CN117826248A - Surface wave dispersion extraction method based on multiscale observation background noise bunching - Google Patents
Surface wave dispersion extraction method based on multiscale observation background noise bunching Download PDFInfo
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Abstract
The invention discloses a method for extracting surface wave dispersion based on multi-scale observation background noise bunching, which comprises the following steps: and designing a multi-scale observation system and extracting a dispersion curve through a beam-focusing algorithm. The processing method establishes the relation between space and time through seismic wave velocity, suppresses noise through bunching and superposition, and reduces effective signal loss caused by filtering; the invention improves the resolution of the detection area by dense observation and improves the detection depth by the restraint of the peripheral reference station. In conclusion, the method has the advantages of low observation cost, high resolution, large detection depth, simple logic and the like, and has high practical value and popularization value in the technical fields of seismic short-period dense array observation and background noise imaging.
Description
Technical Field
The invention relates to the technical field of seismic short-period dense array observation and background noise imaging, in particular to a surface wave dispersion extraction method based on multi-scale observation and beam-focusing analysis.
Background
With the continuous development of underground resources, resources with obvious characteristics and easy exploration are less and less. Meanwhile, it is known that the reservoir location and morphology of deep resources are shifted as the production proceeds, resulting in a reduction in the efficiency of the production system. Therefore, optimizing the exploration method, refining the deep structure and enabling the distribution form of the deep mineral deposit to be more definite is an important guarantee for green development and utilization of dominant resources and mineral resources.
In the prior art, mineral resource exploration means and parameters are relatively single, and the position and the morphology of a deep reservoir are restrained mainly through conductivity change of the deep reservoir. While with progressive perfection of the survey, the multi-solution of the inversion does not decrease simultaneously. Seismic velocity has achieved great success in oil and gas exploration, and mineral resource enrichment can also cause seismic velocity anomalies, especially high temperature anomalies in geothermal reservoirs can lead to seismic velocity degradation.
Currently, existing seismic exploration techniques observe artificially excited seismic waves through ultra-dense detector arrays. The method has strong reliability and high precision, but also has high cost. In addition, large-equivalent blasting is prone to damage to the earth's surface and is not suitable for working in cities or areas where exploration facilities are already built. The natural source seismic exploration is low in cost and environment-friendly, the seismic surface waves propagate along the surface of the earth, and the propagation speed is an important parameter for restraining the near-surface fine structure. However, the distance between stations in the traditional station arrangement mode is relatively uniform, high-frequency signals can be extracted by small-distance arrangement, a shallow speed structure is obtained, the constraint on a deep structure is insufficient, and huge calculation is caused if large-scale intensive observation is performed; the arrangement of the large bench distance can extract low-frequency signals to obtain a deep speed structure, but the constraint on the fine structure is insufficient. Finding a balance between detection accuracy and scale and increasing computation speed is a significant challenge in face-wave dispersion extraction techniques.
For example, "patent publication No.: CN112861721a, name: a Chinese patent invention of a method and a device for automatically extracting background noise dispersion curves, the method comprises the following steps: background noise data recorded by an earthquake station are obtained, the background noise data are preprocessed, cross-correlated and overlapped to obtain an empirical green function, and then vector wave number domain transformation is carried out on the empirical green function to obtain a frequency dispersion spectrum; obtaining a candidate dispersion area according to the dispersion spectrum and the Res-Unet++ network model; and extracting and classifying the dispersion points of the candidate dispersion area according to a gradient method and a chase method to obtain a target dispersion curve. The technology aims at improving the extraction efficiency of the dispersion curve, and does not consider improvement of data quality with short observation time and low signal-to-noise ratio.
And the patent publication number is as follows: CN116165706a, name: a chinese invention patent of an underground structure imaging method based on background noise surface wave double-beam focusing, comprising: acquiring original seismic data, preprocessing the original seismic data, and calculating a cross-correlation function between station pairs based on the preprocessed original seismic data; selecting a dual-beam-forming calculation parameter based on the cross-correlation function, and determining source beam forming and receiving beam forming based on the dual-beam-forming calculation parameter, wherein the dual-beam-forming calculation parameter comprises a beam forming center point and a beam forming width; setting a local phase velocity and azimuth search range, correcting and superposing cross-correlation waveforms in the source bunching and the receiving bunching to the bunching center point, and searching an optimal measurement result corresponding to an envelope maximum energy spectrum of the superposed waveforms, wherein the optimal measurement result comprises an optimal phase velocity and an optimal incidence azimuth; moving the source beam and receiving the beam to obtain a plurality of groups of optimal measurement results, generating a phase velocity diagram based on each optimal measurement result, and generating azimuth anisotropy information according to weak anisotropy medium fitting; subsurface structure information is generated based on the phase velocity map and the azimuthal anisotropy information. The method is used for carrying out high-density observation on a research area with a large observation area, and the signal to noise ratio of the research area is improved through source beam focusing and beam focusing receiving. However, for studying small scale structures, large scale observations must be made at the periphery of the investigation region to obtain deeper structures. It would be wasteful of observation cost if it were to lay out intensive observations in non-study areas.
