Fault deformation probability prediction method and system for engineering resistance to breaking

Abstract

The present application relates to the technical field of engineering earthquake and disaster prevention engineering, and particularly relates to a fault deformation probability prediction method and system for engineering rupture resistance. The method comprises the following steps: obtaining basic data of a target active fault, wherein the basic data comprises geometric characteristics of the fault, kinematic characteristics of the fault, magnitude upper limit, recurrence period, elapsed time, focal depth and deformation width; inputting the basic data as input parameters into a calculation model; and calculating the length of the fault according to the geometric characteristics of the fault to construct a two-dimensional grid for calculating along the fault and across the fault. The present application is a new probability prediction method for evaluating permanent displacement of the crustal shallow layer caused by fault dislocation of a great earthquake and strong deformation of the crustal shallow layer near the fault. The method can provide sub-millimeter fault dislocation data and cross-fault deformation data under a certain exceeding probability of an important engineering site.

Description

Technical Field

[0001] This invention relates to the field of engineering earthquake and disaster prevention engineering technology, and in particular to a method and system for predicting the probability of fault deformation for engineering fault resistance. Background Technology

[0002] Major earthquakes can cause severe damage to engineering projects near the fault, primarily due to two factors: strong ground shaking (involving seismic resistance) and permanent displacement of the shallow crust caused by fault movement, as well as intense deformation of the shallow crust near the fault (involving fault resistance). Research on seismic and fault resistance parameter evaluation can provide scientific and reasonable parameters for engineering construction, which is crucial for ensuring the standardized construction and safe operation of major national infrastructure projects (especially those crossing or near active faults). Currently, my country mainly relies on the "avoidance principle" for fault resistance in engineering projects, requiring major projects to avoid active faults where major earthquakes are likely to occur. However, for long-distance linear projects, it is difficult to completely avoid active faults, and the occurrence of a major earthquake can cause enormous losses. Furthermore, the industry commonly uses deterministic evaluation methods, which use the maximum possible dislocation of the fault as the basis for fault resistance design. This method does not consider the probabilistic characteristics of earthquake occurrence, often leading to overly conservative engineering designs, resulting in wasted resources and increased costs. To address the aforementioned problems, existing technologies lack comprehensive prediction methods that can coordinate earthquake occurrence probability with the spatial distribution of fault deformation, making it difficult to provide scientifically sound and economically feasible fault-resistance parameters. Therefore, there is an urgent need to develop a method capable of quantifying the distribution of fault dislocations and surrounding deformation under different exceedance probabilities, thereby providing a refined and probabilistic basis for fault-resistant design in cross-fault or near-fault engineering projects. This invention aims to provide such a probabilistic prediction method for dislocation-type and distributed deformation in large earthquakes. By integrating multi-source data from seismology, geology, geophysics, and geodesy, and combining statistical regularities and probabilistic analysis, it achieves probabilistic prediction of surface deformation caused by future large earthquakes, providing more scientific and economical technical support for determining engineering fault-resistance parameters. Summary of the Invention

[0003] (1) Technical problems to be solved The purpose of this invention is to provide a method and system for predicting the probability of fault deformation in engineering projects, in order to solve the problems in the prior art that lead to conservative engineering design and waste of resources due to reliance on deterministic maximum dislocation assessment and "avoidance principle", as well as the inability to provide scientific, economical and probabilistic deformation prediction and fault resistance parameters for major linear projects that cannot avoid faults.

