A strong earthquake disaster area prediction method and system based on residence surface and frequency analysis

Abstract

The application discloses a strong earthquake disaster area prediction method and system based on crustal interior and seismic frequency analysis, and relates to the technical field of strong earthquake disaster area prediction. The method comprises the following steps: determining a land crustal interior depth model according to seismic literature data of a research area; the seismic literature data comprises aviation magnetic method measurement data; determining a seismic frequency distribution map of the research area according to historical earthquake catalog data; determining a seismic frequency superimposed crustal interior depth map of the research area according to the land crustal interior depth model and the seismic frequency distribution map; determining an Ms >= 7 strong earthquake potential risk area in the research area based on the seismic frequency superimposed crustal interior depth map of the research area; determining an Ms >= 7 strong earthquake potential disaster area marking map of the research area according to the Ms >= 7 strong earthquake potential risk area in the research area; and using the Ms >= 7 strong earthquake potential disaster area marking map to determine the strong earthquake potential disaster area of the research area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of strong earthquake disaster area prediction, in particular to a strong earthquake disaster area prediction method and system based on intraplate and seismic frequency analysis. BACKGROUND

[0002] In recent years, the demand for accurate and quantitative judgment of strong earthquake potential risk area has been increasing, and the research has gradually shifted from regional earthquake belt prediction to local area or seismogenic fault zone analysis. Domestic scholars have also conducted related series of research on strong earthquake potential risk area. For example, Liu Ming et al. (2015) proposed to study the risk of future Ms≥7.0 earthquakes on the NE large-scale left-lateral strike-slip fault zone, and delineated five Ms≥7.0 strong earthquake potential risk areas.

[0003] The Ms≥7.0 strong earthquake potential risk area marking method (i.e. the analysis method of Liu Ming et al. (2015)) is a relatively applicable method for determining the disaster area. Through the marking of the Ms≥7.0 strong earthquake potential risk area, the prediction of the strong earthquake disaster area can be realized. However, due to the limitations of historical earthquake research, there are multiple explanations of the seismogenic mechanism and rules for strong earthquake prediction. Therefore, the Ms≥7.0 strong earthquake potential risk area marking method lacks quantitative observation data constraints, and thus has obvious deficiencies in marking accuracy. At the same time, this Ms≥7.0 strong earthquake potential risk area marking method is relatively dependent on traditional qualitative analysis methods of strong earthquakes, and also lacks the constraints of multiple observation information such as seismic history frequency statistics and physical property measurements. SUMMARY

[0004] The purpose of the present application is to provide a strong earthquake disaster area prediction method and system based on intraplate and seismic frequency analysis, which uses fine quantitative indicators to quantitatively mark the strong earthquake potential disaster area by fully introducing historical earthquake observation data and multi-source geophysical research results, and improves the marking accuracy.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] The present application provides a strong earthquake disaster area prediction method based on intraplate and seismic frequency analysis, comprising:

[0007] determining a land intraplate depth model according to the seismic literature data of the research area; the seismic literature data at least includes airborne magnetic survey data;

[0008] determining a seismic frequency distribution map of the research area according to historical earthquake catalog data;

[0009] determining a seismic frequency superimposed intraplate depth map of the research area according to the land intraplate depth model and the seismic frequency distribution map of the research area;

[0010] determine the Ms≥7 strong earthquake potential risk area in the research region based on the seismic frequency superposition and intraplate depth map of the research region;

[0011] determine the Ms≥7 strong earthquake potential disaster area marking map of the research region according to the Ms≥7 strong earthquake potential risk area in the research region; the Ms≥7 strong earthquake potential disaster area marking map is used to determine the strong earthquake potential disaster area in the research region.

[0012] Optionally, the seismic frequency distribution map of the research region is determined according to historical earthquake catalog data, specifically including:

[0013] The research region is divided based on historical earthquake catalog data to obtain a 42×62 unit grid region;

[0014] The Ms data of each unit grid region is determined according to the seismic literature data of the research region;

[0015] The number of Ms>1 earthquakes in each unit grid region is counted to obtain the seismic frequency distribution map of the research region.

