Seismic wave impedance high-resolution inversion method, device and equipment based on physical model constraint and storage medium

Abstract

A seismic wave impedance high-resolution inversion method and device based on physical model constraint, equipment and storage medium are disclosed. The method comprises the following steps: training a forward network by using well wave impedance data and corresponding seismic data; training the inversion network based on well constraint by using well logging data, initial low-frequency wave impedance data and seismic trace position text data; training the inversion network based on cycle consistency constraint by using unlabelled data under the constraint of the forward network; obtaining initial predicted wave impedance data and reflection coefficient by using the inversion network; iteratively training the inversion network, training the inversion network by using well constraint and cycle consistency constraint in each iteration, and using the inversion result of the last iteration as input, and outputting the final inversion result when the iteration termination condition is met. The combination of the forward physical model and the inversion network model forms a closed-loop network, fully utilizes the unlabelled data, and improves the physical interpretability of the inversion result.

Description

Technical Field

[0001] The present disclosure relates to the field of geophysics, and in particular to a method, apparatus, device and storage medium for high-resolution inversion of seismic wave impedance based on physical model constraints. Background Art

[0002] In geophysical exploration, high-resolution processing of seismic data plays a vital role in clearly understanding subsurface geological structures and accurately describing oil and gas reservoir environments. However, due to the heterogeneity and imperfect elasticity of real formation media, seismic waves propagate through the formation medium and are affected by factors such as formation absorption and wavefront diffusion. This causes the energy of the seismic waves to attenuate as they propagate through the formation, ultimately altering the frequency of the seismic waves. In particular, high-frequency seismic waves are more susceptible to attenuation than low-frequency seismic waves. If seismic data can be represented as a linear time-invariant system consisting of a finite-band seismic wavelet convolved with a broadband reflection coefficient, the goal of high-resolution seismic processing is to obtain the broadband reflection coefficient through signal inversion. Due to the band-limited nature of seismic data, high-resolution seismic inversion is subject to multiple solutions and uncertainty.

[0003] Currently, deconvolution methods such as optimal Wiener filtering, pulse deconvolution, predictive deconvolution, and homomorphic deconvolution are used to improve the resolution of seismic data. These methods are inverse processes to the convolution process and have good physical interpretability, but they require estimating the seismic wavelet and assume a Gaussian distribution for the reflection coefficient sequence. This results in poor amplitude preservation and is sensitive to noise. High-resolution seismic processing methods based on time-frequency domain analysis, such as short-time Fourier transform, continuous wavelet transform, synchro-compressed wavelet transform, and empirical mode decomposition, can characterize the temporal frequency variation of nonstationary signals in the time-frequency spectrum, that is, estimate dynamic wavelets. However, accurately estimating the time-frequency spectrum of seismic signals requires overcoming the conflicting relationship between time-domain and frequency-domain resolution. Inverse Q filtering can also estimate dynamic wavelets, directly compensating for frequency loss and amplitude attenuation, but the Q model is relatively complex, and the challenge lies in accurately estimating the Q value. Recently, some high-resolution seismic data processing methods based on deep learning, such as those based on end-to-end networks, generative adversarial networks, multi-task learning, and seismic phase guidance, are data-driven and have achieved higher accuracy. However, these methods require a large number of labeled samples and lack physical interpretability.

[0004] Therefore, a high-resolution inversion method for seismic wave impedance is needed that can ensure physical interpretability and make full use of existing labeled data and unlabeled data. Summary of the Invention

[0005] This paper proposes a high-resolution seismic impedance inversion scheme constrained by a physical model. This scheme utilizes a forward physical model and an inversion network model to form a closed-loop network. Unlabeled data is incorporated into the training process. Physical information can be used to optimize wavelets, improving the physical interpretability and controllability of high-resolution seismic data processing results and reducing the multi-solution potential of high-resolution inversion problems.

[0006] According to one embodiment of the present disclosure, a high-resolution inversion method for seismic impedance based on physical model constraints is proposed, comprising:

[0007] The forward modeling network is trained using the well logging impedance data Z and the corresponding seismic data S.

