阅读说明：本技术 基于岩石特征的震源机制反演方法及装置 (Seismic source mechanism inversion method and device based on rock characteristics ) 是由 郑晶 孟令彬 彭苏萍 于 2020-12-30 设计创作，主要内容包括：本发明提供了一种基于岩石特征的震源机制反演方法及装置,涉及微地震监测的技术领域,先采集目标监测区域的微地震信号；然后基于目标关系模型,预测与微地震信号对应的目标岩石特征；最后基于目标岩石特征反演目标监测区域内破裂事件发生的震源机制。本发明通过采集目标监测区域的微地震信号的方式可以保证反演震源机制所用数据的真实性,通过根据目标关系模型预测与微地震信号对应的目标岩石特征的方式,可以得到目标监测区域内破裂事件发生的岩石特征,该岩石特征可以避免震源机制的多解性,进而得到真实、有效且唯一的震源机制。(The invention provides a seismic source mechanism inversion method and device based on rock characteristics, which relate to the technical field of micro-seismic monitoring and comprise the steps of firstly collecting micro-seismic signals of a target monitoring area; then, predicting target rock characteristics corresponding to the micro seismic signals based on a target relation model; and finally, inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics. According to the method, the authenticity of data used for inverting the seismic source mechanism can be ensured by acquiring the micro-seismic signals of the target monitoring area, the rock characteristics of the occurrence of the fracture event in the target monitoring area can be obtained by predicting the target rock characteristics corresponding to the micro-seismic signals according to the target relation model, the rock characteristics can avoid the multi-solution of the seismic source mechanism, and therefore a real, effective and unique seismic source mechanism can be obtained.)

1. A seismic source mechanism inversion method based on rock features is characterized by comprising the following steps:

acquiring micro seismic signals of a target monitoring area;

predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model; the target relation model is the corresponding relation between rock characteristics and microseism signals, and the target rock characteristics are rock characteristics of the occurrence of fracture events in the target monitoring area;

and inverting the mechanism of the seismic source of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

2. The method of claim 1, further comprising:

obtaining a rock sample of a sample area, and determining a rock characteristic sample according to the rock sample; wherein the sample region comprises: the target monitoring area and/or non-target monitoring area;

performing a triaxial stress experiment based on the rock sample to generate an acoustic emission signal sample;

and acquiring a micro-seismic signal sample of the sample area, and constructing the target relation model based on the micro-seismic signal sample, the acoustic emission signal sample and the rock characteristic sample.

3. The method of claim 2, wherein constructing the object relationship model based on the microseismic signal samples, the acoustic emission signal samples, and the rock signature samples comprises:

establishing a first relation model by utilizing a first target deep learning network based on the rock characteristic sample and the acoustic emission signal sample; wherein the first relation model is a relation model between rock characteristics and acoustic emission signals;

establishing a second relation model by utilizing a second target deep learning network based on the micro-seismic signal sample and the acoustic emission signal sample; wherein the second relationship model is a relationship model between microseismic signals and the acoustic emission signals;

and constructing the target relation model based on the first relation model and the second relation model.

4. The method of claim 1, wherein the rock signature comprises: petrophysical parameters and/or petromechanical parameters; wherein the petrophysical parameter comprises at least one of: density, porosity, permeability, magnetic properties and electrical resistivity, said mechanical parameters comprising at least one of: poisson's ratio, young's modulus, lame constant, tensile strength, and longitudinal and transverse wave velocity.

5. The method of claim 2, further comprising:

in the process of conducting the triaxial stress experiment based on the rock sample, a rock fracture mechanism corresponding to the rock sample is also generated.

6. The method of claim 1, further comprising:

judging whether early warning is needed or not according to the seismic source mechanism;

and if so, carrying out early warning through an early warning mechanism corresponding to the seismic source mechanism.

7. The method of claim 2, further comprising:

and adding a randomly generated simulated interference signal in the process of constructing the target relation model.