[0016] Optionally, the Ms≥7 strong earthquake potential risk area in the research region is determined based on the seismic frequency superposition and intraplate depth map of the research region, specifically including:

[0017] The strong earthquake potential occurrence area is determined based on the seismic frequency superposition and intraplate depth map of the research region and a first constraint condition; the first constraint condition is that the region with a seismic frequency of more than 9 times is determined as the strong earthquake potential occurrence area;

[0018] The Ms≥7 strong earthquake potential risk area is determined according to the strong earthquake potential occurrence area.

[0019] Optionally, the Ms≥7 strong earthquake potential risk area is determined according to the strong earthquake potential occurrence area, specifically including:

[0020] The Ms≥7 strong earthquake potential risk area is determined according to the strong earthquake potential occurrence area and a second constraint condition;

[0021] The second constraint condition is that: first, the Ms≥7 strong earthquake potential risk area should be located at the periphery of the active fault zone; second, the Ms≥7 strong earthquake potential risk area should be located in the region with a relatively shallow intraplate gradient; and third, the Ms≥7 strong earthquake potential risk area should be located in the region with a very high historical seismic activity frequency.

[0022] Optionally, the research region is the western region of Yunnan Province in the southeast margin of the Qinghai-Tibet Plateau.

[0023] Optionally, the Ms≥7 strong earthquake potential disaster area marking map of the research region is determined according to the Ms≥7 strong earthquake potential risk area in the research region, specifically including:

[0024] Based on the empirical model of the seismic intensity attenuation of the research area based on the 8-level earthquake, the area with the intensity of IX or more caused by the earthquake with the magnitude of 7 or more is calculated.

[0025] According to the area with the intensity of IX or more caused by the earthquake with the magnitude of 7 or more and the satellite observation data, the marking map of the potential disaster area of the Ms≥7 strong earthquake in the research area is determined.

[0026] Optionally, the method further comprises:

[0027] According to the marking map of the potential disaster area of the Ms≥7 strong earthquake in the research area and the existing delineation map of the potential disaster area of the strong earthquake, three kinds of potential disaster areas of the Ms≥7 strong earthquake are obtained.

[0028] The three kinds of potential disaster areas of the Ms≥7 strong earthquake are as follows: the first kind is the potential disaster area of the Ms≥7 strong earthquake determined according to the marking map of the potential disaster area of the Ms≥7 strong earthquake in the research area and the existing delineation map of the potential disaster area of the strong earthquake, and the confidence of the potential disaster is high; the second kind is the potential disaster area of the Ms≥7 strong earthquake determined according to the marking map of the potential disaster area of the Ms≥7 strong earthquake in the research area or the existing delineation map of the potential disaster area of the strong earthquake, and the confidence of the potential disaster is medium; and the third kind is the potential disaster area of the Ms≥7 strong earthquake predicted by the corrosion expansion operation and the eight-connected method, and the confidence of the potential disaster is low.

[0029] The application provides a strong earthquake disaster area prediction system based on the analysis of the living place and the seismic frequency, which comprises:

[0030] A land living place depth model determination module is configured to determine a land living place depth model according to seismic literature data of a research area, wherein the seismic literature data at least comprises aviation magnetic survey data.

[0031] A seismic frequency distribution map determination module is configured to determine a seismic frequency distribution map of the research area according to historical earthquake catalog data.

[0032] A seismic frequency superimposed living place depth map determination module is configured to determine a seismic frequency superimposed living place depth map of the research area according to the land living place depth model and the seismic frequency distribution map of the research area.

[0033] A strong earthquake potential risk area determination module is configured to determine a Ms≥7 strong earthquake potential risk area in the research area based on the seismic frequency superimposed living place depth map of the research area.

[0034] A strong earthquake potential disaster area marking map determination module is configured to determine a Ms≥7 strong earthquake potential disaster area marking map of the research area according to the Ms≥7 strong earthquake potential risk area in the research area, and the Ms≥7 strong earthquake potential disaster area marking map is used to determine a strong earthquake potential disaster area of the research area.

