Processing method, device and processing equipment for estimating model of permanent displacement of post-earthquake surface

Abstract

The present application provides a processing method, device and processing equipment for a post-earthquake permanent surface displacement estimation model, which is used to provide a set of specific model processing solutions and is improved in details, so as to obtain a post-earthquake permanent surface displacement estimation model that is universal for various scenarios, convenient to configure input data and has high processing efficiency, which meets the high-quality prediction needs for post-earthquake permanent surface displacement in actual situations and provides accurate and effective data support for engineering activities.

Description

Technical Field

[0001] The present application relates to the field of post-earthquake permanent surface displacement analysis, and specifically to a processing method, device and processing equipment for a post-earthquake permanent surface displacement estimation model. Background Art

[0002] Earthquakes are ground movements caused by the rapid release of energy from the earth's crust. The main cause of earthquakes is the collision between plates. The place where an earthquake begins is called the epicenter, and the ground directly above the epicenter is called the epicenter. Earthquakes often cause serious casualties and losses, and may also cause secondary disasters such as mudslides, tsunamis, and landslides.

[0003] In actual situations, many projects mainly adopt avoidance measures to reduce the impact of fault movement. However, many projects inevitably cross active faults. Therefore, it is very necessary to reasonably estimate the permanent displacement of the surface after an earthquake for the construction of these projects.

[0004] However, the inventors of the present application have discovered that the acquisition of permanent surface displacement after an earthquake in the prior art is usually based on a baseline correction method, which requires the deployment of a large number of stations to obtain sufficient measured seismic motion records. Even if there are sufficient seismic motion records, the proportion of records containing permanent displacement is very low, which makes it difficult to adapt to various types of earthquake scenarios and sites. The use of seismic motion simulation technology is overly dependent on the accuracy of the seismological model, resulting in large errors in actual applications and often failing to accurately simulate the permanent displacement of a specific site. Summary of the invention

[0005] The present application provides a processing method, device and processing equipment for a post-earthquake permanent surface displacement estimation model, which is used to provide a set of specific model processing solutions and is improved in details, so as to obtain a post-earthquake permanent surface displacement estimation model that is universal for various scenarios, convenient to configure input data and has high processing efficiency, which meets the high-quality prediction needs for post-earthquake permanent surface displacement in actual situations and provides accurate and effective data support for engineering activities.

[0006] In a first aspect, the present application provides a method for processing a post-earthquake surface permanent displacement estimation model, the method comprising:

[0007] Obtaining earthquake acceleration time history data of the target area;

[0008] Based on the earthquake acceleration time history data, the corresponding peak displacement and permanent displacement are obtained;

[0009] Obtain the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area;

[0010] A post-earthquake surface permanent displacement estimation model is established based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity. The post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

[0011] In combination with the first aspect of the present application, in a first possible implementation of the first aspect of the present application, obtaining corresponding peak displacement and permanent displacement based on seismic acceleration time history data includes:

[0012] The acceleration time history data of the earthquake motion acceleration time history is subjected to baseline correction and filtering to obtain the acceleration time history, and then the maximum absolute value of the displacement time history obtained by the secondary integration is taken to obtain the peak displacement;

[0013] Based on the baseline correction method of permanent displacement recovery, the earthquake acceleration time history data are processed to obtain the permanent displacement.

[0014] In combination with the first aspect of the present application, in a second possible implementation of the first aspect of the present application, the quantitative formula involved in the post-earthquake surface permanent displacement estimation model is specifically:

[0015] ln(PD)=α 1 ln(PGD)+F source +F path +F site +δB+δW,

[0016] Where PD is the estimated permanent displacement, α 1 is the first coefficient obtained by random effect model regression analysis, PGD is the peak displacement parameter, F Source is the source term, F Path is the path item, F Site is the site term, δB is the inter-event residual, δW is the intra-event residual, the source term is determined by the moment magnitude parameter, fault type parameter and focal depth parameter, the path term is determined by the moment magnitude parameter, reference magnitude parameter and fault distance parameter, and the site term is determined by the site shear wave velocity parameter.

[0017] In combination with the second possible implementation of the first aspect of the present application, in a third possible implementation of the first aspect of the present application, the source term is specifically expressed as:

[0018] F Source =α 2 M w +e i F sofi +α 3depth,

[0019] Among them, α 2 is the second coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, F sofi is the fault type parameter, e i is the preset coefficient, i=1,2,3, corresponding to strike-slip fault, reverse fault and normal fault respectively, α 3 is the third coefficient obtained by random effect model regression analysis, and depth is the focal depth parameter.

