CN110568495B - Rayleigh wave multi-mode dispersion curve inversion method based on generalized objective function - Google Patents
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- 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
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- G01V2210/622—Velocity, density or impedance
- G01V2210/6222—Velocity; travel time
Abstract
The invention provides a Rayleigh wave multi-mode dispersion curve inversion method based on a generalized target function, which comprises the following steps: picking up actually measured frequency dispersion points from the actually measured frequency dispersion spectrum; performing point separation processing on the picked actually measured frequency dispersion points to obtain fundamental frequency dispersion points and non-fundamental frequency dispersion points; separating the separated fundamental order frequency dispersion points by taking the selection rate as a reference; performing point empowerment processing on all frequency dispersion points after the point separation processing; assembling an inversion target function of the multimode dispersion curve according to the point weighting processing result; and carrying out inversion according to the inversion target function to obtain a shear wave velocity profile. The invention can quickly and accurately finish inversion under the condition of small dependence on the mode confirmation of the dispersion curve, thereby obtaining the transverse wave velocity of the stratum.
Description
Technical Field
The invention relates to the technical field of near-surface seismic surface wave exploration, in particular to a Rayleigh wave multi-mode dispersion curve inversion method based on a generalized objective function.
Background
Rayleigh wave (Rayleigh wave) exploration is an engineering geophysical exploration method which is developed rapidly in recent years, has the characteristics of no damage, high efficiency, economy, high signal-to-noise ratio and the like, and therefore, has wide application in shallow surface engineering exploration, geological disaster investigation and other aspects. The core link of Rayleigh wave exploration is to extract an actual measurement dispersion curve from an actual measurement seismic record for inversion. According to the inversion result, the transverse wave velocity structure of the underground geological medium can be obtained, and the guidance of numerous scientific and engineering problems can be realized.
The key of the Rayleigh wave dispersion curve inversion is to determine an inversion target function, and once the target function is determined, the inversion can be carried out by combining a quasi-linear (such as a damped least square method and the like) or a complete non-linear inversion method (such as a particle swarm optimization algorithm, a genetic algorithm and the like).
The most commonly used objective function at present is the root mean square value of the difference between the calculated dispersion curve and the measured dispersion curve, and is therefore called root mean square objective function. The root-mean-square objective function requires that the orders (or modes) of the measured dispersion curve are determined one by one, which makes the information of the measured high-order dispersion curve difficult to be utilized in the inversion or often mistakenly recognized as a fundamental mode, so the accuracy of the inversion result based on the objective function is often limited or even wrong. Particularly, for weak interlayers, such as Rayleigh wave data acquired on urban roads, the transverse wave speed of a concrete road is higher than that of a lower stratum, so that the extracted dispersion curve is mainly in a high-order mode in a medium-high frequency band, but the order of the high-order mode cannot be accurately obtained, and an accurate detection result cannot be obtained. On the other hand, the higher-order dispersion curve has higher sensitivity than the fundamental-order dispersion curve, the inversion accuracy can be improved by including the higher-order dispersion curve in the inversion, but it is very difficult to determine the accurate order of each extracted dispersion point.
For the above two reasons, how to reduce the influence of the determination of the frequency point order on the inversion result in rayleigh wave multi-mode inversion will be an important issue.
In recent years, a scholars proposes an objective function which can carry out inversion without determining the order of an actually measured frequency dispersion curve, and the objective function is called a determinant objective function, solves the problem that the order of the root mean square objective function needs to be determined, but the inherent local minimum value of the objective function seriously interferes with the optimization solution of an inversion algorithm, and further development of the objective function is restricted by the objective function.
Disclosure of Invention
The invention provides a Rayleigh wave multi-mode dispersion curve inversion method based on a generalized target function, and aims to solve the problems of high dependence on dispersion curve mode confirmation and low precision when a multi-mode Rayleigh wave dispersion curve is used for inversion.
