data in turn to obtain the final static correction amount. Correct the target seismic data according to the acquired static correction amount to realize static correction processing.
  • FIG. 7A and FIG. 7B are respectively the first arrival picking before the static correction processing and the first arrival picking after the static correction processing.
  • a seismic data static correction processing device including:
  • a target seismic data acquisition module 310 configured to acquire target seismic data
  • the first neural network training module 320 is configured to input the target seismic data into a pre-trained first-arrival picking neural network model for training 330 to obtain first-arrival picking data including first first-arrival time data;
  • the second neural network training module 340 is configured to input the first-arrival pickup data including the first first-arrival time data into a static correction processing neural network for training, and obtain the static corrected first-arrival pickup data and the first-arrival pickup data after static correction processing. Second arrival time data;
  • the static correction amount calculation module 350 is configured to calculate the static correction amount according to the second first arrival time data and the first first arrival time data after the static correction processing;
  • the static correction module 360 is configured to correct the seismic data according to the static correction amount.
  • the seismic data static correction processing device further includes:
  • the sample data acquisition module is configured to acquire sample data in a preset format, wherein the sample data in the preset format is each track data of the shot collection data, and the format of the seismic track data includes file header, track header data of each track and a track data body, the track data body records the amplitude value at each sampling point;
  • the first-arrival picking neural network model training and obtaining module is configured to input the sample data in the preset format into the first-arriving picking neural network for training to obtain the first-arriving picking neural network model.
  • the seismic data static correction processing device further includes:
  • a first-arrival picking neural network building module configured to construct a first-arrival picking neural network including a first input layer, a first intermediate layer, and a first output layer, wherein the first input layer is configured to input a piece of seismic data, After being calculated by the first intermediate layer, the first output layer outputs a piece of data that is consistent with the sample number of the input seismic trace data, and the value of each sample point in the output data is the value of the first sample point or the second sample value. point value.
  • the step of inputting the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtaining the first-arriving picking data including the first first-arrival time data includes:
  • the target seismic data is input into a pre-trained first-arrival picking neural network model for training, and the first-arrival picking neural network model outputs the first-arrival picking data including sample point values, wherein the sample point values include The first sample value and the second sample value;
  • the first-arrival picking data whose sample point value is the first sample point value is extracted through the first-arrival picking neural network model, and all the first-arriving picking data corresponding to the first sample point value are obtained. Describe the first arrival time data.
  • the second neural network training module includes:
  • a first-arrival pickup data input unit configured to input the first-arrival pickup data including the first first-arrival time data to a static correction processing neural network for training;
  • the target elevation output unit is configured to reset the statically corrected elevation for the target seismic data requiring static correction processing through the static correction processing neural network, and output the first arrival time of the target elevation.
  • the seismic data static correction processing device further includes:
  • a statics processing neural network building block configured to build a statics processing neural network including a second input layer, a second intermediate layer, and a second output layer.
  • the static correction amount calculation module is further configured to calculate the difference between the second first arrival time data and the first first arrival time data after static correction processing to obtain the static correction amount.
  • Each unit in the above-mentioned seismic data static correction processing apparatus can be implemented in whole or in part by software, hardware and combinations thereof.
  • the above units may be embedded in or independent of the processor in the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above units.
  • a computer device is provided. Its internal structure diagram can be shown in Figure 4.
  • the computer equipment includes a processor, memory, a network interface, a display screen, and an input device connected by a system bus.
  • the processor of the computing device is configured to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium, an internal memory.
  • the nonvolatile storage medium stores an operating system and a computer program, and deploys a database configured to store a first-arrival pickup neural network model and a static correction processing neural network model.
  • the internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium.
  • the network interface of the computer device is configured to communicate with other computer devices.
  • the computer program when executed by the processor, implements a static correction processing method for seismic data.
  • the display screen of the computer equipment may be a liquid crystal display screen or an electronic ink display screen
  • the input device of the computer equipment may be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer equipment , or an external keyboard, trackpad, or mouse.
  • FIG. 4 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment on which the solution of the present application should be configured.
  • a device may include more or fewer components than shown in the figures, or combine certain components, or have a different arrangement of components.
  • a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:
  • Step 210 acquiring target seismic data.
  • Step 220 Input the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtain first-arrival picking data including first first-arrival time data.
  • the data volume of the sample seismic data is extracted from the target seismic data, and the file header description information of the sample seismic data and the trace header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the acquired seismic data volume is extracted one data at a time, and the amplitude value of each sampling point of one channel is input as input data into each node of the first-arrival picking neural network model, and the corresponding input channel contains the first-arrival time.