8. A seismic source mechanism inversion device based on rock characteristics is characterized by comprising:

the acquisition unit is used for acquiring microseism signals of a target monitoring area;

the prediction unit is used for predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model; the target relation model is the corresponding relation between rock characteristics and microseism signals, and the target rock characteristics are rock characteristics of the occurrence of fracture events in the target monitoring area;

and the inversion unit is used for inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1 to 7.

Technical Field

The invention relates to the technical field of microseism monitoring, in particular to a seismic source mechanism inversion method and device based on rock characteristics.

Background

In the microseism monitoring process, no matter for monitoring the hydraulic fracturing condition or for ensuring the stability of migration and sequestration of carbon dioxide in the injection process, the method can acquire the fracture signals generated by media such as an underground reservoir and the like due to the damaged primary stress balance, and further invert a seismic source mechanism. However, the existing seismic source mechanism inversion method is only simulated in an experiment, and the simulation result has multi-solution, so that the prior art faces the technical problems that the seismic source mechanism has multi-solution and a real and effective seismic source mechanism cannot be further obtained.

Disclosure of Invention

The invention aims to provide a seismic source mechanism inversion method and device based on rock characteristics, so as to solve the technical problem that a real and effective seismic source mechanism cannot be obtained due to the fact that simulation results have multiple solutions in the prior art.

In a first aspect, the present invention provides a seismic source mechanism inversion method based on rock features, including: acquiring micro seismic signals of a target monitoring area; predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model; the target relation model is the corresponding relation between rock characteristics and microseism signals, and the target rock characteristics are rock characteristics of the occurrence of fracture events in the target monitoring area; and inverting the mechanism of the seismic source of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

Further, the method further comprises: obtaining a rock sample of a sample area, and determining a rock characteristic sample according to the rock sample; wherein the sample region comprises: the target monitoring area and/or non-target monitoring area; performing a triaxial stress experiment based on the rock sample to generate an acoustic emission signal sample; and acquiring a micro-seismic signal sample of the sample area, and constructing the target relation model based on the micro-seismic signal sample, the acoustic emission signal sample and the rock characteristic sample.

Further, constructing the target relationship model based on the micro-seismic signal sample, the acoustic emission signal sample and the rock feature sample, including: establishing a first relation model by utilizing a first target deep learning network based on the rock characteristic sample and the acoustic emission signal sample; wherein the first relation model is a relation model between rock characteristics and acoustic emission signals; establishing a second relation model by utilizing a second target deep learning network based on the micro-seismic signal sample and the acoustic emission signal sample; wherein the second relationship model is a relationship model between microseismic signals and the acoustic emission signals; and constructing the target relation model based on the first relation model and the second relation model.

Further, the rock features include: petrophysical parameters and/or petromechanical parameters; wherein the petrophysical parameter comprises at least one of: density, porosity, permeability, magnetic properties and electrical resistivity, said mechanical parameters comprising at least one of: poisson's ratio, young's modulus, lame constant, tensile strength, and longitudinal and transverse wave velocity.

Further, the method further comprises: in the process of conducting the triaxial stress experiment based on the rock sample, a rock fracture mechanism corresponding to the rock sample is also generated.

Further, the method further comprises: judging whether early warning is needed or not according to the seismic source mechanism; and if so, carrying out early warning through an early warning mechanism corresponding to the seismic source mechanism.

Further, the method further comprises: and adding a randomly generated simulated interference signal in the process of constructing the target relation model.

In a second aspect, the present invention provides a seismic source mechanism inversion apparatus based on rock features, including: the acquisition unit is used for acquiring microseism signals of a target monitoring area; the prediction unit is used for predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model; the target relation model is the corresponding relation between rock characteristics and microseism signals, and the target rock characteristics are rock characteristics of the occurrence of fracture events in the target monitoring area; and the inversion unit is used for inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

In a third aspect, the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program executable on the processor, and the processor executes the steps of the rock feature-based source mechanism inversion method implemented when the computer program is executed.

In a fourth aspect, the present invention also provides a computer readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method for rock feature based source mechanism inversion.