[0020] In combination with the second possible implementation of the first aspect of the present application, in a fourth possible implementation of the first aspect of the present application, the path item is specifically represented as:

[0021] ,

[0022] Among them, α 4 is the fourth coefficient obtained from the random effect model regression analysis, M w is the moment magnitude parameter, M ref is the reference magnitude parameter, α 5 is the fifth coefficient obtained from the random effect model regression analysis, R rup is the fault distance parameter, α 6 The sixth coefficient is introduced to account for the near-field saturation effect.

[0023] In combination with the second possible implementation of the first aspect of the present application, in a fifth possible implementation of the first aspect of the present application, the site item is specifically represented as:

[0024] F site =α 7 ln(V / 800),

[0025] Among them, α 7 is the seventh coefficient obtained by random effect model regression analysis, and V is the site shear wave velocity parameter.

[0026] In combination with the first aspect of the present application, in a sixth possible implementation of the first aspect of the present application, after establishing a post-earthquake surface permanent displacement estimation model based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance, and site shear wave velocity, the method further includes:

[0027] Obtaining data to be processed in the target area, the data to be processed including target peak displacement, moment magnitude, target focal depth, target fault type, target fault distance and target site shear;

[0028] Inputting the data to be processed into the post-earthquake permanent surface displacement estimation model so that the post-earthquake permanent surface displacement estimation model can carry out corresponding post-earthquake permanent surface displacement estimation processing;

[0029] Extract the post-earthquake surface permanent displacement estimation results output by the post-earthquake surface permanent displacement estimation model.

[0030] In a second aspect, the present application provides a processing device for a post-earthquake surface permanent displacement estimation model, the device comprising:

[0031] A first acquisition unit, used to acquire earthquake acceleration time history data of a target area;

[0032] A second acquisition unit is used to acquire corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data;

[0033] The third acquisition unit is used to obtain the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area;

[0034] A unit is established for establishing a post-earthquake surface permanent displacement estimation model based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity, wherein the post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

[0035] In conjunction with the second aspect of the present application, in a first possible implementation of the second aspect of the present application, the second acquisition unit is specifically configured to:

[0036] The acceleration time history data of the earthquake motion acceleration time history is subjected to baseline correction and filtering to obtain the acceleration time history, and then the maximum absolute value of the displacement time history obtained by the secondary integration is taken to obtain the peak displacement;

[0037] Based on the baseline correction method of permanent displacement recovery, the earthquake acceleration time history data are processed to obtain the permanent displacement.

[0038] In conjunction with the second aspect of the present application, in a second possible implementation of the second aspect of the present application, the quantitative formula involved in the post-earthquake surface permanent displacement estimation model is specifically:

[0039] ln(PD)=α 1 ln(PGD)+F source +F path +F site +δB+δW,

[0040] Where PD is the estimated permanent displacement, α1 is the first coefficient obtained by random effect model regression analysis, PGD is the peak displacement parameter, F Source is the source term, F Path is the path item, F Site is the site term, δB is the inter-event residual, δW is the intra-event residual, the source term is determined by the moment magnitude parameter, fault type parameter and focal depth parameter, the path term is determined by the moment magnitude parameter, reference magnitude parameter and fault distance parameter, and the site term is determined by the site shear wave velocity parameter.

[0041] In combination with the second possible implementation of the second aspect of the present application, in a third possible implementation of the second aspect of the present application, the source term is specifically expressed as:

[0042] F Source =α 2 M w +e i F sofi +α 3 depth,

[0043] Among them, α 2 is the second coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, F sofi is the fault type parameter, e i is the preset coefficient, i=1,2,3, corresponding to strike-slip fault, reverse fault and normal fault respectively, α 3 is the third coefficient obtained by random effect model regression analysis, and depth is the focal depth parameter.

[0044] In combination with the second possible implementation of the second aspect of the present application, in a fourth possible implementation of the second aspect of the present application, the path item is specifically represented as:

[0045] ,

[0046] Among them, α 4 is the fourth coefficient obtained from the random effect model regression analysis, M w is the moment magnitude parameter, M ref is the reference magnitude parameter, α 5 is the fifth coefficient obtained from the random effect model regression analysis, R rup is the fault distance parameter, α 6 The sixth coefficient is introduced to account for the near-field saturation effect.

[0047] In combination with the second possible implementation of the second aspect of the present application, in a fifth possible implementation of the second aspect of the present application, the site item is specifically represented as:

[0048] F site=α 7 ln(V / 800),

[0049] Among them, α 7 is the seventh coefficient obtained by random effect model regression analysis, and V is the site shear wave velocity parameter.

