CN113640882B - Acquisition footprint noise removal method, electronic device and computer readable storage medium - Google Patents
Acquisition footprint noise removal method, electronic device and computer readable storage medium Download PDFInfo
- Publication number
- CN113640882B CN113640882B CN202110913600.7A CN202110913600A CN113640882B CN 113640882 B CN113640882 B CN 113640882B CN 202110913600 A CN202110913600 A CN 202110913600A CN 113640882 B CN113640882 B CN 113640882B
- Authority
- CN
- China
- Prior art keywords
- seismic data
- initial
- initial seismic
- main energy
- denoising
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 69
- 238000003860 storage Methods 0.000 title claims abstract description 10
- 238000001914 filtration Methods 0.000 claims abstract description 51
- 239000013598 vector Substances 0.000 claims abstract description 49
- 230000009466 transformation Effects 0.000 claims abstract description 33
- 238000005070 sampling Methods 0.000 claims description 24
- 238000004891 communication Methods 0.000 claims description 10
- 238000010586 diagram Methods 0.000 description 24
- 108091006146 Channels Proteins 0.000 description 21
- 230000006870 function Effects 0.000 description 10
- 238000004590 computer program Methods 0.000 description 7
- 230000001788 irregular Effects 0.000 description 6
- 238000009826 distribution Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000000354 decomposition reaction Methods 0.000 description 3
- 239000004215 Carbon black (E152) Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 150000002430 hydrocarbons Chemical class 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application discloses a method for removing noise of collected footprint, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring initial seismic data corresponding to a plurality of seismic channels; performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data; and denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed. By adopting the method and the device, the acquisition footprint noise in the seismic data can be accurately and effectively removed, and the quality of the seismic data is further improved.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a method for removing noise of acquired footprint, electronic equipment and a computer readable storage medium.
Background
With the continuous penetration of lithologic hydrocarbon reservoirs and hidden hydrocarbon reservoir exploration, higher requirements are put on the resolution of seismic imaging and the detailed depiction of seismic data. The acquisition footprint is a noise generated by uneven seismic attribute distribution among the subsurface elements due to an irregular mode of acquisition or spatial sampling of the wave detection points in the seismic data acquisition process.
The seismic data (seismic data) acquired on land are characterized in that noise emitted from underground leaks into superimposed data to generate acquisition footprint noise due to the distribution characteristics of azimuth angles and cannon distances among the surface elements in space. The seismic data collected by the ocean can cause irregular spatial sampling of the wave detection points due to the influence of the feathered cable along with sea water swaying and drifting, ocean current tides and other collecting factors, so that collecting footprint noise is generated. From the above, it can be seen that: the residual time difference, offset or amplitude change caused by the processing means adopted by the seismic data often leads to further enhancement of the collected footprint noise, and influences the subsequent seismic data processing and interpretation work. Therefore, there is a need to propose a seismic data denoising scheme.
Disclosure of Invention
The embodiment of the application can accurately and effectively remove the collected footprint noise in the seismic data by providing the collected footprint noise removing method, thereby improving the quality of the seismic data.
In one aspect, the present application provides a method for removing noise in an acquisition footprint, the method comprising:
Acquiring initial seismic data corresponding to a plurality of seismic channels, wherein the initial seismic data are the seismic data of a time-space domain acquired by the seismic channels at different sampling points and comprise acquisition footprint noise;
performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data;
And denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed.
Optionally, the performing frequency wave number radial fkm transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data includes:
performing frequency wave number conversion on the initial seismic data to convert the initial seismic data in the time space domain into intermediate seismic data in the frequency wave number domain;
And performing angle scanning on the intermediate seismic data in a preset inclination angle range so as to identify and obtain a main energy inclination angle corresponding to the initial seismic data in the radial direction.
Optionally, the performing frequency wave number transformation on the initial seismic data to transform the initial seismic data in a time space domain into intermediate seismic data in a frequency wave number domain includes:
performing non-uniform Fourier transform on the initial seismic data in the time space domain to obtain transformed seismic data in the frequency space domain;
And performing non-uniform Fourier inverse transformation on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency wave number domain.
Optionally, performing non-uniform fourier transform on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency-wave number domain includes:
Calculating Fourier coefficients of the initial seismic data corresponding to each seismic channel;
selecting a maximum fourier coefficient from a plurality of fourier coefficients of the initial seismic data;
And performing Fourier inverse transform recalculation on the transformed seismic data in the frequency space domain by using the maximum Fourier coefficient so as to obtain intermediate seismic data in the frequency wave number domain.
Optionally, after the obtaining the primary energy dip of the initial seismic data, the method further includes:
sorting the main energy dip angles of the plurality of initial seismic data to find out the main energy dip angle of the initial seismic data with dip angles larger than a preset threshold;
the denoising processing of the initial seismic data by adopting multi-directional vector median filtering according to the main energy inclination angle of the initial seismic data comprises the following steps:
and denoising the initial seismic data by adopting multidirectional vector median filtering according to the found main energy inclination angle of the initial seismic data.
Optionally, denoising the initial seismic data by using multi-directional vector median filtering according to the main energy dip angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed, including:
leveling the main energy dip angle of the initial seismic data to remove the acquired footprint noise in the initial seismic data by adopting multi-directional vector median filtering along the dip angle direction, so as to obtain denoising seismic data;
and carrying out anti-leveling operation on the denoising seismic data so as to obtain the target seismic data.
