Thin interbed lithologic structure identification method based on deep learning

Abstract

The invention discloses a thin interbed lithologic structure identification method based on deep learning, and the method comprises the steps: carrying out the manual collection and processing, and obtaining seismic data; performing well-seismic calibration and depth-time conversion to obtain a lithologic structure and seismic data of a thin interbed section at a well point; performing time-frequency analysis and sliding a time window to obtain a time-frequency spectrum image in a local time window of a seismic channel beside a well, and taking the image as a deep learning network input sample; obtaining a thin interbed lithologic structure in a local time window, and classifying and coding the lithologic structure as a sample label; finishing thin interbed lithologic structure identification network model training to obtain optimal network parameters; traversing all seismic traces by using the optimal network parameters to obtain lithology classification results of the thin interbed in all time windows; and processing results in the overlapped time windows, and determining final spatial distribution of the thin interbed lithologic structure. Compared with the prior art, the method has the advantages that logging and seismic data are combined, seismic resolution can be broken through, spatial continuous distribution of the thin interbed lithologic structure is obtained, wavelet information is not needed, the recognition precision is high, and popularization is convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir prediction in artificial seismic exploration, and further to the field of lithology and thin interbedded reservoir exploration, and specifically to a method for identifying thin interbedded lithology structures based on deep learning. Background Art

[0002] Artificial seismic exploration refers to the artificial stimulation of seismic waves on the surface. When propagating underground, the seismic waves will be reflected and transmitted when encountering the interface of rock layers with different medium properties. The seismic waves are received by detectors on the surface to obtain seismic records. The characteristics of seismic records are related to the properties and structure of underground rock layers. By processing and interpreting seismic records, the properties and morphology of underground rock layers can be inferred. Since artificial seismic exploration is superior to other geophysical exploration methods in terms of exploration precision and accuracy, it plays an important role in oil and gas reservoir prediction.

[0003] In recent decades, with the continuous deepening of oil and gas exploration, large-scale and easy-to-find structural traps have been explored, and the oil and gas exploration targets have gradually shifted to lithologic and complex traps with small thickness and fast lithologic changes. Thin interbedded reservoirs are commonly developed in oil and gas basins in my country. Large-area thin interbedded lithologic oil and gas reservoirs are the focus of increasing reserves and production in the field of conventional oil and gas exploration at this stage. Thin interbedded strata have small single-layer thickness, frequent interbedding in the vertical direction, and fast lithologic and thickness changes in the lateral direction. It is difficult to accurately identify and predict their lithology. Therefore, conducting research on the identification of thin interbedded lithologic structures is a very meaningful task in oil and gas exploration.

[0004] In actual artificial seismic exploration, strata with a single layer thickness less than 1 / 4 of the main wavelength of the seismic wavelet are usually considered to be thin layers, and multiple thin layers are vertically stacked to form thin interlayers. Limited by the vertical resolution of seismic data, it is difficult to accurately identify the internal lithological structure of thin interlayers. When using seismic data for lithological structure identification, layer-by-layer seismic attributes and seismic inversion methods are usually used. The resolution of the seismic attribute method is equivalent to that of the seismic data. For thin interlayers, it cannot effectively solve the problem of internal lithological structure identification; the seismic inversion method can integrate well data, effectively improve the resolution of the results, and provide useful help for thin interlayer identification, but the inversion method requires seismic wavelet information, and the accuracy of the wavelet directly affects the accuracy of the inversion. The present invention provides a thin interlayer lithological structure identification method based on deep learning, which uses the two-dimensional time-frequency spectrum of seismic data as input, automatically extracts features related to the thin interlayer lithological structure in the time-frequency spectrum with the help of deep learning, establishes a mapping relationship between the local time-frequency spectrum and the lithological structure, and realizes the accurate identification of the thin interlayer lithological structure. Compared with the existing methods, this method combines artificial seismic data and well data to break through the resolution of seismic data and obtain the spatial continuous distribution of thin interbedded lithological structures. It does not require seismic wavelet information, has high recognition accuracy, and is easy to promote and use. Summary of the invention

[0005] The purpose of the present invention is to provide a thin interbedded lithological structure identification method based on deep learning, so as to improve the accuracy of identifying and predicting the internal lithological structure of the thin interbedded layers.