[0035] According to the specific embodiments provided by the application, the following technical effects are achieved:

[0036] The present application presents reliable prediction results based on aviation geophysical scanning detection observation, historical earthquake frequency statistics and other quantifiable indexes, and adopts a more refined calibration method. The present application will further optimize the identification method of strong earthquake potential danger area and strong earthquake potential disaster area, and provide a new research perspective and effective decision criterion for the systematized work of earthquake disaster area prediction, such as earthquake prevention and disaster reduction and emergency rescue. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor under the premise of the drawings.

[0038] Figure 1 The flowchart of the strong earthquake disaster area prediction method based on the crustal thickness and earthquake frequency analysis provided by the embodiments of the present application is shown in the figure.

[0039] Figure 2 The land crustal thickness depth map of the research area provided by the embodiments of the present application is shown in the figure.

[0040] Figure 3 The earthquake frequency statistical data distribution map of the research area provided by the embodiments of the present application is shown in the figure.

[0041] Figure 4 The research area earthquake frequency superimposed crustal thickness depth map provided by the embodiments of the present application is shown in the figure.

[0042] Figure 5 The Ms≥7 strong earthquake potential occurrence area map of the research area provided by the embodiments of the present application is shown in the figure.

[0043] Figure 6 The Ms≥7 earthquake danger area IX level and above intensity area map provided by the embodiments of the present application is shown in the figure.

[0044] Figure 7 The Ms≥7 strong earthquake potential disaster area map of the research area provided by the embodiments of the present application is shown in the figure.

[0045] Figure 8 The prior art and the present application calibration superimposed map provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0047] The present application focuses on the strong earthquake risk area in the western region of Yunnan, quantitatively marks the strong earthquake risk area based on historical earthquake statistics and geophysical observation analysis, provides a new perspective for earthquake risk assessment and prediction, and provides a technical reference for earthquake prevention and disaster reduction and emergency rescue.

[0048] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0049] Embodiment one

[0050] There is a very big strong earthquake (Ms≥7) risk in the western region of Yunnan in the southeast margin of the Qinghai-Tibet Plateau. Therefore, carrying out earthquake prediction research in the southeast margin of the Qinghai-Tibet Plateau will provide important and efficient decision criteria for earthquake prevention and post-earthquake emergency rescue work.

[0051] The present embodiment summarizes the earthquake prediction results of the local strong earthquake potential risk area in the southeast margin of the Qinghai-Tibet Plateau, analyzes the limitations of earthquake disaster emergency, and proposes a method of jointly marking the strong earthquake (Ms≥7) potential risk area based on historical earthquake frequency and intracrustal depth. Based on the earthquake attenuation formula and remote sensing identification technology, the strong earthquake (Ms≥7) potential disaster area is identified. The research results show that the joint marking and identification method more fully utilizes the historical earthquake data and the latest "sweeping" geophysical research results than the previous method. Although there are differences in the risk areas identified by the two methods, the "quantitative" quantitative index and method can obtain a higher confidence strong earthquake potential disaster area identification model, which provides effective decision-making and research new perspective for disaster prevention and reduction and earthquake emergency.

[0052] The Curie point is the temperature interface of the crust 580℃, which indicates the distribution state of the continental crust magnetic layer depth and the average geothermal gradient, and has important practical significance in the research of continental dynamics, seismogenic mechanism and geological resource effect. At present, the magnetic anomaly data obtained by the two ways of airborne magnetic survey and satellite magnetic survey are used to inverse the Curie point depth.

[0053] According to the historical research data of the Curie surface in the study area (taking the western region of Yunnan Province in the southeast edge of the Qinghai-Tibet Plateau as an example) obtained by inverting the airborne magnetic survey and satellite magnetic survey, it can be found that there are two obvious uplift regions and four obvious depression regions of the Curie surface in the study area. One of the uplift regions is located in the EN direction of the Nantinghe fault zone, which is a triangular region surrounded by three depressions of the EW extension of the Wanding fault zone, the Wulangshan fault zone and the Honghe fault zone, and the WS segment of the Menglian fault zone (in Myanmar); the other uplift region is located between the Dayingjiang fault zone and the Longling-Ruili fault zone, which is a belt region surrounded by two depressions of the EW extension of the Wanding fault zone and the Myitkyina fault zone (in Myanmar); the two uplift regions are connected by the uplift region of the WN segment of the Honghe fault zone. According to the above conclusion, the prone area of strong earthquakes in the study area should be concentrated in the part of the Curie surface gradient zone deviating from the gradient uplift, which is consistent with the results obtained from a large number of historical geological research data.

