CN111273349B - Transverse wave velocity extraction method and processing terminal for seabed shallow sediment layer - Google Patents
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Abstract
The invention relates to a transverse wave velocity extraction method and a processing terminal for a seabed shallow sediment layer, wherein the method comprises the following steps: step 1: generating horizontal layered geological models of a plurality of seabed shallow sedimentary layers; step 2: simulating each horizontal layered geological model by adopting a seismic wave field numerical simulation method to obtain corresponding OBS data including a vertical component and a horizontal component; and step 3: preprocessing the horizontal component of the OBS data, wherein the preprocessing comprises time correction of the OBS data to obtain preprocessed OBS data; and 4, step 4: combining the OBS data and the transverse wave velocity into label data, and inputting the label data into a neural network for training; and 5: and acquiring and preprocessing actual OBS data, inputting the actual OBS data into a neural network for processing, and multiplying an output result by a normalization factor to obtain a final transverse wave velocity. The invention has the advantages of less manual intervention components, less calculation amount and higher accuracy of transverse wave speed.
Description
Technical Field
The invention relates to the technical field of submarine seismic data processing, in particular to a transverse wave velocity extraction method and a processing terminal for a submarine shallow sediment layer.
Background
The transverse wave velocity of a sediment layer on the shallow part of the sea bottom plays a very important role in marine exploration, particularly in the fields of marine drilling, hydrate investigation and the like of marine engineering. At present, the following methods are mainly used for extracting the transverse wave velocity: (1) inverting the surface wave dispersion curve; (2) ocean Bottom Seismograph (OBS) data travel time inversion; (3) inverting the full waveform of the elastic wave; (4) and (6) well drilling measurement. The surface wave information is required to be recorded in seismic data by a surface wave dispersion curve inversion-based method, but the surface wave information is difficult to acquire in a deep sea environment. The method is characterized in that an accurate longitudinal wave velocity structure needs to be determined firstly based on OBS data traveling time inversion transverse wave velocity parameters, and then converted transverse waves recorded in OBS data correspond to an interface generating the transverse waves, so that the subjective factor is large, and the error is large. Moreover, the traveling time inversion transverse wave velocity based on OBS data has large workload, and is not suitable for large-scale development. Theoretically, the full waveform inversion of the elastic wave can directly extract the transverse wave velocity structure of the sediment at the shallow part of the sea bottom, but the method has very high requirements on the quality of original data and the stability of an inversion algorithm, has very high calculation amount, and is still in a development stage at present. The measurement of the well drilling is the most reliable method for extracting the velocity parameter of the shear wave, but the cost of well drilling is very high, and the well drilling is time-consuming and expensive, and especially the well drilling cost is higher in a deep sea environment.
Therefore, a method which is low in cost and can extract high-reliability shear wave velocity is needed, and the problem can be solved based on a neural network. At present, seismic velocity is extracted based on a neural network, for example, Gunter and the like directly extract longitudinal wave velocity information from multi-channel seismic data by using the neural network; the conog beam proposes the use of a BP neural network to extract shear wave velocity structures from data recorded at a single seismic station. However, the problem of superficial shear wave velocity in deep sea environment is not considered.
Disclosure of Invention
In view of the shortcomings of the prior art, one of the objectives of the present invention is to provide a shear wave velocity extraction method for shallow sediment layers on the seabed, which can solve the problem of shear wave velocity extraction;
it is another object of the present invention to provide a processing terminal capable of solving the problem of extracting the shear wave velocity.
The technical scheme for realizing one purpose of the invention is as follows: a shear wave velocity extraction method for a seabed shallow sediment layer comprises the following steps:
step 1: generating horizontal layered geological models of a plurality of seabed shallow sedimentary layers, wherein each horizontal layered geological model is provided with at least two stratums including a sea water layer, the sea water layer is a first layer of all the stratums, each horizontal layered geological model at least comprises stratum thickness, longitudinal wave speed, transverse wave speed and density, each stratum is provided with corresponding longitudinal wave speed, transverse wave speed and density, and numerical values of the longitudinal wave speed, the transverse wave speed and the density are random values;
step 2: simulating each horizontal layered geological model by adopting a seismic wave field numerical simulation method to obtain corresponding OBS data, wherein the OBS data comprises a vertical component and a horizontal component;
and step 3: preprocessing the horizontal component of the OBS data obtained in the step (2), wherein the preprocessing comprises time correction of the OBS data to obtain preprocessed OBS data, and normalization processing is carried out on the transverse wave velocity of each horizontal laminar geological model divided by a normalization factor;
and 4, step 4: forming label data by the preprocessed OBS data and the normalized transverse wave velocity, and inputting the label data into a neural network for training to obtain a trained neural network;
and 5: acquiring actual OBS data, preprocessing the horizontal component of the actual OBS data, wherein the preprocessing of the actual OBS data is the same as that in the step 3, so that the preprocessed actual OBS data is obtained, inputting the preprocessed actual OBS data into the neural network trained in the step 4 for processing, so that an output result is obtained, and multiplying the output result by a normalization factor, so that the final transverse wave speed is obtained.