[0006] To achieve the above object, the present invention provides a method for identifying thin interbedded lithological structures based on deep learning, and the present invention includes:

[0007] Collect artificial seismic data in the field and obtain post-stack seismic data through indoor processing;

[0008] Conduct well-seismic calibration and deep-time conversion to obtain the thin interbedded lithology structure and seismic data at the well point;

[0009] Perform time-frequency analysis on the seismic data of the target thin interbedded section to obtain a two-dimensional time-frequency spectrum of the seismic trace;

[0010] The two-dimensional time-frequency spectrum images in the local time window are obtained from the seismic traces near the well, and are used as input samples of the deep learning network after preprocessing;

[0011] Obtain the thin interbedded lithology structure in the local time window from the logging interpretation lithology data, and use it as the sample label after classification and coding;

[0012] Build a deep learning network architecture, define the loss function, select the network model evaluation method, complete the thin interbedded lithology structure identification network model training, and obtain the optimal network parameters;

[0013] Traverse the two-dimensional time-frequency spectrum of all seismic traces to obtain the entire set of two-dimensional time-frequency spectrum images within the local time window;

[0014] Using the optimal network parameters, all sets of preprocessed two-dimensional time-frequency spectrum images are converted into thin interbed lithology classification results;

[0015] The lithology classification results in the overlapping time windows are processed to determine the final spatial distribution of the thin interbedded lithology structure.

[0016] The beneficial effects of the present invention are as follows: compared with the method of using layer-by-layer seismic attributes to identify lithological structures, the method proposed by the present invention combines seismic and well data, has a higher vertical resolution, and does not require artificial matching of the correspondence between seismic attributes and lithology, thereby reducing the subjectivity and multiple solutions of the identification results. Compared with the method based on seismic inversion, this method does not require known seismic wavelet information, does not require the establishment of an initial inversion model, does not rely on the accuracy of the wavelet and the initial model, and is easy to promote and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. It is obvious that the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 This is a flow chart of a method for identifying thin interbedded lithologic structures based on deep learning according to an embodiment of the present invention;

[0019] Figure 2 The seismic data including the thin interbedded section according to the embodiment of the present invention;

[0020] Figure 3 A seismic trace and a two-dimensional time-frequency spectrum image thereof according to an embodiment of the present invention;

[0021] Figure 4 The sample and label making method of the embodiment of the present invention;

[0022] Figure 5 A deep learning network architecture according to an embodiment of the present invention;

[0023] Figure 6 A diagram showing a ratio of data set division according to an embodiment of the present invention;

[0024] Figure 7 A network hyperparameter table of an embodiment of the present invention;

[0025] Figure 8 1 is a loss curve and an accuracy curve of an embodiment of the present invention;

[0026] Fig. 9 This is the result of thin interlayer identification using seismic relative wave impedance in this embodiment;

[0027] Fig.10 This is the thin interbedded lithological structure recognition result based on deep learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Figure 1 FIG. 1 is a flow chart of a method for identifying thin interbedded lithology structures based on deep learning according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0030] S101. Collect artificial seismic data on the surface, process seismic data indoors, and obtain post-stack seismic data. Specifically, in step S101, artificial earthquake refers to an earthquake that is artificially stimulated for the purpose of exploration. When conducting artificial seismic acquisition in the field, it can be three-dimensional acquisition along a surface on the surface, or it can be two-dimensional acquisition along a line, and the post-stack seismic data obtained can be three-dimensional data or two-dimensional data. An underground exploration target in a certain work area in a certain place has a two-dimensional line of wells connected with three-dimensional seismic data obtained by three-dimensional acquisition, and the application of an embodiment of the present invention is demonstrated. The underground exploration target in the area includes a set of thin interbedded sections, which are delta front deposits, sand and mudstone lithology, and a multi-layer sand and mudstone interbedded structure developed from bottom to top. The spatial relationship of the reservoir sand body is complex, the lateral changes are fast, and the heterogeneity is strong. Intercept the seismic data of the target thin interbedded section, Figure 2 This is the seismic data containing thin interbedded sections in an embodiment of the present invention. The arrows above indicate the locations of the wells, that is, there are 5 wells drilled in the area, and the well data are complete.