The invention provides a seismic source mechanism inversion method and device based on rock characteristics, which comprises the following steps: firstly, acquiring micro seismic signals of a target monitoring area; then, predicting target rock characteristics corresponding to the micro seismic signals based on a target relation model; and finally, inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics. According to the method, the authenticity of data used for inverting the seismic source mechanism can be ensured by acquiring the micro-seismic signals of the target monitoring area, the rock characteristics of the occurrence of the fracture event in the target monitoring area can be obtained by predicting the target rock characteristics corresponding to the micro-seismic signals according to the target relation model, the rock characteristics can avoid the multi-solution of the seismic source mechanism, and therefore a real, effective and unique seismic source mechanism can be obtained.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a flowchart of a seismic source mechanism inversion method based on rock features according to an embodiment of the present invention;

FIG. 2 is a flow chart of another seismic source mechanism inversion method based on rock features according to an embodiment of the present invention;

FIG. 3 is a flowchart of another seismic source mechanism inversion method based on rock features according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a seismic source mechanism inversion apparatus based on rock features according to an embodiment of the present invention.

Icon:

11-a collecting unit; 12-a prediction unit; 13-inversion unit.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but 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.

The existing seismic source mechanism inversion method is to simulate by only using a GCTS triaxial pressure instrument in an experiment, and because the seismic source mechanism inversion method is not associated with a micro seismic signal which is actually acquired, a simulation result has multi-solution, so that the prior art faces the technical problems that the seismic source mechanism has multi-solution and a real and effective seismic source mechanism cannot be further obtained.

Based on the above, the embodiment of the invention provides a seismic source mechanism inversion method and device based on rock characteristics, the authenticity of data used for inverting a seismic source mechanism can be ensured by acquiring micro seismic signals of a target monitoring area, the rock characteristics of a fracture event in the target monitoring area can be obtained by predicting the target rock characteristics corresponding to the micro seismic signals according to a target relation model, the rock characteristics can avoid the multi-solution of the seismic source mechanism, and a true, effective and unique seismic source mechanism can be obtained.

For the understanding of the embodiment, a detailed description will be given to a seismic source mechanism inversion method based on rock features disclosed in the embodiment of the present invention.

Example 1:

in accordance with an embodiment of the present invention, there is provided an embodiment of a method for seismic source mechanism inversion based on rock signatures, it being noted that the steps illustrated in the flowchart of the accompanying drawings may be performed in a computer system such as a set of computer executable instructions and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.

Fig. 1 is a flowchart of a seismic source mechanism inversion method based on rock features according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:

and S101, acquiring micro seismic signals of a target monitoring area. In this embodiment, the definition of the target monitoring area may be determined according to actual conditions, for example, the target monitoring area may refer to a sequestration area for a gas such as carbon dioxide, or may refer to an area where hydraulic fracturing is located. It should be noted that the present embodiment does not specifically limit the manner in which the micro-seismic signals are acquired. The micro seismic signals can be expanded into seismic signals of deep natural earthquakes, and the seismic signals of the deep natural earthquakes on the rock surface can be acquired when the seismic signals of the deep natural earthquakes are acquired because the seismic signals of the deep natural earthquakes on the deep rock core are not easy to directly acquire.

And S102, predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model. The target relation model is the corresponding relation between the rock characteristics and the micro seismic signals, and the target rock characteristics are the rock characteristics of the cracking events in the target monitoring area.

And S103, inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

According to the seismic source mechanism inversion method based on the rock characteristics, provided by the embodiment of the invention, the authenticity of data used for inverting the seismic source mechanism can be ensured by acquiring the micro-seismic signals of the target monitoring area, the rock characteristics of the occurrence of the fracture event in the target monitoring area can be obtained by predicting the target rock characteristics corresponding to the micro-seismic signals according to the target relation model, the rock characteristics can avoid the multi-solution of the seismic source mechanism, and then the true, effective and unique seismic source mechanism is obtained.