Further, in the step 1, the number of the horizontal lamellar geological models is more than or equal to 10000.
Further, the thickness of the strata of the horizontal stratigraphic geological model is 1000 meters.
Further, each horizontal stratigraphic geological model sets the number of strata to 10.
Further, the longitudinal wave velocity is 1500-3000m/s, the transverse wave velocity is 0-2000m/s, and the density is 1-2g/cm3。
Further, in step 3, an envelope of the OBS data is extracted, and time correction is performed according to the envelope of the OBS data.
Further, the normalization factors of step 3 and step 5 are the same and are both 2000.
Further, the neural network is a fully-connected multilayer neural network.
The second technical scheme for realizing the aim of the invention is as follows: a processing terminal, comprising,
a memory for storing program instructions;
a processor for executing the program instructions to perform the steps in the shear wave velocity extraction method for the seafloor sediment layer.
The invention has the beneficial effects that: the invention aims at extracting the transverse wave velocity from a seabed shallow sediment layer applied to a deep sea environment, and the transverse wave velocity is extracted from a seabed seismograph through a trained neural network. The method mainly includes the steps of generating a large number of submarine geological models, generating a large number of OBS data through numerical forward modeling, preprocessing the OBS data and the geological models, then training a neural network, obtaining the trained neural network, and further processing actual OBS data to extract transverse wave speed. Compared with other methods, the method has the advantages of fewer components and less calculation amount in manual intervention, improves the working efficiency and can provide more accurate transverse wave speed.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2(a) is a horizontal layered seismic model set up;
FIG. 2(b) is a horizontal component of the OBS data modeled in accordance with FIG. 2 (a);
FIG. 3(a) is the OBS data after being pre-processed by this step;
FIG. 3(b) is the envelope data extracted from FIG. 3 (a);
FIG. 3(c) is the envelope data after time correction of the envelope data of FIG. 3 (a);
FIG. 4 is a schematic diagram of the shear velocity obtained according to the present method compared to the actual shear velocity;
fig. 5 is a schematic structural diagram of a processing terminal according to the present invention.
Detailed description of the preferred embodiments
The invention will be further described with reference to the accompanying drawings and specific embodiments:
as shown in fig. 1 to 4, a shear wave velocity extraction method for a shallow sediment layer on the seabed comprises the following steps:
step 1: and generating a plurality of horizontal layered geological models of the seabed shallow sedimentary layers, wherein the number of the horizontal layered geological models of the seabed shallow sedimentary layers is usually more than or equal to 10000, and each horizontal layered geological model at least comprises the thickness of the stratum, the longitudinal wave speed, the transverse wave speed and the density. The maximum stratum thickness of each model is set to be a meter, a is usually 1000 meters, and the depth of a deep sea refers to the depth of water below 500 meters in the seabed shallow sedimentary deposit under the deep sea environment, so that a is more than or equal to 500 meters, and the value of a can be adjusted according to actual needs. Each horizontal layered geological model is provided with b stratums at least comprising a seawater layer, wherein b is more than or equal to 2, the value of b is usually 10, and the seawater layer is the first layer, namely the seawater layer is positioned at the uppermost stratum. The thickness of each formation is randomly chosen, but the sum of the thicknesses of all the formations is a meters.
The method comprises the steps that corresponding longitudinal wave velocity, transverse wave velocity and density are set for each stratum, numerical values of the longitudinal wave velocity, the transverse wave velocity and the density are random values, and the value ranges of the parameters are mainly obtained according to petrophysical research results, geological interpretation results, drilling results and the like. In this embodiment, the longitudinal wave velocity is 1500-3000m/s (m/s), the transverse wave velocity is 0-2000m/s (m/s), and the density is 1-2g/cm3。
In practical application, a good layer thickness range, a longitudinal wave velocity range, a transverse wave velocity range and a density range can be preset, values are randomly taken in the parameter ranges, and then combination is carried out, so that a large number of horizontal layered geological models are obtained. This is substantially the same as the above-described step process.
