Seismic exploration velocity analysis method and device, computer equipment and storage medium

Abstract

The invention provides a seismic exploration velocity analysis method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the processing of a depth domain velocity model set based on a two-way wave equation, and obtaining the gather data; processing the gather data based on a stack velocity spectrum making formula to obtain sample set data; extracting a time domain root-mean-square velocity curve from the depth domain velocity model set to obtain label set data; and training a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis. And inputting the calculated label set data and sample set data into a preset deep learning model, training the preset deep learning model, determining parameters in the preset deep learning model, and inputting the stack velocity spectrum data into the seismic exploration velocity modeling system in actual exploration of the seismic exploration velocity modeling system. And a result for automatically explaining the stacking velocity spectrum can be obtained through calculation.

Description

Technical Field

[0001] The present invention relates to the field of seismic exploration technology, and in particular to a seismic exploration velocity analysis method, device, computer equipment and storage medium. Background Art

[0002] In the framework of seismic exploration theory, seismic data processing can be summarized as an inversion problem. The key to seismic exploration is the inversion of underground physical parameters. After the velocity model is obtained, seismic migration imaging and subsequent reservoir and reservoir inversion and interpretation are performed. Recent seismic exploration velocity inversion mainly includes two categories: time domain and depth domain. The velocity analysis in the time domain is mainly based on picking the maximum energy of the stacked velocity spectrum to obtain the seismic root mean square velocity model. The velocity modeling in the depth domain includes reflection, refraction tomography and full waveform inversion, which are the most commonly used techniques in velocity modeling.

[0003] However, the velocity modeling process in the time domain of seismic exploration involves a large amount of manual interaction and time-consuming velocity analysis, resulting in a long analysis time for seismic exploration velocity. Summary of the invention

[0004] Based on this, it is necessary to provide a seismic exploration velocity analysis method, device, computer equipment and storage medium to address the above technical problems.

[0005] A seismic exploration velocity analysis method, comprising:

[0006] The depth domain velocity model set is processed based on the two-way wave equation to obtain the gather data;

[0007] The gather data are processed based on a stacking velocity spectrum production formula to obtain sample set data;

[0008] Extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data;

[0009] A preset deep learning model is trained based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0010] In one embodiment, the step of processing the depth domain velocity model set based on the two-way wave equation to obtain the gather data includes:

[0011] The geological conditions of multiple seismic exploration areas are obtained, the geological conditions of each seismic exploration area are analyzed to obtain geological structural characteristics, and the geological conditions of multiple seismic exploration areas are obtained based on the geological structural characteristics, the geological conditions of each seismic exploration area are analyzed to obtain geological structural characteristics, and the depth domain velocity model set is established based on the geological structural characteristics.

[0012] In one embodiment, the step of processing the depth domain velocity model set based on the two-way wave equation to obtain the gather data includes:

[0013] Performing two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data;

[0014] Gain compensation processing is performed on the synthetic seismic data to obtain gather data.

[0015] In one embodiment, the step of processing the gather data based on the stacking velocity spectrum preparation formula to obtain the sample set data includes:

[0016] Based on the stacking velocity spectrum preparation formula, extracting features from the gather data to obtain gather features, and converting the gather features into stacking velocity spectrum data;

[0017] The stacking velocity spectrum data is subjected to piecewise normalization processing to obtain sample set data.

[0018] In one embodiment, the step of extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data comprises:

[0019] Extracting gather point velocities from the depth-domain velocity model set to obtain an interval velocity curve;

[0020] Converting the layer velocity curve into the time-domain root mean square velocity curve;

[0021] The time domain root mean square speed curve is normalized to obtain label set data.

[0022] In one embodiment, the step of normalizing the time domain root mean square speed curve comprises:

[0023] The time domain root mean square speed curve is normalized based on the maximum and minimum speeds.

[0024] A seismic exploration velocity analysis device, comprising:

[0025] The gather data extraction module processes the depth domain velocity model set based on the two-way wave equation to obtain the gather data;

[0026] A sample set data acquisition module processes the gather data based on a stacking velocity spectrum production formula to obtain sample set data;

[0027] A label set data acquisition module extracts a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data;

[0028] A model training module trains a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0029] In one embodiment, the seismic exploration velocity analysis device comprises:

[0030] The velocity model establishment module is used to obtain the geological conditions of multiple seismic exploration areas, analyze the geological conditions of each seismic exploration area to obtain geological structural characteristics, obtain the geological conditions of multiple seismic exploration areas based on the geological structural characteristics, analyze the geological conditions of each seismic exploration area to obtain geological structural characteristics, and establish the depth domain velocity model set based on the geological structural characteristics.

[0031] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the following steps when executing the computer program:

[0032] The depth domain velocity model set is processed based on the two-way wave equation to obtain the gather data;

[0033] The gather data are processed based on a stacking velocity spectrum production formula to obtain sample set data;

[0034] Extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data;

[0035] A preset deep learning model is trained based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0037] The depth domain velocity model set is processed based on the two-way wave equation to obtain the gather data;

[0038] The gather data are processed based on a stacking velocity spectrum production formula to obtain sample set data;

[0039] Extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data;

[0040] A preset deep learning model is trained based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0041] The above-mentioned seismic exploration velocity analysis method, device, computer equipment and storage medium input the calculated label set data and sample set data into a preset deep learning model, train the preset deep learning model, determine the parameters in the preset deep learning model, and obtain a seismic exploration velocity modeling system. Therefore, in actual exploration, the stacking velocity spectrum data can be input into the seismic exploration velocity modeling system, and the result of automatic interpretation of the stacking velocity spectrum can be calculated. In the process of calculating the seismic exploration velocity analysis results, no manual interaction is required, which shortens the analysis time of the seismic exploration velocity. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of a seismic exploration velocity analysis method in one embodiment;

[0043] Figure 2 is a structural block diagram of a seismic exploration velocity analysis device in one embodiment;

[0044] Figure 3 is an internal structure diagram of a computer device in one embodiment;

[0045] Figure 4 is a flow chart of a seismic exploration velocity analysis method in another embodiment;

[0046] Figure 5 An example diagram of a depth domain velocity model in an embodiment;

[0047] Figure 6 A shot gather seismic record simulated by using an acoustic wave equation and an example diagram after gain in one embodiment;

[0048] Figure 7 An example diagram of CMP arrangement of seismic records for shot gather records sort in one embodiment;

[0049] Figure 8 An example diagram of a stacking velocity spectrum prepared for a CMP seismic record and an example diagram after piecewise normalization in one embodiment.