Optionally, before the obtaining the target seismic data, the method further includes:
judging whether the collected footprint noise is completely removed from the de-noised seismic data subjected to the anti-leveling operation;
if yes, determining the denoising seismic data subjected to the inverse flattening operation as the target seismic data;
if not, taking the de-noised seismic data after the anti-leveling operation as the initial seismic data, and repeatedly executing the frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data and the subsequent steps.
Optionally, the determining whether the collected footprint noise is completely removed from the denoised seismic data after the anti-leveling operation includes:
Judging whether the denoising seismic data after the anti-leveling operation corresponding to each seismic channel are continuous or not;
If continuous, determining that the collected footprint noise is completely removed; otherwise, it is determined that the acquisition footprint noise is not completely removed.
In another aspect, the present application provides, by an embodiment of the present application, an acquisition footprint noise-removing device, the device comprising: acquisition module, processing module and denoising module, wherein:
The acquisition module is used for acquiring initial seismic data corresponding to a plurality of seismic channels, wherein the initial seismic data are the seismic data of a time space domain acquired by the seismic channels at different sampling points and comprise acquisition footprint noise;
the processing module is used for carrying out frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data;
The denoising module is used for denoising the initial seismic data by adopting multi-directional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquisition footprint noise is removed.
In another aspect, the present application provides, by an embodiment of the present application, an electronic device including a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface are connected through the bus and complete communication with each other; the memory stores executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for performing the acquisition footprint noise removal method as provided in the method embodiment above.
In another aspect, the present application provides, by an embodiment of the present application, a computer readable storage medium storing program code which, when run on an electronic device, causes it to perform the acquisition footprint noise removal method as provided in the method embodiment above.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages: and finally, denoising the initial seismic data by adopting multi-directional vector median filtering according to the main energy inclination angle of the initial seismic data, thereby obtaining the target seismic data from which the acquired footprint noise is removed. Therefore, the collected footprint noise in the seismic data can be accurately and effectively removed under the condition of not damaging the seismic data, and the quality of the seismic data is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for removing noise in an acquisition footprint according to an embodiment of the present application.
Fig. 2 (a) -fig. 2 (c) are schematic diagrams of transformation of seismic data between different domains according to embodiments of the present application.
Fig. 3 (a) and fig. 3 (b) are schematic diagrams of a multi-directional vector filtering median and filtering process according to an embodiment of the present application.
Fig. 4-15 are schematic diagrams of interfaces of seismic data before and after denoising according to embodiments of the present application.
Fig. 16 is a schematic structural diagram of a device for removing noise in collected footprint according to an embodiment of the present application.
Fig. 17 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The applicant found in the course of proposing the present application that: the periodic characteristic of the acquired footprint noise in the land seismic data acquisition is relatively weak because the marine seismic data acquisition is influenced by factors such as cable drift, ocean current and tide and the like. A targeted filter is generally required to better suppress the acquisition footprint noise. Currently, in the technology of noise suppression of collected footprint, a principal component decomposition method and a two-dimensional and three-dimensional frequency wave number domain filtering method are generally adopted. Wherein:
The principal component decomposition method is to estimate the clean signal by decomposing the noisy signal vector space into two subspaces that are dominated by the clean signal and the noise signal, respectively, and then by simply removing the noisy signal vector component that falls in the noise space. The two-dimensional frequency wave number domain method is to set a low-pass filter to suppress noise by using the horizontal or vertical characteristics of the acquired footprint noise along the time slice direction. The three-dimensional frequency wave number domain method is a current common method for mainstream commercial software, and the collected footprint noise is suppressed by arranging a notch filter by utilizing the periodic characteristics of the collected footprint noise. However, in practice, it has been found that the use of principal component decomposition methods to suppress/remove the collected footprint noise is inefficient and of insufficient purity. Because the low wave number acquisition footprint noise overlaps with the effective seismic data, the effective seismic data is likely to be easily damaged when the two-dimensional/three-dimensional frequency wave number domain filtering method is adopted to remove the acquisition footprint noise, namely the acquisition footprint noise in the seismic data cannot be removed accurately.
Aiming at the problems of unsatisfactory effect of suppressing low wave number noise (collecting footprint noise) and effective seismic data loss at present, the application provides a frequency wave number radial (FKR) method. In order to re-orthogonalize the global Fourier basis function at irregular sampling, the method reduces leakage of spectrum energy as much as possible, so that an accurate spectrum of irregular data is obtained. From the perspective of modern signals, the method repeatedly applies the sampling theorem to estimate Fourier coefficients, and simple subtraction is applied to replace inversion iteration to achieve re-orthogonalization of the basis functions in Fourier transformation, so that the space aliasing phenomenon is effectively improved. A novel angle scanning method is simultaneously adopted in the frequency wave number (FK) domain to accurately identify the dominant dip in the FK domain. In addition, by combining a multidirectional vector median filtering method, the seismic signals are separated from the acquired footprint noise, so that the seismic data quality is improved.
The embodiment of the application solves the technical problems of low noise removal accuracy, easiness in damaging effective seismic data and the like in the prior art by providing the method for removing the noise of the acquired footprint.
The technical scheme of the embodiment of the application aims to solve the technical problems, and the overall thought is as follows: acquiring initial seismic data corresponding to a plurality of seismic channels, wherein the initial seismic data are the seismic data of a time-space domain acquired by the seismic channels at different sampling points and comprise acquisition footprint noise; performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data; and denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
First, the term "and/or" appearing herein is merely an association relationship describing associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 is a schematic flow chart of a method for removing noise in an acquisition footprint according to an embodiment of the present application. The method as shown in fig. 1 comprises the following implementation steps:
S101, acquiring initial seismic data corresponding to a plurality of seismic channels. The initial seismic data are the seismic data of time space domains acquired by the seismic channels at different sampling points, and the initial seismic data contain acquisition footprint noise.
