CN110687610B - Gravity and magnetic data correlation analysis-based field source positioning and attribute identification method - Google Patents
Gravity and magnetic data correlation analysis-based field source positioning and attribute identification method Download PDFInfo
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Abstract
The invention discloses a field source positioning and attribute identification method based on gravity and magnetic data correlation analysis, which comprises the following steps: inputting gravity anomaly data and magnetic anomaly data to be processed; secondly, enabling the gravity anomaly data and the magnetic anomaly data to be processed to meet the data number of fast Fourier transform by using an edge expanding method; step three, calculating a vertical first-order derivative of the gravity anomaly in a wave number domain, and removing an edge expanding part; step four, calculating the abnormal magnetic force of the polarized pole in a wave number domain, and removing an edge expanding part; fifthly, calculating a Pearson correlation coefficient by utilizing a vertical first-order derivative of the gravity anomaly and the magnetic anomaly of the polarizing pole; and step six, calculating a gravity magnetic field source positioning and attribute identification parameter F. The identification parameters obtained by the invention keep the information of the bit field strength, reflect the property characteristics of the gravity magnetic similarity, effectively narrow the range of the gravity magnetic field source and have better field source positioning identification performance and gravity magnetic field strength information.
Description
Technical Field
The invention belongs to the technical field of geophysical exploration, relates to a correlation-based geophysical data processing method, and particularly relates to a gravity magnetic data correlation analysis-based field source positioning and attribute identification method, which can be used for rapidly and accurately performing range delineation and attribute identification on homologous gravity magnetic field sources.
Background
Geophysical interpretation is based on data-identified features for geological interpretation. The heavy and magnetic anomalies have higher resolution on the plane, and better identification capability on the plane distribution of underground lithology, structure and the like. The gravity anomaly reflects the density characteristic of the geologic body, and the magnetic anomaly reflects the magnetic characteristic of the geologic body, so that the identification precision of lithology can be improved by combining the gravity anomaly and the magnetic anomaly. How to effectively and quickly identify rock mass distribution and attributes is an important research content of qualitative explanation of gravity and magnetism. At present, the analysis of the correlation between gravity and magnetism based on the poisson theorem of the gravity and magnetism positions adopts a sliding window to calculate the pearson correlation coefficient between the magnetic anomaly of the pole and the first-order vertical derivative of the gravity anomaly, if the pearson correlation coefficient tends to be +1, the correlation between the magnetic anomaly of the gravity and the magnetism is positive, if the pearson correlation coefficient tends to be-1, the correlation between the magnetic anomaly of the pole and the gravity anomaly is negative, and if the pearson correlation coefficient tends to be 0, the correlation between the magnetic anomaly of the gravity and the magnetism is poor or not. In addition, the absolute value of the skin-specific correlation coefficient tends to 1 at the field source. But the correlation coefficient is meaningless outside the field source body, and the pearson correlation coefficient can not provide the information of the field, so that the explanation of the heavy correlation coefficient and the magnetic correlation coefficient is difficult. In general, abnormal geologic bodies have heavy and magnetic homology, and the rapid and effective location of the geologic bodies and the identification of attribute characteristics of the geologic bodies through researching the correlation of heavy and magnetic abnormalities has important significance.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention provides a method for positioning a field source and identifying attributes based on gravity and magnetic data correlation analysis, which solves the problems in the prior art. The method aims to realize the positioning and attribute identification of a field source quickly and effectively based on the correlation of the gravity and magnetic data, improve the efficiency of qualitative interpretation of gravity and magnetic geophysical, and be directly used for gravity and magnetic detection research.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a field source positioning and attribute identification method based on gravity and magnetic data correlation analysis, which comprises the following steps:
inputting gravity anomaly data and magnetic anomaly data to be processed, and carrying out gridding processing to obtain gridded gravity anomaly data delta g and gridded magnetic anomaly data delta T;
step two, performing two-dimensional edge expanding treatment on the gridded gravity anomaly data and the gridded magnetic force anomaly data obtained in the step one by using a cosine edge expanding method to obtain the edge expanded gravity anomaly data delta meeting the data number of fast Fourier transformgexpandAnd the magnetic force anomaly data after edge expansion is delta Texpand；
Step three, calculating a gravity anomaly vertical first-order derivative spectrum in a wave number domain, then performing inverse Fourier transform, removing an edge expanding part, and obtaining a gravity anomaly vertical first-order derivative delta g of a space domainVDR；
Calculating a polarized magnetic force abnormal spectrum in a wave number domain, then performing inverse Fourier transform, and removing an edge expanding part to obtain a polarized magnetic force abnormal RTP in a space domain;
step five, calculating a Pearson correlation coefficient by using the vertical first derivative of the gravity anomaly obtained in the step three and the polarized magnetic anomaly obtained in the step four;
step six, the gravity vertical first derivative delta g obtained in the step three is subjected toVDRAnd normalizing the polarized magnetic force abnormity RTP obtained in the fourth step to eliminate the difference of the dimension and the amplitude of the polarized magnetic force abnormity RTP and sum the normalized vertical first derivative of the gravity and the polarized magnetic force abnormity data, then normalizing the summed data, multiplying the normalized data by the Pearson correlation coefficient obtained in the fifth step, and performing the positioning of the gravity magnetic field source and the calculation of the attribute identification parameter F by adopting the following formula:
in the above formula, (x, y) is the plane coordinate of the point to be calculated; Δ gNVDR(x, y) is a normalized gravity anomaly vertical first derivative of a coordinate (x, y) point; NRTP (x, y) is normalized pole magnetic anomaly for coordinate (x, y) points; max is taken to be (Δ g)NVDR(x, y) + NRTP (x, y)) maximum operation; r (x, y) is a Pearson correlation coefficient of a coordinate (x, y) point; the extreme value area range of the first term on the right side of the formula reflects the distribution range of the homologous gravity magnetic field, so that the field source positioning can be carried out, and the strength information of the original gravity magnetic field can be represented; the second term on the right side of the formula is a Pearson correlation coefficient which can reflect whether the attribute of the gravity field source is positively correlated or negatively correlated; since the first term is a value greater than zero or equal to zero, the identity of the calculated identification parameter F is positive or negativeThe method is characterized in that positive and negative correlation of gravity and magnetic anomaly is expressed, namely, the positive and negative correlation is used for explaining the gravity and magnetic field source attribute, the extreme value area of the identification parameter F indicates the position of a field source, namely, the field source is positioned, and the magnitude of the absolute value of the identification parameter F also reflects the strength of the gravity and magnetic field. Namely, the gravity magnetic field source positioning and attribute identification parameter F is obtained. The purpose of normalization is to eliminate the difference between different dimensions and numerical amplitudes of the heavy magnetic field and to make the calculation result between-1 and +1, thereby facilitating data interpretation.
The invention also comprises the following technical characteristics:
specifically, if the gravity anomaly data and the magnetic anomaly data to be processed input in the step one are random net data, performing the following gridding processing on the random net data:
carrying out gridding processing on gravity anomaly data and magnetic anomaly data to be processed, and gridding the gravity anomaly data and the magnetic anomaly data into the same grid spacing and grid size by adopting a kriging method or a minimum curvature method, wherein M points exist in the east direction and N points exist in the north direction after gridding; let the gravity anomaly data after gridding be Δ g and the magnetic anomaly data after gridding be Δ T.