[0050] Fig. 9 FIG. 1 is a diagram showing an example of converting a velocity curve at a CMP point into label data in one embodiment.

[0051] Fig.10 A network model diagram constructed in an embodiment;

[0052] Fig.11 This is a result diagram of automatic interpretation of stacking velocity spectra using a neural network model in an embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] Embodiment 1

[0055] In this embodiment, Figure 1 As shown, a seismic exploration velocity analysis method is provided, which includes:

[0056] Step 110, processing the depth domain velocity model set based on the two-way wave equation to obtain gather data.

[0057] In this embodiment, firstly, a plurality of depth domain velocity models are obtained to establish a depth domain velocity model set. Specifically, according to the geological conditions of the seismic exploration area, the specific geological structure characteristics are analyzed and abstractly represented to obtain a plurality of depth domain velocity models, and a depth domain velocity model set with different geological characteristics is established, so as to ensure that the model covers the breadth of the geological structure characteristics of the actual processing area. Figure 5 is the depth domain velocity model set.

[0058] In this embodiment, the depth-domain velocity model set includes a plurality of depth-domain velocity models, and each depth-domain velocity model is processed by a two-way wave equation to obtain gather data.

[0059] Step 120, processing the gather data based on the stacking velocity spectrum preparation formula to obtain sample set data.

[0060] In this embodiment, stacking velocity spectrum data is obtained from the gather data by using the stacking velocity spectrum production formula as sample set data. The corresponding gather data is generated by using the acoustic wave forward modeling method, and then the stacking velocity spectrum data is generated by scanning and stacking as sample set data.

[0061] Step 130 : extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data.

[0062] The time domain RMS velocity curve is used because it has a more direct spatial correspondence with the stacked velocity spectrum data, which is conducive to extracting the corresponding local features. Moreover, the time domain RMS velocity curve and the stacked velocity spectrum data have the same time length, which makes it easier to determine the specific depth.

[0063] In this embodiment, since the stacked velocity spectrum data is time-dependent, in order to associate the sample set data with the label set data, a time-domain root mean square velocity curve is extracted from the depth-domain velocity model set, wherein the time-domain root mean square velocity curve is time-dependent.

[0064] Step 140: Training a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0065] In this embodiment, the sample set data and the label set data are input into a preset deep learning model, the preset deep learning model is trained, the parameters of the preset deep learning model are determined, and the trained preset deep learning model is obtained as a seismic exploration velocity modeling system.

[0066] In the above embodiment, the seismic exploration velocity analysis method of the present application inputs the calculated label set data and sample set data into a preset deep learning model, trains the preset deep learning model, determines the parameters in the preset deep learning model, and obtains a seismic exploration velocity modeling system. Therefore, in actual exploration, the stacking velocity spectrum data can be input into the seismic exploration velocity modeling system, and the result of automatic interpretation of the stacking velocity spectrum can be calculated.

[0067] In one embodiment, after the step of training a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis, the step further includes:

[0068] The stacking velocity spectrum data to be measured is obtained, and the stacking velocity spectrum data to be measured is input into a seismic exploration velocity modeling system, and the stacking velocity spectrum data to be measured is processed by the seismic exploration velocity modeling system to obtain interpretation result data.

[0069] In this embodiment, the stacking velocity spectrum data to be tested is the stacking velocity spectrum data obtained in actual exploration and needs to be tested. The seismic exploration velocity modeling system calculates the stacking velocity spectrum data to be tested to obtain the seismic exploration velocity analysis results. In the process of calculating the seismic exploration velocity analysis results, no manual interaction is required, thereby shortening the analysis time of the seismic exploration velocity.

[0070] In one embodiment, the depth domain velocity model set is processed based on the two-way wave equation to obtain the track set data, which includes: acquiring the geological conditions of multiple seismic exploration areas, analyzing the geological conditions of each of the seismic exploration areas to obtain geological structural characteristics, and establishing the depth domain velocity model set based on the geological structural characteristics.

[0071] In this embodiment, in order to obtain more sample set data and label set data to train the preset deep learning model, multiple depth velocity domain velocity models are established, thereby more sample set data and label set data can be obtained from multiple depth velocity domain velocity models as training sets, and a large number of training sets are provided to better calculate the parameters in the preset deep learning model. The obtained seismic exploration velocity modeling system can analyze the stacking velocity spectrum data more accurately and output more accurate calculation results.

[0072] In this embodiment, according to the geological complexity of the domestic seismic exploration area, the specific geological structure characteristics are analyzed and abstractly represented, and a depth domain velocity model set with different geological characteristics is established to ensure that the depth domain velocity model set covers the breadth of the geological structure characteristics of the actual processing area.