In order to achieve the above object, an embodiment of the present invention provides a method for inverting a rayleigh wave multi-mode dispersion curve based on a generalized objective function, including:
picking up actually measured frequency dispersion points from the actually measured frequency dispersion spectrum;
performing point separation processing on the picked actually measured frequency dispersion points to obtain fundamental frequency dispersion points and non-fundamental frequency dispersion points;
separating the separated fundamental order frequency dispersion points by taking the selection rate as a reference;
performing point empowerment processing on all frequency dispersion points after the point separation processing;
assembling an inversion target function of the multimode dispersion curve according to the point weighting processing result;
and carrying out inversion according to the inversion target function to obtain a shear wave velocity profile.
The step of performing point separation processing on the picked actually measured frequency dispersion points to obtain fundamental frequency dispersion points and non-fundamental frequency dispersion points includes:
dividing the picked actually measured frequency dispersion points into fundamental frequency dispersion points and non-fundamental frequency dispersion points; all the fundamental frequency dispersion points are marked as a set B, and all the non-fundamental frequency dispersion points are marked as a set N.
The step of separating the separated fundamental order frequency dispersion points by taking the selection rate as a reference comprises the following steps of:
uniformly selecting partial frequency dispersion points from the set B, taking the selected frequency dispersion points as F points, and marking all the F points as a set F;
taking other dispersion points except the f point in the set B and the dispersion points in the set N as H points, and marking all the H points as a set H;
let L be F ∪ H, and the number of dispersion points included in each of the set B, F, H, and L be nB、nF、nHAnd nL，nL＝nF+nHThe selection rate R of f is nF/nB×100％。
Wherein, the step of performing point-empowerment processing on all frequency dispersion points after the point separation processing comprises:
by a weight matrix function
Weighting the frequency dispersion points in the set L; wherein, ciIs the ith dispersion point, w, in the set LiDenotes ciThe weight of (c).
The step of assembling the inversion target function of the multimode dispersion curve according to the point weighting processing result comprises the following steps:
by passing
Assembling an inversion target function of the multimode dispersion curve; wherein G (M) represents an inversion target function of the multimode dispersion curve, M represents any n-layer horizontal model, and the fundamental phase velocity of M is as follows under the corresponding frequency of the ith dispersion point in the set L
The absolute value of the dispersion function of M is D at the position of the ith dispersion point in the set Li，M＝[VP,VS,ρ,h]，VP、VSVector parameters are formed by longitudinal wave velocity and transverse wave velocity of each layer respectively, rho and h are density and layer thickness of each layer respectively,
is the phase velocity value of the ith dispersion point in the set L.
Wherein, when the layer thickness h of the last layer in the n layers isnVector h includes the layer thicknesses of the first n-1 layers → ∞.
Wherein, when R is 0, F is phi, H is L, phi is [0, w ═ L]，
Wherein when R is 100% and H is Φ, F is L, Φ is [ w,0 ═ L]，
Wherein the content of the first and second substances,
wherein the content of the first and second substances,
represents the weight of the ith f-point,
representing the weight of the ith h point.
Wherein, the step of inverting according to the inversion target function to obtain the shear wave velocity profile comprises the following steps:
calculating a function value of an inversion iteration initial model through the inversion target function;
and gradually updating and iterating the model, calculating a function value of the model after the updating and iterating through the inversion objective function when the model after the updating and iterating is obtained, and finishing an iteration optimization solution process until the function value obtained through the calculation of the inversion objective function is smaller than a preset threshold value or the updating and iterating times of the model reach preset times, and outputting an optimal individual of the model to obtain a transverse wave velocity profile.