[0050] In combination with the second aspect of the present application, in a sixth possible implementation manner of the second aspect of the present application, the apparatus further includes an application unit, configured to:

[0051] Obtaining the data to be processed in the target area, the data to be processed including the target peak displacement, moment magnitude, target focal depth, target fault type, target fault distance and target site shear wave velocity;

[0052] Inputting the data to be processed into the post-earthquake permanent surface displacement estimation model so that the post-earthquake permanent surface displacement estimation model can carry out corresponding post-earthquake permanent surface displacement estimation processing;

[0053] Extract the post-earthquake surface permanent displacement estimation results output by the post-earthquake surface permanent displacement estimation model.

[0054] In a third aspect, the present application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation method of the first aspect of the present application is executed.

[0055] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the method provided by the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0056] From the above content, it can be concluded that the present application has the following beneficial effects:

[0057] Aiming at the goal of estimating permanent surface displacement after an earthquake, this application provides a set of specific model processing solutions, which can obtain a post-earthquake permanent surface displacement estimation model that is universal for various scenarios, convenient to configure input data and has high processing efficiency, meeting the high-quality prediction needs for permanent surface displacement after an earthquake in actual situations and providing accurate and effective data support for engineering activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flow chart of a processing method for estimating a post-earthquake permanent displacement model of the surface of the present application;

[0060] Figure 2 A schematic diagram of the structure of a processing device for the post-earthquake surface permanent displacement estimation model of the present application;

[0061] Figure 3 A schematic diagram of the structure of the processing equipment of this application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0063] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0064] The division of modules in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0065] Before introducing the processing method of the post-earthquake surface permanent displacement estimation model provided by the present application, the background content involved in the present application is first introduced.

[0066] The processing method, device and computer-readable storage medium of the post-earthquake permanent surface displacement estimation model provided in this application can be applied to processing equipment to provide a set of specific model processing solutions and improve them in details, so as to obtain a post-earthquake permanent surface displacement estimation model that is universal for various scenarios, convenient to configure input data and has high processing efficiency, which meets the high-quality prediction needs for post-earthquake permanent surface displacement in actual situations and provides accurate and effective data support for engineering activities.

[0067] The processing method of the post-earthquake permanent displacement estimation model mentioned in the present application may be executed by a processing device of the post-earthquake permanent displacement estimation model, or a server, a physical host, or a user equipment (UE) or other different types of processing equipment that integrates the processing device of the post-earthquake permanent displacement estimation model. The processing device of the post-earthquake permanent displacement estimation model may be implemented in hardware or software, and the UE may be a terminal device such as a smart phone, a tablet computer, a laptop computer, a desktop computer, or a personal digital assistant (PDA), and the processing device may be set in a device cluster.

[0068] It is understandable that, in practical applications, considering that the present application scheme is usually a training process carried out on the basis of existing data, the processing equipment for executing the processing method of the post-earthquake surface permanent displacement estimation model of the present application only needs to have the corresponding data processing capabilities, and the specific equipment type and equipment deployment form are relatively flexible.

[0069] If subsequent model applications are involved, corresponding adjustments can be made based on the specific application scenarios. For example, the processing equipment can be related equipment at the engineering site, or it can be related equipment that provides remote data support services to the engineering site in the form of remote services such as web services.

[0070] As an example, the processing equipment may include a first processing equipment for performing model training in the background and a second processing equipment for performing model application at the engineering site. The model obtained by the first processing equipment through initial training or continuous updating after actual application can be sent to the second processing equipment for model deployment, so as to perform specific model applications on site and provide data support for permanent surface displacement after an earthquake at the engineering site.

[0071] Next, the processing method of the post-earthquake surface permanent displacement estimation model provided by the present application is introduced.

[0072] First, see Figure 1 , Figure 1 A schematic flow chart of a processing method for estimating a post-earthquake permanent displacement model of the surface is shown. The processing method for estimating a post-earthquake permanent displacement model of the surface provided by the present application may specifically include the following steps S101 to S104:

[0073] Step S101, obtaining earthquake acceleration time history data of a target area;

[0074] It can be understood that the post-earthquake surface permanent displacement estimation model to be constructed in this application is linked to a specific area. The specified area is recorded in this application as a target area (geographical area, actual area). In this way, the post-earthquake surface permanent displacement estimation model can be highly targeted to the target area and can achieve a high-precision post-earthquake surface permanent displacement estimation effect.