[0031] S102, using well logging data and wellside seismic traces to perform well seismic calibration and deep-time conversion on the target layer segment, and obtain the lithological structure data and seismic data of the thin interlayer segment at the well point; specifically, in step S102, for the embodiment of the present invention, the well logging curves used are acoustic time difference curves and density curves, and the well logging interpretation results data used refer to lithological data. In this example, well seismic calibration adopts the method of making artificial synthetic seismic records. First, the well logging acoustic time difference curve is used to obtain the acoustic wave velocity curve through reciprocal operation, and the acoustic wave velocity curve is multiplied with the density curve to obtain the longitudinal wave impedance curve. The longitudinal wave velocity curve is subjected to point-by-point reflection coefficient operation to obtain a discrete longitudinal wave reflection coefficient sequence, and a sub-wave matching the main frequency of the seismic data is given to perform convolution operation with the reflection coefficient sequence to obtain a synthetic seismic record, and the well or wellside seismic traces are extracted from the actual seismic data, and the wellside seismic traces are matched and calibrated with the synthetic seismic record to determine the time-depth relationship at the well. When calibrating well seismic, the matching degree of the target thin interlayer segment is focused on. The same calibration operation is performed on all wells in the work area. Using the obtained time-depth relationship, the lithology data interpreted on each well is converted into lithology structure data with time coordinates. The converted lithology structure data is consistent with the time coordinates of the seismic data and has the same time sampling interval. In this example, the time sampling interval of the seismic data is 1ms.

[0032] S103, perform time-frequency analysis on the target thin interlayer segment seismic data, and convert the one-dimensional waveform data of the seismic trace into a two-dimensional time-frequency spectrum of the seismic trace. The time-frequency analysis in step S103 can be a linear time-frequency analysis, such as short-time Fourier transform, continuous wavelet transform and S transform, or a nonlinear time-frequency analysis, such as Wigner-Ville distribution, pseudo-Wigner-Ville distribution and Cohen-type time-frequency distribution. In this example, the wavelet transform time-frequency analysis method is used.

[0033] In step S103, the frequency range of the time-frequency analysis is based on the main frequency f of the seismic data of the target thin interlayer segment. m Its effective frequency band range [f1, f h ] OK, low frequency starts at 0hz, high frequency takes 2f m +(f h -f1) / 2 and above, the frequency domain sampling interval is no greater than 1 Hz. In this example, the main frequency of the seismic data is f m It is about 30 Hz, the effective frequency band ranges from 8 Hz to 88 Hz, the frequency range of time-frequency analysis is 0 Hz to 100 Hz, and the frequency domain sampling interval is 1 Hz. Figure 3 A seismic trace and a two-dimensional time-frequency spectrum image of an embodiment of the present invention are shown, wherein the left side is a seismic trace waveform diagram, and the right side is a two-dimensional time-frequency spectrum image obtained after the seismic trace is transformed by wavelet.

[0034] S104, using a sliding time window to obtain a two-dimensional time-frequency spectrum image in a local time window from the wellbore seismic trace, and preprocessing it as an input sample for the deep learning network. Specifically, in step S104, the sliding time window has a time window length based on the main frequency f of the target thin interlayer segment seismic data. m OK, minimum is 1 / f m twice, the maximum is 1 / f m 4 times of the time sampling interval and an integer multiple of the time sampling interval; the sliding step length of the sliding window is at least one time sampling interval and at most one time window length. In this example, the main frequency of the seismic data f m It is about 30hz, the length of the sliding time window is determined to be 100ms, and the sliding step size adopts the minimum step size, that is, 1 time sampling interval is 1ms.