Specifically, the step two of performing two-dimensional edge extension processing on the gridded gravity abnormal data and the gridded magnetic force abnormal data by using a cosine edge extension method includes:
firstly, determining the number of points after the edge expansion, and assuming that the eastern direction has Mexpand points and the northern direction has Nexpand points after the edge expansion, wherein the number of points after the edge expansion meets the condition that Mexpand is 2(INT(log2(M))+1)，Nexpand＝2(INT(log2(N))+1)Wherein INT is a down rounding operation and log is a logarithm operation;
after the number of points after edge expansion is determined, expanding the edge of the gravity abnormal data after gridding and the magnetic abnormal data after gridding of the M multiplied by N grid in the step one to Mexpanded multiplied by Nexpanded by a cosine edge expansion method; the Mexpand and the Nexpand meet the requirement of the number of fast Fourier transform;
the gravity anomaly data after the edge expansion is delta gexpandThe magnetic force anomaly data after the edge expansion is delta Texpand。
Specifically, the third step specifically includes the following steps:
fourier transform is carried out on the edge-expanded gravity anomaly data obtained in the step two by adopting Fast Fourier Transform (FFT) to obtain a two-dimensional wave number spectrum of gravity anomaly
The two-dimensional wavenumber spectrum is then multiplied by a vertical first derivative factor
u and v are wave numbers in the east and north directions, respectively; completing calculation of a first-order vertical wave spectrum of the gravity anomaly;
then, carrying out Fourier inverse transformation, and transforming the gravity anomaly vertical first-order derivative spectrum of the wave number domain to a space domain; then removing the data of the area of the edge expanding part, outputting the data range in the step one, and obtaining the first order derivative delta g of the gravity anomaly vertical direction of the space domainVDR。
Specifically, the fourth step specifically includes the following steps:
fourier transform is carried out on the magnetic force abnormal data after the edge expansion obtained in the step two by adopting Fast Fourier Transform (FFT), and a two-dimensional wave number spectrum of the magnetic force abnormality is obtained
The two-dimensional wavenumber spectrum is then multiplied by a polarization factor
(wherein i is an imaginary unit, u and v are wave numbers in the east and north directions, respectively, l, m and n are direction cosines of effective magnetization, and l ', m ' and n ' are direction cosines of normal geomagnetic field vectors), namely, magnetic anomaly polar wave spectrum calculation is completed;
then, carrying out Fourier inverse transformation, and transforming the polarized magnetic abnormal wave spectrum of the wave number domain to a space domain;
and then removing the data of the edge expanding part area, and outputting the data range in the step one to obtain the polarized magnetic force abnormal RTP of the space domain.
Specifically, the fifth step specifically includes the following steps:
adopting a rectangular sliding window such as 3 multiplied by 3, 5 multiplied by 5, 7 multiplied by 7 and the like, wherein the window is an odd window, and the sliding step length is 1 point; using formulas
Calculating a Pearson correlation coefficient between a vertical first derivative of the gravity anomaly and the magnetic anomaly of the polarizing pole; cov (RTP,. DELTA.g), among othersVDR) For polarizing the vertical first derivative deltag of the polar magnetic anomaly RTP and the gravity anomaly RTPVDRThe covariance of (a) of (b),
and
respectively a polarized magnetic force anomaly RTP and a gravity anomaly vertical first-order derivative deltagVDRThe mean square error of (c).
Specifically, the sixth step specifically includes the following steps:
first, for Δ gVDRAnd RTP is used for normalization processing to eliminate the difference of the dimension and the amplitude of the two, and the normalized gravity anomaly vertical first-order derivative delta g is obtained respectivelyNVDRAnd normalized polarized magnetic anomaly NRTP; then, the following formula is adopted to carry out the positioning of the gravity magnetic field source and the calculation of the attribute identification parameter F:
for the gravity magnetic homologous geologic body, the vertical first derivative of the gravity anomaly and the polarized magnetic anomaly have a linear relation, the vertical first derivative of the gravity anomaly and the polarized magnetic anomaly can define the plane range of a geologic body field source, and the Pearson correlation coefficient can reflect the gravity magnetic homology and the attribute characteristics of the gravity magnetic homology and the polarized magnetic anomaly. Thus combining both field source localization and attribute identification. The first term (the fractional term) on the right side of the formula, the extreme value area range of the first term reflects the distribution range of the homologous gravity magnetic field, so that the field source positioning can be carried out, and the strength information of the original gravity magnetic field can be represented; the second term on the right of the formula is the Pearson correlation coefficient, which reflects the nature of the source of the gravity field, whether it is positively or negatively correlated. Since the first term is a value greater than or equal to zero, the calculated positive and negative of the identification parameter F represents the positive and negative correlation of the gravity magnetic anomaly, that is, the correlation is used to explain the attribute of the gravity magnetic field source, the extremum region of the identification parameter F indicates the position of the field source, that is, the field source is located, and the magnitude of the absolute value of the identification parameter F also reflects the strength of the gravity magnetic field. The identification parameter has the following advantages: firstly, the judgment parameter obtains an extreme value in the field source range, and tends to zero outside the field source body, so that the defect that the absolute value of the correlation coefficient tends to 1 outside the field source is overcome, the interpretation interference is effectively removed, the interpretation is simple, and the gravity and magnetic homologous geologic body is easy to identify. The amplitude of the discrimination parameter reflects the strength of gravity and magnetism anomaly and the distribution scale of the field source, and qualitative explanation can be carried out on the attribute of the gravity and magnetism source.