[0004] (2) Technical solution To achieve the above objectives, on the one hand, this invention provides a method for predicting the probability of fault deformation in engineering applications. The method is characterized by providing the risk of dislocations and deformations at different locations on and near the fault under different exceedance probabilities based on fundamental data of active faults. The fundamental data includes fault geometry data, kinematic characteristic data, seismic activity characteristic data, and large earthquake deformation characteristic data. The method includes the following steps: Step S1: Read the basic data (.shp file) of the target active fault to obtain basic data such as the geometric features, kinematic features, upper limit of magnitude, recurrence period, elapsed time, focal depth and deformation width of the fault; use the basic data as input parameters to input the calculation model of probabilistic fault dislocation hazard analysis. Step S2: Calculate the fracture length based on the geometric characteristics of the fault and construct a two-dimensional calculation grid for along and across the fault to obtain the amount of dislocation or deformation at different locations along and across the fault. Step S3: Select a model for magnitude distribution based on the characteristics of active fault earthquake activity, such as GR relationship, truncated GR relationship, characteristic earthquakes, etc., and generate an artificial earthquake catalog; based on the elapsed time of major earthquakes on active faults, select a target time period (such as the next 50 years or 100 years) and calculate the occurrence rate of different magnitude ranges (with magnitude 0.1 as one range) of major earthquakes of magnitude 6 or above; then generate a secondary artificial earthquake catalog that follows a Poisson distribution based on the Poisson model. Step S4: Determine the spatial distribution of dislocations and deformations of the fault under a single earthquake event based on the preset fault dislocation distribution model; the fault dislocation distribution model includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model (selected according to the deformation distribution characteristics of the fault in a large earthquake). Step S5: Based on the preset cross-fault deformation attenuation model, determine the spatial distribution of the distributed deformation of the fault under a single earthquake event; the attenuation mode of the cross-fault deformation adopts an exponential attenuation law. Step S6: Based on the empirical formulas for magnitude-rupture length and magnitude-maximum dislocation, calculate the spatial distribution of fault dislocation and the spatial distribution of fault distributed deformation to obtain the dislocation deformation and distributed deformation caused by the next earthquake event of a given magnitude. Step S7: Based on the probabilistic seismic hazard analysis model combined with seismic activity simulation, target time period setting and Monte Carlo simulation, perform probabilistic integration of dislocation deformation and distributed deformation to calculate the fault dislocation and cross-fault deformation at each location along and across the fault within the future target time period under different exceedance probabilities. Step S8: Based on the calculation results of fault dislocation and cross-fault deformation under different exceedance probabilities, extract the fault resistance design parameters at the engineering site.

[0005] Furthermore, the basic data is derived from at least one of seismic geological exploration, geophysical exploration, and geodesy.

[0006] Furthermore, the fault dislocation distribution model is selected based on the geometric and kinematic characteristics of the fault and the deformation behavior of a large earthquake.

[0007] Furthermore, the cross-fault deformation attenuation model is an exponential attenuation function model.

[0008] Furthermore, the empirical formulas for magnitude-rupture length and magnitude-maximum dislocation (Wells & Coppersmith, 1994) include: The empirical formula for magnitude-rupture length is: ; The empirical formula for magnitude-maximum dislocation is: , ; in, The fracture length is... The maximum dislocation amount For surface wave magnitude, coefficient , , , The value is determined based on the fault type.

[0009] Furthermore, when the fault is strike-slip, , , , When the fault is a thrust fault, , , , When the fault is a normal fault, , , , .

[0010] Furthermore, in step S7, the probabilistic seismic hazard analysis model includes the following sub-steps: Step S71: Based on the upper limit of magnitude, recurrence period, elapsed time, focal depth and deformation width, simulate and generate an artificial earthquake catalog containing multiple magnitude-location pairs.

[0011] Step S72: Set the design reference period of the project as the target time period.

[0012] Step S73: Using the Monte Carlo simulation method, repeat step S6 to calculate the deformation caused by a large number of random earthquake sequences within the target time period.

[0013] Step S74: Perform statistical analysis on the deformation results to obtain the deformation score corresponding to the specified exceedance probability.

[0014] Furthermore, the fracture resistance design parameters include dislocation or deformation amounts corresponding to at least one of the following thresholds: 10%, 5%, 2%, and 1% over a 50-year or 100-year period.