[0075] Once the target area is determined, the present application can obtain the corresponding seismic acceleration time history data (also referred to as seismic acceleration time history records, seismic data, seismic records, etc.) from local storage space, other equipment or manual entry. These data are the original data obtained during the earthquake recording process, which can be obtained through corresponding mathematical models. They are themselves ready-made data in this field. Considering that how to process them is not the focus of the present application, this application will not elaborate on them.

[0076] As an example, the earthquake acceleration time history data of the target area can be conveniently extracted from the existing relevant earthquake database.

[0077] It can also be seen from here that the seismic acceleration time history data obtained by this application can itself be directly used as an existing data product, which makes the application of the solution more convenient.

[0078] In specific operations, in order to facilitate data management and data processing, a seismic acceleration time history database can also be constructed for the target area to record the obtained seismic acceleration time history data in the form of a database.

[0079] When the original earthquake acceleration time history data is obtained, it can also be appropriately processed or screened to obtain more simplified data that meets the requirements of the present application.

[0080] Specifically, data screening processing may be performed based on data screening strategies / rules involving aspects such as data quality, magnitude, and peak acceleration.

[0081] Step S102, obtaining corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data;

[0082] Based on the obtained seismic acceleration time history data, data processing can be performed to obtain the peak displacement and permanent displacement involved in the solution of the present application, the former corresponding to the input of the model, and the latter corresponding to the output of the model.

[0083] It can be understood that peak displacement and permanent displacement are two common terms in earthquake analysis, which have mature and common concepts and processing methods. Therefore, the corresponding peak displacement and permanent displacement data attributes can be extracted from the data content of the seismic acceleration time history data obtained previously through existing technical means.

[0084] Of course, it is also understandable that in actual operations, further optimization and improvement can be made on the basis of existing technical means, or novel self-developed technical means can be used to obtain these two data.

[0085] As an exemplary embodiment, obtaining the corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data may specifically include:

[0086] The acceleration time history data of the earthquake motion acceleration time history is subjected to baseline correction and filtering to obtain the acceleration time history, and then the maximum absolute value of the displacement time history obtained by the secondary integration is taken to obtain the peak displacement;

[0087] The baseline correction method based on permanent displacement recovery avoids filtering and processes the earthquake acceleration time history data to obtain the permanent displacement.

[0088] It can be understood that the method for obtaining the peak displacement and permanent displacement in the embodiment here is itself a mature existing solution, and its purpose is to provide a more specific and convenient implementation supporting solution for practical applications.

[0089] In addition, after obtaining the peak displacement and permanent displacement, the previous seismic acceleration time history data can be screened for the second time to further simplify the data.

[0090] As an example, the earthquake acceleration time history data that does not contain post-earthquake permanent displacement or whose post-earthquake permanent displacement is less than a given threshold (such as 5 cm) may be deleted.

[0091] Step S103, obtaining the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area;

[0092] It can be understood that the model design of the present application and the model input involve not only peak displacement but also other auxiliary parameters. In other words, in the process of estimating the permanent surface displacement after an earthquake, the present application uses peak displacement as a secondary parameter while considering other auxiliary parameters.

[0093] These auxiliary parameters may specifically involve moment magnitude, focal depth, fault type, fault distance and site shear wave velocity (shear wave velocity 30m below the horizontal site / 30m below the surface).

[0094] In this regard, the corresponding moment magnitude, focal depth, fault type, fault distance and site shear wave velocity can be extracted from the previous seismic acceleration time history data or from data in other places in the target area.

[0095] It should be noted that some data may not be included in the previous seismic acceleration time history data. The acquisition of these parameters here is relatively flexible. In addition to pulling ready-made data or doing simple data analysis and secondary processing, manual entry is also possible.

[0096] For example, the fault distance can be calculated based on the random finite fault model, and the site shear wave velocity can be calculated based on the site geological data of the station.

[0097] In addition, it can also be seen that the data processing in step S103 usually has no specific limiting relationship with the previous steps S101 and S102 in terms of timing.

[0098] Step S104, establishing a post-earthquake surface permanent displacement estimation model based on the peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity, wherein the post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

[0099] After completing the peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity used in training the model (these data can be used as training samples for training the model), specific model training work can be carried out to train a post-earthquake surface permanent displacement estimation model specifically used for post-earthquake surface permanent displacement estimation processing.