[0054] Ms≥7 earthquakes are the important research object of the present embodiment. As one of the most important empirical formulas in seismology research, Gutenbery-Richter formula defines the relationship between earthquake activity frequency and intensity, and forms an empirical formula lgN=a-b×M, which is simply referred to as G-R relationship (in the formula, M value is earthquake magnitude, N value is earthquake frequency, a value is a constant representing the seismic activity level of the statistical area, and b value reflects the proportion between the number of large and small earthquakes in a certain period in a certain area). There may be a close relationship between earthquake intensity and earthquake activity frequency.

[0055] In analyzing the potential risk area of Ms≥7 strong earthquakes, historical geological and seismic research data in the study area should be fully combined, and the seismogenic characteristics of the spatial distribution of active faults, the depth of the Curie surface, and the earthquake frequency should be focused on, and the following three prerequisites should be met:

[0056] First, the potential risk area should be located in the periphery of the active fault zone, second, the potential risk area should be located in the area with relatively shallow gradient of the Curie surface, and third, the potential risk area should be located in the area with extremely high historical earthquake activity frequency. Based on this, the present embodiment carries out the following work.

[0057] As shown in Figure 1 , the strong earthquake disaster area prediction method based on the Curie surface and earthquake frequency analysis provided by the present embodiment includes the following steps.

[0058] Step 100: determining the land Curie surface depth model according to the seismic literature data of the study area; the seismic literature data at least includes airborne magnetic survey data.

[0059] Since the orbit height of the satellite for measuring the lithospheric magnetic field is generally between 400-500km, it reflects the large-scale magnetic changes of the lower crust and upper mantle, so the satellite magnetic survey data are mainly used for global or regional geomagnetic field modeling, geomagnetic field multi-scale changes and mechanism research, etc., focusing on large-scale problems and time-varying problems of the Earth's magnetic field. Considering the limitations of the current satellite magnetic survey data and the limited accuracy of the inversion of the inner core obtained therefrom, the airborne magnetic survey data with regional "scanning" advantages are used for inversion calculation to obtain the land inner core depth model in this embodiment. Taking the western region of Yunnan Province in the southeast margin of the Qinghai-Tibet Plateau as an example, the land inner core depth model of the region is shown in Figure 2 .

[0060] Step 200: Determine the seismic frequency distribution map of the study area according to the historical earthquake catalog data.

[0061] In this embodiment, step 200 specifically includes:

[0062] First, obtain the historical earthquake catalog data (including source location and magnitude) in the prior art; second, divide the study area based on the historical earthquake catalog data to obtain a 42x62 unit grid region; then determine the Ms data of each unit grid region according to the seismic literature data of the study area; finally, count the number of earthquakes with Ms>1 in each unit grid region to obtain the seismic frequency distribution map of the study area.

[0063] An example: based on the historical earthquake catalog data (1964-2022) of the International Seismological Center, the study area is divided into a 42x62 unit grid region with a unit of 0.1°, and the number of earthquakes with Ms>1 in each unit grid region is counted to obtain the seismic frequency distribution map; taking the western region of Yunnan Province in the southeast margin of the Qinghai-Tibet Plateau as an example, the seismic frequency distribution of the region is shown in Figure 3 . Wherein, different sizes of Ο represent different frequencies, which are specifically marked in the lower left corner of Figure 3 .

[0064] Step 300: Determine the seismic frequency superimposed inner core depth map of the study area according to the land inner core depth model and the seismic frequency distribution map of the study area.

[0065] Step 400: Determine the potential risk area of Ms≥7 strong earthquake in the study area based on the seismic frequency superimposed inner core depth map of the study area.

[0066] In this embodiment, step 400 specifically includes:

[0067] First, based on the focal depth map and the first constraint condition of the study area, the potential strong earthquake occurrence area is determined; the first constraint condition is that the area with more than 9 earthquakes is determined as the potential strong earthquake occurrence area; then, according to the potential strong earthquake occurrence area, the potential risk area of Ms≥7 strong earthquake is determined.