[0073] In this embodiment, the depth domain velocity model set is defined according to equation (1):

[0074] V depth =Γ(V m , N dip , M layer , D abnormal )

[0075] V m ∈[1000, 7000], N dip ∈[-35°, 35°]and different with layers,

[0076] M laver ∈[1, 14], D abnormal ∈[0, 1]

[0077] Among them, V depth represents the total velocity parameter of the current depth domain velocity model, function Γ is the abstract velocity model representation, V m The layer velocity magnitude of the Nth layer of the current depth domain velocity model, where N is between 0-M layer Mlayer is the number of layers, and the dip angle of the layer is N dip , where N dip Between -35 and 35 degrees, D abnormal Indicates whether there is an abnormal body in the velocity layer. That is, according to the number of layers Mlayer, the dip angle Ndip, the velocity parameter Vm, and the depth parameter Dabnormal of the generated velocity anomaly body, a depth domain velocity model is generated. The depth domain velocity model is established based on the dip angle, velocity range, geological anomaly body and number of strata, making the depth domain velocity model set more comprehensive and adaptable to the complex geological conditions of domestic seismic exploration areas.

[0078] In one embodiment, the step of processing the depth domain velocity model set based on the two-way wave equation to obtain the gather data includes:

[0079] Step 111, performing two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data.

[0080] Step 112, performing gain compensation processing on the synthetic seismic data to obtain gather data.

[0081] In this embodiment, the two-way wave equation is an acoustic two-way wave equation, and the depth domain velocity model set is processed by the acoustic two-way wave equation, and a forward simulation strategy is performed to prepare synthetic seismic data.

[0082] In this embodiment, the step of performing gain compensation processing on the synthetic seismic data to obtain gather data includes: performing gain compensation processing on the synthetic seismic data to obtain compensated seismic data; sorting the compensated seismic data in a CMP (Common Middle Point, CMP gather) arrangement format to obtain gather data. It should be understood that when preparing stacking velocity spectrum data from synthetic seismic data, the input data sorting format is CMP sorting.

[0083] In one embodiment, the step of processing the gather data based on the stacking velocity spectrum preparation formula to obtain the sample set data includes:

[0084] Step 121, based on the stacking velocity spectrum making formula, extracting features from the gather data to obtain gather features, and converting the gather features into stacking velocity spectrum data;

[0085] Step 122: performing piecewise normalization processing on the stacking velocity spectrum data to obtain sample set data.

[0086] In this embodiment, feature extraction is performed on the gather data according to formula (2):

[0087]

[0088] Among them, Spec amp Indicates the prepared superposition velocity spectrum data, v indicates the current scanning velocity, and the range of v is v start With v end The specific value of v is determined according to the velocity range of the depth domain velocity model, dt is the data sampling point interval, ntrace represents the total number of seismic data, and CMP represents the current gather data to be processed. nt is the number of sampling points, it is the index of the sampling point, ntrace is the total number of gather data, itrace is the index of the number of tracks, and v start is the starting point of the scanning speed, v endis the end point of the scanning speed, the speed parameter dt is the sampling interval, v is the current scanning speed, X offset is the offset length corresponding to the current seismic trace. In this embodiment, the velocity range of the depth domain velocity model is 1000m / s-7350m / s. In this embodiment, the data sampling point interval dt is 0.5ms.

[0089] In this embodiment, the stacking velocity spectrum data is subjected to piecewise normalization processing according to equation (3) to obtain sample set data;

[0090] Spec norm (v, it) = Spec amp (v,it) / Max(Spec amp (v, it)) it∈[t_start, t_end](3)

[0091] Among them, Spec norm Represents the sample set data, t_start and t_end depend on the time window size. In this embodiment, the time window length is 10 sampling points.

[0092] In one embodiment, the step of extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data comprises:

[0093] Step 131, extracting gather point velocities from the depth-domain velocity model set to obtain an interval velocity curve;

[0094] Step 132, converting the layer velocity curve into the time domain root mean square velocity curve;

[0095] Step 133: normalize the time domain RMS speed curve to obtain label set data.

[0096] In this embodiment, the CMP center point layer velocity curve is extracted from the depth domain velocity model to obtain the layer velocity curve.

[0097] In this embodiment, based on the DIX formula, the layer velocity curve is converted into a time-domain root mean square velocity curve.

[0098] In one embodiment, the step of normalizing the time domain root mean square speed curve comprises:

[0099] The time domain root mean square speed curve is normalized based on the maximum and minimum speeds.

[0100] In this embodiment, the maximum and minimum speed normalization processing is performed on the time domain RMS speed curve according to equation (4):

[0101] V=(VV min ) / (Vmax -V min )

[0102] Where V represents the seismic velocity model, V min and V max Indicates the normalized speed range set, V min represents the normalized minimum velocity, V max In this embodiment, the normalized speed range is 1000m / s-7350m / s, V min 1000m / s, V max It is 7350m / s.

[0103] In this embodiment, in the maximum and minimum speed normalization process, the maximum speed and the minimum speed are determined according to the characteristics of the work area.

[0104] In step 140, the preset deep learning model is pre-built, but the parameters of the preset deep learning model are not determined. The sample set data obtained in step 120 and the label set data obtained in step 130 are input into the preset deep learning model, the parameters of the preset deep learning model are determined, and the parameters of the trained preset deep learning model are obtained as the seismic exploration velocity modeling system. The parameters of the preset deep learning model are saved to the local disk, and the preset deep learning model saved to the local disk is deployed to the distributed processing cluster, so that different users can use the same preset deep learning model to interpret the stacking velocity spectrum separately, thereby improving processing efficiency. When the parameters of the preset deep learning model are deployed in the distributed processing cluster, each cluster node user first loads the parameters of the preset deep learning model into the preset deep learning model when using this module, and then uses the preset deep learning model to automatically interpret the stacking velocity spectrum to be processed, thereby improving work efficiency.

[0105] In one embodiment, the preset deep learning model is a neural network model based on deep learning.

[0106] In this embodiment, the neural network model is a network structure based on Unet+++.

[0107] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0108] Embodiment 2

[0109] In this embodiment, a seismic exploration velocity analysis method is provided, comprising:

[0110] Step 410, obtaining geological conditions of a plurality of seismic exploration work areas, analyzing the geological conditions of each of the seismic exploration work areas to obtain geological structural characteristics, and establishing the depth domain velocity model set based on the geological structural characteristics.

