CN114779328B - Earth internal structure detection method based on self-correlation noise imaging - Google Patents

Earth internal structure detection method based on self-correlation noise imaging Download PDF

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CN114779328B
CN114779328B CN202210431853.5A CN202210431853A CN114779328B CN 114779328 B CN114779328 B CN 114779328B CN 202210431853 A CN202210431853 A CN 202210431853A CN 114779328 B CN114779328 B CN 114779328B
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CN114779328A (en
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张衡
李云月
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    • 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. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
    • 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. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G01V1/303—Analysis for determining velocity profiles or travel times
    • G01V1/305—Travel times
    • 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. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • 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. analysis, for interpretation, for correction
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention discloses an earth internal structure detection method based on autocorrelation noise imaging, which comprises the following steps: adopting a single station to process background noise in an autocorrelation way; acquiring approximate coupling signals of a source and a receiving instrument based on the seismic wave convolution model; acquiring the reflectivity below the receivers at other positions, and representing the reflectivity by a waveform; calibrating an average P-wave velocity based on the reflectivity waveform; the engineered bedrock depth at other receiver locations is estimated. The invention uses the autocorrelation noise imaging technology which has no requirement on the starting and stopping time of the arrangement of each station, thereby being particularly convenient for adding the subsequent collected data and greatly improving the data use efficiency. Meanwhile, the method can obtain a shallow detection result with high precision by combining engineering data.