The scheme of the invention has at least the following beneficial effects:
in the embodiment of the invention, firstly, the actually measured frequency dispersion points in the actually measured frequency dispersion spectrum are extracted, then point separation is carried out on the actually measured frequency dispersion points by using point separation processing and point weighting processing, an inversion target function of the multimode frequency dispersion curve is assembled after the point separation is finished, and inversion is carried out by using the inversion target function to obtain the transverse wave velocity profile. The point separation processing can quickly classify the actually measured frequency dispersion points, and is different from the requirement that the conventional multi-mode discrimination needs to accurately obtain the order of each frequency point, which can hardly be realized, and the point separation method only requires an operator to judge the actually measured frequency dispersion points to be classified into the points of a discriminable mode and the points of an indistinguishable mode according to own experience, so that the possibility of mode misjudgment is reduced, and the inversion accuracy is improved. Meanwhile, the assembled inversion target function is used for inversion, all orders of dispersion points can be fully used for inversion, all actually measured dispersion information is fully used, and therefore the inversion multi-solution is reduced. On the other hand, the classification of the frequency dispersion points can be flexibly changed through the selection rate, so that the distribution of the weight matrix during point weighting processing is changed, and the speed of the inversion speed can be adjusted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flowchart of a method for Ruire wave multi-mode dispersion curve inversion based on a generalized objective function according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of measured dispersion points after point separation processing according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a rayleigh wave multi-mode dispersion curve inversion method based on a generalized objective function, in a related link in rayleigh wave exploration, the inversion method includes upper bearing surface wave data processing and lower bearing geological interpretation, and is located at a core inversion position, wherein an actually measured dispersion spectrum is obtained after surface wave data processing. Specifically, the inversion method includes:
and 11, picking up actually measured frequency dispersion points from the actually measured frequency dispersion spectrum.
In the embodiment of the invention, the actually measured frequency dispersion points are picked from the actually measured frequency dispersion spectrum, and specifically, the energy maximum value points of the frequency dispersion spectrum which can be regarded as a frequency dispersion curve are all extracted. The measured dispersion spectrum and the picked measured dispersion points are shown in fig. 2.
And step 12, performing point separation processing on the picked actually measured frequency dispersion points to obtain fundamental frequency dispersion points and non-fundamental frequency dispersion points.
In the embodiment of the present invention, in step 12, it is specifically determined according to the experience of the worker that the actually measured frequency dispersion points are classified into discriminable mode points and indistinguishable mode points. Generally, the fundamental frequency dispersion point with the highest energy and low speed in the actually measured frequency dispersion points is a distinguishable mode point, and the modes of the other points are difficult to distinguish and are indistinguishable mode points, i.e., non-fundamental frequency dispersion points.
Specifically, the specific implementation manner of step 12 is as follows: and dividing the picked actually measured frequency dispersion points into fundamental frequency dispersion points and non-fundamental frequency dispersion points. All the fundamental frequency dispersion points are marked as a set B, and all the non-fundamental frequency dispersion points are marked as a set N. In addition, let L*B ∪ N, i.e. L*The set of all the separated measured dispersion points is obtained.
And step 13, separating the separated fundamental order frequency dispersion points by taking the selection rate as a reference.
In an embodiment of the present invention, the specific implementation manner of step 13 is to uniformly select some dispersion points from the set B, use the selected dispersion points as F points, and mark all F points as a set F, use other dispersion points in the set B except the F points, and the dispersion points in the set N as H points, and mark all H points as a set H, and let L be F ∪ H, where the numbers of dispersion points contained in the set B, the set F, the set H, and the set L are NB、nF、nHAnd nL，nL＝nF+nHThe selection rate R of f is nF/nB× 100%, R represents the selection rate of f point.
And step 14, performing point-empowerment processing on all frequency dispersion points subjected to point separation processing.
In an embodiment of the present invention, a specific implementation manner of the step 14 is as follows: by a weight matrix function
The dispersion points in the set L are weighted. Wherein, ciIs the ith dispersion point, w, in the set LiDenotes ciThe weight of (c). In particular by i traversing from 1To nLThe point-weighting process of all the dispersion points in the set L can be completed.
And step 15, assembling an inversion target function of the multimode dispersion curve according to the point weighting processing result.