[0100] It can be understood that for the post-earthquake surface permanent displacement estimation model to be configured in this application, the model type involved, the model training architecture or loss function used in the training process, can adopt existing solutions, or optimize and improve on the basis of existing solutions, or adopt self-developed novel solutions, which are all possible in actual situations.

[0101] The focus of the post-earthquake surface permanent displacement estimation model is to configure the post-earthquake surface permanent displacement estimation logic involved in this application, which is based on the peak displacement input by the model, and refers to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model to estimate the corresponding post-earthquake surface permanent displacement.

[0102] As an exemplary embodiment, the quantitative formula involved in the post-earthquake surface permanent displacement estimation model designed in this application may be:

[0103] ln(PD)=α 1 ln(PGD)+F source +F path +F site +δB+δW,

[0104] Where PD is the estimated permanent displacement (in cm), α 1 is the first coefficient obtained by random effect model regression analysis, PGD is the peak displacement parameter (in cm), F Source is the source term, F Path is the path item, F Siteis the site term, δB is the inter-event residual, δW is the intra-event residual, the source term is determined by the moment magnitude parameter, fault type parameter and focal depth parameter, the path term is determined by the moment magnitude parameter, reference magnitude parameter and fault distance parameter, and the site term is determined by the site shear wave velocity parameter.

[0105] It can be seen that in the embodiment here, the present application is the estimation logic involved in the post-earthquake surface permanent displacement estimation model. Starting from the formula level, compared with the original "black box" state, a set of more specific quantitative formulas is specially designed. Therefore, in practical applications, the solution can be applied more conveniently, and it also helps to achieve a more stable and accurate estimation effect, and has better practical value.

[0106] It should be noted that for the two parameters PGD and PD, which are the input and output of the post-earthquake permanent surface displacement estimation model, in terms of details, they can be the horizontal geometric mean, RotD50, the values ​​perpendicular to the fault direction and parallel to the fault direction, or the vertical component.

[0107] Furthermore, for the above-mentioned post-earthquake permanent displacement estimation formula, as an exemplary embodiment, the source term can be specifically expressed as:

[0108] F Source =α 2 M w +e i F sofi +α 3 depth,

[0109] Among them, α 2 is the second coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, F sofi is the fault type parameter (dummy variable), e i is the preset coefficient, i=1,2,3, corresponding to strike-slip fault (SS), reverse fault (TF) and normal fault (NF), α 3 is the third coefficient obtained by random effect model regression analysis, and depth is the focal depth parameter.

[0110] Furthermore, for the above-mentioned post-earthquake permanent displacement estimation formula, as an exemplary embodiment, the path term can be specifically expressed as:

[0111] ,

[0112] Among them, α 4 is the fourth coefficient obtained from the random effect model regression analysis, M w is the moment magnitude parameter, M refis the reference magnitude parameter (determined by the magnitude distribution), α 5 is the fifth coefficient obtained from the random effect model regression analysis, R rup is the fault distance parameter (unit: km), α 6 A coefficient introduced to account for near-field saturation effects (can be determined by trial and error).

[0113] Furthermore, for the above-mentioned post-earthquake permanent displacement estimation formula, as an exemplary embodiment, the site term can be specifically expressed as:

[0114] F site =α 7 ln(V / 800),

[0115] Among them, α 7 is the seventh coefficient obtained by random effect model regression analysis, and V is the site shear wave velocity parameter (unit: m / s).

[0116] It can be understood that the above three exemplary embodiments are the three items involved in the above-mentioned formula for estimating the permanent displacement of the post-earthquake surface. Continuing to start from the formula level to provide more specific supporting solutions will of course have better practical significance and help to complete the solution deployment more conveniently and obtain more accurate permanent displacement estimation results.

[0117] And, it is worth noting that for α 1 , α 2 , α 3 , α 4 , α 5 , α 7 , which is a coefficient determined during the model training process, α 6 The coefficient introduced to account for the near-field saturation effect is, for example, determined by trial and error to be 5.0, and Mref is determined based on the magnitude distribution, for example, to be 6.7.

[0118] Of course, these parameters may also be preset parameters or manually entered parameters, which are all possible in practical applications and can be adjusted according to actual conditions.

[0119] After completing the model training, or during the training process, model verification processing may also be involved. Specifically, the rationality of the model can be verified through residual analysis. If the distribution of the residual and the corresponding explanatory variable is close to an unbiased distribution, then the established seismic motion model can be considered reasonable, and the model can be put into practical use.

[0120] After completing the construction and configuration of the model, it can obviously be put into actual use. Correspondingly, the application method can also involve corresponding model application links.