[0068] One example: take the western region of Yunnan Province in the southeast margin of the Qinghai-Tibet Plateau as an example, as shown in Figure 4 In addition to the two concentrated areas in the Lijiang River depression area of the Wuliang Mountain fault zone and the Red River fault zone, the area with more than 9 earthquakes is found to be in the same position as the basic condition of the Ms≥7 strong earthquake potential risk area set. Therefore, the area with more than 9 earthquakes (dark area) is determined as the potential strong earthquake occurrence area in this embodiment.

[0069] As shown in Figure 5 Three Ms≥7 strong earthquake potential risk areas are finally marked from the dark area, and the marking conditions of the three Ms≥7 strong earthquake potential risk areas are: in order to maximize the three prerequisite conditions that the Ms≥7 strong earthquake potential risk area should have in this embodiment: first, the Ms≥7 strong earthquake potential risk area should be located in the periphery of the active fault zone (the edge of the earthquake zone in the figure), second, the Ms≥7 strong earthquake potential risk area should be located in the area with relatively shallow gradient of the Lijiang River, and third, the Ms≥7 strong earthquake potential risk area should be located in the area with extremely high historical earthquake activity frequency.

[0070] The method for marking the Ms≥7 strong earthquake potential risk area in this embodiment is extended from the identification method of Ms≥7 strong earthquake potential risk area by Liu Ming et al. (2015). First, the "elliptical" earthquake isoseismal line pattern in Yunnan is used to mark the unit grid area; second, the elliptical long axis direction is used to mark the same way as the geological active fault closest to the unit grid area; then, the elliptical shape is used to mark the way that at least two corners of the unit grid area are tangent to the 0.1°×0.1° latitude and longitude; finally, the elliptical shape is used as the basis to mark the center of the latitude and longitude unit grid area, the four corners are connected, and the proportion is expanded.

[0071] Step 500: According to the Ms≥7 strong earthquake potential risk area in the study area, determine the Ms≥7 strong earthquake potential disaster area marking map of the study area; the Ms≥7 strong earthquake potential disaster area marking map is used to determine the potential disaster area of strong earthquake in the study area.

[0072] Taking the western region of Yunnan Province in the southeast margin of the Qinghai-Tibet Plateau as an example, the above step 500 specifically includes:

[0073] S1: Mark the area with IX intensity or above caused by earthquakes above 7 magnitude as a heavy disaster area;

[0074] According to the historical earthquake statistics of the International Seismological Center from 1964 to 2022, the highest magnitude earthquake occurring within the boundaries of Yunnan Province was the Tonghai 7.7 magnitude earthquake on January 5, 1970, while the highest magnitude earthquake occurring within the study area was the Lancang / Gengma 7.2 magnitude earthquake in 1988. The epicentral intensity of both earthquakes was X degree, which is the most destructive earthquake in Yunnan Province. The areas with intensity above IX degree after the earthquake are considered as severely affected areas.

[0075] Based on the damage caused by the two strong earthquakes, it is assumed that the highest magnitude of the earthquake does not exceed Ms 8. In this embodiment, the areas with intensity above IX degree caused by earthquakes with magnitude above 7 are considered as severely affected areas.

[0076] Based on the seismic intensity attenuation law in western Yunnan, the areas with intensity above IX degree caused by earthquakes with magnitude above 7 can be predicted from the Ms≥7 strong earthquake risk area. The relevant principles and calculation methods are detailed in the Ju et al (2023) paper.

[0077] Sub-step 2: Construct the empirical model of seismic intensity attenuation in the study area; wherein the empirical formula of seismic intensity attenuation in the study area is:

[0078] I a = 68053 + 1.2972Ms-4.7603 log(R a + 22) (1).

[0079] I b = 5.3315 + 1.2013M s - 4.1917 log(R a + 10) (2).

[0080] In the formula: I is the seismic intensity, a and b represent the long and short axes respectively; M s is the magnitude; R a and R b are the lengths of the long and short axes of the ellipse isoseismal line with intensity I.

[0081] Sub-step 3: Based on the empirical model of seismic intensity attenuation in the study area for an earthquake with magnitude 8, calculate the areas with intensity above IX degree caused by earthquakes with magnitude above 7, as shown in Figure 6 .