[0008] Using the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L , seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints;

[0009] Forward Modeling Network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Conduct the first round of training based on cycle consistency constraints;

[0010] Using the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculate the corresponding reflection coefficient

[0011] The inversion network is iteratively trained. The i+1th round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

[0012] In some embodiments, the training forward network include:

[0013] Based on the forward physical constraint loss term L f (W F ) Train the forward network

[0014]

[0015] Among them, W F are the forward modeling network parameters.

[0016] In some embodiments, the inversion network The first round of training based on well logging constraints includes:

[0017] Based on the first well logging constraint loss term L b1 (W B ) for the inversion network Perform the first round of training based on well logging constraints:

[0018]

[0019] Among them, W B are the inversion network parameters.

[0020] In some embodiments, the inversion network The first round of training based on cycle consistency constraints includes:

[0021] Based on the first cycle consistency constraint L bf1 (W B ,W F ) for the inversion network Perform the first round of training based on cycle consistency constraints:

[0022]

[0023] Among them, W F is the forward network parameter, W B are the inversion network parameters.

[0024] In some embodiments, the inversion network is trained in an iterative manner. Training based on well logging constraints includes:

[0025] Based on the second well logging constraint loss term L b2 (W B) for the inversion network Perform training based on well logging constraints:

[0026]

[0027] Among them, W B are the inversion network parameters.

[0028] In some embodiments, the inversion network is trained in an iterative manner. Training with cycle consistency constraints involves:

[0029] Based on the second cycle consistency constraint L bf2 (W B , W F ) for the inversion network Perform training based on cycle consistency constraints:

[0030]

[0031] Among them, W F is the forward modeling network parameter, W B are the inversion network parameters.

[0032] In some embodiments, during iterative training, the inversion network During the training based on cycle consistency constraints, the forward network also outputs the reflection coefficient, and the reflection coefficient output by the forward network is compared with the reflection coefficient obtained after the first round of training. The inversion network is trained using the loss term between .

[0033] In some embodiments, the forward modeling network is a network based on a convolution physical model, and the forward modeling network parameter W F One-dimensional convolution kernel used to simulate seismic wavelets.

[0034] In some embodiments, the inversion network adopts a one-dimensional U-Net network structure.

[0035] In some embodiments, the iteration termination condition is that the number of iterations reaches a preset number of iterations.

[0036] According to one embodiment of the present disclosure, a high-resolution inversion device for seismic wave impedance based on physical model constraints is proposed, comprising:

[0037] Forward network training unit, used to train the forward network using logging wave impedance data Z and corresponding seismic data S

[0038] The first initial training unit of the inversion network is used to use the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L, seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints;

[0039] The second initial training unit of the inversion network is used to make the forward network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Conduct the first round of training based on cycle consistency constraints;

[0040] Initial prediction unit, used to utilize the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculating the reflection coefficient

[0041] The inversion network iterative training unit is used to iteratively train the inversion network. The i+1th round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

[0042] According to one embodiment of the present disclosure, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions executable on the processor, and the processor is used to implement any of the above methods when executing the computer instructions.

[0043] According to one embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described in any one of the above items is implemented.

[0044] The high-resolution inversion scheme of seismic wave impedance based on physical model constraints proposed in the present disclosure effectively overcomes the shortcomings of the existing technology by combining the forward physical model with the inversion network model to form a closed-loop network structure and introducing unlabeled data to participate in the training process. The scheme uses the forward physical model to constrain the inversion results, so that the inversion results have good physical interpretability; at the same time, spatial constraints are achieved by introducing seismic trace position text data, further improving the controllability of the inversion results. The present disclosure adopts an iterative optimization strategy, using the wave impedance data and reflection coefficient obtained from the previous round of inversion as new constraints to train the inversion network. As the number of iterations increases, the accuracy of the inversion results continues to improve, especially in the recovery of high-frequency details. In addition, the present disclosure makes full use of unlabeled data to participate in network training, significantly improving the generalization performance of the model under limited logging data conditions, reducing the multi-solution problem of high-resolution inversion of seismic wave impedance, and making the inversion results more reliable.