[0111] In this embodiment, according to the complex geological conditions of the domestic seismic exploration area, the specific geological structure characteristics are analyzed and abstractly represented, and a depth domain velocity model set with different geological characteristics is established to ensure that the model covers the breadth of the geological structure characteristics of the actual processing area.

[0112] In this embodiment, by analyzing the geological conditions of various seismic exploration areas in China, the following geological structure characteristics are summarized, and the main influencing factors are inclination, velocity range, geological anomalies and the number of strata. In this embodiment, the depth domain velocity model set is defined by the following formula:

[0113] V depth =Γ(V m , N dip , M layer , D abnormal )

[0114] V m ∈[1000, 7000], N dip ∈|-35°, 35°|and different with layers,

[0115] M layer ∈[1, 14], D abnormal ∈[0, 1]

[0116] Among them, V depth is the total velocity parameter of the current depth domain velocity model, function Γ is the abstract representation of the depth domain velocity model, V m The layer velocity magnitude of the Nth layer of the current depth domain velocity model, where N is between 0-M layer The dip angle of this layer is N dip , where N dip Between -35 and 35 degrees, Dabnormal Indicates whether there is an abnormal body in the velocity layer. The depth domain velocity model is randomly generated according to the number of layers Mlayer, the dip angle Ndip, the velocity parameter Vm and the depth parameter Dabnormal of the generated velocity anomaly body.

[0117] like Figure 5 As shown, the model set diagram of the depth domain velocity model, the depth domain velocity model set includes multiple depth domain velocity models, and different depth domain velocity models correspond to different geological conditions of the seismic exploration area.

[0118] Step 420, processing the depth domain velocity model set based on the two-way wave equation to obtain gather data.

[0119] In this embodiment, a two-way wave equation forward modeling is performed on the established depth domain velocity model to obtain a large amount of synthetic seismic data, and the simulated synthetic data is gain compensated and extracted as CMP gathers.

[0120] In this embodiment, the acoustic two-way wave forward modeling strategy is adopted to prepare synthetic data. First, according to a large number of depth domain velocity models established in step 410, a unified observation system is arranged for all depth domain velocity models in the following manner: the shot detection points are all placed on the surface, wherein the shot point is located in the center of the model, and the detection points are arranged horizontally on the surface at equal intervals to ensure that the distances between the detection points on both sides of the shot point are the same; the obtained synthetic record data are automatically gain compensated and sorted into CMP arrangement format seismic data. Figure 6 An example diagram of a shot-gather seismic record simulated using the acoustic wave equation. Figure 7 An example diagram of seismic records arranged by CMP for sorting shot gather records.

[0121] Step 430: Process the gather data based on the stacking velocity spectrum preparation formula to obtain sample set data.

[0122] In this embodiment, according to the stacking velocity spectrum preparation formula used in the conventional seismic exploration field, CMP features are extracted and converted into stacking velocity spectrum data; the acquired stacking velocity spectrum data is converted into sample set data that can be input into the neural network using a piecewise normalized data enhancement algorithm.

[0123] In this embodiment, the feature extraction of the gather data is mainly performed using the following formula:

[0124]

[0125] Among them, Spec amp Indicates the prepared superposition velocity spectrum data, v indicates the current scanning velocity, and the range of v is v start With vend The specific value of v is determined according to the model velocity range. In this embodiment, 1000m / s-7350m / s is used. dt is the data sampling point interval, and the data sampling point interval dt is 0.5ms. ntrace represents the total number of seismic data, where CMP represents the seismic data to be processed. nt is the number of sampling points, it is the index of the sampling point, ntrace is the total number of channels in this cmp channel set, itrace is the index of the channel number, vstart and vend are the start and end speed parameters of the scanning speed, and X offset It is the offset length corresponding to the current track.

[0126] Figure 8 This is an example diagram of stacking velocity spectra for CMP gathers.

[0127] In this embodiment, the stacking velocity spectrum data obtained by the above operation also needs to be subjected to a piecewise normalization operation, and the processing operation is expressed by the following formula:

[0128] Spec norm (v, it) = Spec amp (v,it) / Max(Spec amp (v,it))it∈[t_start,t_end]

[0129] Among them, t_start and t_end depend on the time window size, Spec norm In this embodiment, the time window length used is 10 sampling points.

[0130] Step 440: extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data.

[0131] In this embodiment, for the depth domain velocity model set established in step 410, the depth domain layer velocity curve at the CMP point is extracted, the depth domain layer velocity curve is converted into time domain RMS velocity curve data, and the appropriate maximum and minimum velocity ranges are selected according to the characteristics of the work area for normalization processing to obtain label set data that can be input into the neural network. For the velocity spectrum data prepared in step 430, the depth domain layer velocity curve at the cmp point corresponding to the data is extracted.

[0132] In this embodiment, the depth domain velocity model obtained in step 410 is subjected to CMP center point layer velocity curve extraction, the layer velocity curve is converted into an RMS velocity curve using the DIX formula, and then normalized using the maximum and minimum velocities:

[0133] V=(VV min ) / (V max -V min )

[0134] Where V represents the seismic velocity model, V min and V max Indicates the set normalized speed range. In this embodiment, the speed range is normalized using 1000m / s-7350m / s. Fig. 9 The label set data is obtained after normalization processing, that is, the label set instance diagram, that is, the time domain root mean square speed curve instance diagram.

[0135] Step 450: Train a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0136] In this embodiment, a neural network model of seismic exploration velocity analysis technology based on deep learning is built, and the sample set data prepared in step 430 and the label set data set prepared in step 440 are input into the neural network model based on deep learning for training processing, the parameters of the neural network model are obtained, and the parameters of the neural network model are saved to the local disk. Fig.10 As shown in the figure, the network model constructed. Fig.11 The stacking velocity spectrum is automatically interpreted by the seismic exploration velocity modeling system.