The initial seismic data are three-dimensional data, the seismic data are obtained on the ground surface in the actual seismic acquisition process, the acquisition form is described by two dimensions of shot point position coordinates and detector point position coordinates, and time is an additional dimension for a data set. In other words, the initial seismic data is three-dimensional data described in three dimensions of shot position coordinates, receiver position coordinates, and time. The initial seismic data for multiple seismic traces to which the present application relates may be collectively referred to as a seismic data gather.
S102, performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data.
In one embodiment, the present application may perform a frequency wavenumber FK domain transform on the initial seismic data in the time-space TX domain to transform the initial seismic data in the TX domain into intermediate seismic data in the FK domain. And then, performing angle scanning on the intermediate seismic data of the FK domain by adopting an angle scanning method within a preset inclination angle range so as to convert the intermediate seismic data of the FK domain into the FKR domain, and simultaneously obtaining the main energy inclination angle corresponding to each initial seismic data.
In a particular embodiment, the present application may employ a Fourier transform FFT to convert the initial seismic data in the TX domain into transformed seismic data in the frequency space (FX) domain; then, the transformed seismic data of FX domain is transformed into intermediate seismic data of FK domain by Fourier inverse transformation. Specifically, the normalized non-uniform fourier transform and the non-uniform inverse fourier transform on the irregular grid are specifically shown in the following formula (1):
Where x l is the initial seismic data for the TX domain. f (x) is the fourier transform. w (x l) is an integral weight, and generally, the difference between offset distances of two adjacent points (two sampling points) is roughly taken. Δx is a normalization factor that contains all sampled integration weights. N p is the number of input samples. Is the fourier coefficient of wavenumber k. f k(xl) is the input data corresponding to wavenumber k (i.e., the seismic data in the frequency-time FX domain).
In practice it is found that for regular sampling, f k(xl) only affects the fourier coefficient estimation of the wavenumber k. But for irregular sampling, f k(xl) will leak into all other frequency components because the sampling breaks the orthogonality of the basis functions. Therefore, the present application can first estimate the fourier coefficients of all wavenumber k components, i.e., the present application can calculate the fourier coefficients of each initial seismic data using equation (1) above. Then selects a maximum energy Fourier coefficient (called maximum Fourier coefficient for short)And returns it to the input grid, finally by subtracting the chosen k-value component/>, from the input data (initial seismic data)To update the input data, which is specifically calculated as shown in the following equation (2):
In other words, in particular implementations, the present application may initialize all fourier components to 0, calculate fourier coefficients of all initial seismic data f u(xl) based on equation (1) above, select the largest fourier coefficient with the largest energy from among them, accumulate its contribution to the existing fourier components, subtract the contribution of this coefficient from the initial seismic data based on equation (2) above, repeat the above steps to calculate fourier coefficients … … of all initial seismic data based on equation (1) above, subtract the contribution of this coefficient from the initial seismic data based on equation (2) above, until the FK domain initial seismic data obtained after updating is sufficiently small (e.g., less than a preset threshold, etc.).
In another embodiment, the present application may employ a precise angle sweep method to transform intermediate seismic data of the FK domain to the FKR domain. The method performs angle scanning within a preset inclination angle range to identify and obtain a corresponding main energy inclination angle. This tilt angle range needs to include not only the low frequency end but also all frequencies or any frequency band. The origin of the angle ray is located at the origin of the frequency wavenumber FK domain.
Let D (t, x) and D (f, k) be expressions of seismic data in the time space TX domain and the frequency wavenumber FK domain, respectively. The origin of the angular ray is located at (f, k) = (0, 0). Because of the symmetry of the frequency axis in the domain, the application takes the calculation of the positive frequency part only as an example. Furthermore, in order to simplify the theoretical calculation, the present application will use gain with normalized frequency and normalized wavenumber. The normalized frequency axis and normalized wavenumber axis are calculated as Δt=1 and Δx=1, respectively, such that the normalized frequency axis range is 0 < f < 0.5, and the normalized wavenumber axis range is-0.5 < k < 0.5. In other words, the application can normalize the intermediate seismic data of FK domain according to frequency and wave number, the normalized frequency is in the following range: 0 < f < 0.5, the normalized wavenumber of which is in the following range: -0.5 < k < 0.5. The map of the principal energy tilt angle can be obtained by summing along the angle rays, which is specifically calculated as shown in equation (3) below:
where p is the slope of the curve, or dip direction, of the intermediate seismic data distribution for the FK domain. n is a certain sampling point of the normalized frequency, i.e. the nth sampling point, which comprises an arbitrary frequency interval. N is the preset number, ω is the inclination slope corresponding to the input data (intermediate seismic data), and ω n is the inclination slope of the nth intermediate seismic data. Is the largest integer of Yu.
Note that, the ray path shown in the above formula (3) may explain the aliasing information around the entire frequency axis in the FK domain. These pseudo-tilt angles are defined by p > 1 and p < -1. The determination of the p-value range is determined according to the severity of the aliasing and the dip limit of the seismic data, with different value ranges in different cases. This range can be divided equally into several parts for calculating the p-value of the tilt scan. In order to correctly identify the change law of the main energy inclination, it is critical to select a suitable interval of values. In practical situations, because of complexity of the same phase axis in the seismic data and interference of noise, the inclination angle interval must be small enough to identify the false frequency interference, so in practical music, the angle rays are connected with each positive frequency sampling point from the beginning, and the total capacity of the rays is calculated and is the total number of the frequency sampling points. So that any one of the main energy dip angles is not missed.