[0035] The preprocessing in step S104 is specifically to perform normalization and resampling on the two-dimensional time-frequency spectrum image in the local time window, wherein normalization is necessary, and resampling is selectively performed according to the requirements of recognition accuracy and computational efficiency. Resampling can be performed in the vertical and horizontal directions of the two-dimensional time-frequency spectrum image in the local time window, or only in one direction. In this example, the preprocessing is performed by normalization and resampling. Normalization is based on the mean and standard deviation of pixel values, mapping pixels to the range of [-1, 1]. After normalization, the data distribution is centered on 0, which is conducive to the optimization of gradient descent. The formula is:

[0036]

[0037] Where x is the pixel value of the image; μ is the mean used for normalization; σ is the standard deviation used for normalization; x norm is the normalized pixel value.

[0038] In this embodiment, the resampling process uses a linear interpolation method to improve the resolution of the time-frequency spectrum. The specific operation is to perform three rounds of linear interpolation on each line of time-frequency data. Suppose the original data is x=[x1, x2, ..., x n ], after one interpolation, the interpolation data is:

[0039]

[0040] Repeat the above steps three times to obtain the final interpolated data.

[0041] S105. Using the same sliding time window as in step S104, obtain the thin interbedded lithology structure in the local time window from the logging interpretation lithology data, and classify and encode its lithology as the label of the sample. In step S105, the thin interbedded lithology structure in the local time window is the logging interpretation lithology conclusion with the same time sampling interval and number of samples as the seismic data in the local time window. The sliding time window is the same as in step S104, with the same time window length and the same sliding step length. Figure 4 The sample and label making method of the embodiment of the present invention are shown. In this example, the time window length and the sliding step length are 100ms and 1ms respectively.

[0042] In step S105, the lithology classification coding is performed according to the following rules: the first lithology: classification coding value = 1; the second lithology: classification coding value = 2; ..., the nth lithology: classification coding value = m; wherein n is the total lithology category of the well logging interpretation, m is the total coding category, m≤n, and the lithology with excessive indication is coded by adjacent values, such as the three lithologies of mudstone, muddy sandstone and sandstone interpreted by the well logging. When coding, coding method 1 (mudstone = 1, muddy sandstone = 2, sandstone = 2) or coding method 2 (mudstone = 1, muddy sandstone = 2, sandstone = 3) can be used. Which coding method is used is determined according to the lithology identification accuracy requirements. In this example, there are two types of lithology conclusions interpreted by the well logging interpretation, which are marked as mudstone and sandstone, and the lithology codes used are mudstone = 1 and sandstone = 2.

[0043] In step S105, the encoded sample label is a discrete numerical one-dimensional vector, the numerical values ​​in the vector are composed of 1, 2, ..., m, and the length of the vector is the same as the number of time sampling points of the sample. In this example, the label of each sample is a one-dimensional vector composed of numerical values ​​1 and 2, and the vector length is 100. In this example, a single label indicates whether the lithology at each time sampling point position in the corresponding 100ms local time window is mudstone or sandstone. As the time window slides, the numerical changes of adjacent labels indicate the vertical changes of lithology.

[0044] S106, match samples and labels, divide training sets and validation sets, build deep learning network architecture, define loss function, select network model evaluation method, test network hyperparameters, complete thin interbedded lithology structure recognition network model training, and obtain optimal network parameters. A deep learning network refers to a deep network with three or more feature extraction layers, and its feature extraction layer uses two-dimensional convolution operations. In this example, a deep network with a U-shaped structure is used. Figure 5 This is a deep learning network architecture diagram of an embodiment of the present invention. Its network structure is an encoder-decoder structure. The encoder consists of four step-by-step downsampling modules, each of which contains two consecutive 3×3 convolution operations and one 2×2 maximum pooling operation. The decoder consists of four step-by-step upsampling modules, each of which contains one 2×2 deconvolution and two 3×3 convolution operations. The output of each layer in the encoder is spliced ​​with the upsampling result of the corresponding layer in the decoder using jump connections to achieve the fusion of image features of different scales.

[0045] Figure 6 This is a diagram showing the proportion of data sets divided according to an embodiment of the present invention. In this example, there are 5 wells with a total of 3840 labeled samples. Samples from 4 wells are used as training sets. Figure 2 The middle well was used as a blind well for verification, and its samples were used as the verification set. The training set and verification set accounted for 80% and 20% respectively.