In an alternative embodiment, the rock features include: petrophysical parameters and/or petromechanical parameters; wherein the petrophysical parameters include at least one of: density, porosity, permeability, magnetic properties and electrical resistivity, the mechanical parameters including at least one of: poisson's ratio, young's modulus, lame constant, tensile strength, and longitudinal and transverse wave velocity.

In an alternative embodiment, as shown in fig. 2, the following steps S104 to S106 are performed before step S102:

and step S104, obtaining a rock sample of the sample area, and determining a rock characteristic sample according to the rock sample. Wherein the sample region comprises: a target monitoring area and/or a non-target monitoring area;

and S105, performing a triaxial stress experiment based on the rock sample to generate an acoustic emission signal sample.

And S106, acquiring a micro-seismic signal sample of the sample area, and constructing a target relation model based on the micro-seismic signal sample, the acoustic emission signal sample and the rock characteristic sample.

Since the waveform is data carrying time, peak-to-peak value, and Y value, and only Y value is generated for the signal, the acoustic emission signal can be replaced with an acoustic emission waveform in order to enrich the data content. Since both the acoustic emission waveform sample and the micro-seismic signal sample generated by the laboratory are generated by rock fracture, a certain correlation exists between the two samples, and the rock samples with different rock characteristics have different waveform characteristics when being fractured.

In the process of executing step S105, the present embodiment may also generate a rock breaking mechanism corresponding to the rock sample.

After the target relation model is constructed, as shown in fig. 3, on one hand, rock characteristics can be predicted by using the target relation model constructed by deep learning and micro seismic signals acquired on site, and then a seismic source mechanism is inverted; then, acoustic emission signals can be predicted according to the second relation model; on the other hand, triaxial stress experiments can produce rock fracture mechanisms.

When the sample area is a subsurface formation, the triaxial stress experiment can obtain the relation between the basic stress parameters (namely the mechanical parameters) of different rock samples and time under the condition of simulating the environment (such as high temperature and high pressure) of the subsurface formation. Furthermore, the basic stress parameters and the environment of the rock sample have certain relations with the instability precursors of the rock sample, and the different basic stress parameters and the time curve relations are mainly shown. The rock sample develops a fracture morphology after fracturing and venting. In general, different rock failure mechanisms result in multiple patterns of fracture morphology. The embodiment can receive acoustic emission waveform samples with different characteristics through the sensor, further realize internal positioning of cracks in the rock sample according to the acoustic emission waveform samples and the arrival time difference of P waves, and also determine the corresponding relation between a rock fracture mechanism (such as shearing, tension and the like) and the acoustic emission waveform, wherein the main characteristics of the acoustic emission waveform are receiving polarity, P wave energy and time-frequency information.

In an alternative embodiment, the present application may utilize deep learning to predict the correlation between rock features and microseismic signals, which may occur in the form of microseismic waveforms. In step S106, a target relationship model is constructed based on the micro-seismic signal sample, the acoustic emission signal sample and the rock characteristic sample, and the method includes the following steps S201 to S203:

step S201, a first relation model is established by utilizing a first target deep learning network based on the rock characteristic sample and the acoustic emission signal sample. Wherein the first relation model is a relation model between rock characteristics and acoustic emission signals.

And S202, establishing a second relation model by utilizing a second target deep learning network based on the micro-seismic signal samples and the acoustic emission signal samples. And the second relation model is a relation model between the micro-seismic signals and the acoustic emission signals. The present embodiment may determine a relationship model between the acoustic emission signal and the micro-seismic signal, for the following reasons: one of them can be used to guide the research of the mechanism of the seismic source, and the other can further guide the analysis of rock characteristics, for example, the rock characteristics of cap fracture are different from the rock characteristics of reservoir fracture.

The embodiment of the invention can guide the inversion unique solution of a seismic source mechanism to obtain the specific fracture characteristics (namely rock characteristics) of the fracture position where the micro seismic event occurs, and particularly has important significance in a carbon dioxide sequestration early warning project. Therefore, the embodiment can determine the fracture type of the microseism event according to the actually acquired microseism signals, and further invert a unique seismic source mechanism.

