CN117237667A - Robust high-resolution geologic body boundary identification method based on bit field data - Google Patents

Robust high-resolution geologic body boundary identification method based on bit field data Download PDF

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CN117237667A
CN117237667A CN202311226378.9A CN202311226378A CN117237667A CN 117237667 A CN117237667 A CN 117237667A CN 202311226378 A CN202311226378 A CN 202311226378A CN 117237667 A CN117237667 A CN 117237667A
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bit field
field data
steps
geologic body
resolution
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刘洁
李三忠
索艳慧
周洁
王旭
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Ocean University of China
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Ocean University of China
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

The invention relates to a robust high-resolution geologic body boundary identification method based on bit field data, and belongs to the technical field of geophysical exploration. The method comprises the following steps: denoising the bit field data, and taking the denoised data as an input of edge detectionf 1 The method comprises the steps of carrying out a first treatment on the surface of the Calculation off 1 Is obtained by the vertical derivative of (2)f 2 The method comprises the steps of carrying out a first treatment on the surface of the Calculation off 2 Is filtered by Harris to obtainf 3 The method comprises the steps of carrying out a first treatment on the surface of the For a pair off 3 Performing two-dimensional Gaussian filtering to eliminate possible singular points to obtainf 4 The method comprises the steps of carrying out a first treatment on the surface of the For a pair off 4 Further performing ILTHG filtering to obtainf 5 The method comprises the steps of carrying out a first treatment on the surface of the Extraction off 5 And (3) the maximum point position set is used as a final geologic body boundary recognition result. The invention has the advantages of strong anti-interference capability, capability of generating a geologic body edge detection result with high resolution, and difficult generation of false boundaries. The problem of compromise between noise immunity and resolution ratio faced by the existing edge detection method is solved to a certain extent, and the boundary recognition effect of the geologic body is better than that of the traditional edge detection method.