Therefore, it is needed to provide a method for extracting the surface wave dispersion based on multi-scale observation background noise bunching, which has simple logic, high signal to noise ratio and low observation cost.
Disclosure of Invention
The invention aims to provide a multi-scale observation background noise bunching-based surface wave dispersion extraction method, provides a detection means which takes the observation scale and the resolution into consideration for near-surface fine structure detection, and solves the problem of acquiring deep structure information while ensuring high resolution. The technical scheme adopted by the invention is as follows:
the surface wave dispersion extraction method based on multi-scale observation background noise bunching comprises the following steps:
designing an observation system according to a research target, and acquiring seismic data of a detection area by adopting multi-scale observation;
preprocessing the seismic data, and extracting a surface wave signal by using cross-correlation calculation;
parameterizing the detection area model, and obtaining a dispersion curve according to a beam-forming algorithm.
Further, the seismic data of the detection area is acquired by adopting multi-scale observation, and the method comprises the following steps:
according to the research target, determining a detection area and designing an observation system: and arranging beam focusing stations at small station intervals in the detection area, performing intensive observation, and arranging reference stations at large station intervals outside the detection area. And determining the distance between stations of dense observation according to the detection precision, namely, not more than the detection abnormal scale. In addition, the coverage range of the constraint station is determined according to the detection depth, and the sensitivity depth of the surface wave is positioned at one of three positions of the wavelength; the wavelength of the lowest frequency detectable between pairs of noise-observing stations is not more than one third of the inter-station distance, and the distance between the furthest points can be estimated to be not less than 9 times the detection depth.
And synchronously observing and acquiring continuous background noise data at the positions of the beam focusing station and the reference station.
Further, the preprocessing includes: the method comprises the steps of removing the mean value, removing the linear trend, normalizing the frequency spectrum, carrying out band-pass filtering, intercepting data, and superposing and extracting the surface wave signals.
Further, the method comprises the steps of performing model parameterization on the detection area and obtaining a dispersion curve according to a beamforming algorithm, wherein the method comprises the following steps:
step S31, dividing grid points in a detection area to serve as a beam focusing center according to the detection precision requirement of an observation system, and selecting a beam focusing radius r according to the signal-to-noise ratio and the detection precision requirement; bunching the bunching stations in the bunching radius by taking the reference station as a constraint;
step S32, selecting one of the beam focusing centers, and determining the distance d between the reference station and the selected beam focusing center and the reference velocity interval [ v ] of the surface wave signal min ，v max ]Obtaining an arrival time window and an interception time window of a face wave signalThe T represents a period;
step S33, calculating the correction time of the beam focusing station relative to the selected beam focusing center according to the formula (1), wherein the expression is as follows:
τ(x，y，u，θ)＝(x-x c )usinθ+(y-y c )ucosθ (1)
wherein (x, y) represents the position of the beaming station; u represents slownessA modulus of a vector of (inverse of velocity), the θ representing an azimuth angle of the reference station to the beam center; (x) c ，y c ) Representing the position of the selected bunching center;
step S34, traversing all the reference stations according to the formula (2); performing time correction of the surface wave signals and overlapping;
wherein p represents waveform data of the plane wave signal; b (t, u) represents waveform data after time correction and bunching superposition; n is n b Representing the number of beaming stations within a beaming radius around a beaming center; n is n c Representing the number of reference stations; t represents the time of the original waveform;
step S35, presetting a range of slowness, and scanning waveform data b (t, u) subjected to bunching correction and superposition by adopting the slowness to obtain the optimal speed of a narrow band; traversing all the narrow bands, and splicing the optimal speeds of all the narrow bands to obtain a dispersion curve under the selected bunching center;
step S36, traversing all beam focusing centers, and repeating the steps S32 to S35 to obtain the dispersion curves under all beam focusing centers.
Further, the method for extracting the surface wave dispersion based on multi-scale observation background noise bunching further comprises the following steps: prior to step S32, waveform data of the face wave signal is subjected to narrowband filtering in a frequency band range.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, by improving an observation system, fine structure information and detection scale of a research area are considered in multi-scale observation. The method combines with the beam-focusing technique, suppresses noise by spatial coherence superposition, not only retains high-frequency active ingredients in signals, but also solves the problem of insufficient low-frequency signals of a dense array, thereby providing fine and reliable data support for seismic wave velocity inversion. In addition, the invention reduces the observation cost when researching the fine structure by designing the multi-scale observation system. In conclusion, the method has the advantages of low observation cost, high resolution, large detection depth, simple logic and the like, and has high practical value and popularization value in the technical fields of seismic short-period dense array observation and background noise imaging.