[0046] In step S106, the loss function adopts the cross entropy function. If the target thin interbed section has two lithologies, the binary cross entropy function is adopted. If the target thin interbed section has three or more lithologies, the multivariate cross entropy function is adopted. In this example, the target thin interbed section has two lithologies: mudstone and sandstone, so the binary cross entropy function is adopted. The binary cross entropy loss function is:

[0047]

[0048] Where X is the training sample; Y is the label of X; is the probability matrix predicted by the network model; p x represents a sampling point in X; For p x The label value of is the p predicted by the network model x probability.

[0049] In step S106, the network model evaluation method adopts a dual evaluation method of loss curve and accuracy curve. The loss curve is generated by the loss function, and the accuracy curve is generated by the classification result confusion matrix. The loss curve is generated by the change of the value of the loss function with the number of training iterations, reflecting the optimization process of the model. The confusion matrix of multi-classification is a K×K matrix, where K represents the number of classification categories. The element X in the matrix ij Indicates the number of samples whose true category is i and the model predicts j. The diagonal elements X ii Indicates the number of samples classified correctly, non-diagonal elements X ij (i≠j) represents the number of samples that are misclassified. The confusion matrix can be used to calculate the classification accuracy, and its formula is:

[0050]

[0051] The numerator is the total number of samples that are correctly classified, and the denominator is the total number of samples. Accuracy is an important indicator to measure the performance of a classification model. The higher the accuracy, the stronger the model's ability to distinguish between categories. Figure 7 This is a network hyperparameter table of an embodiment of the present invention. In this example, the Adam optimization method is used, and the ideal batch size obtained through testing is 1, the epoch is 150, and the learning rate is 0.001. Figure 8 These are the loss curve and accuracy curve of the embodiment of the present invention. The loss curve and accuracy curve of the training set and the validation set can converge normally, indicating that the network training has achieved the expected effect, the training of the thin interbedded lithology structure identification network model has been completed, and the optimal network parameters have been obtained.

[0052] S107. Use a sliding time window of the same length as in step S104 to traverse the two-dimensional time-frequency spectrum of all seismic traces, obtain the entire set of two-dimensional time-frequency spectrum images in the local time window, and preprocess all images in the set. In step S107, the length of the sliding time window is the same as the sliding time window length in step S104; the sliding step size can be the same as the sliding step size in step S104, or it can be different. In this example, a sliding time window exactly the same as that in step S104 is used, and the time window length and sliding step size are 100ms and 1ms respectively. In step S107, the same preprocessing method as in step S104 is used. In this example, normalization and resampling preprocessing are performed.

[0053] S108, using the optimal network parameters, converting the entire set of preprocessed two-dimensional time-frequency spectrum images in the local time window into the lithology classification results of the thin interbeds in the local time window. Specifically, in step S108, the optimal network is the deep network with the optimal weight obtained in step S106, and the conversion process refers to inputting each image in the preprocessed two-dimensional time-frequency spectrum image set in the local time window into the optimal network, and its output is the lithology classification result of the thin interbeds in the corresponding local time window.

[0054] S109, according to the time position of the center point of the sliding time window of each seismic trace, the lithology classification results in the overlapping time window are processed to determine the final spatial distribution of the thin interbedded lithology structure. Specifically, in step S109, the sliding time window is the same sliding time window as that in step S107, with the same time window length and sliding step length; the overlapping time window refers to the overlapping part of the time window existing when the sliding step length of adjacent sliding time windows is less than the time window length, and the processing method of the lithology classification results in the overlapping time window is:

[0055]