In an embodiment of the present invention, a specific implementation manner of the step 15 is as follows: by passing
And assembling an inversion target function of the multimode dispersion curve. Wherein G (M) represents an inversion target function of the multimode dispersion curve, M represents any n-layer horizontal model, and the fundamental phase velocity of M is as follows under the corresponding frequency of the ith dispersion point in the set L
The absolute value of the dispersion function of M is D at the position of the ith dispersion point in the set Li，M＝[VP,VS,ρ,h]，VP、VSThe longitudinal wave velocity and the transverse wave velocity of each layer (namely each layer in the n layers) respectively form vector parameters, rho and h are the density and the layer thickness of each layer (namely each layer in the n layers) respectively,
is the phase velocity value of the ith dispersion point in the set L.
Wherein, as a preferred example, when the layer thickness h of the last layer of the n layers isnThe vector h comprises only the layer thicknesses of the first n-1 layers, i.e. of each of the 1 st to n-1 layers →.
Wherein, when R is 0, F is phi, H is L, phi is [0, w ═ L]The inverse objective function g (m) degenerates to:
when R is 100% and H is phi, F is L, phi is w,0]The inverse objective function G (M) degrades to:
the formula is similar to a classical root mean square objective function and is the same as the classical oneThe root objective functions differ by only a constant
It is obvious that a determinant objective function or a root mean square objective function is a special case of a generalized objective function (i.e. the inverse objective function described above in the embodiments of the present invention) in different situations. In general, the set L is a mixture of f-points and h-points, and the generalized objective function (i.e., the above-mentioned inversion objective function in the embodiment of the present invention) is organically composed of a determinant objective function and a root-mean-square objective function, and can be decomposed into:
wherein the content of the first and second substances,
represents the weight of the ith f-point,
representing the weight of the ith h point.
And
can pass through a weight matrix function phiiTo obtain its value. Before inversion, all Φ can be calculatediThe calculation of unnecessary phase velocity and dispersion function value is reduced, and the inversion speed is accelerated.
And step 16, performing inversion according to the inversion target function to obtain a shear wave velocity profile.
In an embodiment of the present invention, a specific implementation manner of the step 16 is as follows: calculating a function value of an inversion iteration initial model through the inversion target function; and gradually updating and iterating the model, calculating a function value of the model after the updating and iterating through the inversion objective function when the model after the updating and iterating is obtained, and finishing an iteration optimization solution process until the function value obtained through the calculation of the inversion objective function is smaller than a preset threshold value or the updating and iterating times of the model reach preset times, and outputting an optimal individual of the model to obtain a transverse wave velocity profile.
It will be appreciated that the model may also be a population among a global optimization algorithm, such as a genetic algorithm, a particle swarm algorithm, and the like. That is, the specific implementation manner of step 16 includes the following steps:
step one, embedding an inversion target function into an optimization solution algorithm to serve as a basis for evaluating the quality of an iteration result, specifically, after the inversion target function is assembled through steps 11 to 15, giving an initial model (or population), and calculating a function value of the model (or population) through the inversion target function;
step two, updating the model (or the population), calculating a function value of the new model (or the population) by inverting the objective function, and when the function value of the new generation model (or the population) is smaller than the function value of the previous generation (namely the function value calculated last time), considering that the new generation model (or the population) is better than the previous generation model (or the population), and updating the model (or the population) correctly;
and step three, repeating the step two until the function value obtained by calculating the inversion target function is smaller than a preset threshold value or the iteration number is larger than the maximum iteration number, considering that the iteration optimization solution is completed, thereby completing the inversion and obtaining the shear wave velocity profile.