Description

Robust high-resolution geologic body boundary identification method based on bit field data
Technical Field
The invention relates to a robust high-resolution geologic body boundary identification method based on bit field data, and belongs to the technical field of geophysical exploration.
Background
The potential field edge detection technology is a geological boundary identification means which has long history and wide application. The technology can highlight the information which is not easy to perceive in the original potential field data, and plays an important role in construction explanation of various scales, such as mining body detection, fault identification, delineation of different geological bodies, division of regional construction grids and the like. Based on past studies, bit field edge detection can be largely divided into three main categories: (1) derivative based class, (2) phase or ratio based class and (3) statistics/sliding window based class. Among them, the horizontal and vertical derivatives (VDR method) are the basis of the derivative-like method, for example, the total horizontal derivatives (THDR method) proposed earlier are made up of horizontal derivatives in different directions. Tilt angle (TA method) is a typical phase or ratio class based method. Recently, the ILTHG method, the NHF method based on sliding window, and the like have also been proposed.
It has been found that hybrid edge detection filters that use sequential combinations and ratios simultaneously have higher horizontal or vertical resolution and are less prone to artifacts than conventional single derivative, ratio or sliding window based edge detectors. However, hybrid filters with higher derivatives, such as the ILTHG method, tend to be more susceptible to noise interference, in which case they are less effective than NHF filters based on statistical time windows. In general, although bit field edge detection techniques are continually evolving, most filters currently have the following limitations: (1) The response to field sources of different depths, different sizes and different intensities is difficult to balance; (2) The low order edge detector has limited resolution and resistance to noise decreases rapidly with increasing derivative order, i.e. a compromise between resolution and noise immunity is faced.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a novel robust high-resolution geologic body boundary identification method based on bit field data, which can improve the anti-interference capability, generate a high-resolution edge detection result and is not easy to generate false boundaries.
The invention is realized by adopting the following technical scheme: the invention relates to a robust high-resolution geologic body boundary identification method based on bit field data, which comprises the following steps:
step one: judging whether the signal-to-noise ratio of the bit field data meets the requirement, if the signal-to-noise ratio is too low, performing upward continuation or other denoising processing on the bit field data, and taking the denoised bit field data as input data of edge detectionf 1 ；
Step two: calculating input dataf 1 Is defined as the vertical first derivative of:
s1: the calculation formula is as follows:
(1)
this step is typically performed in the frequency-wavenumber domain and then transformed into the spatial domain by an inverse fourier transform to obtainf 2 ；
Step three: for a pair off 2 Normalized Harris filtering:
s2: within any 3 x 3 size window, calculatef 2 Edge of the framex, yDerivative matrix M of direction to extractf 2 Is characterized by the following specific formula
(2)
Wherein,ncorresponding to small displacementf 2 The number of neighbors along the row, column and diagonal,and->Respectively aref 2 At the position ofxDirection and directionyDirectional derivatives of the square;
s3: the response function of Harris filtering is calculated, and the specific formula is as follows
(3)
Wherein Det (M) represents a determinant of M,trace representing M->Is a parameter that adjusts sharpness or smoothness. Normalizing the upper envelope surface of NHF to obtainf 3 ；
Step four: for a pair off 3 And performing two-dimensional Gaussian smoothing to further eliminate potential singular points. If it isf 3 This step can be omitted if no singular points already exist;
s4: two-dimensional Gaussian smoothing is specifically expressed as
(4)
Wherein Gaussian () represents a two-dimensional Gaussian distribution function, thereby yieldingf 4 ；
Step five: for a pair off 4 ILTHG filtering is carried out, and the specific steps are as follows:
s5: separately calculatef 4 At the position ofxThe direction of the light beam is changed,ydirection and directionzDirection derivative of direction, />And->；
S6: will be, />And->Substituting the ILTHG filter formula:
(5)
wherein exp represents an exponential function based on a natural constant e,is a power exponent, generally between 1 and 10;
step six: calculating equation (5) to obtainf 5 Extractingf 5 And (3) the maximum point position set is used as a final geologic body boundary recognition result.
The beneficial effects of the invention are as follows: by adopting the robust high-resolution geologic body boundary identification method based on the bit field data, the advantage of the various edge detection filters is fused, so that a high-resolution geologic body boundary identification result can be obtained, false boundaries are not easy to generate, the robustness is still realized under certain noise intensity, the problem that the existing method cannot achieve both high resolution and strong noise resistance is solved, the noise resistance and the boundary resolution are superior to those of the traditional method, and the boundary identification result is reliable.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a 3D theoretical model;
FIG. 3 is a gravity anomaly graph generated by a model;
FIG. 4 (a) is a graph of VDR geological boundary recognition results without noise;
FIG. 4 (b) is a graph of the result of THDR method geological boundary recognition under the condition of no noise;
FIG. 4 (c) is a graph of the result of TA geological boundary recognition under the condition of no noise;
FIG. 4 (d) is a diagram of the result of NHF-method geological boundary recognition under the condition of no noise;
FIG. 4 (e) is a graph of the ILTHG method geological boundary recognition result without noise;
FIG. 4 (f) is a graph of the result of identifying the geological boundary of the method of the present invention in the absence of noise;
fig. 5 (a) is a random noise pattern to be added;
FIG. 5 (b) is a gravity anomaly graph after random noise addition;
FIG. 6 (a) is a graph of VDR geological boundary recognition results after random noise is added;
FIG. 6 (b) is a graph of the result of THDR method geological boundary recognition after adding random noise;
FIG. 6 (c) is a graph of the TA geological boundary recognition result after random noise is added;
FIG. 6 (d) is a diagram of the result of NHF-method geological boundary recognition after random noise addition;
FIG. 6 (e) is a graph of ILTHG method geological boundary recognition results after random noise is added;
FIG. 6 (f) is a graph of the geological boundary recognition result of the method of the present invention after adding random noise;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects and technical solutions of the present invention more apparent. The flow chart of the invention, as shown in fig. 1, comprises the following steps:
step one: judging whether the signal-to-noise ratio of the bit field data meets the requirement, if the signal-to-noise ratio is too low, performing upward continuation or other denoising processing on the bit field data, and taking the denoised bit field data as input data of edge detectionf 1 ；