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For a clearer description of the technical solutions of the embodiments of the present invention, reference will be made to the accompanying drawings, which are used in the embodiments, for the sake of simplicity, it being understood that the following drawings only illustrate some embodiments of the invention and are therefore not to be considered as limiting the scope of protection, and that other related drawings can also be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a logic flow diagram of the present invention.
Fig. 2 is a flow chart of an embodiment of the present invention.
Fig. 3 is a schematic diagram of a station arrangement according to the present invention.
Fig. 4 is a schematic diagram of the beam-forming theory of the present invention.
Detailed Description
As shown in fig. 1 to 4, the present embodiment provides a method for extracting surface wave dispersion based on multi-scale observation background noise bunching, which includes the following steps:
firstly, designing an observation system according to a research target, and acquiring seismic data of a detection area: the method also comprises the presetting of parameters such as a multiscale dense station observation system, a data sampling rate, a duration and the like. As shown in fig. 3, a study area (an inner small rectangular area) in which the stations are laid out with high density is determined. The constraint range (outer large rectangular area) in which the stations are laid out at large inter-station distances is determined at the periphery of the investigation region. The present embodiment constrains shallow fine structures by dense stations of the investigation region and deep structures by peripheral stations. And arranging seismometers in the research area and the constraint area to acquire continuous seismic waveform data. It is generally considered that 1/3 of the signal wavelength is the detection depth, while the extraction surface wave signal stage spacing should be greater than 3 times the signal wavelength. The distance of the peripheral station from the investigation region is determined by the depth of the desired constraint.
Step two, preprocessing the seismic data, and extracting a surface wave signal by adopting cross-correlation calculation:
removing the instrument response from the vertical component of the continuous waveform data recorded in the first step, and performing single-unit data preprocessing: removing average value, removing linear trend, normalizing frequency spectrum, band-pass filtering, intercepting data, etc. The purpose of this step is to remove the long period and the non-uniform interference of the noise source, ensure the accuracy of the subsequent calculation, and are conventional means, and will not be described here.
For the preprocessed data, cross-correlating all the preprocessed data of the stations; and (5) carrying out time sequence phase weighting superposition on the data after cross correlation to obtain the wave signal waveform data for beam focusing. The purpose of this step is to increase the computational speed while guaranteeing performance and flexibility.
Thirdly, carrying out model parameterization on the detection area, and obtaining a dispersion curve according to a beam-focusing algorithm:
as shown in fig. 4 (a), m bunching centers (x) c ，y c ) Determining a beam-gathering radius r and referencing a stationThe study area was bunched as a constraint.
Selecting a beam focusing center according to the distance d between the reference station and the beam focusing center and the reference speed interval [ v ] min ，v max ]Estimating a face wave arrival time window, wherein the intercepted time window isWhere T represents a period. The purpose of this operation is to extend the face-wave intercept window forward and backward by one cycle to ensure a full-cycle face-wave signal within the window.
Calculating a correction time of the station with respect to the beam-forming center according to formula (1):
τ(x，y，u，θ)＝(x-x c )usinθ+(y-y c )ucosθ (1)
wherein (x, y) represents the position of the reference station; u represents slownessModulus of vector (inverse of velocity); the theta represents the incident direction angle thereof; (x) c ，y c ) Indicating the location of the selected focus.
Traversing all reference stations in a beam-focusing center, and completing time correction and superposition calculation of the surface wave signals according to a formula (2):
wherein p represents waveform data of the face wave signal after the time correction processing; b (t, u) represents waveform data after beamforming correction and superposition; n is n b Representing the number of beaming stations within a beaming radius around a beaming center; n is n c The number of reference stations is represented, and t represents the time of the original data.
Fig. 4 (b) to 4 (c) are schematic diagrams of the plane wave signal interception, time correction and superposition.