[0015] Based on the same inventive concept, the present invention also provides a fault deformation probability prediction system for engineering fault resistance, including a data input module, a data processing module, a seismic activity module, a deformation calculation module, a probability analysis module, and a parameter output module; The data input module is used to acquire the basic data of the target active fault, including the geometric features of the fault, the kinematic features of the fault, the upper limit of magnitude, the recurrence period, the elapsed time, the focal depth, and the deformation width; the basic data is used as input parameters to input the calculation model. The data processing module is used to calculate the length of the fracture based on the geometric characteristics of the fault and to construct a two-dimensional computational grid for along and across the fault. The seismic activity module is used to establish an artificial earthquake catalog spanning earthquake cycles based on the actual historical earthquake frequency of the fault. Then, based on the elapsed time of the last major earthquake, it selects a target time period and calculates the occurrence rate of different magnitude ranges within that time period. Based on the Poisson model, it establishes a secondary artificial earthquake catalog within the target time period. The deformation calculation module is used to determine the spatial distribution of dislocations along the fault under a single earthquake event based on a preset fault dislocation distribution model. The fault dislocation distribution model includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model. It also determines the distributed deformation spatial distribution of the fault under a single earthquake event based on a preset cross-fault deformation attenuation model. Finally, it calculates the dislocation-type deformation and distributed deformation caused by a given earthquake event based on the magnitude-rupture length empirical formula and the magnitude-maximum dislocation empirical formula. The probability analysis module is used to perform probabilistic integration of dislocation deformation and distributed deformation based on the probabilistic seismic hazard analysis model combined with seismic activity simulation, target time period setting and Monte Carlo simulation, and to calculate the fault dislocation and cross-fault deformation at the target location under different exceedance probabilities in the future target time period. The parameter output module is used to output the distribution maps of fault dislocation and cross-fault deformation under different exceedance probabilities. Based on the distribution of fault dislocation and cross-fault deformation, the fault resistance design parameters at the engineering site under a certain exceedance probability are extracted.

[0016] (3) Beneficial effects Compared with existing technologies, this invention is a novel probabilistic prediction method for assessing the permanent displacement of the shallow crust caused by fault displacement during large earthquakes, as well as the intense deformation of the shallow crust near the fault. It can provide sub-meter-level data on fault-related dislocation and cross-fault deformation at important engineering sites with a certain exceedance probability. This method is applicable not only to large-scale linear foundation lifeline projects spanning active faults, but also to point-like projects near active faults (reservoirs, dams, power stations, etc.), providing more scientific, accurate, and reasonable fault resistance parameters for project construction and avoiding unnecessary resource waste caused by currently widely used deterministic methods. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a fault deformation probability prediction method for engineering fault resistance according to an embodiment of the present invention; Figure 2 This is a block diagram of a fault deformation probability prediction system for engineering fault resistance according to an embodiment of the present invention; Figure 3 For technical flowcharts; Figure 4 A schematic diagram showing the distribution characteristics of deformation along the fracture and at different distances from the fracture. Figure 5 A schematic diagram showing the attenuation characteristics of cross-fracture deformation at different locations; Figure 6 This is a schematic diagram of the three-dimensional deformation distribution during fracture. Figure 7 This is a schematic diagram of the probabilistic fault dislocation hazard analysis results. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: As Figure 1 As shown in the figure, this embodiment provides a method for predicting the probability of fault deformation for engineering fault resistance, characterized in that the method includes the following steps: Step S1: Read the basic data (.shp file) of the target active fault to obtain basic data such as the geometric features, kinematic features, upper limit of magnitude, recurrence period, elapsed time, focal depth and deformation width of the fault; use the basic data as input parameters to input the calculation model of probabilistic fault dislocation hazard analysis. Step S2: Calculate the fracture length based on the geometric characteristics of the fault and construct a two-dimensional calculation grid for along and across the fault to obtain the amount of dislocation or deformation at different locations along and across the fault. Step S3: Select a model for magnitude distribution based on the characteristics of active fault earthquake activity, such as GR relationship, truncated GR relationship, characteristic earthquakes, etc., and generate an artificial earthquake catalog; based on the elapsed time of major earthquakes on active faults, select a target time period (such as the next 50 years or 100 years) and calculate the occurrence rate of different magnitude ranges (with magnitude 0.1 as one range) of major earthquakes of magnitude 6 or above; then generate a secondary artificial earthquake catalog that follows a Poisson distribution based on the Poisson model. Step S4: Determine the spatial distribution of dislocations and deformations of the fault under a single earthquake event based on the preset fault dislocation distribution model; the fault dislocation distribution model includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model (selected according to the deformation distribution characteristics of the fault in a large earthquake). Step S5: Based on the preset cross-fault deformation attenuation model, determine the spatial distribution of the distributed deformation of the fault under a single earthquake event; the attenuation mode of the cross-fault deformation adopts an exponential attenuation law. Step S6: Based on the empirical formulas for magnitude-rupture length and magnitude-maximum dislocation, calculate the spatial distribution of fault dislocation and the spatial distribution of fault distributed deformation to obtain the dislocation deformation and distributed deformation caused by the next earthquake event of a given magnitude. Step S7: Based on the probabilistic seismic hazard analysis model combined with seismic activity simulation, target time period setting and Monte Carlo simulation, perform probabilistic integration of dislocation deformation and distributed deformation to calculate the fault dislocation and cross-fault deformation at each location along and across the fault within the future target time period under different exceedance probabilities. Step S8: Based on the calculation results of fault dislocation and cross-fault deformation under different exceedance probabilities, extract the fault resistance design parameters at the engineering site.

