INVERSION METHOD AND SYSTEM FOR ADAPTIVE MULTI-SEGMENT DISTRIBUTION CONSTRAINT OF PHYSICAL PROPERTIES

Abstract

Disclosed is an inversion method and system for adaptive multi—segment distribution constraint of physical properties, including: inverting an initial model parameter to be inverted in a regularized objective function to obtain an inverted. model parameter; counting distribution intervals of the inverted. model parameter; selecting a plurality of intervals, and multiplying by a corresponding interval factor to obtain physical property distribution interval segments; regulating the physical property distribution interval segments according to prior information; constructing za multi—segment transformation function; performing nmlti—segment transformation on the initial model parameter to be inverted to obtain a new model parameter; performing iterative inversion and inverse transformation on the new model parameter to obtain a true value of the model parameter; and obtaining a value of the model parameter to be inverted in response to the true value of the model parameter reaching a preset convergence criterion.

Description

I INVERSION METHOD AND SYSTEM FOR ADAPTIVE MULTI-SEGMENT DISTRIBUTION CONSTRAINT OF PHYSICAL PROPERTIES TECHNICAL FIELD [OI] The present application relates to the field of inversion technologies, in particular to an inversion method and system. for adaptive multisegment distribution constraint of physical properties. BACKGROUND ART

[02] In the field of inversion, due to purity, impurity content, fragmentation and other reasons, physical property values of a medium have different distribution range intervals, and each of these distribution intervals is called a segment of the physical property distribution. In an inversion space region, the physical property values of an object and a surrounding medium are often distributed in different range intervals, that is, the physical properties of the medium in the inversion space show the multisegment distribution.

[03] In the prior art, in order to determine a solution of a model parameter to be inverted, it is necessary to set a large search range interval of a physical property value for the model parameter to be inverted. This search range interval of the physical property value is generally far beyond the distribution of physical property segments of the whole inversion space, so that the search range of the model parameter to be inverted is too large, which is not conducive to the effective progress of the inversion, resulting in low accuracy of the finally determined value of the model parameter to be inverted. SUMMARY

[04] An object of the present application is to provide an inversion method and system for adaptive multisegment distribution constraint of physical properties, which can perform adaptive constraint on a search range of a model parameter to be inverted, so as to improve the accuracy of solving a value of the model parameter to be inverted.

[05] In order to fulfill the above object, the present application provides the following schemes.

[06] According to a first aspect, the present application provides an inversion. method. for adaptive :multisegment distribution constraint of physical properties. The inversion method for adaptive multisegment distribution constraint of physical properties includes:

[07] constructing a regularized objective function, the regularized objective function being composed of a misfit function, a regularization term.and a regularization factor, and the regularization term being constructed by a prior model and a model parameter to be inverted;

[08] inverting an initial model parameter to be inverted in the regularized objective function by using an optimized inversion algorithm to obtain an inverted model parameter;

[09] counting distribution intervals of the inverted model parameter at a fixed interval;

[10] selecting' a plurality of intervals in which the inverted model parameter is concentratedly distributed from the distribution intervals, and multiplying the plurality of intervals by a corresponding interval factor to obtain a plurality of physical property distribution interval segments;

[11] regulating the number of the physical property distribution interval segments and a segmentation range of each physical property distribution interval segment according to prior information, and taking the plurality of regulated physical property distribution interval segments as final model parameter change interval segments;

[12] constructing a multisegment transformation function based on the model parameter change interval segments;

[13] performing multisegment transformation on the initial model parameter to be inverted in the regularized objective function by using the multisegment transformation function to obtain a new model parameter;

[14] performing iterative inversion on the new model parameter by using a linear or nonlinear optimization method until a solution that makes the regularized objective function reach a minimum value is determined, and applying an inverse transformation of the multisegment transformation to this solution to obtain a true value of the model parameter; and

[15] taking the true value of the model parameter as a value of the model parameter to be inverted in response to the true value of the model parameter reaching a preset convergence criterion.

[16] According to a second aspect, the present application further provides a computer system. The computer system includes a memory, a processor, and a computer program that is stored in the memory and operable on the processor, the processor, when executing the computer program, being configured to implement the inversion method for adaptive multisegment distribution constraint of physical properties.

[17] According to the specific embodiments provided in the present application, the present application discloses the following technical effects.