Compared with the prior art, the invention has the beneficial technical effects that:
the invention provides a new identification parameter of the gravity and magnetic anomaly sources, the value not only retains the information of the bit field strength and reflects the attribute characteristics of the gravity and magnetic similarity, but also effectively reduces the range of the gravity and magnetic field sources, and the identification parameter has better field source positioning and identification performance and gravity and magnetic field strength information compared with the traditional Pearson correlation coefficient. When the identification parameter is greater than 0, the identification parameter is positive correlation, and represents that the region has a heavy magnetic field source and a magnetic field source with high magnetic density or low magnetic density; when the identification parameter is less than 0, the identification parameter is negative correlation, and represents that the heavy and magnetic field sources with high magnetic low density or low magnetic high density exist in the area; when the identification parameter approaches zero, no gravity source region is indicated. The size of the identification parameter represents the information of the strength of the gravity magnetic field and reflects the scale of the field source. The method overcomes the problem that the absolute value of the Pearson correlation coefficient of the field source body tends to be 1 and is difficult to explain.
Drawings
FIG. 1 is a schematic diagram showing the distribution of a model plane in which five horizontally and vertically stacked hexahedral models are placed according to example 1 of the present invention;
FIG. 2 is a vertical first derivative of gravity anomaly corresponding to the models in Table 1 and FIG. 1 of example 1 of the present invention;
FIG. 3 shows the magnetic pole anomalies corresponding to the models in Table 1 and FIG. 1 of example 1 of the present invention;
FIG. 4 shows the Pearson correlation coefficient for gravity magnetic anomaly in example 1 of the present invention;
fig. 5 shows the source location and attribute identification parameters of the magnetic plumes in embodiment 1.
Detailed Description
Patent subsidization project: the subject of a national key development planning project, namely 'typical coverage area aviation geophysical technology demonstration and processing interpretation software platform development' (2017YFC0602200) 'aviation geophysical data comprehensive processing interpretation method research and software development' (2017YFC0602202).
The invention discloses a field source positioning and attribute identification method based on gravity and magnetic data correlation analysis, which is characterized by comprising the following steps of:
inputting gravity anomaly data and magnetic anomaly data to be processed, and carrying out gridding processing to obtain gridded gravity anomaly data delta g and gridded magnetic anomaly data delta T;
step two, performing two-dimensional edge expanding treatment on the gridded gravity anomaly data and the gridded magnetic force anomaly data obtained in the step one by using a cosine edge expanding method, and obtaining the expanded gravity anomaly data delta g meeting the data number of fast Fourier transformexpandAnd the magnetic force anomaly data after edge expansion is delta Texpand；
Step three, calculating a gravity anomaly vertical first-order derivative in a wave number domain, then performing inverse Fourier transform, removing an edge expanding part, and obtaining a gravity anomaly vertical first-order derivative delta g of a space domainVDR；
Calculating the polarized magnetic force abnormity in a wavenumber domain, then performing inverse Fourier transform, and removing an edge expanding part to obtain the polarized magnetic force abnormity RTP of a space domain;
step five, calculating a Pearson correlation coefficient by using the vertical first derivative of the gravity anomaly obtained in the step three and the polarized magnetic anomaly obtained in the step four;
step six, the gravity vertical first derivative delta g obtained in the step three is subjected toVDRAnd normalizing the polarized magnetic force abnormity RTP obtained in the fourth step to eliminate the difference of the dimension and the amplitude of the polarized magnetic force abnormity RTP and sum the normalized vertical first derivative of the gravity and the polarized magnetic force abnormity data, then normalizing the summed data, multiplying the normalized data by the Pearson correlation coefficient obtained in the fifth step, and performing the positioning of the gravity magnetic field source and the calculation of the attribute identification parameter F by adopting the following formula:
in the above formula, (x, y) is the plane coordinate of the point to be calculated; Δ gNVDR(x, y) is a normalized gravity anomaly vertical first derivative of a coordinate (x, y) point; NRTP (x, y) is normalized pole magnetic anomaly for coordinate (x, y) points; max is taken to be (Δ g)NVDR(x, y) + NRTP (x, y)) maximum operation; r (x, y) is a Pearson correlation coefficient of a coordinate (x, y) point; the extreme value area range of the first term on the right side of the formula reflects the distribution range of the homologous gravity magnetic field, so that the field source positioning can be carried out, and the strength information of the original gravity magnetic field can be represented; the second term on the right side of the formula is a Pearson correlation coefficient which can reflect whether the attribute of the gravity field source is positively correlated or negatively correlated; since the first term is a value greater than or equal to zero, the calculated positive and negative of the identification parameter F represents the positive and negative correlation of the gravity magnetic anomaly, that is, the correlation is used to explain the attribute of the gravity magnetic field source, the extremum region of the identification parameter F indicates the position of the field source, that is, the field source is located, and the magnitude of the absolute value of the identification parameter F also reflects the strength of the gravity magnetic field. Namely, the gravity magnetic field source positioning and attribute identification parameter F is obtained. The purpose of normalization is to eliminate the difference between different dimensions and numerical amplitudes of the heavy magnetic field and to make the calculation result between-1 and +1, thereby facilitating data interpretation.
In the present embodiment, in the first step, if the gravity anomaly data and the magnetic anomaly data to be processed are input as random net data, the following gridding processing is performed on the random net data: carrying out gridding processing on gravity anomaly data and magnetic anomaly data to be processed, and gridding the gravity anomaly data and the magnetic anomaly data into the same grid spacing and grid size by adopting a kriging method or a minimum curvature method, wherein M points exist in the east direction and N points exist in the north direction after gridding; let the gravity anomaly data after gridding be Δ g and the magnetic anomaly data after gridding be Δ T.
In the second step, the step of performing two-dimensional edge expansion on the gridded gravity abnormal data and the gridded magnetic force abnormal data by adopting a cosine edge expansion method comprises the following steps: firstly, determining the number of points after the edge expansion, and assuming that the eastern direction has Mexpand points and the northern direction has Nexpand points after the edge expansion, wherein the number of points after the edge expansion meets the condition that Mexpand is 2(INT(log2(M))+1)，Nexpand＝2(INT (log2(N))+1)Wherein INT is a down rounding operation and log is a logarithm operation; after the number of points after edge expansion is determined, expanding the edge of the gravity abnormal data after gridding and the magnetic abnormal data after gridding of the M multiplied by N grid in the step one to Mexpanded multiplied by Nexpanded by a cosine edge expansion method; the Mexpand and the Nexpand meet the requirement of the number of fast Fourier transform; the gravity anomaly data after the edge expansion is delta gexpandThe magnetic force anomaly data after the edge expansion is delta Texpand。
The third step specifically comprises the following steps: fourier transform is carried out on the edge-expanded gravity anomaly data obtained in the step two by adopting Fast Fourier Transform (FFT) to obtain a two-dimensional wave number spectrum of gravity anomaly
The two-dimensional wavenumber spectrum is then multiplied by a vertical first derivative factor
u and v are wave numbers in the east and north directions, respectively; the gravity anomaly vertical first-order calculation in the wave number domain is completed; then, carrying out Fourier inverse transformation, and transforming the first vertical derivative of the wave number domain with the gravity anomaly to a space domain; then removing the data of the area of the edge expanding part, outputting the data range in the step one, and obtaining the first order derivative delta g of the gravity anomaly vertical direction of the space domainVDR。
Step (ii) ofThe fourth step specifically comprises the following steps: fourier transform is carried out on the magnetic force abnormal data after the edge expansion obtained in the step two by adopting Fast Fourier Transform (FFT), and a two-dimensional wave number spectrum of the magnetic force abnormality is obtained
The two-dimensional wavenumber spectrum is then multiplied by a polarization factor
(wherein i is an imaginary unit, u and v are wave numbers in the east and north directions, respectively, l, m and n are direction cosines of effective magnetization, and l ', m ' and n ' are direction cosines of normal geomagnetic field vectors), namely, the calculation of the magnetic anomaly pole in the wave number domain is completed; then, carrying out Fourier inverse transformation, and transforming the polarized magnetic anomaly result of the wavenumber domain to a space domain; and then removing the data of the edge expanding part area, and outputting the data range in the step one to obtain the polarized magnetic force abnormal RTP of the space domain.