Wherein, the peak value in M (p) represents the main energy included in the inclination angle, which is called as main energy inclination angle for short. If M (p) is greater than two adjacent values, the tilt angle may be considered a peak. In practical application, several peak values can be identified in M (p), but the application only needs to keep the largest several values, and can be realized in the following two modes. One retains the largest few M (p) values and the other retains M (p) values greater than the threshold. Regardless of the manner employed, each seismic data may correspond to a plurality of L primary energy dip angles that can be identified, respectively: p 1,p2,…,pL. In other words, in an alternative embodiment, the present application may sort the main energy dip angles corresponding to the plurality of initial seismic data, for example, from the largest to the smallest, and take the main energy dip angles of the first m initial seismic data for subsequent flattening, denoising, and anti-flattening operations, which are described in detail below. Therefore, the FKR transformation constructed by combining the anti-aliasing window FK transformation and the angle scanning method has good anti-aliasing capability, and can effectively interpolate regular real seismic data, and the specific schematic diagram of the FKR transformation is shown in figures 2 (a) -2 (c). Fig. 2 (a) to 2 (b) show transformation diagrams of the seismic data converted from the time space TX domain to the frequency wavenumber FK domain, and fig. 2 (b) to 2 (c) show transformation diagrams of the seismic data converted from the frequency wavenumber FK domain to the frequency wavenumber radial EFR domain.
S103, denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquisition footprint noise is removed.
In one embodiment, the application can perform leveling operation on main energy dip angles of a plurality of initial seismic data so as to remove acquisition footprint noise in the initial seismic data by adopting multi-directional vector median filtering along the dip angle direction, so as to obtain corresponding denoising seismic data; and further carrying out anti-leveling operation on the denoising seismic data, thereby obtaining target seismic data after the acquisition footprint noise is removed, wherein the target seismic data also does not have the seismic data of the time space TX domain.
In particular, the main energy dip angles identified in FKR fields are used for leveling the main energy dip angles of each initial seismic data by adopting a four-point difference algorithm so as to perform leveling operation in a small window range. Aiming at the noise characteristics that the collected footprint noise does not belong to the same phase axis of the main energy dip angle and the capability distribution is uneven, the method adopts a multidirectional vector median filtering method to suppress or remove the collected footprint noise. Embodiments of median filtering correlation are described below.
Median filtering is a nonlinear signal processing technology based on ordering statistical theory, which can effectively attenuate noise. The median filtering method is to take discrete amplitude values of x ji from a plurality of channels x i (t) in time, wherein i is a channel sequence number and j is a time sampling point number. The amplitude values of the trace are arranged according to the absolute value, the value of the edge trace can be zero-filled, and the amplitude value of the midpoint is used as the output value of the middle seismic trace position. And then taking one step as a step length to move downwards, arranging the amplitudes according to the absolute value, and taking the midpoint amplitude value as the output value of the step length. And so on, and finally outputting to obtain x ij. The weak inclined wave group exists near the time T j, the weak inclined wave group is obviously weakened after being filtered, and the filtering can be seen to play a role in smoothing the inclined wave group. Such median filtering can strengthen the phase-coincident in-phase axis and suppress the inclined in-phase axis and noise. By its nature, is a nonlinear filter. The method has two advantages, namely, noise peaks can be absolutely prevented, and the median filtering only takes the median and never takes the abnormal constant; and secondly, median filtering does not change the position of the step function in space and time. Starting from the basic scalar median filtering method, a distance function is defined as shown in equation (4) below assuming that there is a set of scalar quantities that can be expressed as { x i |i=1, …, N }:
Where r is a norm, x i and x j are any two input points (i.e., seismic data), N is the number of sampling points, and the number is preset.
The median output x m of the upper set of scalars can be expressed as shown in equation (5) below:
the practice finds that: the scalar median filtering method has a good effect on the horizontal homophase axis, but has a poor protection effect on the inclined homophase axis. Considering further the vector median filtering method, assuming that a set of vectors can be expressed as { x i |i=1, …, N }, the distance function of the set of vectors can be expressed as shown in the following equation (6):
Wherein l * is the norm.
The median output vector X m of the upper set of vectors can be expressed as shown in equation (7) below:
Please refer to fig. 3 (a) and fig. 3 (b) for a schematic diagram of multi-directional vector median output and filtering, respectively. As shown in fig. 3 (a), the meaning of the median vector can be described, and the point a is the median point outputted after the multidirectional vector processing is illustrated. When denoising is performed by vector median filtering, effective horizontal and inclined phase axis signals can be protected, but effective signals (namely effective seismic data) are damaged to a certain extent. The present application therefore proposes a multidirectional vector median filtering method. Vector median filtering, abbreviated as multidirectional vector median filtering, can be performed along different tilt directions as shown in fig. 3 (b). Illustratively, the present application defines a multidirectional vector median filter by using 1 norm, the expression of which is shown in the following formula (8):
Xj(p)∈{Xi(p)∣i＝1,…,N,p＝pmin,…,pmax}
the median vector X m (p) of its corresponding output can be expressed as shown in the following equation (9):
Wherein p represents the tilt direction, and X j (p) is a vector along one of the directions p.