Step S203, a target relation model is constructed based on the first relation model and the second relation model. In the process of constructing the target relation model, a randomly generated simulation interference signal is added for improving the reliability of the target relation model.

In practical application, if carbon dioxide is injected and sealed in a target monitoring area, in order to comply with the policy of energy conservation and emission reduction, cracking needs to be avoided as much as possible in the process of injecting and sealing the carbon dioxide to prevent generated gas from escaping, influence on the ecological environment, establish the relationship between rock characteristics, acoustic emission waveforms and actually acquired microseismic waveforms, and can perform effective seismic source mechanism analysis on microseismic events generated in the process of injecting and sealing the carbon dioxide. The source mechanisms include, but are not limited to: the source location and the moment tensor matrix.

In the microseism monitoring process, no matter in order to prevent hydraulic fracturing, or guarantee the stability of migration and sequestration in the carbon dioxide injection process, a fracture signal (namely a microseism signal) generated by a medium such as an underground reservoir and the like due to the fact that the balance of the original stress is broken is acquired. The microseism signal is similar to the nature of an air gun active source and a natural seismic signal, and can be transmitted through different stratum structure media according to wave, so that the expressions of reflected and direct P waves and S waves are different. The microseism signal monitoring and acquisition is mainly used for researching the signal characteristics of direct waves. Destabilizing fractures of subsurface rocks in confined spaces are primarily related to the source radiation pattern (e.g., double force couple DC, isotropic ISO, compensated linear vector dipole CLVD). The rock fracture mechanism is a mechanical process of rock units with a certain scale at the fracture part of the micro-seismic event. The seismic source mechanism is used for analyzing the direct influence generated by the micro-seismic waveform (micro-seismic signal) and is beneficial to analyzing the motion characteristics and stress distribution relation of the rock at the fracture.

Through the sample data analysis, on the basis of the given rock characteristic sample, the acoustic emission signal sample corresponding to the rock characteristic sample and the corresponding micro-seismic signal sample, a relation model between the rock characteristic and the acoustic emission signal and a relation model between the acoustic emission signal and the micro-seismic signal can be established through a deep learning network, and then a seismic source mechanism of deep medium fracture can be obtained through prediction.

In an alternative embodiment, after step S103, the method further includes the following steps S301 to S302:

step S301, judging whether early warning is needed or not according to a seismic source mechanism;

and S302, if yes, early warning is carried out through an early warning mechanism corresponding to the seismic source mechanism.

The embodiment can establish a sound early warning mechanism, and further realize early warning on a series of applications, for example: mine water inrush warning, rock fracture warning, water injection and gas sequestration warning and the like. It should be noted that different rock samples may correspond to different warning intervals.

The whole technical scheme of this embodiment can be described as the following 5 steps: step 1, measuring rock characteristics of different rock samples (e.g. rock samples of a reservoir and rock samples of a cap rock), for example: density, P-wave velocity, S-wave velocity, elastic modulus, Lame constant μ, and Poisson' S ratio; step 2, respectively carrying out acoustic emission experiments on different rock samples, and analyzing the characteristics of the microseismic waveforms; step 3, constructing a relational model of the microseismic waveform and the rock characteristics according to deep learning, adding analog signals and random samples as interference in the construction process, and verifying the reliability of the relational model; step 4, predicting rock characteristics causing an actual microseism event by taking the actually acquired microseism signals as samples, and further inverting a seismic source mechanism; and 5, judging the position of the type of the rock in the target monitoring area in the reservoir composition according to the rock characteristics, predicting the rock fracture condition according to the rock fracture mechanism, and judging the possibly occurring fracture form.

Example 2:

the embodiment of the invention provides a seismic source mechanism inversion device based on rock characteristics, which is mainly used for executing the seismic source mechanism inversion method based on rock characteristics provided by the embodiment 1, and the seismic source mechanism inversion device based on rock characteristics provided by the embodiment of the invention is specifically described below.