Step 2: simulating each horizontal layered geological model obtained in the step 1 by adopting a reflectivity method in a seismic wave field numerical simulation method to obtain corresponding OBS data d (x, t), wherein x represents the horizontal distance between the OBS and a seismic source point, and t represents time, namely calculating theoretical OBS data d (x, t) based on the reflectivity method, wherein the OBS data d (x, t) comprises a horizontal component h (x, t) and a vertical component v (x, t), and only using the horizontal component h (x, t) of the OBS data in the subsequent steps. Fig. 2(a) is a horizontal layered seismic model set up, and fig. 2(b) is the horizontal component of the OBS data modeled in accordance with fig. 2 (a).
And step 3: and (3) preprocessing the horizontal component h (x, t) of the OBS data d (x, t) obtained in the step (2). Firstly, extracting an envelope E (x, t) of an OBS data horizontal component h (x, t) according to a formula (I):
where Hh (x, t) represents the hilbert transform of the horizontal component h (x, t).
Then, the time of envelope E (x, t) is corrected according to equation two:
v1representing the velocity, is a constant, typically 1500m/s (meters per second), and other velocity values may also be obtained experimentally. After the time correction value is calculated through a formula II, the time correction value is subtracted from the original recording time to obtain preprocessed OBS data, namely the OBS data is processed according to an expression III:
p(x,t-dt)＝E(x,t)------③
e (x, t) obtained through calculation of the expression is an envelope for preprocessing the horizontal component h (x, t) of the OBS data, and p (x, t-dt) is the preprocessed OBS data. Fig. 3(a) shows OBS data subjected to the preprocessing of this step, fig. 3(b) shows envelope data extracted from fig. 3(a), and fig. 3(c) shows envelope data subjected to time correction with respect to the envelope data of fig. 3 (a).
In this step, the method further includes normalizing the shear wave velocity of each horizontal stratiform geological model, where the normalization factor is a constant, usually 2000. Namely obtaining a normalized transverse wave velocity parameter V according to a formulas，VsThat is, the shear wave velocity after pretreatment:
Vs' denotes the shear velocity before normalization, and f denotes a normalization factor, which is a constant, typically 2000.
Through the above processing, OBS data after preprocessing is obtained.
And 4, step 4: and 3, using the preprocessed OBS data obtained in the step 3 as input data, using the preprocessed shear wave velocity as target data, and inputting the input data and the target data together to form label data to a neural network for training, wherein a deep neural network is generally adopted. The neural network is a fully-connected multi-layer neural network model, of course, a type of neural network model may also be adopted, nodes in the neural network are connected in a fully-connected manner, and the excitation function of the last layer is an exponential linear unit function (ELU).
In this step, the objective function of the neural network is defined as the two-norm of the difference between the output vector of the neural network and the target vector, as shown in formula (v):
wherein e represents the difference between the transverse wave velocity output by the neural network and the transverse wave velocity of the known geological model, N represents the number of sample data, M represents the parameters of the neural network, including the weight, bias and the like of the neural network, and the initial weight and bias are random numbers in accordance with normal distribution.
Adopting a random gradient algorithm to make the target function converge, wherein the updating formula is shown as the formula (II):
wherein M iskRepresenting the neural network parameter, α, after the k-th updatekWhich represents a learning rate, for controlling a learning step size,
is the gradient of the objective function, the elements of which are the objective functionWith respect to the partial derivatives of the neural network weights and biases,
is achieved by back-propagation of the error.
After the processing, dividing the label data into two parts, namely training data and testing data, wherein the training data is used for training a neural network to find the optimal neural network; the test data is used for evaluating the generalization ability of the trained neural network so as to determine whether the neural network has over-fitting and under-fitting problems.
The above processing is prior art of neural networks and is not described here in detail.
And 5: and preprocessing the acquired actual OBS data to obtain a horizontal component of the actual OBS data, so as to obtain the preprocessed actual OBS data, wherein the preprocessing mode is the same as the steps. Inputting the actual OBS data after preprocessing into the neural network trained in the step 4 for processing to obtain an output result, and multiplying the output result by the normalization factor to obtain a final transverse wave velocity, namely extracting the transverse wave velocity.
Fig. 4 is a schematic diagram showing the comparison between the shear wave velocity obtained by the present method and the actual shear wave velocity, in which the solid line represents the actual extracted shear wave velocity and the dotted line represents the shear wave velocity obtained by the present method. As can be seen from fig. 4, the two shear wave velocities have good consistency, which indicates that the method can extract the shear wave velocity that meets the actual situation.
The invention aims at extracting the transverse wave velocity from a sedimentary layer at the shallow part of the seabed in the deep sea (the water depth is more than 500 meters) environment and extracting the transverse wave velocity from an Ocean Bottom Seismograph (OBS) through a trained neural network. The method mainly includes the steps of generating a large number of submarine geological models, generating a large number of OBS data through numerical forward modeling, preprocessing the OBS data and the geological models, then training a neural network, obtaining the trained neural network, and further processing actual OBS data to extract transverse wave speed. Compared with other methods, the method has the advantages of fewer components and less calculation amount in manual intervention, improves the working efficiency and can provide more accurate transverse wave speed.