Finally, the application can carry out anti-leveling operation on the denoising seismic data obtained after the denoising of the multidirectional vector median filter. Since the initial seismic data of multiple seismic traces is simply referred to as a seismic data trace set, its corresponding multi-directional vector median filtered seismic data may also be referred to as a filtered seismic data trace set. Correspondingly, the application can adopt a four-point difference algorithm to reversely flatten the filtered trace set back to obtain the target seismic data for removing the noise of the acquired footprint.
In an optional embodiment, the application may further perform a noise total removal determination on the seismic data after the anti-leveling operation, specifically determine whether the collected footprint noise is completely removed from the seismic data after the anti-leveling operation, for example, by determining whether the seismic data after the anti-leveling operation is continuous, and if so, determine that the collected footprint noise is completely removed currently; otherwise, it is determined that the acquisition footprint noise is not currently completely removed. And after the fact that the collected footprint noise of the seismic data subjected to the anti-leveling operation is completely removed is determined, the seismic data subjected to the anti-leveling operation can be used as target seismic data to be output. Otherwise, the seismic data after the anti-leveling operation is re-input as initial seismic data, and the steps of the steps S102 and S103 are repeatedly executed.
It can be seen by practicing the present application that: in the scheme for removing the collected footprint noise, which is provided by the application, FK transformation and median filtering are adopted, so that the algorithm noise removing efficiency is high and the operation speed is high. And also an iterative algorithm, the iteration can be repeated until the acquired footprint noise is completely removed.
For an intuitive and better understanding of the embodiments of the present application, specific experimental examples are set forth below. Fig. 4-7 are schematic diagrams showing seismic data before and after the removal of the noise of the acquired footprint in accordance with an embodiment of the present application. Fig. 4 and 6 each show a schematic view of the interface of the initial seismic data of different seismic traces input before denoising, with slightly different viewing display angles. In the figure, the upper and lower rows (upper and lower abscissas) represent the number of tracks, and the left and right columns (left and right abscissas) represent the number of sampling points in the time direction. Iterative FKR filtering is carried out on the initial seismic data, the deep seismic event also becomes continuous while shallow acquisition footprint noise is suppressed, and the seismic data is better protected. Fig. 5 and 7 show schematic interface diagrams of target seismic data for different seismic traces after denoising.
Referring to fig. 8-15, an interface diagram of seismic data observed in different directions is shown. In the present example, the seismic data before and after filtering may be viewed from inline (inline), crossline (crossline) and time slice directions at different depths. FIG. 8 shows a schematic interface diagram of initial seismic data observed in the inline direction prior to denoising. Seismic data in the inline direction after denoising the foot print using FKR acquisition is shown in FIG. 9. FIG. 10 shows a schematic interface diagram of initial seismic data observed in the crossline direction prior to denoising, where dark black represents the main energy and continuous black represents the acquisition footprint noise. Fig. 11 shows the seismic data after fkm acquisition footprint noise removal. Fig. 12 and 14 show interface distribution diagrams of initial seismic data acquired before denoising at shallow (e.g., sampling time t=100 ms) and deep (e.g., sampling time t=380 ms), respectively. Fig. 13 and 15 show schematic interface diagrams of shallow and deep denoised seismic data, respectively. As can be seen from the figure, the acquisition footprint noise in the initial seismic data can be well suppressed or removed by adopting the scheme of the application.
According to the method, initial seismic data corresponding to a plurality of seismic channels are acquired, frequency wave number radial transformation is carried out on the initial seismic data to obtain a main energy dip angle of the initial seismic data, and finally, multi-directional vector median filtering is adopted to carry out denoising processing on the initial seismic data according to the main energy dip angle of the initial seismic data, so that target seismic data with the acquired footprint noise removed is obtained. Therefore, the collected footprint noise in the seismic data can be accurately and effectively removed under the condition of not damaging the seismic data, and the quality of the seismic data is improved.
Fig. 16 is a schematic structural diagram of a device for removing noise from collected footprint according to an embodiment of the present application. The apparatus shown in fig. 16 includes: acquisition module 161, processing module 162 and denoising module 163, wherein:
The acquisition module 161 is configured to acquire initial seismic data corresponding to a plurality of seismic traces, where the initial seismic data is seismic data of a time-space domain acquired by the seismic traces at different sampling points, and includes acquisition footprint noise;
the processing module 162 is configured to perform frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data;
The denoising module 163 is configured to denoise the initial seismic data by using multi-directional vector median filtering according to a main energy inclination angle of the initial seismic data, so as to obtain target seismic data from which the acquisition footprint noise is removed.
Optionally, the processing module 162 is specifically configured to:
performing frequency wave number conversion on the initial seismic data to convert the initial seismic data in the time space domain into intermediate seismic data in the frequency wave number domain;
And performing angle scanning on the intermediate seismic data in a preset inclination angle range so as to identify and obtain a main energy inclination angle corresponding to the initial seismic data in the radial direction.
Optionally, the processing module 162 is further specifically configured to:
performing non-uniform Fourier transform on the initial seismic data in the time space domain to obtain transformed seismic data in the frequency space domain;
And performing non-uniform Fourier inverse transformation on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency wave number domain.
Optionally, the processing module 162 is further specifically configured to:
Calculating Fourier coefficients of the initial seismic data corresponding to each seismic channel;
selecting a maximum fourier coefficient from a plurality of fourier coefficients of the initial seismic data;
And performing Fourier inverse transform recalculation on the transformed seismic data in the frequency space domain by using the maximum Fourier coefficient so as to obtain intermediate seismic data in the frequency wave number domain.
