CN105425307B - A kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections - Google Patents

A kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections Download PDF

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CN105425307B
CN105425307B CN201510870525.5A CN201510870525A CN105425307B CN 105425307 B CN105425307 B CN 105425307B CN 201510870525 A CN201510870525 A CN 201510870525A CN 105425307 B CN105425307 B CN 105425307B
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CN105425307A (en
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沈鸿雁
严月英
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Xian Shiyou University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • 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
    • G01V1/30—Analysis
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/70—Other details related to processing

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Abstract

A kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections, step are：The first step, it will containmIndividual sampled point, put away from for △xOne-dimensional potential field anomaly digital independent to one-dimension arrayfIn；Second step, it is rightfCarry out one-dimensional discrete Multiscale Wavelet Decomposition；3rd step, extraction is every respectivelyOneThe high fdrequency component of multi-scale wavelet details, and carry out one-dimensional discrete inverse wavelet transform；4th step, scale dimension is equivalent to intend depth dimension, and by the potential field anomaly data that multiple dimensioned high fdrequency component is set up by the order of small yardstick to large scale, forms 2-D dataF；5th step, to 2-D dataFGridding and interpolation processing are carried out, is then depicted as Two-dimensional Potential Field abnormal profile figure, just realizes one-dimensional potential field anomaly curve structure Two-dimensional Potential Field abnormal profile, the two dimensional cross-section of acquisition has that abnormal information displaying is directly perceived, is easy to the advantages of geologic interpretation.