  • the track data is used as the output inspection data.
  • the seismic data is not input in sequence, but input a data at a certain distance, and loops in turn, and finally completes the training of all input data.
  • a seismic trace data volume is extracted from the seismic data that needs first-arrival picking, and is input into the first-arrival picking neural network model according to the track sequence.
  • the sampling position of the seismic trace corresponding to the node whose node value of the output layer is 1 is taken as the first arrival time of the target trace. All the seismic data that needs to be picked up by the first arrival are input into the neural network in turn, and finally the first arrival time of all the data is obtained.
  • the output layer By inputting a piece of seismic data to the first-arrival picking neural network model, and after calculating through the first-arrival picking neural network model, the output layer outputs a piece of data that is consistent with the input seismic trace data samples, and the value of each sample point in the data is 1 or 0. 1 indicates that the sample point is the first arrival time point, and 0 indicates that the sample point is not the first arrival time point.
  • the first arrival time point corresponding to the sampling point is the first first arrival time data
  • the first first arrival time data is the first arrival time obtained by picking up the actual elevation.
  • Step 230 Input the first-arrival pickup data including the first first-arrival time data into a static correction processing neural network for training, and obtain the first-arrival pickup data and the second first-arrival time number after static correction processing.
  • the goal of the static correction processing neural network is to establish the relationship between the surface elevation and the time of first arrival.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the shot coordinates and elevations (Sx, Sy, Sz) and the receiver coordinates and elevations (Rx, Ry, Rz) of each track of the seismic data are extracted, and the extracted six parameters are input into the input layer of the neural network , the first arrival time of the input channel is used as the verification data of the output layer.
  • Neural network training is performed on each track of the target seismic data in turn.
  • Step 240 Calculate a static correction amount according to the second first arrival time data and the first first arrival time data after the static correction processing.
  • the step of calculating the static correction amount according to the second first arrival time data and the first first arrival time data after static correction processing includes: calculating the first arrival time after static correction processing. The difference between the second first arrival time data and the first first arrival time data is to obtain the static correction amount.
  • the neural network establishes the relationship between the elevation data of each track and the first arrival time, resets the static correction elevation for the target seismic data that needs static correction processing, inputs it into the neural network, and calculates the output value through the neural network.
  • the first arrival time of the target elevation is subtracted from the first arrival time picked up by the actual elevation, and the difference obtained is the static correction amount of the target elevation.
  • Step 250 Correct the seismic data according to the static correction amount.
  • first-arrival automatic pick-up and direct static correction calculation are realized.
  • the method does not need to manually pick up the first arrival data, avoids the complex near-surface modeling process, and realizes an efficient and accurate static correction processing function.
  • the processor further implements the following steps when executing the computer program:
  • sample data in a preset format wherein the sample data in the preset format is each trace of shot collection data, and the format of the seismic trace data includes a file header, trace header data for each trace, and a trace data body.
  • the data volume records the amplitude value at each sampling point; the sample data in the preset format is input into the first-arrival picking neural network for training, and the first-arrival picking neural network model is obtained.
  • the neural network model needs to be trained with sample data to obtain the first-arrival picking neural network model.
  • the sample data adopts the accurate first arrival time data obtained by manual picking.
  • the input sample is each track data of shot set data, and the format of seismic track data includes file header, track header data of each track and track data body.
  • the trace data body records the amplitude value at each sampling point.
  • the neural network input data only needs the trace data body, so it is necessary to reconstruct the input seismic data, strip the file header description data and the trace header data of each trace, and retain the data of each trace.
  • the data body is the sample data that constitutes the pure data body.
  • the output sample data is constructed from the input sample data and first arrival time.
  • the value of the corresponding sampling point of each track of data in the output sample data is set to 1 to obtain the output sample data.
  • the training process of the neural network is as follows: the generated input sample data is input in the order of the channels, and each time the data is input to the input layer of the neural network, each sampling point of the input channel data corresponds to a node of the input layer.
  • the output data is the output sample data of the corresponding channel. The sampling point of each output sample corresponds to a node of the output layer.
  • the processor further implements the following steps when executing the computer program:
  • a first-arrival picking neural network including a first input layer, a first intermediate layer and a first output layer, wherein the first input layer is configured to input a piece of seismic data, and after calculation by the first intermediate layer, the The first output layer outputs a piece of data that is consistent with the sample number of the input seismic trace data, and each sample point value in the output data is a first sample point value or a second sample point value.
  • the first-arrival picking neural network is provided with an input layer, an intermediate layer and an output layer.