Step two: calculating input dataf 1 Is defined as the vertical first derivative of:
s1: the calculation formula is as follows:
(1)
this step is typically performed in the frequency-wavenumber domain and then transformed into the spatial domain by an inverse fourier transform to obtainf 2 ；
Step three: for a pair off 2 Normalized HarrisAnd (3) filtering:
s2: within any 3 x 3 size window, calculatef 2 Edge of the framex, yDerivative matrix M of direction to extractf 2 Is characterized by the following specific formula
(2)
Wherein,ncorresponding to small displacementf 2 The number of neighbors along the row, column and diagonal,and->Respectively aref 2 At the position ofxDirection and directionyDirectional derivatives of the square;
s3: the response function of Harris filtering is calculated, and the specific formula is as follows
(3)
Wherein Det (M) represents a determinant of M,trace representing M->Is a parameter that adjusts sharpness or smoothness. Normalizing the upper envelope surface of NHF to obtainf 3 ；
Step four: for a pair off 3 And performing two-dimensional Gaussian smoothing to further eliminate potential singular points. If it isf 3 This step can be omitted if no singular points already exist;
s4: two-dimensional Gaussian smoothing is specifically expressed as
(4)
Wherein Gaussian () represents a two-dimensional Gaussian distribution function, thereby yieldingf 4 ；
Step five: for a pair off 4 ILTHG filtering is carried out, and the specific steps are as follows:
s5: separately calculatef 4 At the position ofxThe direction of the light beam is changed,ydirection and directionzDirection derivative of direction, />And->；
S6: will be, />And->Substituting the ILTHG filter formula:
(5)
wherein exp represents an exponential function based on a natural constant e,is a power exponent, generally between 1 and 10;
step six: calculating equation (5) to obtainf 5 Extractingf 5 And (3) the maximum point position set is used as a final geologic body boundary recognition result.
The model test of the present invention will be explained and illustrated with reference to the following embodiments.
Embodiment one:
in order to explain the implementation thought and the implementation process of the method and prove the effectiveness of the method, a three-dimensional model is built for testing, and the three-dimensional model is compared with the result of the traditional edge detection method.
S1, establishing a theoretical three-dimensional model shown in fig. 2. The model consists of three prisms with positive density and three prisms with negative density, wherein the length of the prism (1) is 15 km, the width is 50 km, the height is 5 km, and the density difference is-0.3 g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The prism (2) has a length of 15 km, a width of 50 km, a height of 5 km and a density difference of 0.35 g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The prism body (3) has the length of 20 km, the width of 20 km, the height of 5 km and the density difference of 0.4 g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The prism (4) has a length of 80 km, a width of 20 km, a height of 5 km and a density difference of 0.3 g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The prism (5) has a length of 60 km, a width of 60 km, a height of 5 km, and a density difference of-0.5 g/cm 3 The method comprises the steps of carrying out a first treatment on the surface of the The prism (6) has a length of 20 km, a width of 20 km, a height of 5 km, and a density difference of-0.4 g/cm 3 。
And S2, in the range of the model, arranging gravity observation points along the horizontal ground surface (z=0 km), wherein the distance between the measurement points is 2 km.
S3, calculating gravity anomaly generated by the modelf 1 As shown in fig. 3.
S4, calculating input dataf 1 Is obtained by the vertical first derivative of (2)f 2 。
S5, pairingf 2 Normalized Harris filtering is carried out to obtainf 3 。
S6, pairingf 3 Performing two-dimensional Gaussian smoothing and ILTHG filtering to obtainf 4 Andf 5 。
s7, extractingf 5 And (3) the maximum point position set is used as a final geologic body boundary recognition result.
To illustrate the effectiveness of the method of the present invention, the boundary recognition results of the present invention are compared with those of the existing methods (including VDR method, THDR method, TA method, NHF method, and ILTHG method). Fig. 4 (a) to (e) are the geological boundary recognition results of the VDR method, the THDR method, the TA method, the NHF method and the ILTHG method, respectively, and fig. 4 (f) is the geological boundary recognition result of the present invention. As is evident from the figure, the VDR method corresponds well to the true bulk boundary position around the zero point, however, not all zero points indicate the true boundary position and therefore VDR has ambiguities and false boundaries. The maximum of the THDR method indicates the location of the boundary, but with lower resolution, the sensitivity to deep-source small objects (prisms (3)) is lower. The TA method is substantially equivalent to the VDR method in performance, and inherits the disadvantage of the VDR method, namely that obvious excessive zero crossing points exist, and false boundaries are generated. The NHF method has a stronger resolution than the THDR method, but again cannot detect deep patches. In contrast, the ILTHG method and the method of the invention are more capable of balancing the amplitude intensities of field sources of different sizes and different depths, and six block boundaries are detected. Compared with the ILTHG method, the method has finer edge linewidth and sharper block boundary performance, and shows higher resolution.
Embodiment two:
to further illustrate the noise immunity and robustness of the present method, the addition of random noise as shown in fig. 5 (a) to gravity anomalies generated in the first mode of example was tested and compared to the results of prior methods, including VDR, THDR, TA, NHF and ILTHG methods. Gravity anomalies after the addition of random noise are shown in fig. 5. Carrying out 4km upward continuation processing on the gravity anomaly added with random noise as input data of edge detectionf 1 . Then, the specific steps of the second embodiment are the same as those of S4 to S7 of the first embodiment. Fig. 6 (a) to (e) are the geological boundary recognition results of the VDR method, the THDR method, the TA method, the NHF method, and the ILTHG method, respectively, after the random noise is added, and fig. 6 (f) is the geological boundary recognition result after the random noise is added.
It can be seen that the results of the VDR and THDR methods are relatively ambiguous and that there is a boundary shift where the prisms (3) meet (5). The results of the TA method are similar to those of the two methods above, but appear to be more affected by noise. The NHF method is less affected by noise, but still has limited resolution. The boundary resolution of the ILTHG method is relatively high, but there are false trivial spots, and the large noise level causes instability of the ILTHG method. Compared with the ILTHG method, the method greatly reduces the possibility of detecting discrete false points as boundaries. Compared with the NHF method, the method of the invention obviously improves the horizontal resolution of edge detection, and the positioning of the boundary is more definite and reliable. It can be seen that the method of the present invention is superior to the existing edge detection methods.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.