[0045] Other features and advantages of the present disclosure are described in detail below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0047] It should be noted that Figures 2 to 10 Some characters may not be clear enough due to resolution issues, but these characters do not affect the understanding of the solution by those skilled in the art.

[0048] Figure 1 A flowchart of a high-resolution inversion method for seismic wave impedance based on physical model constraints according to an embodiment of the present disclosure is shown.

[0049] Figure 2 A schematic diagram of a closed-loop network training process according to an exemplary embodiment of the present disclosure is shown.

[0050] Figure 3 The original seismic data and the predicted reflection coefficient data profiles at different iteration numbers are shown.

[0051] Figure 4 Spectral plots of the original seismic data and predicted reflection coefficients are shown.

[0052] Figure 5 The real wave impedance data and the wave impedance data profiles inverted at different iteration numbers are displayed.

[0053] Figure 6 The comparison between the real high-frequency wave impedance data and the inverted high-frequency wave impedance data is shown.

[0054] Figure 7 Cross plots of real wave impedance data and inversion error data are shown.

[0055] Figure 8 Wave impedance from 1st to 6th inversion of single channel data is shown.

[0056] Figure 9 Reflection coefficient from 1st to 6th inversion of single channel data is shown.

[0057] Figure 10 Plot of iteration number versus inversion cross correlation coefficient and inversion error is shown.

[0058] Figure 11 is a structural schematic of an electronic device according to at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0059] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description of the exemplary embodiments is intended to apply to various alternative embodiments as well. It is to be understood that the description of the exemplary embodiments is not intended to be exhaustive or to be limited to a single embodiment. The description of the exemplary embodiments is intended to be illustrative, and to present the concepts in the best light to enable others skilled in the art to best utilize the exemplary embodiments with various modifications as are suited to the particular use contemplated. In the description of the exemplary embodiments, the terminology used is for the purpose of describing the exemplary embodiments only and is not intended to limit the scope of the disclosure.

[0060] Embodiments of the present disclosure can be applied to a computer system / server, which can operate in a distributed cloud computing environment. Use of the exemplary embodiments in this context will be appreciated when considered in light of the following disclosure. Examples of cloud computing

[0061] The computer system / server can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system / server can operate in a distributed cloud computing environment where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0062] Figure 1A flowchart of a high-resolution inversion method for seismic wave impedance based on physical model constraints according to an embodiment of the present disclosure is shown. As shown in the figure, the method includes steps 1 to 5.

[0063] Step 1: Use the well logging impedance data Z and the corresponding seismic data S to train the forward modeling network

[0064] In some embodiments, the forward modeling network is a network based on a convolution physical model, which is constructed by a one-dimensional convolution kernel W F To simulate seismic wavelets. The physical model of this forward network can be expressed as:

[0065]

[0066] Where r(t) is the reflection coefficient, w(t) is the seismic wavelet, and * represents the convolution operation. The reflection coefficient r(t) can be calculated from the wave impedance using the following formula:

[0067]

[0068] In some embodiments, the forward modeling network is based on the following forward physical constraint loss term L f (W F ) for training:

[0069]

[0070] Among them, W F is the forward network parameter. In some examples, W F It is represented as a one-dimensional convolution kernel and is used to simulate seismic wavelets.

[0071] The forward modeling physical constraint loss term can be optimized by using the stochastic gradient descent method to obtain the optimal forward modeling network parameter W F The training process of the forward modeling network is essentially an estimation process of the seismic wavelet. After training, the forward modeling network will be used as a physical constraint in the subsequent inversion network training process.

[0072] Step 2: Using the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L , seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints.

[0073] In some embodiments, the inversion network A one-dimensional U-Net network structure is used.

[0074] In the first round of training, the inversion network is trained with the well side channel seismic data S corresponding to the well logging wave impedance data Z, the initial low frequency wave impedance data Z L, seismic trace position text data T as input, and output predicted wave impedance data.