Description

A kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections
Technical field
The invention belongs to potential field data processing technical field, more particularly to a kind of 1D potential field anomalies curve structure 2D potential fields are different The method of normal section.
Background technology
Potential field includes gravitational field, magnetic field and electric field.The exception of potential field is integrated from different depth, different scale field source body Response and the result being superimposed jointly.Itself limited by potential field exploitation method theory, target seeker gyro collection in field is difficult to obtain deeply (dimension) information is spent, causes the geologic interpretation of potential field anomaly not directly perceived enough, so as to increase the difficulty of potential field data geologic interpretation work Degree, and reduce the accuracy of geologic interpretation.
The content of the invention
In order to overcome the above-mentioned deficiencies of the prior art, it is an object of the invention to propose a kind of 1D potential field anomalies curve structure The method for building 2D potential field anomaly sections, this method have abnormal information displaying directly perceived, the advantages of being easy to geologic interpretation.
To achieve these goals, the technical solution adopted by the present invention is：A kind of 1D potential field anomalies curve builds 2D potential fields The method of abnormal profile, comprises the following steps：
The first step, m sampled point will be contained, put away from the one-dimensional potential field anomaly digital independent for △ x into one-dimension array f；
Second step, one-dimensional discrete Multiscale Wavelet Decomposition direct transform, one-dimensional discrete multi-scale wavelet are carried out to one-dimension array f Decomposing direct transform is：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；ψ*For ψ conjugate function；A represents coefficient of dilatation, reflects specific The width (also referred to as yardstick) of basic function；B represents translation coefficient, specifies the position translated along x-axis, and And make a0=2, b0=1, j ∈ Z, k ∈ Z；△ x for point away from；I=0,2 ..., m-1, it is sampling sequence number；
3rd step, extracts the high fdrequency component of each multi-scale wavelet details respectively, and carries out one-dimensional discrete multi-scale wavelet point Inverse transformation is solved, one-dimensional discrete Multiscale Wavelet Decomposition contravariant is changed to：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；J ∈ Z, k ∈ Z；X=i △ x, sat for data sampling point Mark, △ x for point away from；I=0,2 ..., m-1, it is sampling sequence number；
4th step, scale dimension is equivalent to intend depth dimension, and multiple dimensioned height is set up by by the order of small yardstick to large scale The potential field anomaly data of frequency component, form 2-D data F；
5th step, gridding and interpolation processing are carried out to 2-D data F, are then depicted as Two-dimensional Potential Field abnormal profile Figure, just realize one-dimensional two-dimentional (2D) potential field anomaly section of (1D) potential field anomaly curve structure.
The beneficial effects of the invention are as follows：
This method is of a relatively high based on potential field anomaly frequency caused by shallow-layer field source body, and potential field caused by deep layer field source body Abnormal frequency is relatively low, and increases with depth, and the frequency of potential field anomaly is gradually being reduced to supposed premise, by it is one-dimensional from Multiscale Wavelet Decomposition technology is dissipated, the potential field anomaly from different depth, different field sources is subjected to multi-resolution decomposition, then chi Degree dimension is equivalent to intend depth dimension, and is combined potential field anomaly by the order by small yardstick to large scale, so that will be one-dimensional (1D) potential field anomaly curve, which is expanded, constructs two-dimentional (2D) potential field anomaly section, and this two-dimentional (2D) section can be directly used for geology solution Release；This method has abnormal information displaying directly perceived, the advantages of being easy to geologic interpretation.
Brief description of the drawings
Fig. 1 is actual one-dimensional (1D) bouguer gravity anomaly curve map.
Fig. 2 is the Scale Decomposition of one-dimensional discrete small echo 13 of the present invention and reconstructs the different scale high fdrequency component Bouguer gravity of acquisition Abnormal curve figure.
Fig. 3 is two dimension (2D) Bouguer weight that the one-dimensional Scale Decomposition of (1D) bouguer gravity anomaly curve 13 structure of the present invention obtains Power abnormal profile figure.
Embodiment
The present invention is described in more detail with reference to the accompanying drawings and examples.
Referring to Fig. 1,2,3, a kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections, comprise the following steps：