[0137] In this embodiment, a network structure based on Unet+++ is adopted. The sample set data and the label set data are input into the established neural network model for training, and the parameters of the neural network model are saved to the local disk after the training is completed. The network model saved to the local disk is deployed to the distributed processing cluster, so that different users can use the same network model to interpret the stacking velocity spectrum respectively, thereby improving processing efficiency. In this embodiment, the parameters of the acquired neural network model are deployed in the distributed processing cluster. When each cluster node user uses this module, the parameters of the neural network model are first loaded into the neural network, and then the neural network is used to automatically interpret the stacking velocity spectrum to be processed, thereby improving work efficiency.

[0138] Embodiment 3

[0139] In this embodiment, Figure 4As shown, a seismic exploration velocity analysis method is provided, including: randomly generating a depth domain velocity model training set; using an acoustic wave equation to forward simulate the depth domain velocity model set to obtain synthetic seismic data, automatically performing gain compensation on the synthetic seismic data, extracting CMP gather data, and preparing a stacked velocity spectrum by using a stacked velocity spectrum production formula; segmentally normalizing the stacked velocity spectrum into sample data; extracting CMP point velocity from the depth domain velocity model to obtain a layer velocity curve; converting the layer velocity curve into an RMS curve by using a DIX formula; normalizing the RMS curve to obtain label data; inputting the sample data and label data into a constructed convolutional neural network and training it, and obtaining the trained neural network as a seismic exploration velocity modeling system. In actual seismic exploration, the stacked velocity spectrum data is input into the seismic exploration velocity modeling system, and the seismic exploration velocity modeling system outputs the prediction results.

[0140] Embodiment 4

[0141] In this embodiment, Figure 2 As shown, a seismic exploration velocity analysis device is provided, comprising:

[0142] A gather data extraction module 210 processes the depth domain velocity model set based on the two-way wave equation to obtain gather data;

[0143] The sample set data acquisition module 220 processes the gather data based on the stacking velocity spectrum production formula to obtain the sample set data;

[0144] The label set data acquisition module 230 extracts the time domain root mean square velocity curve from the depth domain velocity model set to obtain the label set data;

[0145] The model training module 240 trains a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0146] In summary, the seismic exploration velocity analysis device of the present application inputs the calculated label set data and sample set data into a preset deep learning model, trains the preset deep learning model, determines the parameters in the preset deep learning model, and obtains a seismic exploration velocity modeling system. Therefore, in actual exploration, the stacking velocity spectrum data can be input into the seismic exploration velocity modeling system, and the result of automatic interpretation of the stacking velocity spectrum can be calculated.

[0147] The gather data extraction module 210 processes the depth domain velocity model set based on the two-way wave equation to obtain gather data.

[0148] In this embodiment, the depth-domain velocity model set includes a plurality of depth-domain velocity models, and each depth-domain velocity model is processed by a two-way wave equation to obtain gather data.

[0149] The sample set data acquisition module 220 processes the gather data based on the stacking velocity spectrum preparation formula to obtain the sample set data.

[0150] In this embodiment, stacking velocity spectrum data is obtained from the gather data as sample set data through the stacking velocity spectrum preparation formula.

[0151] The label set data acquisition module 230 extracts the time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data.

[0152] In this embodiment, since the stacked velocity spectrum data is time-dependent, in order to associate the sample set data with the label set data, a time-domain root mean square velocity curve is extracted from the depth-domain velocity model set, wherein the time-domain root mean square velocity curve is time-dependent.

[0153] The model training module 240 trains a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0154] In this embodiment, the sample set data and the label set data are input into a preset deep learning model, the preset deep learning model is trained, the parameters of the preset deep learning model are determined, and the trained preset deep learning model is obtained as a seismic exploration velocity modeling system.

[0155] In the model training module 240, the preset deep learning model is pre-built, but the parameters of the preset deep learning model are not determined. The sample set data obtained by the sample set data acquisition module 220 and the label set data obtained by the label set data acquisition module 230 are input into the preset deep learning model, the parameters of the preset deep learning model are determined, and the parameters of the trained preset deep learning model are obtained as the seismic exploration velocity modeling system. The parameters of the preset deep learning model are saved to the local disk, and the preset deep learning model saved to the local disk is deployed to the distributed processing cluster, so that different users can use the same preset deep learning model to interpret the stacking velocity spectrum separately, thereby improving processing efficiency. When the parameters of the preset deep learning model are deployed in the distributed processing cluster, each cluster node user first loads the parameters of the preset deep learning model into the preset deep learning model when using this module, and then uses the preset deep learning model to automatically interpret the stacking velocity spectrum to be processed, thereby improving work efficiency.

[0156] In one embodiment, the seismic exploration velocity analysis device comprises:

[0157] The velocity model building module 250 is used to obtain the geological conditions of multiple seismic exploration areas, analyze the geological conditions of each seismic exploration area to obtain geological structural characteristics, and build the depth domain velocity model set based on the geological structural characteristics.

[0158] In this embodiment, in order to obtain more sample set data and label set data to train the preset deep learning model, multiple depth velocity domain velocity models are established, thereby more sample set data and label set data can be obtained from multiple depth velocity domain velocity models as training sets, and a large number of training sets are provided to better calculate the parameters in the preset deep learning model. The obtained seismic exploration velocity modeling system can analyze the stacking velocity spectrum data more accurately and output more accurate calculation results.

[0159] In this embodiment, according to the geological complexity of the domestic seismic exploration area, the specific geological structure characteristics are analyzed and abstractly represented, and a depth domain velocity model set with different geological characteristics is established to ensure that the depth domain velocity model set covers the breadth of the geological structure characteristics of the actual processing area.