[0075] In some embodiments, the first well logging constraint loss term L may be used as follows: b1 (W B ) for the inversion network To train:

[0076]

[0077] Among them, W B is the inversion network parameter; Z is the logging wave impedance data; S is the seismic data; Z L is the initial low-frequency wave impedance data; T is the seismic trace position text data; By adopting this loss term, the wave impedance data output by the inversion network can be made as close as possible to the known logging wave impedance data, thereby achieving preliminary training of the inversion network.

[0078] The seismic trace position text data T introduced in this step can realize spatial constraints and improve the controllability of the inversion results. L The introduction of provides low-frequency background information for the inversion network, which helps to improve the reliability of the inversion results.

[0079] Step 3: Make the forward network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Perform the first round of training based on cycle consistency constraints.

[0080] This step will invert the network The output of the inverted wave impedance data is used as the forward network The input of the forward network and inversion network The series connection forms a closed loop structure. Take the unlabeled earthquake data S * , unlabeled initial low-frequency wave impedance data and unlabeled seismic trace position text data T * As input, the predicted wave impedance data is output, and the predicted wave impedance data is input to the forward modeling network In the forward network Synthetic seismic data is generated based on the wave impedance data. Through this closed-loop structure, the constraints of the forward physical model can be introduced into the inversion network. during the training process.

[0081] On this basis, in some embodiments, the following first cycle consistency constraint loss term L can be used: bf1 (W B , W F ) for the inversion network To train:

[0082]

[0083] Among them, S * is unlabeled earthquake data; is the unlabeled initial low-frequency wave impedance data; T * is the unlabeled seismic trace position text data; W F is the forward network parameter obtained in step 1; W B is the inversion network parameter. The loss term L bf1 (W B , W F ) requires synthetic seismic data generated by a closed-loop network Should be compared with the original unlabeled earthquake data S * As close as possible, so that in the unlabeled data S * Implementing the inversion network further optimization.

[0084] Step 4: Use the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculating the reflection coefficient

[0085] After completing the inversion network through steps 2 and 3 After the initial training of * Performing inversion, we can obtain:

[0086]

[0087] Prediction After that, the corresponding reflection coefficient can be further calculated according to the above formula 2

[0088] The predicted wave impedance data and the corresponding reflection coefficient will serve as input conditions for the subsequent iterative training process.

[0089] Step 5: Iteratively train the inversion network. The i+1th round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

[0090] The value of i starts from 1, which means that iterative training begins from the second round.

[0091] Compared with the first round of training, the wave impedance data predicted after the previous round of training is As an inversion network Input and add reflection coefficient as inversion network Because at the beginning of the first round of training, the unlabeled earthquake data S has not yet been obtained. * The corresponding wave impedance data does not get the corresponding reflection coefficient. After completing the first round of training, the inversion network trained initially can be used For seismic data S and S * Perform an inversion (as in step 4) to predict the wave impedance data and calculate the corresponding reflection coefficient. Since the reflection coefficient directly reflects the wave impedance variation characteristics of the underground medium, introducing the reflection coefficient as an input to the inversion network can provide additional constraints for the inversion process, helping to improve the accuracy of the inversion results.

[0092] In some embodiments, in the i+1th round of iterative training, first, based on the second well logging constraint loss term L b2 (W B ) for the inversion network To train:

[0093]

[0094] Among them, W F is the forward modeling network parameter, W B is the inversion network parameter. (i) is the inversion network based on the i-th round of training Predicted wave impedance data, r (i) Based on Z (i)The reflection coefficient is calculated using Formula 2. Compared with the first round of training, the reflection coefficient is added to the inversion network input here, so that the logging constraint term can utilize more prior information.

[0095] Next, the unlabeled data is used for training under the constraints of the forward network. In some embodiments, the second cycle consistency constraint term L bf2 (W B , W F ) for the inversion network Perform training based on cycle consistency constraints:

[0096]

[0097] Among them, W F is the forward network parameter, W B is the inversion network parameter. Similarly, is the inversion network based on the i-th round of training Predicted wave impedance data, Based on The reflection coefficient is calculated using Equation 2.