Description

Earth internal structure detection method based on self-correlation noise imaging
Technical Field
The invention belongs to the technical field of seismic exploration, and particularly relates to an earth internal structure exploration method based on autocorrelation noise imaging.
Background
The minute vibrations that exist at a time on the earth's surface are called micromotion, which is called background noise in natural seismology research. The sources of earth pulsation are complex and are generally considered to be signals that are scattered by the subsurface medium and recorded by the seismic instruments. Pulsing according to the range of the periodic components of the signal is generally divided into two categories: a pulsating signal with a frequency above 1Hz and a long-period signal with a frequency below 1 Hz. The former mainly originates from human activities such as transportation, plant machine operation, etc., and the high-frequency components in the earth's pulses mainly originate from close-range sources because high-frequency signals are attenuated faster during the earth's propagation; the latter is mainly due to natural factors such as wind, river flow, ocean waves, etc., and may contain a greater range of media information in long-period recordings due to their slower decay during propagation. At present, the long-period micro-motion signal is mainly applied to the tomography research of the earth deep structure by extracting the empirical Green function by using the cross-correlation technology.
In the prior art, the cross-correlation noise imaging technology requires that the distribution time of the stations is the same, so that the addition of other newly acquired data in the later period is inconvenient. If new data is required to be added, large-scale data acquisition is required to be carried out again, the cost is greatly improved, and the detection efficiency is greatly reduced.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the earth internal structure detection method based on the autocorrelation noise imaging, so that different data acquired in each time period can be used simultaneously without data re-acquisition.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a detection method of an internal structure of the earth based on autocorrelation noise imaging comprises the following steps:
s1, processing background noise by adopting autocorrelation of a single station;
s2, acquiring approximate coupling signals of a source and a receiving instrument based on the seismic wave convolution model;
s3, obtaining the reflectivity below the receivers at other positions, and representing the reflectivity by a waveform;
s4, calibrating the average P-wave speed based on the reflectivity waveform;
and S5, estimating the depth of the engineering bedrock at the positions of other receivers.
Further: the specific method of the step S1 comprises the following steps:
according to the formula:
Figure GDA0003953543940000024
acquiring a signal; wherein U (xi, t), w (xi, t), r (xi, t) and e (xi, t) represent the original background noise signal, the background noise source signature, the station detector signature and the formation reflection model at each station xi, respectively, the prime symbol represents the cross-correlation,
Figure GDA0003953543940000025
it is a convolution.
Further, the method comprises the following steps: the specific method of step S2 includes the following substeps:
s2-1, acquiring an environmental noise track at any position of two reference drill holes in a frequency domain;
s2-2, obtaining the reflectivity of any position of two reference drill holes in a frequency domain;
s2-3, according to a formula:
Figure GDA0003953543940000021
obtaining approximate coupled signals of source and receiving instruments in frequency domain (omega)
Figure GDA0003953543940000022
Wherein U (x) ref1 orx ref2 ω) is the ambient noise trajectory at any position of the two reference boreholes in the frequency domain;
Figure GDA0003953543940000023
is the reflectivity at any position of the two reference boreholes in the frequency domain.
Further, the method comprises the following steps: the specific method of the step S3 is as follows:
according to the formula:
Figure GDA0003953543940000031
is obtained at x i Waveform reflectivity at receiver
Figure GDA0003953543940000032
Wherein
Figure GDA0003953543940000033
Is a form of the frequency of the same,
Figure GDA0003953543940000034
representing the inverse Fourier transform operator, U (x) i ω) is x i The receiver records the ambient noise trace in the frequency domain.
Further: the specific method of step S4 includes the following substeps:
s4-1, representing the bedrock depth in the time domain by the peak value of the waveform, and acquiring the difference between the engineering bedrock depth and two reference drill holes and the bidirectional propagation time difference;
s4-2, according to a formula:
Figure GDA0003953543940000035
calibrating average P-wave velocity
Figure GDA0003953543940000036
Wherein (d) ref1 -d ref2 )，
Figure GDA0003953543940000037
Is the difference between the engineering bedrock depth and the two reference boreholes and the two-way propagation time difference.
Further: the specific method of step S5 includes the following substeps:
s5-1, acquiring the difference between the depth of the engineering bedrock and any reference drilling hole and the bidirectional propagation time difference.
S5-2, according to a formula:
Figure GDA0003953543940000038
estimating engineering bedrock depth d at other receiver locations i 。
The beneficial effects of the invention are as follows:
the invention uses the autocorrelation noise imaging technology which has no requirement on the starting and stopping time of the arrangement of each station, thereby being particularly convenient for adding the subsequent collected data and greatly improving the data use efficiency. Meanwhile, the method can obtain a shallow detection result with high precision by combining engineering data.
Drawings
FIG. 1 is a flowchart of a detection method proposed in example 1;
FIG. 2 is a diagram showing the inversion results of the basement in example 2;
FIG. 3 is a schematic diagram of a background noise imaging synthesis experiment; wherein (a) the full-scale real model; (b) full-scale noise imaging results; (c) a shallow structure real model; (d) shallow noise imaging results; (e) synthesizing noise data; (f) noise spectra of 5 different stations.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
referring to fig. 1, the method for detecting the internal structure of the earth based on autocorrelation noise imaging comprises the following steps:
s1, processing background noise by adopting autocorrelation of a single station;
s2, acquiring approximate coupling signals of a source and a receiving instrument based on the seismic wave convolution model;
s3, obtaining the reflectivity below the receivers at other positions, and representing the reflectivity by a waveform;
s4, calibrating the average P-wave speed based on the reflectivity waveform;
and S5, estimating the depth of the engineering bedrock at the positions of other receivers.
Specifically, the specific method of step S1 is:
according to the formula:
Figure GDA0003953543940000041
acquiring a signal; wherein U (xi, t), w (xi, t), r (xi, t) and e (xi, t) represent the original background noise signal, the background noise source signature, the station detector signature and the formation reflection model at each station xi, respectively, the prime symbol represents the cross-correlation,
Figure GDA0003953543940000042
it is a convolution.
Specifically, the specific method of step S2 includes the following sub-steps:
s2-1, acquiring an ambient noise track at any position of two reference drill holes in a frequency domain;
s2-2, acquiring the reflectivity of any position of two reference drill holes in a frequency domain;
s2-3, according to a formula:
Figure GDA0003953543940000051
obtaining approximate coupled signals of source and receiving instruments in frequency domain (omega)
Figure GDA0003953543940000052
Wherein U (x) ref1 orx ref2 ω) is the ambient noise trajectory at any position of the two reference boreholes in the frequency domain;
Figure GDA0003953543940000053
is the reflectivity at any position of the two reference boreholes in the frequency domain.
Specifically, the specific method of step S3 is:
according to the formula:
Figure GDA0003953543940000054
is obtained at x i Waveform reflectivity at receiver
Figure GDA0003953543940000055
Wherein
Figure GDA0003953543940000056
Is a form of the frequency of the same,
Figure GDA0003953543940000057
representing the inverse Fourier transform operator, U (x) i ω) is x i The receiver records the ambient noise trace in the frequency domain.
Specifically, the specific method of step S4 includes the following substeps:
s4-1, representing the bedrock depth in the time domain by the peak value of the waveform, and acquiring the difference between the engineering bedrock depth and two reference drill holes and the bidirectional propagation time difference;
s4-2, according to a formula:
Figure GDA0003953543940000058
calibrating average P-wave velocity
Figure GDA0003953543940000059
Wherein (d) ref1 -d ref2 )，
Figure GDA00039535439400000510
Is the difference between the engineering bedrock depth and the two reference boreholes and the two-way propagation time difference.
Specifically, the specific method of step S5 includes the following sub-steps:
s5-1, acquiring the difference between the depth of the engineering bedrock and any reference drilling hole and the bidirectional propagation time difference.
S5-2, according to a formula:
Figure GDA0003953543940000061
estimating engineering bedrock depth d at other receiver locations i 。
Example 2:
example 2 is an actual effect analysis based on the technical means described in example 1.
After recording background noise hours to days, the autocorrelation of these data can approximate the impulse response of the earth. Equivalently, background noise data was converted into zero offset active source seismic experiments with stochastic source wavelets using autocorrelation techniques. Thus, background noise imaging techniques based on autocorrelation can greatly improve the efficiency of use of the amount of imaging data for subsurface structures.
Autocorrelation on a background noise signal is an extension of the basic theory of cross-correlation. The signals in the same time period between the stations are subjected to cross-correlation processing, and a Green function between the two stations can be approximately obtained. The green's function is equivalent to the response obtained at one station as an active signal source and at the other station. Autocorrelation is a special form of cross-correlation. When two stations are in infinite proximity, i.e., a single station background noise signal is used for cross-correlation, this is auto-correlation. When the signal obtained after the autocorrelation processing is carried out on a single station, the signal can be approximated to the experiment of spontaneous excitation and spontaneous collection, and the autocorrelation processing on background noise can be approximated to be a classic seismic wave convolution model.
There is also an advantage in that there is a complementarity between the autocorrelation and cross-correlation methods. Since the signal acquisition and signal processing of the autocorrelation is based entirely on a single station, no synchronization and no co-long signals are required. And in the aspect of processing calculation, the calculation cost is low.
In an embodiment of the invention, the problem is simplified by two realistic assumptions.
1. The source signals being identical in a particular region
2. The effect of the receiving instrument is negligible
At calibrated average P-wave velocity
Figure GDA0003953543940000062
In (1), although the deposit is thick, it is
Figure GDA0003953543940000063
Is considered to apply to all soil layers at the same site.
Using the actual data in the field, we can get a clear underground structure, when there is a basement in a shallow layer (< 100 meters deep), the velocity at the basement is significantly lower than that of the surrounding medium, as shown in fig. 2, and the result is from the actual data.
To further test the effectiveness of the detection method in detecting deep structures in the earth, station background noise data (laboratory synthetic data, non-actual field data) was simulated using the model shown in fig. 3f, as shown in fig. 3 e. The spatial distances between stations are random and the differences in noise spectra of the 5 stations in fig. 3f verify the assumption that the data for each station comes from different observation periods (simulating the case of partial station corruption under real conditions). Meanwhile, it is assumed that the frequency peak of data that can be observed by the fiber-optic seismic station is 1Hz and the observation time period is 72 hours. The results of the background noise imaging based on autocorrelation are shown in fig. 3, and it can be seen that the subsurface structure of both shallow (within 50km, fig. 3 d) and deep (around 200km, fig. 3 b) has higher resolution. The results obtained in figure 3 are from experimental synthesis data.
The invention uses the autocorrelation noise imaging technology which has no requirement on the starting and stopping time of the arrangement of each station, thereby being particularly convenient for adding the subsequent collected data and greatly improving the data use efficiency. Meanwhile, the method combines engineering data to obtain a shallow layer detection result with high precision.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (1)