[18] In order to solve unknown distribution intervals of physical properties, the traditional optimized inversion algorithm is used first to preliminarily invert the model parameter to be inverted in the regularized objective function, the inversion results are statistically separated, and the distribution intervals (i.e., the physical property distribution segments) of the model parameter are extracted. Then, the interval factor is used to finely tune the intervals in which the model parameter is concentratedly distributed, and then the number of segments and the segmentation range are further regulated according to the prior information, so as to adaptively obtain an accurate distribution interval that is more in line with the model parameter of the inversion space. Finally, under the constraint of this exact distribution interval, the regularized objective function is subjected to multisegment transformation and iterative inversion to obtain the accurate value of the model parameter to be inverted that is in line with the preset convergence condition. BRIEF DESCRIPTION OF THE DRAWINGS

[19] To describe the technical solutions of the embodiments of the present application. or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings required for describing the embodiments. Apparently, the accompanying drawings in the following description show only some embodiments of the present application, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts.

[20] FIG. 1 is a flowchart of an inversion method for adaptive multisegment distribution constraint of physical properties according to an embodiment of the present application;

[21] FIG. 2 is an execution diagram of an inversion method for adaptive multisegment distribution constraint of physical properties according to an embodiment of the present application; and

[22] FIG. 3 is a diagram of an internal structure of a computer system according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[23] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction. with accompanying drawings in the embodiments of the present application. Of course, the described embodiments are merely some embodiments, rather than all embodiments, of the present application. Based on the embodiments in the present application, all other embodiments derived by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[24] An object of the present application is to provide an inversion method and system for adaptive multisegment distribution constraint of physical properties, which can perform adaptive constraint on a search range of a model parameter to be inverted, so as to improve the accuracy of solving a value of the model parameter to be inverted.

[25] In order to make the above object, features and advantages of the present application clearer, a further detailed description will be made to the present application below with reference to the accompanying drawings and specific embodiments.

[26] Embodiment l

[27] The present embodiment provides an inversion method for adaptive multisegment distribution constraint of physical properties. .As shown. in FIG. 1, the inversion method for adaptive multisegment distribution constraint of physical properties includes the following steps.

[28] Sl: Construct a regularized objective function, the regularized objective function being composed of a misfit function, a regularization term.and a regularization factor, and the regularization term being constructed by a prior model and a model parameter to be inverted.

[29] In the present embodiment, a calculation formula of the regularized objective function is: ] «><m)=«>d(m)+aR(m),_

[31] where ÓC) is the regularized objective function; QA) is the misfit function; a is the regularization factor; RC) is the regularization item; and is the model parameter to be inverted.

[32] Furthermore, as shown in FIG. 2, the following steps precede Sl.

[33] (1) Acquire observation data and prior information of a target region. The observation data is any one or any combination of the followings: seismic time series data, electromagnetic time domain or frequency domain response data, gravity data, magnetic field data, induced polarizability data, temperature field data, etc.; and the prior information is any one or any combination of the followings: constraint conditions of various physical property jparameters, petrological lithology distribution information, geological structure distribution information, known physical property information, etc.

[34] (2) Construct a prior model of an inversion region based on prior information. The inversion region is determined based on the target region; and the prior model is any one or any combination of the followings: a velocity parameter, a resistivity parameter, a density parameter, a magnetic susceptibility parameter, a polarizability parameter, a dielectric parameter, etc.

[35] (3) Construct the regularization term based on the prior model and. the model parameter to be inverted A calculation formula of the regularization item is:

[36] R <m>=||m-m,,||2 or R <m> =||V <m-m,,,.)||2;

[37] where bm is the prior model, and VC) is a gradient operator.

[38] (4) Select an appropriate forward algorithm according to the model parameter to be inverted and an observation system device, and determine forward data. The observation system device is configured to collect observation data; and the observation data is any one or any combination of the followings: seismic time series data, electromagnetic time domain or frequency domain response data, gravity data, magnetic field data, induced polarizability data, temperature field data, etc.

[39] (5) Construct the misfit function based on residuals of the forward data and the observation data. A calculation formula of the misfit function is: ] Mm) = Hd A <m)||2,_

[41] where dûs is the observation data, and AC) is a forward operator.

[42] 52: Invert an initial model parameter to be inverted in the regularized objective function by using an optimized inversion algorithm to obtain an inverted model parameter.

[43] In the present embodiment, the initial model parameter to be inverted in the regularized objective function is preliminarily inverted by using a least square method, a conjugate gradient method, a Gaussian. Newton method or other traditional inversion algorithm.