[0098] In some embodiments, during cycle consistency constraint training, the forward modeling network may also output a reflection coefficient. By comparing the reflection coefficient output by the forward modeling network with the initial reflection coefficient obtained in step 4 above after the first round of training, A reflection coefficient loss term can be constructed to provide additional constraint information for inversion network training and further improve the reliability of the inversion results.

[0099] In each round of training, after the training based on the cycle consistency constraint is completed, the inversion network of the current training can be used For seismic data S and S * Perform inversion to obtain wave impedance data and the corresponding reflection coefficient in The reflection coefficient can be further calculated using formula 2

[0100] Then it can be determined whether the iteration termination condition is met. In some embodiments, the iteration termination condition is that the number of iterations reaches a preset number of iterations. If not, and Update the corresponding input of the inversion network and enter the next round of iteration; if satisfied, terminate the iteration and output the final inversion result

[0101] The high-resolution seismic impedance inversion scheme based on physical model constraints proposed in this embodiment effectively overcomes the shortcomings of the existing technology by combining the forward physical model with the inversion network model to form a closed-loop network structure and introducing unlabeled data into the training process. This embodiment uses the forward physical model to constrain the inversion results, making the inversion results have good physical interpretability; at the same time, spatial constraints are achieved by introducing seismic trace position text data, further improving the controllability of the inversion results. This embodiment adopts an iterative optimization strategy, using the wave impedance data and reflection coefficient obtained from the previous round of inversion as new constraints to train the inversion network. As the number of iterations increases, the accuracy of the inversion results continues to improve, especially in the recovery of high-frequency details. In addition, this embodiment fully utilizes unlabeled data to participate in network training, significantly improving the generalization performance of the model under limited logging data conditions, reducing the multi-solution problem of high-resolution seismic impedance inversion, and making the inversion results more reliable.

[0102] Figure 2 FIG. 1 shows a schematic diagram of a closed-loop network training process according to an exemplary embodiment of the present disclosure. Figure 2 As shown in Figure 2, the network training process consists of two main stages. The first stage ( Figure 2 The upper part shows the initial training process, including the training of the forward network and the first round of training of the inverse network. A one-dimensional convolution kernel is used to simulate seismic wavelets, and the well logging impedance data and the corresponding seismic data are input into the network for training; the inversion network The training consists of two parts: one is to use the logging wave impedance data for training based on logging constraints, and the other is to use unlabeled data for training based on cycle consistency constraints under the constraints of the forward network.

[0103] Phase II Figure 2 The lower part shows the iterative training process. Compared with the first stage, the input of the inversion network is increased by reflection coefficient data. In each round of iteration, the inversion network The system also undergoes training based on well logging constraints and cycle consistency constraints, and after training is complete, it outputs a new wave impedance prediction result. This prediction result is used to calculate the new reflection coefficient and serves as the input for the next round of iterative training until the predetermined number of iterations is reached.

[0104] Through this closed-loop network structure design, the forward physical model can continuously provide constraints for the training of the inversion network, while making full use of unlabeled data, effectively improving the accuracy and reliability of the inversion results.

[0105] In order to verify the effectiveness and superiority of this scheme, a specific application example is given below to demonstrate the inversion effect obtained according to this scheme.

[0106] This example uses Marmousi II model wave impedance data for the experiment. Reflection coefficients are calculated using a differential operator and convolved with a 20 Hz Ricker wavelet to generate synthetic seismic data. The data consists of 476 seismic traces, with 1651 sampling points and a 1 ms sampling interval. In this experiment, the 250th wave impedance data is selected as the well logging label data. The corresponding seismic trace data, low-frequency wave impedance data, and seismic trace position data are used as labeled data. The remaining data is used as unlabeled data for network training.

[0107] Figure 3 The original seismic data and the predicted reflection coefficient data profiles at different iteration numbers are shown.

[0108] in Figure 3 (a) shows the original earthquake data, Figure 3 (b) to (f) show the reflection coefficients predicted from the 1st to the 5th iteration, respectively. As can be seen from the figure, with the increase of the number of iterations, the predicted reflection coefficient becomes clearer and the characteristics of the stratum interface become more prominent.