[0056] Wherein, M is the total number of overlapping time windows, which is related to the length of the sliding time window and the sliding step; N is the total number of overlapping sampling points, which is related to the time length of the seismic trace and the length of the sliding time window; l ij is the lithology classification prediction value of the i-th sampling point in the j-th overlapping time window, l iis the final lithology classification prediction value of the i-th sampling point, and Round(·) is the nearest integer (rounding off) function. The existence of overlapping time windows enables multiple lithology predictions for the same underground location. Multiple lithology identification results are integrated to determine the final lithology, avoiding the uncertainty of a single identification result and improving the accuracy and robustness of lithology identification. In this example, M=101, N=450. In step S109, the final spatial distribution of the thin interbedded lithology structure refers to the lithology classification result consistent with the lithology conclusion of the logging interpretation obtained by reverse encoding the final lithology classification prediction value of each sampling point in the space according to the lithology classification coding rules described in step S105. In this example, it refers to converting the one-dimensional vector composed of the values ​​1 and 2 predicted by the network into mudstone and sandstone lithologies. In step S109, the obtained lithology spatial distribution has the same dimension as the post-stack seismic data in step S101, that is, when the post-stack seismic data is three-dimensional data, the obtained final spatial distribution of the thin interbedded lithology structure is three-dimensional; when the post-stack seismic data is two-dimensional data, the obtained final spatial distribution of the thin interbedded lithology structure is two-dimensional. In this example, Fig.10 The result of thin interbedded lithological structure recognition based on deep learning in an embodiment of the present invention shows a well line. Fig. 9 Compared with the traditional method of using seismic relative wave impedance to identify thin interlayer effects, Fig. 9 and Fig.10 It can be seen that the method proposed in the present invention has obvious advantages in identifying thin interbeds. The spatial lithological structure of the thin interbeds is clearly depicted, and the vertical superposition relationship of the sand bodies and the horizontal annihilation points are clearly revealed.

[0057] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for identifying thin interbedded lithological structures based on deep learning, characterized in that The steps include: (1) Collect and process artificial seismic data on the surface to obtain post-stack seismic data; (2) Use well logging data and wellside seismic traces to perform well-seismic calibration and deep-time conversion on the target layer to obtain lithologic structure data and seismic data of the thin interbedded layer at the well point; (3) Perform time-frequency analysis on the seismic data of the target thin interlayer segment and convert the one-dimensional waveform data of the seismic trace into a two-dimensional time-frequency spectrum of the seismic trace. The frequency range of the time-frequency analysis is based on the main frequency f of the seismic data of the target thin interlayer segment. m Its effective frequency band [f l ,f h ] OK, low frequency starts at 0hz, high frequency takes 2f m +(f h -f l ) / 2 and above, the frequency domain sampling interval is not greater than 1 Hz; (4) Use a sliding time window to obtain a two-dimensional time-frequency spectrum image in a local time window from the seismic trace near the well, and preprocess it as an input sample for the deep learning network. The time window length of the sliding time window is based on the main frequency f of the seismic data of the target thin interlayer segment. m OK, minimum is 1 / f m twice, the maximum is 1 / f m 4 times of the sliding step length; the minimum sliding step length is a time sampling interval and the maximum sliding step length is a time window length; (5) Using the same sliding time window as in step (4), obtain the thin interbedded lithology structure in the local time window from the logging interpretation lithology data, and classify and encode its lithology as the label of the sample. The lithology classification coding is performed according to the following rules: The first lithology: classification code value = 1; The second lithology: classification code value = 2; … The nth lithology: classification code value = m; Among them, n is the total lithology category of logging interpretation, m is the total coding category, m≤n, and the lithology with excessive indication is coded by adjacent values. The encoded sample label is a discrete numerical one-dimensional vector, and the values ​​in the vector are composed of 1, 2, ..., m. The length of the vector is the same as the number of time sampling points of the sample; (6) Match samples and labels, divide training sets and validation sets, build a deep learning network architecture, define loss functions, select network model evaluation methods, test network hyperparameters, complete thin interbedded lithology structure recognition network model training, and obtain optimal network parameters; (7) using a sliding time window of the same length as in step (4) to traverse the two-dimensional time-frequency spectrum of all seismic traces, obtain a complete set of two-dimensional time-frequency spectrum images within the local time window, and preprocess all images in the set; (8) using the optimal network parameters, converting the entire set of preprocessed two-dimensional time-frequency spectrum images within the local time window into the lithology classification results of the thin interbeds within the local time window; (9) According to the time position of the center point of each seismic trace sliding window, the lithology classification results in the overlapping time window are processed to determine the final spatial distribution of the thin interbedded lithology structure. The overlapping time window refers to the overlapping part of the adjacent sliding time windows when the sliding step length is less than the time window length. The processing method of the lithology classification results in the overlapping time window is as follows: Wherein, M is the number of overlapping time windows, which is related to the length of the sliding time window and the sliding step; N is the number of overlapping sampling points, which is related to the time length of the seismic trace and the length of the sliding time window; l ij is the lithology classification prediction value of the i-th sampling point in the j-th overlapping time window, l i is the final lithology classification prediction value of the i-th sampling point, Round( ˙ ) is the nearest integer (rounding) function.