[44] S3: Count distribution intervals of the inverted model parameter at a fixed interval.

[45] S4: Select a plurality of intervals in. which the inverted model parameter is concentratedly distributed from the distribution intervals, and multiply the plurality of intervals by a corresponding interval factor to obtain a plurality of physical property distribution interval segments.

[46] In the present embodiment, it is only necessary to retain the intervals in which the inverted model parameter is concentratedly distributed. in the above distribution intervals, and discard the intervals in which the inverted model parameter is less distributed.

[47] S5: Regulate the number of the physical property distribution interval segments and a segmentation range of each physical property distribution interval segment according to prior information, and take the plurality of regulated physical property distribution interval segments as final model parameter change interval segments.

[48] S6: Construct a multisegment transformation function based on the model parameter change interval segments.

[49] In the present embodiment, the multisegment transformation function may be applied to the model parameter to be inverted and a sensitivity matrix obtained by the forward algorithm, and the multisegment transformation function also needs to be constructed with the help of an error function, a 5igmoid function, a hyperbolic tangent function, an arctangent function, etc.

[50] In the present embodiment, a multisegment transformation function constructed from an improved Bent Identity function is proposed:

[51] a.=1Ê(1+g(m.m))lî(1+g(m.m(k)))+,8m_,- 2,-1 S 2,.-. " '

[52] where mi is anith original model parameter to kw inverted; 1%, is a Inodel parameter obtained. from. the ith original model parameter to be inverted by means of multi segment transformation; né is a lower boundary of a jth segment; H is an upper boundary of a kth segment; B is a constant; gÇ) is an improved Bent Identity function, and its calculation formula is:

[53] g(x)=\ / 5x2+1r'

[54] where ö :Ms a regulatory factor, whida is used to regulate the boundary smoothness of the multisegment transformation.

[55] S7: Perform multisegment transformation on the initial model parameter to be inverted in the regularized objective function by using the multisegment transformation function to obtain a new model parameter.

[56] S8: Perform. iterative inversion. on the new :model parameter by using a linear or nonlinear optimization method until a solution that makes the regularized objective function reach a minimum value is determined, and applying an inverse transformation of the multisegment transformation to this solution to obtain a true value of the model parameter.

[57] S9: Take the true value of the model parameter as a value of the model parameter to be inverted in response to the true value of the model parameter reaching a preset convergence criterion.

[58] In the present embodiment, the preset convergence criterion. may be the maximum. number of iterations, an objective function fitting error value, etc.

[59] In addition, in response to the true value of the model parameter not reaching the preset convergence criterion, the initial model parameter to be inverted in the regularized objective function is updated, and steps S7S9 are reexecuted.

[60] In summary, in the present embodiment, the conventional inversion. method. such as the least square method, the conjugate gradient method or the Gaussian Newton method is used first for iterative inversion, and a plurality of concentrated physical property distribution intervals is preferably selected as a plurality of physical property segments according to the model parameter distribution. of the inversion result, and. the physical property segments are multiplied by an interval factor as constraint intervals of the multisegment inversion of the physical properties. Then, according to the multisegment distribution of the physical properties, the inverted model parameter is subjected to multisegment transformation, so that the inverted model parameter is searched within respective segment intervals of the physical properties at the same time to realize the constraint on the search space of the inversion model parameter, which overcomes the situation that the traditional conventional method is easy to produce a plurality of solutions or the model parameter of the physical property can only be a discrete value in the largeinterval search on the one hand, and on the other hand, overcomes inaccurate physical properties caused by the need to artificially determine the physical property intervals, thereby improving the accuracy of solving the value of the parameter model to be inverted as a whole.

[61] Embodiment 2

[62] The present embodiment provides a computer system. The computer systen1 may' be a server or a terminal, an internal structural diagram of which may be shown in FIG. 3. The computer system includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, the memory, and the input / output interface are connected. via a system. bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer system is configured to provide computation and control abilities. The memory of the computer system includes a nonvolatile storage medium and an internal memory. The nonvolatile storage medium has an operating system, a computer program and a database stored therein. The internal memory provides an environment for the operating system and the computer program in the nonvolatile storage medium. The database of this computer system is configured to store the data of the inverted model parameter and the observation data of the target region. The input / output interface of this computer system is configured to exchange information between the processor and external devices. The communication interface of this computer system is configured to communicate with an external terminal through network connection. The computer program is executed by the processor to implement the inversion method for adaptive multisegment distribution constraint of physical properties.