  • the number of nodes in the input layer is the same as the number of sampling points of the seismic data one, and is configured to input one seismic data.
  • the middle layer consists of two layers, the number of nodes in each layer is the same as that of the input layer, and the number of nodes in the output layer is the same as the number of nodes in the input layer.
  • the network is a fully connected network.
  • a piece of seismic data is input to the input layer.
  • the output layer outputs a piece of data that is consistent with the number of sample points of the input seismic trace data.
  • the value of each sample point in the data is 1 or 0. 1 means that the sample point is the first arrival time point. 0 indicates that the sample point is not the first arrival time point.
  • the first-arrival picking neural network structure is set.
  • the neural network structure is designed according to one input layer, two middle layers, and one output layer.
  • the number of nodes in the input layer is the same as the number of seismic data sampling points that need to be picked up first, and the number of nodes in the middle two layers and the number of nodes in the output layer are the same as the number of nodes in the input layer. Consistent.
  • training sample data is generated. Part of the seismic data that needs to be picked for the first arrival is selected as the training sample data.
  • the first arrival of the training sample seismic data is manually picked to obtain the accurate first arrival time.
  • the data body of the sample seismic data is extracted, and the file header description information of the sample seismic data and the track header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the training parameters are determined.
  • the training parameters of the neural network are the key factors to determine the training effect. Considering the calculation amount and accuracy of the training, the training of the neural network is controlled by two parameters: the number of cycles and the amount of error.
  • the training error determines the accuracy of the training and prevents overfitting.
  • the number of loops controls the amount of calculation for training, preventing it from falling into multiple loops and failing to end normally.
  • the processor further implements the following steps when executing the computer program:
  • the target seismic data is input into a pre-trained first-arrival picking neural network model for training, and the first-arrival picking neural network model outputs the first-arrival picking data including sample point values, wherein the sample point values include A sample point value and a second sample point value; extract the first arrival pick-up data whose sample point value is the first sample point value through the first arrival picking neural network model, and obtain the first sample point value of the first first arrival time data of the first arrival pickup data corresponding to the value.
  • the trained neural network model is used to generate the input data of the target seismic data that needs to be processed for the first-time pick-up according to the requirements of the input samples, and input them into the trained neural network. Data, extract the sample point with the sampling value of 1 in the output channel, and obtain the position time of the sample point with the value of 1 as the first arrival picking result of this channel.
  • the processor further implements the following steps when executing the computer program:
  • the first-arrival pickup data including the first first-arrival time data is input into the static correction processing neural network for training; the static correction processing neural network is used to reset the static correction for the target seismic data that needs static correction processing. Elevation, output the first arrival time of the target elevation.
  • the shot coordinates and elevations (Sx, Sy, Sz) of each track, and the receiver coordinates and elevations (Rx, Ry, Rz) are extracted from the seismic data for which the first arrivals have been picked up.
  • the obtained six data of each track are input to the six nodes of the input layer of the neural network respectively, and the output verification data is the first arrival time of the input track. All first-arrival seismic traces are picked up according to the above process to extract training samples to train the neural network.
  • the processor further implements the following steps when executing the computer program:
  • a static correction processing neural network is constructed including a second input layer, a second intermediate layer, and a second output layer.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • Step 210 acquiring target seismic data.
  • Step 220 Input the target seismic data into a pre-trained first-arrival picking neural network model for training, and obtain first-arrival picking data including first first-arrival time data.
  • the data volume of the sample seismic data is extracted from the target seismic data, and the file header description information of the sample seismic data and the trace header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the acquired seismic data volume is extracted one data at a time, and the amplitude value of each sampling point of one channel is input as input data into each node of the first-arrival picking neural network model, and the corresponding input channel contains the first-arrival time.
  • the track data is used as the output inspection data.
  • the seismic data is not input in sequence, but input a data at a certain distance, and loops in turn, and finally completes the training of all input data.
  • a seismic trace data volume is extracted from the seismic data that needs first-arrival picking, and is input into the first-arrival picking neural network model according to the track sequence.
  • the sampling position of the seismic trace corresponding to the node whose node value of the output layer is 1 is taken as the first arrival time of the target trace. All the seismic data that needs to be picked up by the first arrival are input into the neural network in turn, and finally the first arrival time of all the data is obtained.
  • the output layer By inputting a piece of seismic data to the first-arrival picking neural network model, and after calculating through the first-arrival picking neural network model, the output layer outputs a piece of data that is consistent with the input seismic trace data samples, and the value of each sample point in the data is 1 or 0. 1 indicates that the sample point is the first arrival time point, and 0 indicates that the sample point is not the first arrival time point.