It should be noted that, in the embodiment of the present invention, the measured dispersion points in the measured dispersion spectrum are extracted first, then the point separation processing and the point weighting processing are applied to perform the point separation on the measured dispersion points, an inversion target function of the multimode dispersion curve is assembled after the point separation is completed, and the inversion is completed by using the inversion target function through multiple iterative optimization, so as to obtain the shear wave velocity profile. The point separation processing can quickly classify the actually measured frequency dispersion points, and is different from the requirement that the conventional multi-mode discrimination needs to accurately obtain the order of each frequency point, which can hardly be realized, and the point separation method only requires an operator to judge the actually measured frequency dispersion points to be classified into the points of a discriminable mode and the points of an indistinguishable mode according to own experience, so that the possibility of mode misjudgment is reduced, and the inversion accuracy is improved. Meanwhile, the assembled inversion target function is used for inversion, all orders of dispersion points can be fully used for inversion, all actually measured dispersion information is fully used, and therefore the inversion multi-solution is reduced. On the other hand, the classification of the frequency dispersion points can be flexibly changed through the selection rate, so that the distribution of the weight matrix during point weighting processing is changed, and the speed of the inversion speed can be adjusted.
In addition, the inversion target function constructed by the invention is fully suitable for optimization solving algorithms such as a quasi-linear or complete non-linear inversion method and the like, and has wide applicability, convenience and flexibility. The inversion target function is utilized to carry out inversion, the accuracy of Rayleigh surface wave exploration can be improved, the application range of the Rayleigh surface wave exploration is expanded, and particularly, the accurate detection effect can be simply and efficiently obtained under the condition that a weak interlayer exists during urban Rayleigh surface wave exploration.
While the foregoing is a preferred embodiment of the present invention, it should be noted that those skilled in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it can still modify the technical solutions described in the foregoing embodiments, or equally replace some technical features (for example, the fundamental phase velocity of the model M in the step 15 is at the frequency corresponding to the ith frequency dispersion point in the set L
"Replacing" the phase at which the maximum displacement of the model M occurs at the frequency corresponding to the ith frequency dispersion point in the set LThe speed position is
", i.e., the phase velocity at the maximum displacement is replaced by the fundamental phase velocity)", and such modifications or replacements do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and all should be included in the protection scope of the present invention.
Claims (10)
1. A Rayleigh wave multi-mode dispersion curve inversion method based on a generalized objective function is characterized by comprising the following steps:
picking up actually measured frequency dispersion points from the actually measured frequency dispersion spectrum;
performing point separation processing on the picked actually measured frequency dispersion points to obtain fundamental frequency dispersion points and non-fundamental frequency dispersion points;
separating the separated fundamental order frequency dispersion points by taking the selection rate as a reference;
performing point empowerment processing on all frequency dispersion points after the point separation processing;
constructing an inversion target function of the multi-mode dispersion curve by combining the phase velocity difference of the dispersion curve in the root-mean-square target function and the absolute value of the dispersion function in the determinant target function according to the point weighting processing result;
and carrying out inversion according to the inversion target function to obtain a shear wave velocity profile.
2. The inversion method according to claim 1, wherein the step of performing point separation on the picked measured dispersion points to obtain fundamental dispersion points and non-fundamental dispersion points comprises:
dividing the picked actually measured frequency dispersion points into fundamental frequency dispersion points and non-fundamental frequency dispersion points; all the fundamental frequency dispersion points are marked as a set B, and all the non-fundamental frequency dispersion points are marked as a set N.
3. The inversion method according to claim 2, wherein the step of separating the separated fundamental dispersion points based on a selection rate comprises:
uniformly selecting partial frequency dispersion points from the set B, taking the selected frequency dispersion points as F points, and marking all the F points as a set F;
taking other dispersion points except the f point in the set B and the dispersion points in the set N as H points, and marking all the H points as a set H;
let L be F ∪ H, and the number of dispersion points included in each of the set B, F, H, and L be nB、nF、nHAnd nL，nL＝nF+nHThe selection rate R of f is nF/nB×100％。
4. The inversion method according to claim 3, wherein the step of performing point-weighted processing on all the frequency dispersion points after the point separation processing comprises:
by a weight matrix function
Weighting the frequency dispersion points in the set L; wherein, ciIs the ith dispersion point, w, in the set LiDenotes ciThe weight of (c).