As shown in fig. 5, the present invention also provides a physical implementation processing terminal 100 for a shear wave velocity extraction method of a shallow sediment layer on the seabed, which comprises,
a memory 101 for storing program instructions;
a processor 102 for executing the program instructions to perform the steps of the shear wave velocity extraction method for shallow seafloor sediment layers.
The embodiments disclosed in this description are only an exemplification of the single-sided characteristics of the invention, and the scope of protection of the invention is not limited to these embodiments, and any other functionally equivalent embodiments fall within the scope of protection of the invention. Various other changes and modifications to the above-described embodiments and concepts will become apparent to those skilled in the art from the above description, and all such changes and modifications are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (9)
1. A shear wave velocity extraction method for a seabed shallow sediment layer is characterized by comprising the following steps:
step 1: generating horizontal layered geological models of a plurality of seabed shallow sedimentary layers, wherein each horizontal layered geological model is provided with at least two stratums including a sea water layer, the sea water layer is a first layer of all the stratums, each horizontal layered geological model at least comprises stratum thickness, longitudinal wave speed, transverse wave speed and density, each stratum is provided with corresponding longitudinal wave speed, transverse wave speed and density, and numerical values of the longitudinal wave speed, the transverse wave speed and the density are random values;
step 2: simulating each horizontal layered geological model by adopting a seismic wave field numerical simulation method to obtain corresponding OBS data, wherein the OBS data comprises a vertical component and a horizontal component;
and step 3: preprocessing the horizontal component of the OBS data obtained in the step (2), wherein the preprocessing comprises time correction of the OBS data to obtain preprocessed OBS data, and normalization processing is carried out on the transverse wave velocity of each horizontal laminar geological model divided by a normalization factor;
and 4, step 4: forming label data by the preprocessed OBS data and the normalized transverse wave velocity, and inputting the label data into a neural network for training to obtain a trained neural network;
and 5: acquiring actual OBS data, preprocessing the horizontal component of the actual OBS data, wherein the preprocessing of the actual OBS data is the same as that in the step 3, so that the preprocessed actual OBS data is obtained, inputting the preprocessed actual OBS data into the neural network trained in the step 4 for processing, so that an output result is obtained, and multiplying the output result by a normalization factor, so that the final transverse wave speed is obtained.
2. The method for extracting shear wave velocity of a shallow sediment layer on the seabed as claimed in claim 1, wherein in the step 1, the number of horizontal laminar geological models is more than or equal to 10000.
3. The shear wave velocity extraction method for shallow seabed sediment layers as claimed in claim 1, wherein the stratum thickness of the horizontal layered geological model is 1000 m.
4. The shear wave velocity extraction method for shallow seabed sedimentary layers as claimed in claim 1, wherein the number of stratums is set to 10 per horizontal stratigraphic geological model.
5. The method as claimed in claim 1, wherein the longitudinal wave velocity is 1500-3000m/s, the transverse wave velocity is 0-2000m/s, and the density is 1-2g/cm3。
6. The shear wave velocity extraction method for a shallow seabed sediment layer as claimed in claim 1, wherein in the step 3, an envelope of OBS data is extracted, and time correction is performed according to the envelope of the OBS data.
7. The shear wave velocity extraction method for shallow seabed sediment layers as claimed in claim 1, wherein the normalization factors of the step 3 and the step 5 are the same and are both 2000.
8. The shear wave velocity extraction method for a shallow seabed sediment layer as claimed in claim 1, wherein the neural network is a fully connected multilayer neural network.
9. A processing terminal, characterized in that it comprises,
a memory for storing program instructions;
a processor for executing said program instructions to perform the steps of the method for shear wave velocity extraction of a shallow sediment layer on the seafloor according to any one of claims 1 to 8.
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| CN101231346A (en) * | 2008-02-26 | 2008-07-30 | 中国矿业大学(北京) | Method for estimating coal, rock mass physical mechanics parameter through seismic wave velocity |
| CN108802812A (en) * | 2017-04-28 | 2018-11-13 | 中国石油天然气股份有限公司 | A kind of formation lithology inversion method of well shake fusion |
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| CN101231346A (en) * | 2008-02-26 | 2008-07-30 | 中国矿业大学(北京) | Method for estimating coal, rock mass physical mechanics parameter through seismic wave velocity |
| CN108802812A (en) * | 2017-04-28 | 2018-11-13 | 中国石油天然气股份有限公司 | A kind of formation lithology inversion method of well shake fusion |
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