[0121] Specifically, as an exemplary embodiment, after establishing a post-earthquake surface permanent displacement estimation model based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity in step S104, the method of the present application may further include:

[0122] Obtaining the data to be processed in the target area, the data to be processed including the target peak displacement, moment magnitude, target focal depth, target fault type, target fault distance and target site shear wave velocity;

[0123] Inputting the data to be processed into the post-earthquake permanent surface displacement estimation model so that the post-earthquake permanent surface displacement estimation model can carry out corresponding post-earthquake permanent surface displacement estimation processing;

[0124] Extract the post-earthquake surface permanent displacement estimation results output by the post-earthquake surface permanent displacement estimation model.

[0125] It is easy to understand that in the actual application of the model, the model input, that is, the data to be processed, corresponds to the training samples in the model training process in terms of data format and data content. Specifically, it may also involve ready-made related data products, or data processed based on ready-made data products.

[0126] The method for acquiring the data to be processed is related to the specific deployment plan of the model under actual circumstances. It can be either manual entry or automatic acquisition by the system according to the corresponding acquisition strategy / rules. For example, the data records of the target area can be continuously monitored on the system so that the corresponding post-earthquake surface permanent displacement estimation processing can be initiated autonomously when there is a data update. For another example, under the corresponding trigger conditions other than data update, the corresponding data records can be autonomously extracted on the system, and then the corresponding post-earthquake surface permanent displacement estimation processing can be initiated autonomously. Obviously, the specific autonomous acquisition method is relatively flexible and can be adjusted according to the business needs in the actual situation.

[0127] After the currently processed data is imported into the post-earthquake surface permanent displacement estimation model, the model will obviously carry out corresponding processing and output the corresponding post-earthquake surface permanent displacement estimation results after completing the processing. At this time, the post-earthquake surface permanent displacement estimation results can be extracted.

[0128] It is understandable that when the estimated results of the permanent displacement of the post-earthquake surface are extracted, data application processing such as local storage, off-site storage, content display, output of completed processing prompts, or further data analysis can be performed according to the preset data application strategy or the real-time configured data application strategy, which is also relatively flexible.

[0129] In addition, it is worth adding that the application scenarios to which this application scheme is applicable in actual applications can be applied not only to engineering projects involving active faults, but also to geological disaster warning and other aspects. As long as there is a need to understand and use the data on permanent surface displacement after an earthquake, it is obvious that these needs can be met through the application of this application scheme.

[0130] Finally, for the above solution contents (including the solution contents of each exemplary embodiment), in general, for the goal of estimating the permanent surface displacement after an earthquake, this application provides a set of specific model processing solutions and improves them in details, so as to obtain a post-earthquake permanent surface displacement estimation model that is universal for various scenarios, convenient to configure input data and has high processing efficiency, which meets the high-quality prediction needs for the permanent surface displacement after an earthquake in actual situations and provides accurate and effective data support for engineering activities.

[0131] The above is an introduction to the processing method of the post-earthquake surface permanent displacement estimation model provided by this application. In order to facilitate better implementation of the processing method of the post-earthquake surface permanent displacement estimation model provided by this application, this application also provides a processing device for the post-earthquake surface permanent displacement estimation model from the perspective of functional modules.

[0132] See also Figure 2 , Figure 2 This is a structural schematic diagram of a processing device for the post-earthquake permanent displacement estimation model of the present application. In the present application, the processing device 200 for the post-earthquake permanent displacement estimation model of the present application may specifically include the following structure:

[0133] A first acquisition unit 201 is used to acquire the earthquake acceleration time history data of the target area;

[0134] A second acquisition unit 202 is used to acquire corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data;

[0135] The third acquisition unit 203 is used to acquire the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area;

[0136] Establishing unit 204, used to establish a post-earthquake surface permanent displacement estimation model based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity, wherein the post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

[0137] As an exemplary embodiment, the second acquisition unit 202 is specifically configured to:

[0138] The acceleration time history data of the earthquake motion acceleration time history is subjected to baseline correction and filtering to obtain the acceleration time history, and then the maximum absolute value of the displacement time history obtained by the secondary integration is taken to obtain the peak displacement;

[0139] Based on the baseline correction method of permanent displacement recovery, the earthquake acceleration time history data are processed to obtain the permanent displacement.