[0022] Furthermore, the basic data is derived from at least one of seismic geological exploration, geophysical exploration, and geodesy.

[0023] Furthermore, the fault dislocation distribution model is selected based on the geometric and kinematic characteristics of the fault and the deformation behavior of a large earthquake.

[0024] Furthermore, the cross-fault deformation attenuation model is an exponential attenuation function model.

[0025] Furthermore, the empirical formulas for magnitude-rupture length and magnitude-maximum dislocation (Wells & Coppersmith, 1994) include: The empirical formula for magnitude-rupture length is: ; The empirical formula for magnitude-maximum dislocation is: , ; in, The fracture length is... The maximum dislocation amount For surface wave magnitude, coefficient , , , The value is determined based on the fault type.

[0026] Furthermore, when the fault is strike-slip, , , , When the fault is a thrust fault, , , , When the fault is a normal fault, , , , .

[0027] Furthermore, in step S7, the probabilistic seismic hazard analysis model includes the following sub-steps: Step S71: Based on the upper limit of magnitude, recurrence period, elapsed time, focal depth and deformation width, simulate and generate an artificial earthquake catalog containing multiple magnitude-location pairs.

[0028] Step S72: Set the design reference period of the project as the target time period.

[0029] Step S73: Using the Monte Carlo simulation method, repeat step S6 to calculate the deformation caused by a large number of random earthquake sequences within the target time period.

[0030] Step S74: Perform statistical analysis on the deformation results to obtain the deformation score corresponding to the specified exceedance probability.

[0031] Furthermore, the fracture resistance design parameters include dislocation or deformation amounts corresponding to at least one of the following thresholds: 10%, 5%, 2%, and 1% over a 50-year or 100-year period.

[0032] Based on the same inventive concept, such as Figure 2 As shown, the present invention also provides a fault deformation probability prediction system for engineering fault resistance, including a data input module, a data processing module, a seismic activity module, a deformation calculation module, a probability analysis module, and a parameter output module; The data input module is used to acquire the basic data of the target active fault, including the geometric features of the fault, the kinematic features of the fault, the upper limit of magnitude, the recurrence period, the elapsed time, the focal depth, and the deformation width; the basic data is used as input parameters to input the calculation model. The data processing module is used to calculate the length of the fracture based on the geometric characteristics of the fault and to construct a two-dimensional computational grid for along and across the fault. The seismic activity module is used to establish an artificial earthquake catalog spanning earthquake cycles based on the actual historical earthquake frequency of the fault. Then, based on the elapsed time of the last major earthquake, it selects a target time period and calculates the occurrence rate of different magnitude ranges within that time period. Based on the Poisson model, it establishes a secondary artificial earthquake catalog within the target time period. The deformation calculation module is used to determine the spatial distribution of dislocations along the fault under a single earthquake event based on a preset fault dislocation distribution model. The fault dislocation distribution model includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model. It also determines the distributed deformation spatial distribution of the fault under a single earthquake event based on a preset cross-fault deformation attenuation model. Finally, it calculates the dislocation-type deformation and distributed deformation caused by a given earthquake event based on the magnitude-rupture length empirical formula and the magnitude-maximum dislocation empirical formula. The probability analysis module is used to perform probabilistic integration of dislocation deformation and distributed deformation based on the probabilistic seismic hazard analysis model combined with seismic activity simulation, target time period setting and Monte Carlo simulation, and to calculate the fault dislocation and cross-fault deformation at the target location under different exceedance probabilities in the future target time period. The parameter output module is used to output the distribution maps of fault dislocation and cross-fault deformation under different exceedance probabilities. Based on the distribution of fault dislocation and cross-fault deformation, the fault resistance design parameters at the engineering site under a certain exceedance probability are extracted.