[0109] Figure 4 Spectral plots of the original seismic data and predicted reflection coefficients are shown. Figure 4 (a) is the spectrum of the original seismic data. Figure 4 (b) to (f) are the spectra of the reflection coefficients predicted from the 1st to the 5th time. Spectral analysis shows that through iterative training, the predicted reflection coefficients contain richer frequency components.

[0110] Figure 5 The real wave impedance data and the wave impedance data profiles inverted at different iteration numbers are displayed. Figure 5 (a) shows the real wave impedance data, Figure 5 (b) to (f) show the wave impedance data of the 1st to 5th inversion, respectively. Figure 6 The comparison between the actual high-frequency impedance data and the inverted high-frequency impedance data is further demonstrated. As can be seen in the arrowed area, the inversion results' ability to depict high-frequency details improves with increasing iterations. The average cross-correlation coefficient of the high-frequency inversion increases from an initial 0.8569 to 0.9040.

[0111] Figure 7 A cross-section diagram showing the true wave impedance data and the inversion error data. Figure 7 (a) is the real wave impedance data, Figure 7(b) to (f) show the error data for the first to fifth inversions. The mean absolute error of the inversions gradually decreases from 316.70 to 277.34. This shows that using the inversion results of the previous training as the input for the next training can improve the subsequent inversion accuracy and better reflect the high-frequency details.

[0112] Figure 8 and Figure 9 The inversion results of single-channel data with a CDP of 350 in the test set are shown. Figure 8 From left to right, the wave impedances of the 1st to 6th inversions are shown. Figure 9 The reflection coefficients predicted from the first to the sixth iteration are shown from left to right, with the solid black lines representing the true values ​​and the dashed lines representing the inversion results. The single-channel results show that the fit between the inversion results and the true values ​​improves with increasing iteration number.

[0113] Figure 10 The relationship between the number of iterations, the inversion correlation coefficient and the inversion error is shown. Figure 10 The middle horizontal axis represents the number of iterations, the left vertical axis represents the mean absolute error (MAE), and the corresponding bar graph shows the trend of the error gradually decreasing with the increase of the number of iterations; the right vertical axis represents the cross-correlation coefficient, and the corresponding broken line shows the trend of the cross-correlation coefficient gradually increasing with the increase of the number of iterations. Figure 10 It is intuitively demonstrated that the high-resolution inversion method of seismic wave impedance based on physical model constraints proposed in this disclosure can simultaneously achieve error reduction and improvement of cross-correlation coefficients through iterative training.

[0114] The above experimental results show that the high-resolution inversion method of seismic wave impedance based on physical model constraints proposed in this disclosure can effectively improve the inversion accuracy, especially showing significant advantages in recovering high-frequency details.

[0115] The present disclosure also proposes a high-resolution inversion device for seismic wave impedance, which includes a forward network training unit, a first initial training unit for an inversion network, a second initial training unit for an inversion network, an initial prediction unit, and an iterative training unit for an inversion network.

[0116] The forward network training unit is used to train the forward network using the logging wave impedance data Z and the corresponding seismic data S

[0117] The first initial training unit of the inversion network is used to use the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L , seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints.

[0118] The second initial training unit of the inversion network is used to make the forward network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Perform the first round of training based on cycle consistency constraints.

[0119] The initial prediction unit is used to utilize the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculating the reflection coefficient

[0120] The inversion network iterative training unit is used to iteratively train the inversion network. The i+1 round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

[0121] Figure 11 An electronic device provided for at least one embodiment of the present disclosure includes a memory and a processor, wherein the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the high-resolution inversion method of seismic wave impedance based on physical model constraints described in any embodiment or implementation of the present disclosure when executing the computer instructions.

[0122] At least one embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the high-resolution inversion method for seismic wave impedance based on physical model constraints as described in any embodiment or implementation of the present disclosure.

[0123] Those skilled in the art will appreciate that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the data processing device embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

[0125] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] Embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by the data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0127] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0128] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer does not necessarily have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0129] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0130] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of the sub-combination.