The fifth step specifically comprises the following steps: adopting a rectangular sliding window such as 3 multiplied by 3, 5 multiplied by 5, 7 multiplied by 7 and the like, wherein the window is an odd window, and the sliding step length is 1 point; using formulas
Calculating a Pearson correlation coefficient between a vertical first derivative of the gravity anomaly and the magnetic anomaly of the polarizing pole; cov (RTP,. DELTA.g), among othersVDR) For polarizing the vertical first derivative deltag of the polar magnetic anomaly RTP and the gravity anomaly RTPVDRThe covariance of (a) of (b),
and
respectively a polarized magnetic force anomaly RTP and a gravity anomaly vertical first-order derivative deltagVDRThe mean square error of (c).
The sixth step specifically comprises the following steps: first, for Δ gVDRAnd RTP is used for normalization processing to eliminate the difference of the dimension and the amplitude of the two, and the normalized gravity anomaly vertical first-order derivative delta g is obtained respectivelyNVDRAnd normalized polarized magnetic anomaly NRTP; then, the following formula is adopted to carry out the positioning of the gravity magnetic field source and the calculation of the attribute identification parameter F:
for the gravity magnetic homologous geologic body, the vertical first derivative of the gravity anomaly and the polarized magnetic anomaly have a linear relation, the vertical first derivative of the gravity anomaly and the polarized magnetic anomaly can define the plane range of a geologic body field source, and the Pearson correlation coefficient can reflect the gravity magnetic homology and the attribute characteristics of the gravity magnetic homology and the polarized magnetic anomaly. Thus combining both field source localization and attribute identification. The first term (the fractional term) on the right side of the formula, the extreme value area range of the first term reflects the distribution range of the homologous gravity magnetic field, so that the field source positioning can be carried out, and the strength information of the original gravity magnetic field can be represented; the second term on the right of the formula is the Pearson correlation coefficient, which reflects the nature of the source of the gravity field, whether it is positively or negatively correlated. Since the first term is a value greater than or equal to zero, the calculated positive and negative of the identification parameter F represents the positive and negative correlation of the gravity magnetic anomaly, that is, the correlation is used to explain the attribute of the gravity magnetic field source, the extremum region of the identification parameter F indicates the position of the field source, that is, the field source is located, and the magnitude of the absolute value of the identification parameter F also reflects the strength of the gravity magnetic field. The identification parameter has the following advantages: firstly, the judgment parameter obtains an extreme value in the field source range, and tends to zero outside the field source body, so that the defect that the absolute value of the correlation coefficient tends to 1 outside the field source is overcome, the interpretation interference is effectively removed, the interpretation is simple, and the gravity and magnetic homologous geologic body is easy to identify. The amplitude of the discrimination parameter reflects the strength of gravity and magnetism anomaly and the distribution scale of the field source, and qualitative explanation can be carried out on the attribute of the gravity and magnetism source.
When the identification parameter is greater than 0, the identification parameter is positive correlation, and represents that the region has a heavy magnetic field source and a magnetic field source with high magnetic density or low magnetic density; when the identification parameter is less than 0, the identification parameter is negative correlation, and represents that the heavy and magnetic field sources with high magnetic low density or low magnetic high density exist in the area; when the identification parameter approaches zero, no gravity source region is indicated. The size of the identification parameter represents the information of the strength of the gravity magnetic field and reflects the scale of the field source. The method overcomes the problem that the absolute value of the Pearson correlation coefficient of the field source body tends to be 1 and is difficult to explain.
The present invention is not limited to the following embodiments, and all equivalent changes based on the technical solutions of the present invention fall within the protection scope of the present invention. The present invention will be described in further detail with reference to examples.
Example 1:
in this embodiment, the identification of the field source is implemented by taking a gravity-magnetic multi-combination model as an example, and according to the invention, the identification specifically comprises the following steps:
(1) raw data were input, as shown in fig. 1, a total of five horizontally and vertically superimposed hexahedral models were placed in this example, and forward evolution of gravity-magnetic anomaly was performed according to the model parameters in table 1. For simple calculation, the declination angle of the magnetic parameters is designed to be 90 degrees, and the declination angle is 0 degree.