[0033] This invention presents a novel probabilistic prediction method for assessing the permanent displacement of the shallow crust caused by fault displacement during large earthquakes, as well as the intense deformation of the shallow crust near the fault. Based on fundamental data from seismology, geophysics, and geodesy of active faults involved in engineering projects, and the statistical distribution patterns of fault-related displacement and cross-fault deformation during large earthquakes, this method combines empirical formulas for magnitude-rupture length and magnitude-maximum displacement of large earthquakes. A basic model for probabilistic prediction of displacement-type and distributed deformation during large earthquakes is established using Monte Carlo simulation. By implementing this method, the distribution of fault-related displacement and cross-fault distributed deformation under different exceedance probabilities of active faults involved in engineering projects can be provided. It also provides deformation data for important engineering sites under a certain exceedance probability, offering more scientific, accurate, and reasonable fault resistance parameters for project construction. This avoids the unnecessary resource waste caused by currently widely used deterministic methods, with benefits potentially exceeding tens of millions of yuan.

[0034] The main contents of this study include the following aspects: (1) Basic input parameters Determine the geometry and kinematic characteristics of the fault, its upper limit of magnitude, the recurrence cycle of major earthquakes, the elapsed time, the focal depth, and the deformation width.

[0035] (2) The important work processes involved A. Basic data reading and processing: Read the geometry of the fracture, calculate the length of the fracture, divide the fracture into cells along and across the fracture, and provide a two-dimensional mesh around the fracture.

[0036] B. The basic laws governing the distribution of dislocations along faults mainly include three types: triangular dislocation distribution, sinusoidal dislocation distribution, and elliptical dislocation distribution. Users can choose one of the three modes as the basic model for subsequent deterministic dislocation distribution calculations in major earthquakes, based on their actual circumstances.

[0037] C. The basic laws of cross-fault distributed deformation: mainly including the exponential decay function model and other decay function models (selected according to the geometric and kinematic characteristics of the fracture).

[0038] D. Empirical relationship between rupture length, maximum displacement, and magnitude of a major earthquake: Referring to Wells & Coppersmith (1994), empirical formulas were established based on statistics for the magnitude, rupture length, and maximum displacement of different types of faults (strike-slip, normal, and reverse faults), as follows: Empirical formula for magnitude-rupture length ;% log10(fracture length) ;% Theoretical fracture length (km) Magnitude-Maximum Dislocation Empirical Formula ;%log10(maximum bit shift) ;% Maximum dislocation (m) Strike-slip faults: a=-3.55;b=0.74;c=-7.03;d=1.03; Thrust fault: a=-2.86;b=0.63;c=-1.84;d=0.29; Normal fault: a=-2.01;b=0.50;c=-5.90;d=0.89; All types of faults: a=-3.22;b=0.69;c=-5.46;d=0.82; E. Calculation method for deterministic fault dislocation and distributed deformation: The calculation process requires calculating the dislocation and distributed deformation distribution of each major earthquake of magnitude 6 or above. This calculation process can give the distribution of dislocation along the fault and the distribution of deformation across the fault caused by a major earthquake. This process will record and visualize the deformation at different locations generated by each earthquake. (4) Probabilistic fault dislocation and distributed deformation analysis model This method is a novel probabilistic prediction approach for fault dislocation and distributed deformation, based on an improvement upon Probabilistic Seismic Hazard Analysis (PSHA). The method includes steps such as simulating seismic activity, selecting target time periods, calculating the occurrence rate of major earthquakes during the target time period, generating a catalog of secondary artificial earthquakes, Monte Carlo simulations (100,000 times), and calculating the corresponding exceedance probability quantiles, ultimately providing visualized calculation results.