Optionally, after the obtaining the primary energy dip of the initial seismic data, the processing module 162 is further configured to:
sorting the main energy dip angles of the plurality of initial seismic data to find out the main energy dip angle of the initial seismic data with dip angles larger than a preset threshold;
the denoising module 163 specifically is configured to:
and denoising the initial seismic data by adopting multidirectional vector median filtering according to the found main energy inclination angle of the initial seismic data.
Optionally, the denoising module 163 is specifically configured to:
leveling the main energy dip angle of the initial seismic data to remove the acquired footprint noise in the initial seismic data by adopting multi-directional vector median filtering along the dip angle direction, so as to obtain denoising seismic data;
and carrying out anti-leveling operation on the denoising seismic data so as to obtain the target seismic data.
Optionally, before obtaining the target seismic data, the apparatus further includes a determining module 164, where the determining module 164 is configured to:
judging whether the collected footprint noise is completely removed from the de-noised seismic data subjected to the anti-leveling operation;
if yes, determining the denoising seismic data subjected to the inverse flattening operation as the target seismic data;
if not, taking the de-noised seismic data after the anti-leveling operation as the initial seismic data, and repeatedly executing the frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data and the subsequent steps.
Optionally, the judging module 164 is specifically configured to:
Judging whether the denoising seismic data after the anti-leveling operation corresponding to each seismic channel are continuous or not;
If continuous, determining that the collected footprint noise is completely removed; otherwise, it is determined that the acquisition footprint noise is not completely removed.
According to the method, initial seismic data corresponding to a plurality of seismic channels are acquired, frequency wave number radial transformation is carried out on the initial seismic data to obtain a main energy dip angle of the initial seismic data, and finally, multi-directional vector median filtering is adopted to carry out denoising processing on the initial seismic data according to the main energy dip angle of the initial seismic data, so that target seismic data with the acquired footprint noise removed is obtained. Therefore, the collected footprint noise in the seismic data can be accurately and effectively removed under the condition of not damaging the seismic data, and the quality of the seismic data is improved.
Based on the same inventive concept, another embodiment of the present application provides an electronic device for implementing the method for removing noise of collected footprint in the embodiment of the present application. Referring to fig. 17, fig. 17 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device of the present embodiment includes: at least one processor 171, communication interface 172, user interface 173, and memory 174, the processor 171, communication interface 172, user interface 173, and memory 174 may be connected via a bus or otherwise, with embodiments of the present application being exemplified by connection via bus 175. Wherein,
The processor 171 may be a general-purpose processor such as a central processing unit (Central Processing Unit, CPU).
The communication interface 172 may be a wired interface (e.g., an ethernet interface) or a wireless interface (e.g., a cellular network interface or using a wireless local area network interface) for communicating with other terminals or websites. In an embodiment of the present invention, the communication interface 172 is specifically configured to transmit the target seismic data or collect the initial seismic data.
The user interface 173 may be a touch panel, including a touch screen and a touch screen, for detecting operation instructions on the touch panel, and the user interface 173 may be a physical button or a mouse. The user interface 173 may also be a display screen for outputting, displaying images or data.
Memory 174 may include Volatile Memory (Volatile Memory), such as random access Memory (Random Access Memory, RAM); the Memory may also include a Non-Volatile Memory (Non-Volatile Memory), such as Read-Only Memory (ROM), flash Memory (Flash Memory), hard disk (HARD DISK DRIVE, HDD), or Solid state disk (Solid-state disk-STATE DRIVE, SSD); memory 174 may also include a combination of the types of memory described above. The memory 604 is used for storing a set of program codes, and the processor 171 is used for calling the program codes stored in the memory 174, performing the following operations:
Acquiring initial seismic data corresponding to a plurality of seismic channels, wherein the initial seismic data are the seismic data of a time-space domain acquired by the seismic channels at different sampling points and comprise acquisition footprint noise;
performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data;
And denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed.
Optionally, the performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data includes:
performing frequency wave number conversion on the initial seismic data to convert the initial seismic data in the time space domain into intermediate seismic data in the frequency wave number domain;
And performing angle scanning on the intermediate seismic data in a preset inclination angle range so as to identify and obtain a main energy inclination angle corresponding to the initial seismic data in the radial direction.
Optionally, the performing frequency wave number transformation on the initial seismic data to transform the initial seismic data in a time space domain into intermediate seismic data in a frequency wave number domain includes:
performing non-uniform Fourier transform on the initial seismic data in the time space domain to obtain transformed seismic data in the frequency space domain;
And performing non-uniform Fourier inverse transformation on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency wave number domain.
Optionally, performing non-uniform fourier transform on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency-wave number domain includes:
Calculating Fourier coefficients of the initial seismic data corresponding to each seismic channel;
selecting a maximum fourier coefficient from a plurality of fourier coefficients of the initial seismic data;
And performing Fourier inverse transform recalculation on the transformed seismic data in the frequency space domain by using the maximum Fourier coefficient so as to obtain intermediate seismic data in the frequency wave number domain.
Optionally, after the obtaining the primary energy dip of the initial seismic data, the processor 171 is further configured to:
sorting the main energy dip angles of the plurality of initial seismic data to find out the main energy dip angle of the initial seismic data with dip angles larger than a preset threshold;
the denoising processing of the initial seismic data by adopting multi-directional vector median filtering according to the main energy inclination angle of the initial seismic data comprises the following steps:
and denoising the initial seismic data by adopting multidirectional vector median filtering according to the found main energy inclination angle of the initial seismic data.