(2) The data edge expansion processing is carried out to meet the data number requirement of the fast Fourier transform (2)nOne).
(3) Calculating a vertical first derivative of the gravity anomaly in a wave number domain, wherein FIG. 2 is a vertical first derivative of the gravity anomaly corresponding to the models in the table 1 and the FIG. 1; it can be seen in fig. 2 that the vertical first derivative of gravity anomaly substantially reflects the density distribution range of the model.
(4) Calculating the polarized magnetic anomalies in a wavenumber domain, wherein the polarized magnetic anomalies corresponding to the models in the table 1 and the figure 1 are shown in figure 3; it can be seen in fig. 3 that the magnetized pole magnetic anomaly substantially reflects the magnetic distribution range of the model.
(5) And calculating the Pearson correlation coefficient by using the vertical first derivative of the gravity anomaly and the magnetic anomaly of the polarized pole. Fig. 4 shows the pearson correlation coefficient of the gravity magnetic data. In fig. 4, it can be seen that the pearson correlation coefficient shows a high correlation outside the field source, the isoline body has a poor correspondence with the model, which causes a large interference to the explanation of the gravity and magnetic data, and it is difficult to identify the distribution range and the attribute of the field source.
(6) Calculating the positioning and attribute identification parameters of the gravity field source, wherein fig. 4 is the positioning and attribute identification parameters of the gravity field source, and fig. 5 shows that the parameters effectively identify the distribution range and the attributes of the field source, the parameters have correlation in the distribution range of the model, the values outside the distribution range of the model tend to be zero, and the size of the parameters corresponds to the scale of the model and the physical property size. Compared with the traditional Pearson correlation coefficient method, the method has the advantage that the field source positioning and attribute identification parameters provided by the invention have good identification effect.
TABLE 1 model parameters
Claims (6)
1. A field source positioning and attribute identification method based on gravity and magnetic data correlation analysis is characterized by comprising the following steps:
inputting gravity anomaly data and magnetic anomaly data to be processed, and carrying out gridding processing to obtain gridded gravity anomaly data delta g and gridded magnetic anomaly data delta T;
step two, performing two-dimensional edge expanding treatment on the meshed gravity anomaly data and the meshed magnetic force anomaly data obtained in the step one by using a cosine edge expanding method to obtain the gravity anomaly data delta g meeting the number of fast Fourier transform dataexpandAnd magnetic force anomaly data of Δ Texpand；
Step three, calculating a first vertical derivative delta g of the gravity anomaly in a wave number domainVDR；
Calculating the polar magnetic force abnormity RTP in a wave number domain;
fifthly, calculating a Pearson correlation coefficient by using the first vertical derivative of the gravity anomaly obtained in the third step and the polarized magnetic anomaly obtained in the fourth step;
step six, the first derivative delta g of the gravity anomaly vertical direction obtained in the step three is subjected toVDRAnd normalizing the polarized magnetic force abnormity RTP obtained in the step four, eliminating the difference of the dimension and the amplitude of the polarized magnetic force abnormity RTP and the normalized gravity abnormity vertical first-order derivative and polarized magnetic force abnormity data, and then summing the normalized gravity abnormity vertical first-order derivative and polarized magnetic force abnormity dataNormalizing the summed data and multiplying the normalized data by the Pearson correlation coefficient obtained in the fifth step; performing gravity magnetic field source positioning and attribute identification parameter F calculation by adopting the following formula:
in the above formula, (x, y) is the plane coordinate of the point to be calculated; Δ gNVDR(x, y) is a normalized gravity anomaly vertical first derivative of a coordinate (x, y) point; NRTP (x, y) is normalized pole magnetic anomaly for coordinate (x, y) points; max is taken to be (Δ g)NVDR(x, y) + NRTP (x, y)) maximum operation; and R (x, y) is a Pearson correlation coefficient of a coordinate (x, y) point, and the gravity field source positioning and attribute identification parameter F is obtained through the formula.