The first step, m sampled point will be contained, put away from the one-dimensional potential field anomaly digital independent for △ x into one-dimension array f, Referring to Fig. 1；
Second step, one-dimensional discrete Multiscale Wavelet Decomposition direct transform, one-dimensional discrete multi-scale wavelet are carried out to one-dimension array f Decomposing direct transform is：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；ψ*For ψ conjugate function；A represents coefficient of dilatation, reflects specific The width (also referred to as yardstick) of basic function；B represents translation coefficient, specifies the position translated along x-axis, and And make a0=2, b0=1, j ∈ Z, k ∈ Z；△ x for point away from；I=0,2 ..., m-1, it is sampling sequence number；
3rd step, extracts the high fdrequency component of each multi-scale wavelet details respectively, and carries out one-dimensional discrete multi-scale wavelet point Inverse transformation is solved, one-dimensional discrete Multiscale Wavelet Decomposition contravariant is changed to：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；J ∈ Z, k ∈ Z；X=i △ x, sat for data sampling point Mark, △ x for point away from；I=0,2 ..., m-1, it is sampling sequence number, referring to Fig. 2；
4th step, scale dimension is equivalent to intend depth dimension, and multiple dimensioned height is set up by by the order of small yardstick to large scale The potential field anomaly data of frequency component, form 2-D data F；
5th step, gridding and interpolation processing are carried out to 2-D data F, are then depicted as Two-dimensional Potential Field abnormal profile Figure, one-dimensional two-dimentional (2D) potential field anomaly section of (1D) potential field anomaly curve structure is just realized, referring to Fig. 3.
Embodiment
155 sampled points will be contained, put and implement step away from the one-dimensional bouguer gravity anomaly data instance explanation of actual measurement for 2km Suddenly：
The first step, 155 sampled points will be contained, put away from the one-dimensional bouguer gravity anomaly digital independent for 2km a to dimension In group f, Fig. 1；
Second step, the Scale Decomposition direct transform of one-dimensional discrete small echo 13, the yardstick of one-dimensional discrete small echo 13 are carried out to one-dimension array f Decomposing direct transform is：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；ψ*For ψ conjugate function；A represents coefficient of dilatation, reflects specific The width (also referred to as yardstick) of basic function；B represents translation coefficient, specifies the position translated along x-axis, and And make a0=2, b0=1, j=0,1 ..., 12, k ∈ Z；△ x=2km, for point away from；I=0,1 ..., 154, it is sampling sequence number；
3rd step, extracts the high fdrequency component of each multi-scale wavelet details respectively, and carries out one-dimensional discrete multi-scale wavelet point Inverse transformation is solved, one-dimensional discrete Multiscale Wavelet Decomposition contravariant is changed to：
In formula, ψ is referred to as wavelet (also referred to as morther wavelet)；J=0,1 ..., 12, k ∈ Z；X=i △ x, are adopted for data Sampling point coordinate；△ x=2km, for point away from；I=0,1 ..., 154, it is sampling sequence number, Fig. 2；
4th step, scale dimension is equivalent to intend depth dimension, and it is high by 13 yardsticks are set up by the order of small yardstick to large scale The bouguer gravity anomaly data of frequency component, form 2-D data F；
5th step, gridding and interpolation processing are carried out to 2-D data F, are then depicted as two-dimentional bouguer gravity anomaly Profile, just realize one-dimensional two-dimentional (2D) bouguer gravity anomaly section of (1D) bouguer gravity anomaly curve structure, Fig. 3.
Case effect explanation：
Fig. 1 is actual one-dimensional (1D) bouguer gravity anomaly curve, and wherein abscissa is sampled point sequence number, and ordinate is Bouguer GRAVITY ANOMALIES △ g, the curve is more apparent along the off-note performance on line direction (transverse direction), but lacks the letter of Depth Domain Which type of geologic body breath, can not clearly judge by, where cause these bouguer gravity anomalies.
Each curve represents the bouguer gravity anomaly curve of different scale in Fig. 2, and wherein abscissa is sampled point sequence number, Ordinate is bouguer gravity anomaly value △ g.
Fig. 3 is to be obtained by one-dimensional (1D) bouguer gravity anomaly curve by one-dimensional discrete Multiscale Wavelet Decomposition and reconstruct Two-dimentional (2D) bouguer gravity anomaly section, wherein abscissa are sampled point sequence number, and ordinate is yardstick (plan depth), and colour code represents Bouguer gravity anomaly value △ g at two dimensional cross-section diverse location, due to the present invention by scale dimension be equivalent to intend depth dimension, so as to open up Depth domain information is put on display, can effectively identify bouguer gravity anomaly in two dimension from the exceptional value size of the profile and abnormal profile Distribution characteristics on section, and qualitatively judge out and cause the geologic body of bouguer gravity anomaly to be high density body or low-density.