Claims (5)

1. A robust high-resolution geologic body boundary identification method based on bit field data is characterized by comprising the following steps:
step one: judging whether the signal-to-noise ratio of the bit field data meets the requirement, if the signal-to-noise ratio is too low, performing upward continuation or other denoising processing on the bit field data, and taking the denoised bit field data as input data of edge detectionf 1 ；
Step two: calculating input dataf 1 Is defined as the vertical first derivative of:
s1: the calculation formula is as follows:
(1)
this step is typically performed in the frequency-wavenumber domain and then transformed into the spatial domain by an inverse fourier transform to obtainf 2 ；
Step three: for a pair off 2 Normalized Harris filtering:
s2: within any 3 x 3 size window, calculatef 2 Edge of the framex, yDerivative matrix M of direction to extractf 2 Is characterized by the following specific formula
(2)
Wherein,ncorresponding to small bitsMove inwardsf 2 The number of neighbors along the row, column and diagonal,and->Respectively aref 2 At the position ofxDirection and directionyDirectional derivatives of the square;
s3: the response function of Harris filtering is calculated, and the specific formula is as follows
(3)
Wherein Det (M) represents a determinant of M,trace representing M->Is a parameter that adjusts sharpness or smoothness. Normalizing the upper envelope surface of NHF to obtainf 3 ；
Step four: for a pair off 3 And performing two-dimensional Gaussian smoothing to further eliminate potential singular points. If it isf 3 This step can be omitted if no singular points already exist;
s4: two-dimensional Gaussian smoothing is specifically expressed as
(4)
Wherein Gaussian () represents a two-dimensional Gaussian distribution function, thereby yieldingf 4 ；
Step five: for a pair off 4 ILTHG filtering is carried out, and the specific steps are as follows:
s5: separately calculatef 4 At the position ofxThe direction of the light beam is changed,ydirection and directionzDirection derivative of direction, />And->；
S6: will be, />And->Substituting the ILTHG filter formula:
(5)
wherein exp represents an exponential function based on a natural constant e,is a power exponent, generally between 1 and 10;
step six: calculating equation (5) to obtainf 5 Extractingf 5 And (3) the maximum point position set is used as a final geologic body boundary recognition result.
2. The method for identifying boundary of robust high resolution geologic volume based on bit field data as recited in claim 1, wherein in said step two, local structural features are extracted byf 2 Edge of the framex, yThe derivative matrix M of the direction.
3. The method for identifying boundary of high-resolution geologic volume based on robust bit field data according to claim 1, wherein in the fourth step, potential singular points are eliminated by using a two-dimensional gaussian smoothing function.
4. The method for identifying boundary of robust high resolution geologic volume based on bit field data as defined in claim 1, wherein said ILTHG filtering in step five is performed by,/>And->Is obtained through exponential function and power operation calculation.
5. The method for identifying boundary of high-resolution geologic volume based on bit field data as defined in claim 1, wherein in step six,f 5 is the boundary of the geologic body.
CN202311226378.9A 2023-09-22 2023-09-22 Robust high-resolution geologic body boundary identification method based on bit field data Pending CN117237667A (en)

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