[63] A.person skilled in the art may understand that, the structure shown in FIG. 3 is merely a block diagram of a partial structure related. to a solution. in the present application, and does not constitute a limitation to the computer system. to which the solution in the present application is applied. Specifically, the computer system may include more or fewer components than those shown in the figure, or have some components combined, or have a different component deployment.

[64] A.person of ordinary skill in the art may understand that all or some of procedures of the method.in the foregoing embodiments may be implemented by a computer program instructing relevant hardware. The computer program may be stored in a nonvolatile computerreadable storage medium. When the computer program is executed, the procedures of the foregoing method embodiments may be implemented. References to the memory, the database and other medium.used in the embodiments provided in the present application may all include at least one of a nonvolatile memory or a volatile memory. The nonvolatile memory may include a read only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a highdensity embedded non volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory may be a random access memory (RAM) or an external cache, etc. As a description rather than a limitation, RAM may be implemented in many forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM).

[65] The databases involved in the respective embodiments provided in the present application may include at least one of a relational database and a nonrelational database. The nonrelational database may include a blockchainbased distributed database, but not be limited thereto. The processors involved in the respective embodiments provided in the present application may be a generalpurpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic processor, a data processing logic processor based on quantum.computing, etc., but not be limited thereto.

[66] The respective embodiments of the present description are described in a progressive manner, the focus of each embodiment illustrates the differences from other embodiments, and the same similar parts among the embodiments may refer to one another.

[67] Specific examples are used herein to illustrate the principles and embodiments of the present application. The description of the above embodiments is only used to help the understanding of the methods and core ideas of the present application. At the same time, for those skilled in the art, according to the ideas of the present application, there will be changes in the specific embodiments and the scope of application. In summary, the content of the present description should not be construed as a limitation of the present application. < / m> < / m>

Claims

l. An inversion method for adaptive multisegment distribution restriction of physical properties, comprising the next steps: constructing a regularized objective function, where the regularized objective function is composed of a mismatch function, a regularization term and a regularization factor, and where the regularization term is constructed by a prior model and a model parameter ter which must be inverted; inverting an initial model parameter that must be inverted into the regularized objective function by using an optimized inversion algorithm to obtain an inverted model parameter; the counting. of. distribution intervals. of. the inverted fixed model parameter at a fixed interval; selecting a from the distribution intervals multiple of intervals in which the inverted model parameter meter concentrated is distributed, and the multiplication of the multiple of intervals with a corresponding inter falling factor around a multiple of physical attribute distribution to obtain interval segments; regulating the number of the physical property distribution interval segments and a segmentation range of each physical property distribution interval segment according to prior ongoing information, and taking the multiple of regular larized physical trait distribution interval segments as final model parameter change interval segments ten; constructing a multisegment transformation function based on the model parameter change interval segments; executing multisegment transformation on the ini key model parameter that must be inverted into the ge regularized objective function by using the multisegment transformation function to a new model parameter meter to obtain; performing iterative inversion on the new mo del parameter by using a linear or non-linear linear optimization method until a solution that the regularized objective function leaves a minimum value achieve, is determined, and the application of an inverse transaction formation of the multisegment transformation on this solution to obtain a true value of the model parameter; and taking the actual value of the model parameter as a value of the model parameter that needs to be inverted in response to the true value of the model parameter that a preset convergence criterion reached.

2. Inversion method for adaptive multisegment ver constraint on the division of physical properties according to conclusion 1, where the optimized inversion algorithm a small square method, a conjugate gradient method or is a Gauss-Newton method.

3. Inversion method for adaptive multisegment ver constraint on the division of physical properties according to conclusion 1, further comprising: updating the initial model p rameter that must be inverted into the regularized objective function in response to the true value of the mo del parameter that the preset convergence criterion not reached, and redoing the step of the executing multisegment transformation on the initial mo delparameter that must be inverted in the regular seerde objective function by using the multi segment transformation function to a new model parameter to acquire.

4. A computer system, comprising a memory, a processor and a computer program that is stored in the memory and that is workable on the processor, where the pro cessor, when it executes the computer program, is ge configured to implement the inversion method for adaptive multisegment distribution restriction of physical properties according to one of claims 1 to 3. FIG. 1

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