[0160] In this embodiment, the depth domain velocity model set is defined according to equation (1):

[0161] V depth =Γ(V m , N dip , M layer , D abnormal )

[0162] V m ∈[1000, 7000], N dip ∈[-35°, 35°]and different with layers,

[0163] M layer ∈[1, 14], D abnormal ∈[0, 1]

[0164] Among them, V depth represents the total velocity parameter of the current depth domain velocity model, function Γ is the abstract velocity model representation, V m The layer velocity magnitude of the Nth layer of the current depth domain velocity model, where N is between 0-M layer Mlayer is the number of layers, and the dip angle of the layer is N dip , where N dip Between -35 and 35 degrees, D abnormalIndicates whether there is an abnormal body in the velocity layer. That is, according to the number of layers Mlayer, the dip angle Ndip, the velocity parameter Vm, and the depth parameter Dabnormal of the generated velocity anomaly body, a depth domain velocity model is generated. The depth domain velocity model is established based on the dip angle, velocity range, geological anomaly body and number of strata, making the depth domain velocity model set more comprehensive and adaptable to the complex geological conditions of domestic seismic exploration areas.

[0165] In one embodiment, the gather data extraction module 210 includes:

[0166] The seismic data synthesis unit 211 performs two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data.

[0167] The gather data acquisition unit 212 performs gain compensation processing on the synthetic seismic data to obtain gather data.

[0168] In this embodiment, the two-way wave equation is an acoustic two-way wave equation, and the depth domain velocity model set is processed by the acoustic two-way wave equation, and a forward simulation strategy is performed to prepare synthetic seismic data.

[0169] In this embodiment, the step of performing gain compensation processing on the synthetic seismic data to obtain gather data includes: performing gain compensation processing on the synthetic seismic data to obtain compensated seismic data; sorting the compensated seismic data in a CMP arrangement format to obtain gather data. It should be understood that when preparing stacking velocity spectrum data from synthetic seismic data, the input data sorting format is CMP sorting.

[0170] In one embodiment, the sample set data acquisition module 220 includes:

[0171] The velocity spectrum acquisition module 221 extracts features from the gather data to obtain gather features based on the stacking velocity spectrum production formula, and converts the gather features into stacking velocity spectrum data;

[0172] The sample set acquisition module 222 performs piecewise normalization processing on the stacking velocity spectrum data to obtain sample set data.

[0173] In this embodiment, feature extraction is performed on the gather data according to formula (2):

[0174]

[0175]

[0176] Among them, Spec amp Indicates the prepared superposition velocity spectrum data, v indicates the current scanning velocity, and the range of v is v start With vend The specific value of v is determined according to the velocity range of the depth domain velocity model, dt is the data sampling point interval, ntrace represents the total number of seismic data, and CMP represents the current gather data to be processed. nt is the number of sampling points, it is the index of the sampling point, ntrace is the total number of gather data, itrace is the index of the number of tracks, and v start is the starting point of the scanning speed, v end is the end point of the scanning speed, the speed parameter dt is the sampling interval, v is the current scanning speed, X offset is the offset length corresponding to the current seismic trace. In this embodiment, the velocity range of the depth domain velocity model is 1000m / s-7350m / s. In this embodiment, the data sampling point interval dt is 0.5ms.

[0177] In this embodiment, the stacking velocity spectrum data is subjected to piecewise normalization processing according to equation (3) to obtain sample set data;

[0178] Spec norm (v, it) = Spec amp (v,it) / Max(Spec amp (v, it)) it∈[t_start, t_end](3)

[0179] Among them, Spec norm represents the sample set data, t_start and t_end depend on the time window size. In this embodiment, the time window length is 10 sampling points.

[0180] In one embodiment, the tag set data acquisition module 230 includes:

[0181] The velocity extraction module 231 extracts the gather point velocity from the depth domain velocity model set to obtain a layer velocity curve;

[0182] The velocity conversion module 232 converts the layer velocity curve into the time domain root mean square velocity curve;

[0183] The label set acquisition module 233 performs normalization processing on the time domain RMS speed curve to obtain label set data.

[0184] In this embodiment, the CMP center point layer velocity curve is extracted from the depth domain velocity model.

[0185] In this embodiment, based on the DIX formula, the layer velocity curve is converted into a time-domain root mean square velocity curve.

[0186] In one embodiment, normalizing the time domain root mean square speed curve includes:

[0187] The time domain root mean square speed curve is normalized based on the maximum and minimum speeds.

[0188] In this embodiment, the maximum and minimum speed normalization processing is performed on the time domain RMS speed curve according to equation (4):

[0189] V=(VV min ) / (V max -V min )

[0190] Where V represents the seismic velocity model, V min and V max Indicates the normalized speed range set, V min represents the normalized minimum velocity, V max In this embodiment, the normalized speed range is 1000m / s-7350m / s, V min 1000m / s, V max It is 7350m / s.

[0191] The specific definition of the seismic exploration velocity analysis device can be found in the definition of the seismic exploration velocity analysis method above, which will not be repeated here. Each unit in the above-mentioned seismic exploration velocity analysis device can be implemented in whole or in part based on software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can 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-mentioned units.

[0192] Embodiment 5

[0193] In this embodiment, a computer device is provided. Its internal structure diagram can be shown as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected based on a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database, which is used to store data on the geological conditions of the seismic exploration area and a depth domain velocity model. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices that deploy application software. When the computer program is executed by the processor, a seismic exploration velocity analysis method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0194] Those skilled in the art will understand that Figure 3 The structure shown in the figure 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 device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0195] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0196] Step 110, processing the depth domain velocity model set based on the two-way wave equation to obtain gather data.

[0197] In this embodiment, the depth-domain velocity model set includes a plurality of depth-domain velocity models, and each depth-domain velocity model is processed by a two-way wave equation to obtain gather data.

[0198] Step 120, processing the gather data based on the stacking velocity spectrum preparation formula to obtain sample set data.

[0199] In this embodiment, stacking velocity spectrum data is obtained from the gather data as sample set data through the stacking velocity spectrum preparation formula.