Optionally, denoising the initial seismic data by using multi-directional vector median filtering according to the main energy dip angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed, including:
leveling the main energy dip angle of the initial seismic data to remove the acquired footprint noise in the initial seismic data by adopting multi-directional vector median filtering along the dip angle direction, so as to obtain denoising seismic data;
and carrying out anti-leveling operation on the denoising seismic data so as to obtain the target seismic data.
Optionally, before the obtaining the target seismic data, the processor 171 is further configured to:
judging whether the collected footprint noise is completely removed from the de-noised seismic data subjected to the anti-leveling operation;
if yes, determining the denoising seismic data subjected to the inverse flattening operation as the target seismic data;
if not, taking the de-noised seismic data after the anti-leveling operation as the initial seismic data, and repeatedly executing the frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data and the subsequent steps.
Optionally, the determining whether the collected footprint noise is completely removed from the denoised seismic data after the anti-leveling operation includes:
Judging whether the denoising seismic data after the anti-leveling operation corresponding to each seismic channel are continuous or not;
If continuous, determining that the collected footprint noise is completely removed; otherwise, it is determined that the acquisition footprint noise is not completely removed.
Since the electronic device described in this embodiment is an electronic device for implementing the method for removing the collected footprint noise in the embodiment of the present application, based on the method for removing the collected footprint noise described in the embodiment of the present application, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how the method in the embodiment of the present application is implemented in this electronic device will not be described in detail herein. Any electronic device used by those skilled in the art to implement the information processing method in the embodiment of the present application is within the scope of the present application.
According to the method, initial seismic data corresponding to a plurality of seismic channels are acquired, frequency wave number radial transformation is carried out on the initial seismic data to obtain a main energy dip angle of the initial seismic data, and finally, multi-directional vector median filtering is adopted to carry out denoising processing on the initial seismic data according to the main energy dip angle of the initial seismic data, so that target seismic data with the acquired footprint noise removed is obtained. Therefore, the collected footprint noise in the seismic data can be accurately and effectively removed under the condition of not damaging the seismic data, and the quality of the seismic data is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. A method of collecting footprint noise removal, the method comprising:
Acquiring initial seismic data corresponding to a plurality of seismic channels, wherein the initial seismic data are the seismic data of a time-space domain acquired by the seismic channels at different sampling points and comprise acquisition footprint noise;
Performing frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data, wherein the method comprises the following steps:
Performing frequency wave number transformation on the initial seismic data to transform the initial seismic data in the time-space domain into intermediate seismic data in the frequency wave number domain, including: performing non-uniform Fourier transform on the initial seismic data in the time space domain to obtain transformed seismic data in the frequency space domain; performing non-uniform Fourier inverse transformation on the transformed seismic data in the frequency space domain to obtain intermediate seismic data in the frequency wave number domain, wherein the method specifically comprises the following steps of: calculating Fourier coefficients of the initial seismic data corresponding to each seismic channel; selecting a maximum fourier coefficient from a plurality of fourier coefficients of the initial seismic data; performing Fourier inverse transform recalculation on the transformed seismic data of the frequency space domain by using the maximum Fourier coefficient to obtain intermediate seismic data of the frequency wave number domain;
Performing angle scanning on the intermediate seismic data in a preset inclination angle range so as to identify and obtain a main energy inclination angle corresponding to the initial seismic data in a radial direction;
And denoising the initial seismic data by adopting multidirectional vector median filtering according to the main energy inclination angle of the initial seismic data, so as to obtain the target seismic data from which the acquired footprint noise is removed.
2. The method of claim 1, wherein after the obtaining the primary energy dip of the initial seismic data, the method further comprises:
sorting the main energy dip angles of the plurality of initial seismic data to find out the main energy dip angle of the initial seismic data with dip angles larger than a preset threshold;
the denoising processing of the initial seismic data by adopting multi-directional vector median filtering according to the main energy inclination angle of the initial seismic data comprises the following steps:
and denoising the initial seismic data by adopting multidirectional vector median filtering according to the found main energy inclination angle of the initial seismic data.
3. The method according to claim 1 or 2, wherein denoising the initial seismic data using multi-directional vector median filtering according to the main energy dip of the initial seismic data, thereby obtaining the target seismic data from which the acquisition footprint noise is removed comprises:
leveling the main energy dip angle of the initial seismic data to remove the acquired footprint noise in the initial seismic data by adopting multi-directional vector median filtering along the dip angle direction, so as to obtain denoising seismic data;
and carrying out anti-leveling operation on the denoising seismic data so as to obtain the target seismic data.
4. A method according to claim 3, wherein prior to said obtaining said target seismic data, said method further comprises:
judging whether the collected footprint noise is completely removed from the de-noised seismic data subjected to the anti-leveling operation;
if yes, determining the denoising seismic data subjected to the inverse flattening operation as the target seismic data;
if not, taking the de-noised seismic data after the anti-leveling operation as the initial seismic data, and repeatedly executing the frequency wave number radial transformation on the initial seismic data to obtain a main energy dip angle of the initial seismic data and the subsequent steps.
5. The method of claim 4, wherein said determining whether the acquired footprint noise is completely removed from the de-noised seismic data after the anti-leveling operation comprises:
Judging whether the denoising seismic data after the anti-leveling operation corresponding to each seismic channel are continuous or not;
If continuous, determining that the collected footprint noise is completely removed; otherwise, it is determined that the acquisition footprint noise is not completely removed.