[0039] (5) Comprehensive evaluation of engineering fracture resistance parameters The calculation results of the dislocation along the fault and the deformation across the fault given by the above model provide a scientific and reasonable dislocation amount at the intersection of the fault and the project, providing a basis for the fault resistance parameters for the construction of the project.

[0040] like Figure 4 The schematic diagram shows the distribution characteristics of deformation along the fault and at different distances from the fault. The magnitude is Mw=7.33, the fault length is about 70km, and the dislocation distribution pattern along the fault is elliptical.

[0041] like Figure 5 The schematic diagram shows the attenuation characteristics of cross-fault deformation at different locations. The magnitude is Mw=7.33, the fault length is about 70km, and the distribution pattern of cross-fault deformation is exponentially attenuated.

[0042] like Figure 6 The schematic diagram of the three-dimensional deformation distribution of the fault is shown. The magnitude is Mw=7.33, the fault length is about 70km, the elliptical dislocation distribution pattern along the fault is shown, and the exponential attenuation cross-fault deformation distribution pattern is shown.

[0043] like Figure 7 The schematic diagram of the probabilistic fault dislocation hazard analysis results shows that the magnitude is Mw=7.33, the fault length is about 70km, the elliptical fault dislocation distribution pattern is along the fault, and the exponential decay cross-fault deformation distribution pattern is also shown. The exceedance probabilities in the next 100 years are 10%, 5%, 2%, and 1%, respectively.

[0044] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0045] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the probability of fault deformation in engineering applications, characterized in that, Based on the fundamental data of active fractures, the method for determining the risk of dislocation and deformation at different locations in and around the fracture under different exceedance probabilities includes the following steps: Step S1: Read the basic data of the target active fault, including the geometric features of the fault, the kinematic features of the fault, the upper limit of magnitude, the recurrence period, the elapsed time, the focal depth, and the deformation width; use the basic data as input parameters to input the calculation model of probabilistic fault dislocation hazard analysis. Step S2: Calculate the fracture length based on the geometric characteristics of the fault, and construct a two-dimensional mesh for calculation along and across the fault to obtain the amount of dislocation or deformation at different locations along and across the fault. Step S3: Based on the seismic activity characteristics of active faults, select a magnitude distribution law model, which includes GR relationship, truncated GR relationship or characteristic earthquake model, and generate an artificial earthquake catalog; based on the elapsed time of major earthquakes on active faults, select a target time period and calculate the occurrence rate of each magnitude range of major earthquakes above magnitude 6; then generate a secondary artificial earthquake catalog that follows a Poisson distribution based on the Poisson model. Step S4: Determine the spatial distribution of dislocations and deformations of the fault under a single earthquake event based on the preset fault dislocation distribution model; the fault dislocation distribution model includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model, and the corresponding model is selected according to the deformation distribution characteristics of a large earthquake along the fault. Step S5: Determine the spatial distribution of the fault-based distributed deformation under a single earthquake event based on the preset cross-fault deformation attenuation model; the cross-fault deformation attenuation model adopts an exponential attenuation law. Step S6: Based on the empirical formulas for magnitude-rupture length and magnitude-maximum dislocation, calculate the spatial distribution of dislocation deformation and distributed deformation of the fault, respectively, to obtain the amount of dislocation deformation and distributed deformation caused by the next earthquake event of a given magnitude. Step S7: Based on the probabilistic seismic hazard analysis model, combined with seismic activity simulation, target time period setting and Monte Carlo simulation, the dislocation deformation and distributed deformation are probabilistically integrated to calculate the fault dislocation and cross-fault deformation under different exceedance probabilities at various locations along and across the fault in the future target time period. Step S8: Based on the calculation results of fault dislocation and cross-fault deformation under different exceedance probabilities, extract the fault resistance design parameters at the engineering site.

2. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 1, characterized in that, The basic data is derived from at least one of seismic geological exploration, geophysical exploration, and geodesy.

3. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 2, characterized in that, The fault dislocation distribution model is selected based on the geometric and kinematic characteristics of the fault and the deformation behavior during a major earthquake.

4. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 1, characterized in that, The cross-fault deformation attenuation model is an exponential attenuation function model.

5. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 1, characterized in that, The empirical formulas for magnitude-rupture length and magnitude-maximum dislocation include: The empirical formula for magnitude-rupture length is: ； The empirical formula for magnitude-maximum dislocation is: ， ； in, The fracture length, The maximum dislocation amount For surface wave magnitude, coefficient , , , The value is determined based on the fault type.

6. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 5, characterized in that, When the fault is a strike-slip type , , , When the fault is a thrust fault, , , , ; When the fault is a normal fault type , , , .

7. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 1, characterized in that, In step S7, the probabilistic seismic hazard analysis model includes the following sub-steps: Step S71: Based on the upper limit of magnitude, recurrence period, elapsed time, focal depth and deformation width, simulate and generate an artificial earthquake catalog containing multiple magnitude-location pairs; Step S72: Use the design reference period of the project as the target time period; Step S73: Using the Monte Carlo simulation method, repeat step S6 within the target time period to calculate the deformation at each location caused by a large number of random earthquake sequences, and sort the simulation results by size. Step S74: Perform statistical analysis on the deformation results and extract the deformation results at the quantile corresponding to the specified exceedance probability.

8. The method for predicting the probability of fault deformation for engineering fault resistance according to claim 7, characterized in that, The fracture resistance design parameters include dislocation or deformation amounts corresponding to at least one of the following levels: 10%, 5%, 2%, and 1% over a 50-year or 100-year period.

9. A fault deformation probability prediction system for engineering fault resistance, used to implement the method as described in any one of claims 1 to 8, characterized in that, The system includes a data input module, a data processing module, a seismic activity module, a deformation calculation module, a probability analysis module, and a parameter output module. The data input module is used to acquire the basic data of the target active fault. The basic data includes the geometric features of the fault, the kinematic features of the fault, the upper limit of the magnitude, the recurrence period, the elapsed time, the focal depth and the deformation width. The basic data is then used as input parameters to input the calculation model. The data processing module is used to calculate the fracture length based on the geometric characteristics of the fault, and to construct the two-dimensional grid required for calculation along and across the fault. The seismic activity module is used to establish an artificial earthquake catalog spanning earthquake cycles based on the actual historical earthquake frequency of the fault; and to select a target time period and calculate the occurrence rate of different magnitude ranges within that time period based on the elapsed time of the last major earthquake, and to establish a secondary artificial earthquake catalog within the target time period based on the Poisson model. The deformation calculation module is used to determine the spatial distribution of fault dislocations under a single earthquake event based on a preset fault dislocation distribution model, which includes a triangular distribution model, a sinusoidal function distribution model, or an elliptical distribution model; and to determine the spatial distribution of fault distributed deformation under a single earthquake event based on a preset cross-fault deformation attenuation model; and to calculate the dislocation-type deformation and distributed deformation caused by a single earthquake event of a given magnitude using empirical formulas for magnitude-rupture length and magnitude-maximum dislocation. The probability analysis module is used to perform probabilistic integration of the dislocation deformation and distributed deformation based on the probabilistic seismic hazard analysis model, combined with seismic activity simulation, target time period setting and Monte Carlo simulation, to calculate the fault dislocation and cross-fault deformation at the target location under different exceedance probabilities in the future target time period. The parameter output module is used to output the distribution maps of fault-along dislocations and cross-fault deformations under different exceedance probabilities, and to extract the fault-resistant design parameters corresponding to the exceedance probabilities at the engineering site.

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