[0200] Step 130 : extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data.

[0201] In this embodiment, since the stacked velocity spectrum data is time-dependent, in order to associate the sample set data with the label set data, a time-domain root mean square velocity curve is extracted from the depth-domain velocity model set, wherein the time-domain root mean square velocity curve is time-dependent.

[0202] Step 140: Training a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0203] In this embodiment, the sample set data and the label set data are input into a preset deep learning model, the preset deep learning model is trained, the parameters of the preset deep learning model are determined, and the trained preset deep learning model is obtained as a seismic exploration velocity modeling system.

[0204] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0205] The geological conditions of a plurality of seismic exploration areas are obtained, the geological conditions of each of the seismic exploration areas are analyzed to obtain geological structural characteristics, and the depth domain velocity model set is established based on the geological structural characteristics.

[0206] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0207] Step 111, performing two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data.

[0208] Step 112, performing gain compensation processing on the synthetic seismic data to obtain gather data.

[0209] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0210] Step 121, based on the stacking velocity spectrum making formula, extracting features from the gather data to obtain gather features, and converting the gather features into stacking velocity spectrum data;

[0211] Step 122: performing piecewise normalization processing on the stacking velocity spectrum data to obtain sample set data.

[0212] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0213] Step 131, extracting gather point velocities from the depth-domain velocity model set to obtain an interval velocity curve;

[0214] Step 132, converting the layer velocity curve into the time domain root mean square velocity curve;

[0215] Step 133: normalize the time domain RMS speed curve to obtain label set data.

[0216] Embodiment 6

[0217] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0218] Step 110, processing the depth domain velocity model set based on the two-way wave equation to obtain gather data.

[0219] In this embodiment, the depth-domain velocity model set includes a plurality of depth-domain velocity models, and each depth-domain velocity model is processed by a two-way wave equation to obtain gather data.

[0220] Step 120, processing the gather data based on the stacking velocity spectrum preparation formula to obtain sample set data.

[0221] In this embodiment, stacking velocity spectrum data is obtained from the gather data as sample set data through the stacking velocity spectrum preparation formula.

[0222] Step 130 : extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data.

[0223] In this embodiment, since the stacked velocity spectrum data is time-dependent, in order to associate the sample set data with the label set data, a time-domain root mean square velocity curve is extracted from the depth-domain velocity model set, wherein the time-domain root mean square velocity curve is time-dependent.

[0224] Step 140: Training a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

[0225] In this embodiment, the sample set data and the label set data are input into a preset deep learning model, the preset deep learning model is trained, the parameters of the preset deep learning model are determined, and the trained preset deep learning model is obtained as a seismic exploration velocity modeling system.

[0226] In summary, in this embodiment, the calculated label set data and sample set data are input into a preset deep learning model, the preset deep learning model is trained, and the parameters in the preset deep learning model are determined to obtain a seismic exploration velocity modeling system. Therefore, in actual exploration, the stacking velocity spectrum data can be input into the seismic exploration velocity modeling system, and the result of automatic interpretation of the stacking velocity spectrum can be calculated.

[0227] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0228] The geological conditions of a plurality of seismic exploration areas are obtained, the geological conditions of each of the seismic exploration areas are analyzed to obtain geological structural characteristics, and the depth domain velocity model set is established based on the geological structural characteristics.

[0229] In this embodiment, in order to obtain more sample set data and label set data to train the preset deep learning model, multiple depth velocity domain velocity models are established, thereby more sample set data and label set data can be obtained from multiple depth velocity domain velocity models as training sets, and a large number of training sets are provided to better calculate the parameters in the preset deep learning model. The obtained seismic exploration velocity modeling system can analyze the stacking velocity spectrum data more accurately and output more accurate calculation results.

[0230] In this embodiment, according to the geological complexity of the domestic seismic exploration area, the specific geological structure characteristics are analyzed and abstractly represented, and a depth domain velocity model set with different geological characteristics is established to ensure that the depth domain velocity model set covers the breadth of the geological structure characteristics of the actual processing area.

[0231] In this embodiment, the depth domain velocity model set is defined according to equation (1):

[0232] V depth =Γ(V m , N dip , M laye r, D abnormal )

[0233] V m ∈[1000, 7000], N dip ∈[-35°, 35°]and different with layers,

[0234] M layer ∈[1, 14], D abnormal ∈[0, 1]

[0235] Among them, V depth represents the total velocity parameter of the current depth domain velocity model, function Γ is the abstract velocity model representation, V m The layer velocity magnitude of the Nth layer of the current depth domain velocity model, where N is between 0-M layer Mlayer is the number of layers, and the dip angle of the layer is N dip , where N dip Between -35 and 35 degrees, D abnormalIndicates whether there is an abnormal body in the velocity layer. That is, according to the number of layers Mlayer, the dip angle Ndip, the velocity parameter Vm, and the depth parameter Dabnormal of the generated velocity anomaly body, a depth domain velocity model is generated. The depth domain velocity model is established based on the dip angle, velocity range, geological anomaly body and number of strata, making the depth domain velocity model set more comprehensive and adaptable to the complex geological conditions of domestic seismic exploration areas.

[0236] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0237] Step 111, performing two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data.

[0238] Step 112, performing gain compensation processing on the synthetic seismic data to obtain gather data.

[0239] In this embodiment, the two-way wave equation is an acoustic two-way wave equation, and the depth domain velocity model set is processed by the acoustic two-way wave equation, and a forward simulation strategy is performed to prepare synthetic seismic data.

[0240] In this embodiment, the step of performing gain compensation processing on the synthetic seismic data to obtain gather data includes: performing gain compensation processing on the synthetic seismic data to obtain compensated seismic data; sorting the compensated seismic data in a CMP arrangement format to obtain gather data. It should be understood that when preparing stacking velocity spectrum data from synthetic seismic data, the input data sorting format is CMP sorting.