[0140] As another exemplary embodiment, the quantitative formula involved in the post-earthquake surface permanent displacement estimation model is specifically:

[0141] ln(PD)=α 1 ln(PGD)+F source +F path +F site +δB+δW,

[0142] Where PD is the estimated permanent displacement, α 1 is the first coefficient obtained by random effect model regression analysis, PGD is the peak displacement parameter, F Source is the source term, F Path is the path item, F Site is the site term, δB is the inter-event residual, δW is the intra-event residual, the source term is determined by the moment magnitude parameter, fault type parameter and focal depth parameter, the path term is determined by the moment magnitude parameter, reference magnitude parameter and fault distance parameter, and the site term is determined by the site shear wave velocity parameter.

[0143] As another exemplary embodiment, the source term is specifically expressed as:

[0144] F Source =α 2 M w +e i F sofi +α 3 depth,

[0145] Among them, α 2 is the second coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, F sofi is the fault type parameter, e i is the preset coefficient, i=1,2,3, corresponding to strike-slip fault, reverse fault and normal fault respectively, α 3 is the third coefficient obtained by random effect model regression analysis, and depth is the focal depth parameter.

[0146] As another exemplary embodiment, the path item is specifically represented as:

[0147] ,

[0148] Among them, α 4 is the fourth coefficient obtained from the random effect model regression analysis, M w is the moment magnitude parameter, M ref is the reference magnitude parameter, α 5 is the fifth coefficient obtained from the random effect model regression analysis, R rup is the fault distance parameter, α 6 The sixth coefficient is introduced to account for the near-field saturation effect.

[0149] As another exemplary embodiment, the venue item is specifically represented as:

[0150] F site =α 7 ln(V / 800),

[0151] Among them, α 7 is the seventh coefficient obtained by random effect model regression analysis, and V is the site shear wave velocity parameter.

[0152] As another exemplary embodiment, the apparatus further includes an application unit 205, configured to:

[0153] Obtaining the data to be processed in the target area, the data to be processed including the target peak displacement, moment magnitude, target focal depth, target fault type, target fault distance and target site shear wave velocity;

[0154] Inputting the data to be processed into the post-earthquake permanent surface displacement estimation model so that the post-earthquake permanent surface displacement estimation model can carry out corresponding post-earthquake permanent surface displacement estimation processing;

[0155] Extract the post-earthquake surface permanent displacement estimation results output by the post-earthquake surface permanent displacement estimation model.

[0156] This application also provides a processing device from the perspective of hardware structure, see Figure 3 , Figure 3 301, a memory 302, and an input / output device 303. The processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 1 The steps of the processing method of the post-earthquake permanent displacement estimation model of the corresponding embodiment; or, the processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 2 Corresponding to the functions of each unit in the embodiment, the memory 302 is used to store the processor 301 executing the above Figure 1 The computer program required for the processing method of the post-earthquake surface permanent displacement estimation model in the corresponding embodiment.

[0157] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302, and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0158] The processing device may include, but is not limited to, a processor 301, a memory 302, and an input / output device 303. Those skilled in the art will appreciate that the illustration is merely an example of a processing device and does not constitute a limitation on the processing device, and may include more or fewer components than shown in the illustration, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 301, the memory 302, the input / output device 303, etc. are connected via a bus.

[0159] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the processing device, and uses various interfaces and lines to connect various parts of the entire device.

[0160] The memory 302 can be used to store computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the processing device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0161] When the processor 301 is used to execute the computer program stored in the memory 302, the following functions can be implemented:

[0162] Obtaining earthquake acceleration time history data of the target area;

[0163] Based on the earthquake acceleration time history data, the corresponding peak displacement and permanent displacement are obtained;

[0164] Obtain the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area;

[0165] A post-earthquake surface permanent displacement estimation model is established based on peak displacement, permanent displacement, moment magnitude, focal depth, fault type, fault distance and site shear wave velocity. The post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

[0166] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the processing device, processing equipment and corresponding units of the post-earthquake permanent displacement estimation model described above can refer to the following. Figure 1 The description of the processing method of the post-earthquake surface permanent displacement estimation model in the corresponding embodiment will not be repeated here in detail.

[0167] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0168] To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the present application as follows: Figure 1 The steps of the processing method of the post-earthquake surface permanent displacement estimation model in the corresponding embodiment, the specific operation can be referred to as follows Figure 1 The description of the processing method of the post-earthquake surface permanent displacement estimation model in the corresponding embodiment will not be repeated here.