Selecting proper frequency band range before beam-gathering and superposition, and making narrow-band filtering on the face wave waveform data, and making target frequencyThe rate range is set up with n narrow bands, and a reasonable slowness range ([ u ] is set up for each band min ，u max ]) And scanning, wherein the slowness value corresponding to the maximum amplitude of the superimposed waveform is the local optimal slowness value of the band bunching. Multiple scans may be employed to increase efficiency, with a larger step size (e.g., a 1/10 slowness window) being selected for the first time to narrow the scan. With the first obtained optimal solution (u x ) Near%d is the time window of the second scanning, which is the first scanning step), the second scanning is carried out by the small slowness step of the second scanning step, namely, the second scanning step is 1/10 of d, namely, the precision is improved by one order of magnitude, thereby rapidly obtaining the high-precision optimal slowness of the cohesive beam center in the corresponding frequency. And scanning for multiple times until the speed precision meets the requirement. As shown in fig. 4 (c), taking a frequency band as an example, the relation diagram of the amplitude and the velocity obtained after the slowness scanning calculation is taken, and the velocity corresponding to the maximum amplitude is the optimal velocity of the beam focusing center. Scanning the filtering range to obtain a dispersion curve below the beam focusing center, and moving the beam focusing center to obtain all dispersion curves below the beam focusing center, namely the three-dimensional speed structure constraint information of the research area.
The above embodiments are only preferred embodiments of the present invention and are not intended to limit the scope of the present invention, but all changes made by adopting the design principle of the present invention and performing non-creative work on the basis thereof shall fall within the scope of the present invention.
Claims (5)
1. The method for extracting the surface wave dispersion based on multi-scale observation background noise bunching is characterized by comprising the following steps of:
designing an observation system according to a research target, and acquiring seismic data of a detection area by adopting multi-scale observation;
preprocessing the seismic data, and extracting a surface wave signal by using cross-correlation calculation;
parameterizing the detection area model, and obtaining a dispersion curve according to a beam-forming algorithm.
2. The method for extracting the surface wave dispersion based on the multi-scale observation background noise bunching according to claim 1, wherein the seismic data of the detection area is acquired by adopting the multi-scale observation, and the method comprises the following steps:
according to the research target, determining a detection area and designing an observation system: a small-station interval is adopted to arrange beam-focusing stations in a detection area, dense observation is carried out, and a large-station interval is adopted to arrange reference stations outside the detection area;
and synchronously observing and acquiring continuous background noise data at the positions of the beam focusing station and the reference station.
3. The method for extracting the surface wave dispersion based on multi-scale observation background noise bunching according to claim 2, wherein the preprocessing comprises: the method comprises the steps of removing the mean value, removing the linear trend, normalizing the frequency spectrum, carrying out band-pass filtering, intercepting data, and superposing and extracting the surface wave signals.
4. A method for extracting surface wave dispersion based on multi-scale observation background noise beamforming according to claim 3, wherein the detection area is parameterized by a model, and a dispersion curve is obtained according to a beamforming algorithm, comprising the steps of:
step S31, dividing grid points in a detection area to serve as a beam focusing center according to the detection precision requirement of an observation system, and selecting a beam focusing radius r according to the signal-to-noise ratio and the detection precision requirement; bunching the bunching stations in the bunching radius by taking the reference station as a constraint;
step S32, selecting one of the beam focusing centers, and determining the distance d between the reference station and the selected beam focusing center and the reference velocity interval [ v ] of the surface wave signal min ，v max ]Obtaining an arrival time window and an interception time window of a face wave signalThe T represents a period;
step S33, calculating the correction time of the beam focusing station relative to the selected beam focusing center according to the formula (1), wherein the expression is as follows:
τ(x，y，u，θ)＝(x-x c )u sinθ+(y-y c )u cosθ (1)
wherein (x, y) represents the position of the beaming station; u represents slownessThe θ represents the azimuth angle of the reference station to the beam center; (x) c ，y c ) Representing the position of the selected bunching center;
step S34, traversing all the reference stations according to the formula (2); performing time correction of the surface wave signals and overlapping;
wherein p represents waveform data of the plane wave signal; b (t, u) represents waveform data after time correction and bunching superposition; n is n b Representing the number of beaming stations within a beaming radius around a beaming center; n is n c Representing the number of reference stations; t represents the time of the original waveform;
step S35, presetting a range of slowness, and scanning waveform data b (t, u) subjected to bunching correction and superposition by adopting the slowness to obtain the optimal speed of a narrow band; traversing all the narrow bands, and splicing the optimal speeds of all the narrow bands to obtain a dispersion curve under the selected bunching center;
step S36, traversing all beam focusing centers, and repeating the steps S32 to S35 to obtain the dispersion curves under all beam focusing centers.
5. The method for extracting the surface wave dispersion based on the multi-scale observation background noise bunching according to claim 4, further comprising: prior to step S32, waveform data of the face wave signal is subjected to narrowband filtering in a frequency band range.
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| 李娜;: "利用基于密集台阵的面波成像方法研究南北地震带北段地壳上地幔速度结构", 国际地震动态, no. 02, 25 February 2018 (2018-02-25) * |
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