[0241] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0242] Step 121, based on the stacking velocity spectrum making formula, extracting features from the gather data to obtain gather features, and converting the gather features into stacking velocity spectrum data;

[0243] Step 122: performing piecewise normalization processing on the stacking velocity spectrum data to obtain sample set data.

[0244] In this embodiment, feature extraction is performed on the gather data according to formula (2):

[0245]

[0246] Among them, Spec amp Indicates the prepared superposition velocity spectrum data, v indicates the current scanning velocity, and the range of v is v start With v endThe specific value of v is determined according to the velocity range of the depth domain velocity model, dt is the data sampling point interval, ntrace represents the total number of seismic data, and CMP represents the current gather data to be processed. nt is the number of sampling points, it is the index of the sampling point, ntrace is the total number of gather data, itrace is the index of the number of tracks, and v staxt is the starting point of the scanning speed, v end is the end point of the scanning speed, the speed parameter dt is the sampling interval, v is the current scanning speed, X offset is the offset length corresponding to the current seismic trace. In this embodiment, the velocity range of the depth domain velocity model is 1000m / s-7350m / s. In this embodiment, the data sampling point interval dt is 0.5ms.

[0247] In this embodiment, the stacking velocity spectrum data is subjected to piecewise normalization processing according to equation (3) to obtain sample set data;

[0248] Spec norm (v, it) = Spec amp (v,it) / Max(SpeC amp (v, it)) it∈[t_start, t_end](3)

[0249] Among them, Spec norm represents the sample set data, t_start and t_end depend on the time window size. In this embodiment, the time window length is 10 sampling points.

[0250] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0251] Step 131, extracting gather point velocities from the depth-domain velocity model set to obtain an interval velocity curve;

[0252] Step 132, converting the layer velocity curve into the time domain root mean square velocity curve;

[0253] Step 133: normalize the time domain RMS speed curve to obtain label set data.

[0254] In this embodiment, the CMP center point layer velocity curve is extracted from the depth domain velocity model.

[0255] In this embodiment, based on the DIX formula, the layer velocity curve is converted into a time-domain root mean square velocity curve.

[0256] In one embodiment, the step of normalizing the time domain root mean square speed curve comprises:

[0257] The time domain root mean square speed curve is normalized based on the maximum and minimum speeds.

[0258] In this embodiment, the maximum and minimum speed normalization processing is performed on the time domain RMS speed curve according to equation (4):

[0259] V=(VV min ) / (V max -V min )

[0260] Where V represents the seismic velocity model, V min and V max Indicates the normalized speed range set, V min represents the normalized minimum velocity, V max In this embodiment, the normalized speed range is 1000m / s-7350m / s, V min 1000m / s, V max It is 7350m / s.

[0261] In this embodiment, in the maximum and minimum speed normalization process, the maximum speed and the minimum speed are determined according to the characteristics of the work area.

[0262] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware based on a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0263] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0264] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A seismic exploration velocity analysis method, characterized in that: include: The depth domain velocity model set is processed based on the two-way wave equation to obtain the gather data; The gather data are processed based on a stacking velocity spectrum production formula to obtain sample set data; Extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data; A preset deep learning model is trained based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

2. The method according to claim 1, characterized in that The steps of processing the depth domain velocity model set based on the two-way wave equation to obtain the gather data include: The geological conditions of multiple seismic exploration areas are obtained, the geological conditions of each seismic exploration area are analyzed to obtain geological structural characteristics, and the geological conditions of multiple seismic exploration areas are obtained based on the geological structural characteristics, the geological conditions of each seismic exploration area are analyzed to obtain geological structural characteristics, and the depth domain velocity model set is established based on the geological structural characteristics.

3. The method according to claim 1, characterized in that The steps of processing the depth domain velocity model set based on the two-way wave equation to obtain the gather data include: Performing two-way wave equation forward modeling on the depth domain velocity model set to obtain synthetic seismic data; Gain compensation processing is performed on the synthetic seismic data to obtain gather data.

4. The method according to claim 1, characterized in that: The steps of processing the gather data based on the stacking velocity spectrum production formula to obtain the sample set data include: Based on the stacking velocity spectrum preparation formula, extracting features from the gather data to obtain gather features, and converting the gather features into stacking velocity spectrum data; The stacking velocity spectrum data is subjected to piecewise normalization processing to obtain sample set data.

5. The method according to any one of claims 1 to 4, characterized in that: The step of extracting a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data comprises: Extracting gather point velocities from the depth-domain velocity model set to obtain an interval velocity curve; Converting the layer velocity curve into the time-domain root mean square velocity curve; The time domain root mean square speed curve is normalized to obtain label set data.

6. The method according to claim 5, characterized in that The step of normalizing the time domain root mean square velocity curve comprises: The time domain root mean square speed curve is normalized based on the maximum and minimum speeds.

7. A seismic exploration velocity analysis device, characterized in that: include: The gather data extraction module processes the depth domain velocity model set based on the two-way wave equation to obtain the gather data; A sample set data acquisition module processes the gather data based on a stacking velocity spectrum production formula to obtain sample set data; A label set data acquisition module extracts a time domain root mean square velocity curve from the depth domain velocity model set to obtain label set data; A model training module trains a preset deep learning model based on the sample set data and the label set data to obtain a seismic exploration velocity modeling system for seismic exploration velocity analysis.

8. The device according to claim 7, characterized in that The seismic exploration velocity analysis device includes: The velocity model establishment module is used to obtain the geological conditions of multiple seismic exploration areas, analyze the geological conditions of each seismic exploration area to obtain geological structural characteristics, obtain the geological conditions of multiple seismic exploration areas based on the geological structural characteristics, analyze the geological conditions of each seismic exploration area to obtain geological structural characteristics, and establish the depth domain velocity model set based on the geological structural characteristics.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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