6. An electronic device, comprising: a processor, a memory, a communication interface, and a bus; the processor, the memory and the communication interface are connected through the bus and complete communication with each other; the memory stores executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory for performing the acquisition footprint noise removal method as set forth in any of the preceding claims 1-5.
7. A computer readable storage medium, characterized in that the computer readable storage medium stores a program which, when run on an electronic device, performs the acquisition footprint noise removal method of any of the preceding claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110913600.7A CN113640882B (en) | 2021-08-10 | 2021-08-10 | Acquisition footprint noise removal method, electronic device and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110913600.7A CN113640882B (en) | 2021-08-10 | 2021-08-10 | Acquisition footprint noise removal method, electronic device and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113640882A CN113640882A (en) | 2021-11-12 |
| CN113640882B true CN113640882B (en) | 2024-06-21 |
Family
ID=78420506
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110913600.7A Active CN113640882B (en) | 2021-08-10 | 2021-08-10 | Acquisition footprint noise removal method, electronic device and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113640882B (en) |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2458642A (en) * | 2008-03-25 | 2009-09-30 | Geco Technology Bv | Noise attenuation of seismic data |
| WO2013090713A2 (en) * | 2011-12-15 | 2013-06-20 | Saudi Arabian Oil Company | Iterative dip-steering median filter for seismic data processing |
| US9702999B2 (en) * | 2014-10-17 | 2017-07-11 | Chevron U.S.A. Inc. | System and method for velocity analysis in the presence of critical reflections |
| CN106353816B (en) * | 2016-08-09 | 2018-10-16 | 中国石油天然气集团公司 | A kind of earthquake-capturing footprint Noise Elimination method and system |
| CN107561588B (en) * | 2017-09-19 | 2019-07-09 | 中国石油天然气股份有限公司 | Seismic data noise suppression method and device |
| CN109164483B (en) * | 2018-08-29 | 2020-04-03 | 中国科学院地球化学研究所 | Multi-component seismic data vector denoising method and multi-component seismic data vector denoising device |
| CN109782346B (en) * | 2019-01-14 | 2020-07-28 | 西安交通大学 | Acquisition footprint pressing method based on morphological component analysis |
| CN109633753B (en) * | 2019-01-30 | 2020-04-28 | 中国科学院地质与地球物理研究所 | Earth surface noise suppression method and device |
| CN110133721B (en) * | 2019-06-04 | 2020-06-16 | 南京加宝囤信息科技有限公司 | Method and system for monitoring hydraulic fracturing process |
| CN112817047B (en) * | 2020-12-31 | 2021-10-08 | 北京东方联创地球物理技术有限公司 | Ocean earthquake self-adaptive ghost wave removing method and device, electronic equipment and medium |
| CN113156514B (en) * | 2021-04-25 | 2022-08-23 | 中南大学 | Seismic data denoising method and system based on dominant frequency wavenumber domain mean value filtering |
-
2021
- 2021-08-10 CN CN202110913600.7A patent/CN113640882B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| Research of liquid CT image de-noising based on improved NL-means algorithm;Huang Liang;《2015 International Carnahan Conference on Security Technology 》;20160125;全文 * |
| 相对保持振幅、频率、相位属性的采集脚印压制技术;张红军 等;《石油地球物理勘探》;20121220;15-19 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113640882A (en) | 2021-11-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | A method for low-frequency noise suppression based on mathematical morphology in microseismic monitoring | |
| Gómez et al. | A simple method inspired by empirical mode decomposition for denoising seismic data | |
| US9612351B2 (en) | System and method for estimating and attenuating noise in seismic data | |
| Zhang et al. | Multicomponent microseismic data denoising by 3D shearlet transform | |
| GB2500302A (en) | Removing non-orthogonal coherent noise from seismic data | |
| US20150301209A1 (en) | Estimating A Wavefield For A Dip | |
| CN105242309A (en) | Earthquake data regular interference suppressing method and device | |
| CN110907995B (en) | Reverse time migration method and device for VSP seismic data in well | |
| Geng et al. | Relative time seislet transform | |
| CN104391325B (en) | Discontinuous heterogeneous geologic body detection method and device | |
| WO2020086662A1 (en) | Seismic random noise attenuation | |
| Wang et al. | Attention-based neural network for erratic noise attenuation from seismic data with a shuffled noise training data generation strategy | |
| CN113640882B (en) | Acquisition footprint noise removal method, electronic device and computer readable storage medium | |
| CN104898166B (en) | A kind of method and device for handling geological data | |
| NO20130064A1 (en) | Device and method for estimating time offset. | |
| CN111929732B (en) | Seismic data denoising method, device and equipment | |
| CN111337973B (en) | Seismic data reconstruction method and system | |
| CN107807391B (en) | Seismic data processing method, device, electronic equipment and computer storage medium | |
| CN115951403B (en) | Microseism offset superposition positioning method and device, electronic equipment and readable storage medium | |
| Yin et al. | Research on interference signal recognition in p wave pickup and magnitude estimation | |
| CN103760599B (en) | A kind of miniature fault detection method and fault detection device | |
| CN111352158A (en) | Seismic signal enhancement method and device | |
| CN111562616A (en) | Seismic data scattering noise suppression method and device | |
| Lu et al. | Automatic source localization of diffracted seismic noise in shallow water | |
| US20230123550A1 (en) | Attenuation of interface waves using single component seismic data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |