CN113820749A - Seismic data velocity field anomaly inversion method based on machine learning - Google Patents
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Abstract
The invention discloses a seismic data velocity field anomaly inversion method based on machine learning, which comprises the following steps of: building a U-Net deep learning network facing seismic data and training the U-Net deep learning network by using a training data set, wherein the training data set consists of three types of seismic oscillograms, velocity field images and abnormal point images; and inputting the generated seismic oscillogram into a U-Net deep learning network, generating a velocity field image and an abnormal point image, and predicting a feature tag set. According to the seismic data velocity field anomaly inversion method based on machine learning, a U-Net deep learning network is established, an obtained seismic wave graph is input into the U-Net deep learning network, a velocity field image and an anomaly point image predicted by the method are generated, compared with an actual velocity field and an actual anomaly point, the method has high precision, and research is carried out on shot gather seismic data before migration imaging, so that errors caused by a migration imaging algorithm can be effectively avoided.
Description
Technical Field
The invention relates to the technical field of geophysical, in particular to a seismic data velocity field anomaly inversion method based on machine learning.
Background
Inversion refers to deducing underground geologic structure characteristics, occurrence states of geologic bodies and physical parameters according to the change characteristics of a geophysical field, and chromatographic inversion and full waveform inversion are two most commonly used methods for modeling a velocity field at present.
In practical application, the tomographic inversion method has higher precision for background velocity field modeling, but has a general inversion effect for velocity field anomalies corresponding to a complex geological structure, and high-precision velocity field anomalies are often required to be artificially introduced in the velocity field updating process to accelerate iterative convergence; the full waveform inversion method is proposed in the eighties of the last century, and the method is widely applied in recent years along with the great improvement of cluster computing capacity, so that the full waveform inversion method becomes an effective high-resolution speed field modeling method. Due to the multi-solution nature of the velocity field model, as an iterative algorithm based on data fitting, the convergence of the full waveform inversion process is highly dependent on the accuracy of the velocity field initial model. When the initial model, especially the abnormal value error therein is large, a large phase difference is generated between the synthetic seismic record and the actual seismic record, and when the phase difference is larger than half of the wavelet period of the seismic source, a period jump phenomenon is generated, so that the algorithm converges to a local minimum point to generate an error inversion result. The traditional speed field modeling means generally processes data by using an offset imaging algorithm, which requires the offset imaging algorithm to have higher precision, but in actual application, the precision of abnormal distribution of the speed field is influenced due to errors of the offset imaging algorithm.
Meanwhile, in practical application, the speed field abnormity pickup method usually needs a large amount of manual intervention, the speed field abnormity is introduced by utilizing the past accumulated experience and through a manual modification mode of processing and interpreters, the mode is excessively dependent on the experience and technical level of technicians, the modeling quality is unstable, and the modeling efficiency is low.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a seismic data velocity field anomaly inversion method based on machine learning.
In order to solve the technical problems, the invention provides the following technical scheme: a seismic data velocity field anomaly inversion method based on machine learning comprises the following steps:
(1) building a U-Net deep learning network facing seismic data and training the U-Net deep learning network by using a training data set, wherein the training data set consists of three types of seismic oscillograms, velocity field images and abnormal point images;
(2) inputting the generated seismic oscillogram into a U-Net deep learning network, generating a velocity field image and an abnormal point image, and predicting a feature tag set;
(3) and (3) performing post-processing on the generated image based on a CNN algorithm (convolutional neural network) to further reduce errors, wherein the image needing the post-processing comprises a velocity field image and an abnormal point image.
Preferably, the U-Net deep learning network structure mainly comprises a convolutional layer, a down-sampling, an up-sampling and a ReLU nonlinear activation function, and the U-Net network also has a characteristic channel while adopting an up-sampling and down-sampling method.
Preferably, the U-Net network generates the velocity field image and the abnormal point image simultaneously, and the error function is a weighted sum of errors of the generated velocity field and the abnormal point.
Preferably, the process that the U-Net network is directly mapped to the right end of the U-Net by the image before the down sampling for the first time is cancelled.
The invention has the following beneficial effects:
1. according to the seismic data velocity field anomaly inversion method based on machine learning, a U-Net deep learning network is established, an obtained seismic wave graph is input into the U-Net deep learning network, a velocity field image and an anomaly point image predicted by the method are generated, compared with an actual velocity field and an actual anomaly point, the method has high precision, and research is carried out on shot gather seismic data before migration imaging, so that errors caused by a migration imaging algorithm can be effectively avoided.
2. According to the seismic data velocity field anomaly inversion method based on machine learning, higher-precision velocity field anomaly distribution can be provided at the initial stage of velocity field modeling, convergence of a modeling iteration process can be accelerated, modeling efficiency is improved, a velocity field anomaly model is established in an automatic identification mode by means of a deep learning algorithm, dependence on people is greatly reduced, and errors caused by manual intervention are reduced.