2. The method according to claim 1, characterized in that the artificial earthquake in step (1) is an earthquake artificially stimulated for the purpose of exploration; When conducting artificial seismic acquisition in the field, three-dimensional acquisition can be performed along a surface on the surface, or two-dimensional acquisition can be performed along a line. The post-stack seismic data obtained can be either three-dimensional data or two-dimensional data.

3. The method according to claim 1, characterized in that the logging data in step (2) includes an acoustic time difference curve, a density curve and lithology data obtained by logging interpretation; The lithologic structural data of the thin interbedded section at the well point refers to the lithologic data in the time domain after depth-time conversion at the well point, which is consistent with the time coordinate of the seismic data and has the same time sampling interval.

4. The method according to claim 1 is characterized in that the time-frequency analysis described in step (3) can be linear time-frequency analysis or nonlinear time-frequency analysis.

5. The method according to claim 1, characterized in that the sliding time window in steps (4) and (5) is the same, having the same time window length and the same sliding step length; The method according to claim 1 is characterized in that the preprocessing described in step (4) refers to normalization and resampling of the two-dimensional time-frequency spectrum image within the local time window, and the resampling can be performed in both the vertical and horizontal directions of the two-dimensional time-frequency spectrum image within the local time window, or in only one direction.

6. The method according to claim 1 is characterized in that the thin interbedded lithologic structure in the local time window described in step (5) is a logging interpretation lithologic conclusion with the same time sampling interval and sampling number as the seismic data in the local time window.

7. The method according to claim 1, characterized in that the deep learning network described in step (6) refers to a deep network with three or more feature extraction layers, and the feature extraction layer is performed using a two-dimensional convolution operation; The loss function adopts a cross entropy function. If there are two lithologies in the target thin interbedded section, a binary cross entropy function is used. If there are three or more lithologies in the target thin interbedded section, a multivariate cross entropy function is used. The network model evaluation method adopts a dual evaluation method of loss curve and accuracy curve, the loss curve is generated by the loss function, and the accuracy curve is generated by the classification result confusion matrix.

8. The method according to claim 1 is characterized in that the length of the sliding time window described in step (7) is the same as the length of the sliding time window in step (4), and the sliding step length is the same as or different from the sliding step length in step (4); the preprocessing adopts the same method as the preprocessing described in step (4).

9. The method according to claim 1, characterized in that the optimal network in step (8) is a deep network with optimal weights obtained in step (6); The conversion refers to inputting each image in the preprocessed two-dimensional time-frequency spectrum image set within the local time window into the optimal network, and the output thereof is the lithology classification result of the thin interbeds within the corresponding local time window.

10. The method according to claim 1 is characterized in that the existence of the overlapping time windows in step (9) enables multiple lithology predictions for the same underground location, and the multiple lithology identification results are integrated to determine the final lithology, thereby avoiding the uncertainty of a single identification result and improving the accuracy and robustness of lithology identification; The final spatial distribution of the thin interbedded lithological structure refers to the lithological classification result that is consistent with the lithological conclusion of the logging interpretation obtained by reversely encoding the final lithological classification prediction value of each sampling point in the space according to the lithological classification coding rule described in step (5); The spatial distribution has the same dimension as the post-stack seismic data described in step (1), that is, when the post-stack seismic data is three-dimensional data, the final spatial distribution of the thin interbedded lithological structure obtained is three-dimensional; when the post-stack seismic data is two-dimensional data, the final spatial distribution of the thin interbedded lithological structure obtained is two-dimensional.

Images