Fig. 4 is a schematic structural diagram of a seismic source mechanism inversion apparatus based on rock features according to an embodiment of the present invention. As shown in fig. 4, the seismic source mechanism inversion apparatus based on rock features mainly includes: acquisition unit 11, prediction unit 12 and inversion unit 13, wherein:

the acquisition unit 11 is used for acquiring microseism signals of a target monitoring area;

the prediction unit 12 is used for predicting target rock characteristics corresponding to the micro seismic signals based on a pre-constructed target relation model; the target relation model is the corresponding relation between rock characteristics and the micro seismic signals, and the target rock characteristics are rock characteristics of the occurrence of a fracture event in a target monitoring area;

and the inversion unit 13 is used for inverting a seismic source mechanism of the occurrence of the fracture event in the target monitoring area based on the target rock characteristics.

According to the seismic source mechanism inversion device based on the rock characteristics, the authenticity of data used for inverting the seismic source mechanism can be guaranteed in a mode of collecting micro seismic signals of a target monitoring area, the rock characteristics of a fracture event in the target monitoring area can be obtained in a mode of predicting the target rock characteristics corresponding to the micro seismic signals according to a target relation model, the rock characteristics can avoid the multi-solution of the seismic source mechanism, and therefore a real, effective and unique seismic source mechanism can be obtained.

Optionally, the apparatus further comprises:

the acquisition unit is used for acquiring a rock sample of the sample area and determining a rock characteristic sample according to the rock sample; wherein the sample region comprises: a target monitoring area and/or a non-target monitoring area;

the generating unit is used for performing a triaxial stress experiment based on the rock sample to generate an acoustic emission signal sample;

and the acquisition and construction unit is used for acquiring the micro-seismic signal sample of the sample area and constructing a target relation model based on the micro-seismic signal sample, the acoustic emission signal sample and the rock characteristic sample.

Optionally, the acquisition construction unit includes a first construction module, a second construction module, and a construction module, wherein:

the first establishing module is used for establishing a first relation model by utilizing a first target deep learning network based on the rock characteristic sample and the acoustic emission signal sample; wherein the first relation model is a relation model between rock characteristics and acoustic emission signals;

the second establishing module is used for establishing a second relation model by utilizing a second target deep learning network based on the micro-seismic signal samples and the acoustic emission signal samples; the second relation model is a relation model between the micro-seismic signals and the acoustic emission signals;

and the construction module is used for constructing the target relation model based on the first relation model and the second relation model.

Optionally, the rock features comprise: petrophysical parameters and/or petromechanical parameters; wherein the petrophysical parameters include at least one of: density, porosity, permeability, magnetic properties and electrical resistivity, the mechanical parameters including at least one of: poisson's ratio, young's modulus, lame constant, tensile strength, and longitudinal and transverse wave velocity.

Optionally, the generating unit is configured to further generate a rock fracture mechanism corresponding to the rock sample in the process of performing the triaxial stress test based on the rock sample.

Optionally, this embodiment further includes a determining unit and an early warning unit, wherein:

the judging unit is used for judging whether early warning is needed or not according to the seismic source mechanism;

and the early warning unit is used for carrying out early warning through an early warning mechanism corresponding to the seismic source mechanism if the seismic source mechanism is the seismic source mechanism.

Optionally, the building unit is further configured to add a randomly generated simulated interference signal in the process of building the target relationship model.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In an optional embodiment, the present embodiment further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method of the foregoing method embodiment.

In an alternative embodiment, the present embodiment also provides a computer readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of the above method embodiment.

In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "connected" and "connected" should be interpreted broadly, e.g., as being fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

In the description of the present embodiment, it should be noted that the terms "in", "up", "in", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be configured in a specific orientation, and operate, and thus, should not be construed as limiting the present embodiment. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the embodiments provided in the present embodiment, it should be understood that the disclosed method and apparatus may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present embodiment or parts of the technical solution may be essentially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.