1. An earth internal structure detection method based on autocorrelation noise imaging is characterized in that: the method comprises the following steps:
s1, performing autocorrelation processing on background noise by adopting a single station;
s2, acquiring approximate coupling signals of a source and a receiver based on a seismic wave convolution model;
s3, obtaining the reflectivity below the receivers at other positions, and representing the reflectivity by a waveform;
s4, calibrating the average P-wave speed based on the reflectivity waveform;
s5, estimating the depth of the engineering bedrock at the position of the receiver;
the specific method of the step S1 is as follows:
according to the formula:
Figure FDA0003953543930000011
acquiring a signal; wherein U (xi, t), w (xi, t), r (xi, t) and e (xi, t) represent the original background noise signal, the background noise source characteristics, the station detector characteristics and the formation reflection mode at xi at each station, respectively, the symbol' representsThe tables are cross-correlated with each other,
Figure FDA0003953543930000012
then it is a convolution;
the specific method of step S2 includes the following substeps:
s2-1, acquiring an environmental noise track at any position of two reference drill holes in a frequency domain;
s2-2, acquiring the reflectivity of any position of two reference drill holes in a frequency domain;
s2-3, according to a formula:
Figure FDA0003953543930000013
obtaining approximate coupled signals of source and receiving instruments in frequency domain (omega)
Figure FDA0003953543930000014
Wherein U (x) ref1 or x ref2 ω) is the ambient noise trajectory at any position of the two reference boreholes in the frequency domain;
Figure FDA0003953543930000015
is the reflectivity at any position of the two reference boreholes in the frequency domain;
the specific method of the step S3 is as follows:
according to the formula:
Figure FDA0003953543930000021
is obtained at x i Waveform reflectivity at receiver
Figure FDA0003953543930000022
Wherein
Figure FDA0003953543930000023
Is a form of the frequency of the same,
Figure FDA0003953543930000024
represents the inverse Fourier transform operator, U (x) i ω) is x i An ambient noise trace recorded by the receiver in the frequency domain;
the specific method of step S4 includes the following substeps:
s4-1, representing the bedrock depth in the time domain by the peak value of the waveform, and acquiring the difference between the engineering bedrock depth and two reference drill holes and the bidirectional propagation time difference;
s4-2, according to a formula:
Figure FDA0003953543930000025
calibrating average P-wave velocity
Figure FDA0003953543930000026
Wherein (d) ref1 -d ref2 )，
Figure FDA0003953543930000027
The difference between the depth of the engineering bedrock and two reference boreholes and the difference of the two-way propagation time;
the specific method of step S5 includes the following substeps:
s5-1, acquiring the difference between the depth of the engineering bedrock and any reference borehole and the bidirectional propagation time difference;
s5-2, according to a formula:
Figure FDA0003953543930000028
estimating an engineered bedrock depth d at a receiver location i 。
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