5. The inversion method of claim 4, wherein the step of constructing the inverse objective function of the multi-mode dispersion curve according to the point-weighted processing result comprises:
by passing
Constructing an inversion target function of the multimode dispersion curve; wherein G (M) represents an inversion target function of the multimode dispersion curve, M represents any n-layer horizontal model, and the fundamental phase velocity of M is as follows under the corresponding frequency of the ith dispersion point in the set L
The absolute value of the dispersion function of M is D at the position of the ith dispersion point in the set Li，M＝[VP,VS,ρ,T]，VP、VSVector parameters composed of longitudinal wave velocity and transverse wave velocity of each layer, rho and T are density and layer thickness of each layer respectively,
is the phase velocity value of the ith dispersion point in the set L.
6. The inversion method according to claim 5, wherein the thickness T is determined when the layer thickness of the last layer of the n layers is TnVector T includes the layer thicknesses of the first n-1 layers → ∞.
7. The inversion method of claim 5, wherein when R is 0, F, H, and Φi＝[0,wi]，
Where φ represents an empty set.
8. The inversion method of claim 5, wherein when R is 100% and H is φ, F is L and φ isi＝[wi，0]，
Where φ represents an empty set.
9. The inversion method according to claim 5,
wherein the content of the first and second substances,
represents the weight of the ith f-point,
representing the weight of the ith h point.
10. The inversion method of claim 1, wherein the step of performing inversion according to the inversion objective function to obtain a shear velocity profile comprises:
calculating a function value of an inversion iteration initial model through the inversion target function;
and gradually updating and iterating the model, calculating a function value of the model after the updating and iterating through the inversion objective function when the model after the updating and iterating is obtained, and finishing an iteration optimization solution process until the function value obtained through the calculation of the inversion objective function is smaller than a preset threshold value or the updating and iterating times of the model reach preset times, and outputting an optimal individual of the model to obtain a transverse wave velocity profile.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019049263A1 (en) * | 2017-09-07 | 2019-03-14 | 国立研究開発法人防災科学技術研究所 | Conversion program for simple profiling method (spm) and conversion method for simple profiling method (spm) |
| WO2019071515A1 (en) * | 2017-10-12 | 2019-04-18 | 南方科技大学 | Surface wave detection method and terminal device |
| CN109799530A (en) * | 2018-12-25 | 2019-05-24 | 核工业北京地质研究院 | Rayleigh waves dispersion curve inversion method for seismic surface wave exploration |
-
2019
- 2019-09-24 CN CN201910902606.7A patent/CN110568495B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019049263A1 (en) * | 2017-09-07 | 2019-03-14 | 国立研究開発法人防災科学技術研究所 | Conversion program for simple profiling method (spm) and conversion method for simple profiling method (spm) |
| WO2019071515A1 (en) * | 2017-10-12 | 2019-04-18 | 南方科技大学 | Surface wave detection method and terminal device |
| CN109799530A (en) * | 2018-12-25 | 2019-05-24 | 核工业北京地质研究院 | Rayleigh waves dispersion curve inversion method for seismic surface wave exploration |
Non-Patent Citations (3)
| Title |
|---|
| Multi-mode inversion of multi-channel analysis of surface waves (MASW) dispersion curves and high-resolution linear radon transform (HRLRT);Julian Ivanov et al.;《SEG Denver 2010 Annual Meeting》;20101231;第1902-1906页 * |
| 基于多模态分离的CMP解析法在面波勘探中的应用;胡晋等;《工程地球物理学报》;20190531;第16卷(第3期);第305-313页 * |
| 新的瑞雷波多模式频散曲线反演目标函数;蔡伟等;《地球科学》;20170930;第42卷(第9期);第1608-1620页 * |
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