3. The seismic data velocity field anomaly inversion method based on machine learning has the characteristics of data self-learning and iteration, continuous optimization of a deep learning network in a technical process is realized by continuously expanding a training data set, and thus training data can be continuously accumulated to continuously exert value.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a comparison between a predicted image and a real image at a single outlier in a normal velocity field according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a comparison between a predicted image and a real image under a normal velocity field and two outliers according to an embodiment of the present invention;
fig. 4 is a schematic diagram of the comparison between a predicted image and a real image under a single outlier with a fault velocity field according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1-4, the invention provides a seismic data velocity field anomaly inversion method based on machine learning, which comprises the following steps:
(1) building a U-Net deep learning network facing seismic data and training the U-Net deep learning network by using a training data set, wherein the training data set consists of three types of seismic oscillograms, velocity field images and abnormal point images;
the U-Net deep learning network structure mainly comprises a convolution layer, a down sampling layer, an up sampling layer and a ReLU nonlinear activation function, and the U-Net network adopts the up sampling method and the down sampling method and simultaneously has a characteristic channel, so that more information of an original image or down sampling texture can be transmitted in a high-resolution layer.
The U-Net network simultaneously generates a speed field image and an abnormal point image, and the error function of the U-Net network is the weighted sum of the generated speed field and the abnormal point error.
The process that the U-Net network is directly mapped to the right end of the U-Net from the image before down-sampling for the first time is cancelled, and the situation that the generated velocity field contains the outline of the seismic oscillogram can be avoided
(2) Inputting the generated seismic oscillogram into a U-Net deep learning network, generating a velocity field image and an abnormal point image, and predicting a feature tag set;
(3) and (3) performing post-processing on the generated image based on a CNN algorithm (convolutional neural network) to further reduce errors, wherein the image needing the post-processing comprises a velocity field image and an abnormal point image.
After a speed field and an abnormal point image generated by U-Net training are obtained, a convolution kernel with the size of 2 multiplied by 2 is used for gradually traversing the whole image to extract features, and a smoothed image is generated after pooling and full-connection operations.
In summary, when the method is actually used for predicting the seismic velocity field and the abnormal points, only the detector is required to receive data and then process the data, the seismic oscillogram is input to the U-Net, the seismic velocity field and the abnormal points are predicted by the method, in the experiment for predicting the seismic velocity field and the abnormal points, the experiment is divided into a plurality of groups for verification, the data are processed after the detector receives the data after the detector is shot at the shot point, fig. 2-4 show that the prediction results when the normal velocity field, the fault velocity field, the single shot point and the multiple shot points are received and compared with the actual velocity field and the abnormal points, the seismic waveforms are shot at the surface and the surface detectors, and then the underground velocity distribution model is obtained through inversion, and the multiple shot points can improve the covering times of related bins and improve the resolution.
Fig. 2 shows the prediction in the case of a normal velocity field and a single anomaly point, as shown in the figure, the velocity field is divided into five layers, only one anomaly point is provided, the velocity field and the anomaly point are respectively predicted in the case of a single shot point and a plurality of shot points, the effect of predicting the velocity field and the anomaly point is shown in the figure, and the prediction precision of the single shot point and the plurality of shot points is high in the case of the normal velocity field and the single anomaly point; fig. 3 shows the prediction under the normal velocity field and two abnormal points, as shown in the figure, the velocity field is divided into two layers, there are two abnormal points, the prediction is performed when a single shot point and a plurality of shot points are respectively performed, the effect of the predicted velocity field and the abnormal points is shown in the figure, and the result shows that the prediction precision of the single shot point and the plurality of shot points is still high under the normal velocity field and the plurality of abnormal points; fig. 4 shows that in the case of a fault velocity field, a single abnormal point velocity field is divided into three layers, a fault is formed, an abnormal point is formed, the effects of the predicted velocity field and the abnormal point are shown in the figure, and the result shows that under the fault velocity field and the single abnormal point, the prediction accuracy of a single shot point and multiple running points is still high.
It should be noted that, in this document, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A seismic data velocity field anomaly inversion method based on machine learning is characterized by comprising the following steps:
(1) building a U-Net deep learning network facing seismic data and training the U-Net deep learning network by using a training data set, wherein the training data set consists of three types of seismic oscillograms, velocity field images and abnormal point images;
(2) inputting the generated seismic oscillogram into a U-Net deep learning network, generating a velocity field image and an abnormal point image, and predicting a feature tag set;
(3) and post-processing the generated image based on the CNN algorithm to further reduce the error, wherein the image needing post-processing comprises a speed field image and an abnormal point image.
2. The seismic data velocity field anomaly inversion method based on machine learning according to claim 1, characterized in that the U-Net deep learning network structure mainly comprises convolution layers, down sampling, up sampling and ReLU nonlinear activation functions, and the U-Net network has characteristic channels while adopting the up sampling and down sampling methods.
3. The seismic data velocity field anomaly inversion method based on machine learning of claim 1, wherein the U-Net network simultaneously generates a velocity field image and an anomaly point image, and the error function is a weighted sum of errors of the generated velocity field and the anomaly point.
4. The machine learning-based seismic data velocity field anomaly inversion method according to claim 1, wherein the U-Net network is cancelled by directly mapping an image before downsampling to the right end of the U-Net network for the first time.
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