[0131] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0132] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the particular order shown or sequential sequence to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0133] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A high-resolution inversion method for seismic impedance based on physical model constraints, characterized by: include: The forward modeling network is trained using the well logging impedance data Z and the corresponding seismic data S. Using the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L , seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints; Forward Modeling Network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Conduct the first round of training based on cycle consistency constraints; Using the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculate the corresponding reflection coefficient The inversion network is iteratively trained. The i+1 round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

2. The method according to claim 1, characterized in that The training forward network include: Based on the forward physical constraint loss term L f (W F ) Train the forward network Among them, W F are the forward modeling network parameters.

3. The method according to claim 1, characterized in that The inversion network The first round of training based on well logging constraints includes: Based on the first well logging constraint loss term L b1 (W B ) for the inversion network Perform the first round of training based on well logging constraints: Among them, W B are the inversion network parameters.

4. The method according to claim 1, wherein The inversion network The first round of training based on cycle consistency constraints includes: Based on the first cycle consistency constraint L bf1 (W B ,W F ) for the inversion network Perform the first round of training based on cycle consistency constraints: Among them, W F is the forward network parameter, W B are the inversion network parameters.

5. The method according to claim 1, wherein In the iterative training of the inversion network Training based on well logging constraints includes: Based on the second well logging constraint loss term L b2 (W B ) for the inversion network Perform training based on well logging constraints: Among them, W B are the inversion network parameters.

6. The method according to claim 1, characterized in that In the iterative training of the inversion network Training with cycle consistency constraints involves: Based on the second cycle consistency constraint L bf2 (W B ,W F ) for the inversion network Perform training based on cycle consistency constraints: Among them, W F is the forward network parameter, W B are the inversion network parameters.

7. The method according to claim 6, characterized in that In iterative training, the inversion network During the training based on cycle consistency constraints, the forward network also outputs the reflection coefficient, and the reflection coefficient output by the forward network is compared with the reflection coefficient obtained after the first round of training. The inversion network is trained with the loss term between .

8. The method according to claim 1, characterized in that The forward modeling network is a network based on a convolution physical model, and the forward modeling network parameter W F One-dimensional convolution kernel used to simulate seismic wavelets.

9. The method according to claim 1, characterized in that The inversion network adopts a one-dimensional U-Net network structure.

10. The method according to claim 1, characterized in that The iteration termination condition is that the number of iterations reaches a preset number of iterations.

11. A high-resolution inversion device for seismic wave impedance based on physical model constraints, characterized in that: include: Forward network training unit, used to train the forward network using logging wave impedance data Z and corresponding seismic data S The first initial training unit of the inversion network is used to use the logging wave impedance data Z and the corresponding seismic data S, the initial low-frequency wave impedance data Z L , seismic trace position text data T for inversion network Conduct the first round of training based on well logging constraints; The second initial training unit of the inversion network is used to make the forward network and inversion network Constructing a closed-loop structure, in the forward modeling network Using unlabeled seismic data S under constraints * , unlabeled initial low-frequency wave impedance data Unlabeled seismic trace position text data T * Inversion Network Conduct the first round of training based on cycle consistency constraints; Initial prediction unit, used to utilize the currently trained inversion network For seismic data S and S * Perform inversion to obtain the initial predicted wave impedance data And according to Calculating the reflection coefficient The inversion network iterative training unit is used to iteratively train the inversion network. The i+1th round of training includes using the wave impedance data predicted after the previous round of training. and the corresponding reflection coefficient As an inversion network Input, in turn, the inversion network Perform training based on logging constraints and training based on cycle consistency constraints, and use the currently trained inversion network after this round of training is completed For seismic data S and S * Perform inversion to obtain wave impedance data And calculate the corresponding reflection coefficient Then determine whether the iteration termination condition is met. If not, enter the next round of iteration. If it is met, terminate the iteration and output the final inversion result.

12. An electronic device, characterized in that: The device includes a memory and a processor, wherein the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the method according to any one of claims 1 to 10 when executing the computer instructions.

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

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