Claims (2)

  1. A kind of 1. method of 1D potential field anomalies curve structure 2D potential field anomaly sections, it is characterised in that comprise the following steps：
    The first step, m sampled point will be contained, put away from the one-dimensional potential field anomaly digital independent for △ x into one-dimension array f；
    Second step, one-dimensional discrete Multiscale Wavelet Decomposition direct transform, one-dimensional discrete Multiscale Wavelet Decomposition are carried out to one-dimension array f Direct transform is：
    <mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>W</mi> <mrow> <mo>(</mo> <mi>a</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mo>|</mo> <mi>a</mi> <mo>|</mo> </mrow> </msqrt> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mrow> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mi>&amp;psi;</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>i</mi> <mo>&amp;CenterDot;</mo> <mi>&amp;Delta;</mi> <mi>x</mi> <mo>-</mo> <mi>b</mi> </mrow> <mi>a</mi> </mfrac> <mo>)</mo> </mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>&amp;CenterDot;</mo> <mi>&amp;Delta;</mi> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>a</mi> <mo>&amp;NotEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>
    In formula, ψ is referred to as wavelet；ψ*For ψ conjugate function；A represents coefficient of dilatation, reflects the width of specific basic function；B tables Show translation coefficient, specify the position translated along x-axis, andAnd make a0=2, b0=1, j ∈ Z, k ∈ Z；△x For point away from；I=0,1 ..., m-1, it is sampling sequence number；
    3rd step, the high fdrequency component of each multi-scale wavelet details is extracted respectively, and it is anti-to carry out one-dimensional discrete Multiscale Wavelet Decomposition Conversion, one-dimensional discrete Multiscale Wavelet Decomposition contravariant are changed to：
    <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </munderover> <mi>W</mi> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> <mi>&amp;psi;</mi> <mrow> <mo>(</mo> <msup> <mn>2</mn> <mrow> <mo>-</mo> <mi>j</mi> </mrow> </msup> <mi>x</mi> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow>
    In formula, ψ is referred to as wavelet；J ∈ Z, k ∈ Z；X=i △ x, it is data sampling point coordinates；△ x for point away from；I=0, 1 ..., m-1, it is sampling sequence number；
    4th step, scale dimension is equivalent to intend depth dimension, and multiple dimensioned high frequency division is set up by by the order of small yardstick to large scale The potential field anomaly data of amount, form 2-D data F；
    5th steps, gridding and interpolation processing are carried out to 2-D data F, are then depicted as Two-dimensional Potential Field abnormal profile figure, Just one-dimensional potential field anomaly curve structure Two-dimensional Potential Field abnormal profile is realized.
  2. 2. a kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections according to claim 1, its feature exist In comprising the following steps：
    The first step, 155 sampled points will be contained, put away from the one-dimensional bouguer gravity anomaly digital independent for 2km to one-dimension array f In；
    Second step, the Scale Decomposition direct transform of one-dimensional discrete small echo 13, the Scale Decomposition of one-dimensional discrete small echo 13 are carried out to one-dimension array f Direct transform is：
    <mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>W</mi> <mrow> <mo>(</mo> <mi>a</mi> <mo>,</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <msqrt> <mrow> <mo>|</mo> <mi>a</mi> <mo>|</mo> </mrow> </msqrt> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mn>154</mn> </munderover> <msup> <mi>&amp;psi;</mi> <mo>*</mo> </msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>i</mi> <mo>&amp;CenterDot;</mo> <mi>&amp;Delta;</mi> <mi>x</mi> <mo>-</mo> <mi>b</mi> </mrow> <mi>a</mi> </mfrac> <mo>)</mo> </mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>&amp;CenterDot;</mo> <mi>&amp;Delta;</mi> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </mtd> <mtd> <mrow> <mi>a</mi> <mo>&amp;NotEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>
    In formula, ψ is referred to as wavelet；ψ*For ψ conjugate function；A represents coefficient of dilatation, reflects the width of specific basic function；B tables Show translation coefficient, specify the position translated along x-axis, andAnd make a0=2, b0=1, j=0,1 ..., 12, k∈Z；△ x=2km, for point away from；I=0,1 ..., 154, it is sampling sequence number；
    3rd step, the high fdrequency component of each multi-scale wavelet details is extracted respectively, and it is anti-to carry out one-dimensional discrete Multiscale Wavelet Decomposition Conversion, one-dimensional discrete Multiscale Wavelet Decomposition contravariant are changed to：
    <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>0</mn> </mrow> <mn>12</mn> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mo>-</mo> <mi>&amp;infin;</mi> </mrow> <mrow> <mo>+</mo> <mi>&amp;infin;</mi> </mrow> </munderover> <mi>W</mi> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <mi>k</mi> <mo>)</mo> </mrow> <mi>&amp;psi;</mi> <mrow> <mo>(</mo> <msup> <mn>2</mn> <mrow> <mo>-</mo> <mi>j</mi> </mrow> </msup> <mi>x</mi> <mo>-</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow>
    In formula, ψ is referred to as wavelet；J=0,1 ..., 12, k ∈ Z；X=i △ x, it is data sampling point coordinates；△ x=2km, For point away from；I=0,1 ..., 154, it is sampling sequence number；
    4th step, scale dimension is equivalent to intend depth dimension, and the high frequency division of 13 yardsticks is set up by by the order of small yardstick to large scale The bouguer gravity anomaly data of amount, form 2-D data F；
    5th step, gridding and interpolation processing are carried out to 2-D data F, are then depicted as two-dimentional bouguer gravity anomaly section Figure, just realize one-dimensional bouguer gravity anomaly curve and build two-dimentional bouguer gravity anomaly section.
CN201510870525.5A 2015-12-01 2015-12-01 A kind of method of 1D potential field anomalies curve structure 2D potential field anomaly sections Expired - Fee Related CN105425307B (en)

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US5673191A (en) * 1995-04-10 1997-09-30 Atlantic Richfield Company Method and apparatus for identifying geological structures using wavelet analysis of potential fields
US6691039B1 (en) * 2002-08-30 2004-02-10 John M. Robinson Removal of noise from seismic data using improved radon transformations
CN103245972B (en) * 2012-02-07 2016-06-08 中国石油天然气集团公司 A kind of method determining complex geological structure in two-dimensional space
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