  • the first arrival time point corresponding to the sampling point is the first first arrival time data
  • the first first arrival time data is the first arrival time obtained by picking up the actual elevation.
  • Step 230 Input the first-arrival pickup data including the first first-arrival time data into a static correction processing neural network for training, and obtain the first-arrival pickup data and the second first-arrival time number after static correction processing.
  • the goal of the static correction processing neural network is to establish the relationship between the surface elevation and the time of first arrival.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.
  • the shot coordinates and elevations (Sx, Sy, Sz) and the receiver coordinates and elevations (Rx, Ry, Rz) of each track of the seismic data are extracted, and the extracted six parameters are input into the input layer of the neural network , the first arrival time of the input channel is used as the verification data of the output layer.
  • Neural network training is performed on each track of the target seismic data in turn.
  • Step 240 Calculate a static correction amount according to the second first arrival time data and the first first arrival time data after the static correction processing.
  • the step of calculating the static correction amount according to the second first arrival time data and the first first arrival time data after static correction processing includes: calculating the first arrival time after static correction processing. The difference between the second first arrival time data and the first first arrival time data is to obtain the static correction amount.
  • the neural network establishes the relationship between the elevation data of each track and the first arrival time, resets the static correction elevation for the target seismic data that needs static correction processing, inputs it into the neural network, and calculates the output value through the neural network.
  • the first arrival time of the target elevation is subtracted from the first arrival time picked up by the actual elevation, and the difference obtained is the static correction amount of the target elevation.
  • Step 250 Correct the seismic data according to the static correction amount.
  • first-arrival automatic pick-up and direct static correction calculation are realized.
  • the method does not need to manually pick up the first arrival data, avoids the complex near-surface modeling process, and realizes an efficient and accurate static correction processing function.
  • the computer program further implements the following steps when executed by the processor:
  • sample data in a preset format wherein the sample data in the preset format is each trace of shot collection data, and the format of the seismic trace data includes a file header, trace header data for each trace, and a trace data body.
  • the data volume records the amplitude value at each sampling point; the sample data in the preset format is input into the first-arrival picking neural network for training, and the first-arrival picking neural network model is obtained.
  • the neural network model needs to be trained with sample data to obtain the first-arrival picking neural network model.
  • the sample data adopts the accurate first arrival time data obtained by manual picking.
  • the input sample is each track data of shot set data, and the format of seismic track data includes file header, track header data of each track and track data body.
  • the trace data body records the amplitude value at each sampling point.
  • the neural network input data only needs the trace data body, so it is necessary to reconstruct the input seismic data, strip the file header description data and the trace header data of each trace, and retain the data of each trace.
  • the data body is the sample data that constitutes the pure data body.
  • the output sample data is constructed from the input sample data and first arrival time.
  • the value of the corresponding sampling point of each track of data in the output sample data is set to 1 to obtain the output sample data.
  • the training process of the neural network is as follows: the generated input sample data is input in the order of the channels, and each time the data is input to the input layer of the neural network, each sampling point of the input channel data corresponds to a node of the input layer.
  • the output data is the output sample data of the corresponding channel. The sampling point of each output sample corresponds to a node of the output layer.
  • the computer program further implements the following steps when executed by the processor:
  • a first-arrival picking neural network including a first input layer, a first intermediate layer and a first output layer, wherein the first input layer is configured to input a piece of seismic data, and after calculation by the first intermediate layer, the The first output layer outputs a piece of data that is consistent with the sample number of the input seismic trace data, and each sample point value in the output data is a first sample point value or a second sample point value.
  • the first-arrival picking neural network is provided with an input layer, an intermediate layer and an output layer.
  • the number of nodes in the input layer is the same as the number of sampling points of the seismic data one, and is configured to input one seismic data.
  • the middle layer consists of two layers, the number of nodes in each layer is the same as that of the input layer, and the number of nodes in the output layer is the same as the number of nodes in the input layer.
  • the network is a fully connected network.
  • a piece of seismic data is input to the input layer.
  • the output layer outputs a piece of data that is consistent with the number of sample points of the input seismic trace data.
  • the value of each sample point in the data is 1 or 0. 1 means that the sample point is the first arrival time point. 0 indicates that the sample point is not the first arrival time point.
  • the first-arrival picking neural network structure is set.
  • the neural network structure is designed according to one input layer, two middle layers, and one output layer.
  • the number of nodes in the input layer is the same as the number of seismic data sampling points that need to be picked up first, and the number of nodes in the middle two layers and the number of nodes in the output layer are the same as the number of nodes in the input layer. Consistent.