2. The gravity and magnetic data correlation analysis-based field source positioning and attribute identification method according to claim 1, wherein if the gravity anomaly data and the magnetic anomaly data to be processed input in the step one are random net data, the random net data is subjected to gridding processing as follows:
carrying out gridding processing on gravity anomaly data and magnetic anomaly data to be processed, and gridding the gravity anomaly data and the magnetic anomaly data into the same grid spacing and grid size by adopting a kriging method or a minimum curvature method, wherein M points exist in the east direction and N points exist in the north direction after gridding; let the gravity anomaly data after gridding be Δ g and the magnetic anomaly data after gridding be Δ T.
3. The gravity and magnetic data correlation analysis-based field source positioning and attribute identification method according to claim 2, wherein the step of performing two-dimensional edge extension processing on the gridded gravity anomaly data and the gridded magnetic anomaly data by using a cosine edge extension method in the step two comprises the steps of:
firstly, determining the number of points after the edge expansion, and supposing that the eastern direction has Mexpand points and the northern direction has Nexpand points after the edge expansionThe point number satisfies Mexpand 2(INT(log2(M))+1)，Nexpand＝2(INT(log2(N))+1)Wherein INT is a down rounding operation and log is a logarithm operation;
after the number of points after edge expansion is determined, expanding the edge of the gravity abnormal data after gridding and the magnetic abnormal data after gridding of the M multiplied by N grid in the step one to Mexpanded multiplied by Nexpanded by a cosine edge expansion method; the Mexpand and the Nexpand meet the requirement of the number of fast Fourier transform; the gravity anomaly data after the edge expansion is delta gexpandThe magnetic force anomaly data after the edge expansion is delta Texpand。
4. The gravity and magnetic data correlation analysis-based field source positioning and attribute identification method according to claim 1, wherein in the third step, the gravity anomaly vertical first-order derivative is calculated in a wave number domain, then inverse Fourier transform is performed, an edge expanding part is removed, and the gravity anomaly vertical first-order derivative Δ g is obtainedVDR(ii) a The method specifically comprises the following steps:
fourier transform is carried out on the edge-expanded gravity anomaly data obtained in the step two by adopting Fast Fourier Transform (FFT) to obtain a two-dimensional wave number spectrum of gravity anomaly
The two-dimensional wavenumber spectrum is then multiplied by a vertical first derivative factor
u and v are wave numbers in the east and north directions, respectively; then carrying out inverse Fourier transform to obtain a first derivative of the expanded abnormal vertical gravity; then removing the data of the area of the edge expanding part, outputting the data range in the step one, and obtaining the first order derivative delta g of the gravity anomaly vertical directionVDR。
5. The gravity-magnetic data correlation analysis-based field source positioning and attribute identification method according to claim 1, wherein in the fourth step, a polarized magnetic anomaly is calculated in a wavenumber domain, and an edge expansion part is removed to obtain a polarized magnetic anomaly RTP; the method specifically comprises the following steps:
fourier transform is carried out on the magnetic force abnormal data after the edge expansion obtained in the step two by adopting Fast Fourier Transform (FFT), and a two-dimensional wave number spectrum of the magnetic force abnormality is obtained
The two-dimensional wavenumber spectrum is then multiplied by a polarization factor
Wherein i is an imaginary unit, u and v are wave numbers in the east and north directions, respectively, l, m and n are direction cosines of effective magnetization, and l ', m ' and n ' are direction cosines of normal geomagnetic field vectors; and then carrying out inverse Fourier transform, then removing data in the edge expanding part area, and outputting the data range in the step one to obtain the polarized magnetic force abnormal RTP.
6. The gravity-magnetic data correlation analysis-based field source positioning and attribute identification method according to claim 1, wherein the step five specifically comprises the steps of:
adopting a rectangular sliding window, wherein the window needs to be an odd window, and the sliding step length is 1 point; using formulas
Calculating a Pearson correlation coefficient between a vertical first derivative of the gravity anomaly and the magnetic anomaly of the polarizing pole; cov (RTP,. DELTA.g), among othersVDR) For polarizing the vertical first derivative deltag of the polar magnetic anomaly RTP and the gravity anomaly RTPVDRThe covariance of (a) of (b),
and
respectively a polarized magnetic force anomaly RTP and a gravity anomaly vertical first-order derivative deltagVDRThe mean square error of (c).
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