An automatic picking method of seismic velocity based on deep learning
A deep learning and automatic picking technology, which is applied in neural learning methods, seismology, seismic signal processing, etc., can solve problems such as time-consuming efficiency and achieve the effects of saving manpower, improving work efficiency, and enhancing relationships
Active Publication Date: 2021-07-13
CHINA UNIV OF MINING & TECH (BEIJING)
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Problems solved by technology
Due to the huge amount of 3D seismic data, it takes a lot of time and is inefficient to simply use manual methods to pick up the velocity spectrum when processing seismic data.
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[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0041] See attached Figure 6 , the embodiment of the present invention discloses a method for automatically picking up seismic velocity based on deep learning, including:
[0042] Step S1: Obtain seismic data and labels;
[0043] Step S2: Input the seismic data and the label into the pre-trained deep learning model to obtain the velocity picking result;
[0044] See attached Figure 4 , the structure of the deep learning model includes: a residual network ...
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The invention discloses an automatic seismic velocity picking method based on deep learning, comprising: acquiring seismic data and labels; inputting the seismic data and the labels into a pre-trained deep learning model to obtain velocity picking results; wherein , the structure of the deep learning model includes: a residual network composed of three residual blocks; a long short-term memory network and a fully connected layer are added after the residual network; where each residual block is composed of three convolutional layers; The activation function between each residual block and the layers of the residual block is the Relu function; the activation function between the long short-term memory network and the fully connected layer is the Relu function. The method for automatically picking up seismic velocity based on deep learning provided by the present invention effectively improves the efficiency of picking up seismic velocity.
Description
technical field [0001] The present invention relates to the technical field of seismic velocity picking, and more specifically relates to an automatic seismic velocity picking method based on deep learning. Background technique [0002] Velocity model building is an important step in seismic data processing. The quality of velocity model building has an important impact on the subsequent processing of seismic data (such as multiple wave suppression, time-depth conversion, migration imaging and inversion, etc.). [0003] Establishing a velocity model requires velocity picking on the velocity spectrum. This process usually requires manual picking by professionals and takes a lot of manual time. When velocity spectrum is used for velocity picking, the accuracy of picking velocity and the resolution of velocity spectrum are affected by many factors, such as offset distribution, stacking times, signal-to-noise ratio, velocity sampling density, and near-surface anomalies. Therefo...
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IPC IPC(8): G01V1/30G01V1/28G06N3/04G06N3/08G06K9/62G06F30/23G06F30/27
CPCG01V1/303G01V1/282G06N3/08G06F30/23G06F30/27G01V2210/6222G06N3/044G06N3/045G06F18/214
Inventor 师素珍李明轩
Owner CHINA UNIV OF MINING & TECH (BEIJING)