[0169] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0170] Due to the instructions stored in the computer-readable storage medium, the present application can be executed. Figure 1The steps of the processing method of the post-earthquake surface permanent displacement estimation model in the corresponding embodiment, therefore, the present application can be implemented as follows Figure 1 The beneficial effects that can be achieved by the processing method of the post-earthquake permanent displacement estimation model of the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0171] The above is a detailed introduction to the processing method, device, processing equipment and computer-readable storage medium of the post-earthquake surface permanent displacement estimation model provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A processing method for estimating a model of permanent displacement of the ground surface after an earthquake, characterized in that: The method comprises: Obtaining earthquake acceleration time history data of the target area; Based on the earthquake acceleration time history data, the corresponding peak displacement and permanent displacement are obtained; Obtaining moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area; A post-earthquake surface permanent displacement estimation model is established based on the peak displacement, the permanent displacement, the moment magnitude, the focal depth, the fault type, the fault distance and the site shear wave velocity, wherein the post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

2. The method according to claim 1, characterized in that The obtaining of the corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data includes: Performing a second integration on the acceleration time history obtained after baseline correction and filtering of the earthquake acceleration time history data, and then taking the maximum absolute value of the displacement time history obtained by the second integration to obtain the peak displacement; Based on the baseline correction method of permanent displacement recovery, the earthquake acceleration time history data is processed to obtain the permanent displacement.

3. The method according to claim 1, characterized in that The quantitative formula involved in the post-earthquake surface permanent displacement estimation model is specifically: ln(PD)=α1ln(PGD)+F source +F path +F site +δB+δW, Among them, PD is the estimated value of permanent displacement, α1 is the first coefficient obtained by random effect model regression analysis, PGD is the peak displacement parameter, F Source is the source term, F Path is the path item, F Site is the site term, δB is the inter-event residual, δW is the intra-event residual, the source term is determined by the moment magnitude parameter, the fault type parameter and the focal depth parameter, the path term is determined by the moment magnitude parameter, the reference magnitude parameter and the fault distance parameter, and the site term is determined by the site shear wave velocity parameter.

4. The method according to claim 3, characterized in that The source term is specifically expressed as: F Source =α2M w +e i F sofi +α3depth, Among them, α2 is the second coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, F sofi is the fault type parameter, e i is a preset coefficient, i=1, 2, 3, corresponding to strike-slip fault, reverse fault and normal fault respectively, α3 is the third coefficient obtained by regression analysis of the random effect model, and depth is the focal depth parameter.

5. The method according to claim 3, characterized in that: The path item is specifically represented as: ， Among them, α4 is the fourth coefficient obtained by random effect model regression analysis, M w is the moment magnitude parameter, M ref is the reference magnitude parameter, α5 is the fifth coefficient obtained by random effect model regression analysis, R rup is the fault distance parameter, and α6 is the sixth coefficient introduced to explain the near-field saturation effect.

6. The method according to claim 3, characterized in that: The site item is specifically represented as follows: F site =α7ln(V / 800), Among them, α7 is the seventh coefficient obtained by regression analysis of the random effect model, and V is the shear wave velocity parameter of the site.

7. The method according to claim 1, characterized in that After establishing a post-earthquake surface permanent displacement estimation model based on the peak displacement, the permanent displacement, the moment magnitude, the focal depth, the fault type, the fault distance and the site shear wave velocity, the method further includes: Acquiring the data to be processed in the target area, wherein the data to be processed includes target peak displacement, moment magnitude, target focal depth, target fault type, target fault distance and target site shear velocity; Inputting the data to be processed into the post-earthquake ground permanent displacement estimation model so that the post-earthquake ground permanent displacement estimation model carries out corresponding post-earthquake ground permanent displacement estimation processing; The post-earthquake permanent surface displacement estimation result output by the post-earthquake permanent surface displacement estimation model is extracted.

8. A processing device for estimating a model of permanent displacement of the ground surface after an earthquake, characterized in that: The device comprises: A first acquisition unit, used to acquire earthquake acceleration time history data of a target area; A second acquisition unit is used to acquire corresponding peak displacement and permanent displacement based on the earthquake acceleration time history data; A third acquisition unit is used to acquire the moment magnitude, focal depth, fault type, fault distance and site shear wave velocity of the target area; A unit is established, which is used to establish a post-earthquake surface permanent displacement estimation model based on the peak displacement, the permanent displacement, the moment magnitude, the focal depth, the fault type, the fault distance and the site shear wave velocity, wherein the post-earthquake surface permanent displacement estimation model is used to estimate the corresponding post-earthquake surface permanent displacement based on the peak displacement input by the model, with reference to the moment magnitude input by the model, the focal depth input by the model, the fault type input by the model, the fault distance input by the model and the site shear wave velocity input by the model.

9. A processing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 7 when calling the computer program in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.

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