  • training sample data is generated. Part of the seismic data that needs to be picked for the first arrival is selected as the training sample data.
  • the first arrival of the training sample seismic data is manually picked to obtain the accurate first arrival time.
  • the data body of the sample seismic data is extracted, and the file header description information of the sample seismic data and the track header information of each track are removed.
  • the acquisition of the verification data volume is to generate a data volume of the same size according to the seismic data volume.
  • the value of each sampling point in the data volume is set to 0, and then the value of the sampling point position corresponding to the first arrival time of each track is set to 1, indicating that The first arrival time position of this data.
  • the training parameters are determined.
  • the training parameters of the neural network are the key factors to determine the training effect. Considering the calculation amount and accuracy of the training, the training of the neural network is controlled by two parameters: the number of cycles and the amount of error.
  • the training error determines the accuracy of the training and prevents overfitting.
  • the number of loops controls the amount of calculation for training, preventing it from falling into multiple loops and failing to end normally.
  • the computer program further implements the following steps when executed by the processor:
  • the target seismic data is input into a pre-trained first-arrival picking neural network model for training, and the first-arrival picking neural network model outputs the first-arrival picking data including sample point values, wherein the sample point values include A sample point value and a second sample point value; extract the first arrival pick-up data whose sample point value is the first sample point value through the first arrival picking neural network model, and obtain the first sample point value of the first first arrival time data of the first arrival pickup data corresponding to the value.
  • the trained neural network model is used to generate the input data of the target seismic data that needs to be processed for the first-time pick-up according to the requirements of the input samples, and input them into the trained neural network. Data, extract the sample point with the sampling value of 1 in the output channel, and obtain the position time of the sample point with the value of 1 as the first arrival picking result of this channel.
  • the computer program further implements the following steps when executed by the processor:
  • the first-arrival pickup data including the first first-arrival time data is input into the static correction processing neural network for training; the static correction processing neural network is used to reset the static correction for the target seismic data that needs static correction processing. Elevation, output the first arrival time of the target elevation.
  • the shot coordinates and elevations (Sx, Sy, Sz) of each track, and the receiver coordinates and elevations (Rx, Ry, Rz) are extracted from the seismic data for which the first arrivals have been picked up.
  • the obtained six data of each track are input to the six nodes of the input layer of the neural network respectively, and the output verification data is the first arrival time of the input track. All first-arrival seismic traces are picked up according to the above process to extract training samples to train the neural network.
  • the computer program further implements the following steps when executed by the processor:
  • a static correction processing neural network is constructed including a second input layer, a second intermediate layer, and a second output layer.
  • the neural network structure is divided into input layer, middle layer and output layer.
  • the input layer is the coordinates and elevation of the shot position and the coordinates and elevation of the receiver position of the seismic trace data. Therefore, six nodes are set in the input layer, corresponding to the shot coordinates and elevations (Sx, Sy, Sz), and the receiver coordinates and elevations (Rx, Ry, Rz) of each data, respectively.
  • the output layer is a node that outputs the first arrival time.
  • the neural network is a fully connected network.

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Abstract

La présente invention concerne un procédé et un appareil de traitement de correction statique pour données sismiques, ainsi qu'un dispositif informatique et un support de stockage. Le procédé comprend les étapes consistant à : acquérir des données sismiques cibles ; entrer les données sismiques cibles dans un modèle de réseau de neurones de capture de première rupture préalablement entraîné à des fins d'apprentissage, de façon à obtenir des données de capture de première rupture contenant des premières données de temps de première rupture ; entrer les données de capture de première rupture contenant les premières données de temps de première rupture dans un réseau de neurones de traitement de correction statique à des fins d'apprentissage, de façon à obtenir des données de capture de première rupture et des secondes données de temps de première rupture qui ont été soumises à un traitement de correction statique ; obtenir, par un calcul, une quantité de correction statique en fonction des secondes données de temps de première rupture et des premières données de temps de première rupture qui ont été soumises à un traitement de correction statique ; et corriger les données sismiques en fonction de la quantité de correction statique. On obtient ainsi une capture automatique de première rupture et un calcul direct de correction statique. Dans le procédé, une capture manuelle de données de première rupture n'est pas nécessaire, de sorte qu'un processus complexe de modélisation de surface proche est évité et qu'une fonction de traitement de correction statique efficace et précise est obtenue.
PCT/CN2021/102018 2020-09-28 2021-06-24 Procédé et appareil de traitement de correction statique pour données sismiques, et dispositif informatique et support de stockage WO2022062508A1 (fr)

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