[0082] Substitute Ms = 8 into the above calculation formula, the results are shown in Table 1.

[0083] Table 1 Calculation results of the empirical model of seismic intensity attenuation in the study area based on an earthquake with magnitude 8

[0084]

[0085] Sub-step 4: Based on the areas with intensity of IX or above caused by earthquakes with magnitude of 7 or above and satellite observation data, the Ms≥7 strong earthquake potential disaster area marking map of the study area is determined.

[0086] Based on the observation data of Terra satellite and Aqua satellite in a whole year, the MCD12Q1 MODIS level 3 data land cover type product is used to extract the planar distribution characteristics of urban buildings, and the Ms≥7 strong earthquake potential disaster area of the study area is obtained as shown in FIG. 2. Figure 7

[0087] In this embodiment, the method provided in the embodiment further includes:

[0088] Based on the Ms≥7 strong earthquake potential disaster area of step 500 and the inaccurate strong earthquake potential disaster area demarcation map in the prior art, the two are compared, Figure 8 The dashed oval in FIG. 5 is the strong earthquake potential disaster area of the embodiment, and the straight line oval is the inaccurate strong earthquake potential disaster area demarcation map in the prior art. By comparing the Ms≥7 strong earthquake risk area identified and predicted by the embodiment and Ju et al. (2023), ID05 is the Ms≥7 strong earthquake potential disaster area identified and predicted by both, ID08 and ID09 are the Ms≥7 strong earthquake potential disaster areas newly identified in the study area. In the process of identification and prediction, by performing erosion and expansion operation on the building distribution data, and taking eight connected methods as the basis for judgment, the Ms≥7 strong earthquake potential disaster areas ID01, ID03 and ID08 with weaker damage are identified and predicted.

[0089] Therefore, according to the Ms≥7 strong earthquake potential disaster area marking map of the study area and the existing strong earthquake potential disaster area demarcation map, three Ms≥7 strong earthquake potential disaster areas are obtained, the first one is the Ms≥7 strong earthquake potential disaster area determined according to the Ms≥7 strong earthquake potential disaster area marking map of the study area and the existing strong earthquake potential disaster area demarcation map, that is, the area ID5 in the overlapping area of the two methods, and the potential disaster confidence is high; the second one is the Ms≥7 strong earthquake potential disaster area determined according to the Ms≥7 strong earthquake potential disaster area marking map of the study area or the existing strong earthquake potential disaster area demarcation map, that is, the area ID02, ID04, ID06, ID07 and ID09 in the area marked by the two methods, and the potential disaster confidence is medium; the third one is the Ms≥7 strong earthquake potential disaster area predicted by the erosion and expansion operation and the eight connected methods, that is, the area ID01, ID03 and ID08 predicted by the erosion and expansion operation and the eight connected methods, and the potential disaster confidence is low.

[0090] ​Theoretical and empirical methods such as identification of strong earthquake risk areas are difficult to be used for accurate prevention of earthquake disasters. Although the current research results of earthquake science have uncertainties for predictive research, the maximum reference to existing research results is an important way to effectively evaluate earthquake disasters and emergency rescue methods.

[0091] According to the identification conditions and marking methods of strong earthquake potential risk areas, the embodiment quantifies a plurality of evaluation indexes such as historical earthquake frequency and "elliptical" long axis direction. At present, there is no Chinese and international standard to be referred to for the above-mentioned indexes, so the embodiment restores the real situation of post-earthquake disaster relief in the western region of Yunnan to the greatest extent by means of literature research and expert interviews. Therefore, the selection of these indexes has certain rationality and good operability. The embodiment believes that in future research, scholars can effectively mark and evaluate Ms≥7 strong earthquake risk areas and disaster relief areas by referring to the existing indexes when they are sorting out and editing historical information.

[0092] The summary of the embodiment is as follows:

[0093] (1) Ms≥7.0 strong earthquakes with extremely high risk often occur in the periphery of active fault zones near the high seismic frequency intraplate gradient belt, and are often located in the region of the intraplate gradient belt.

[0094] (2) Based on the statistical analysis of intraplate depth and seismic frequency, the Ms≥7.0 strong earthquake potential risk area marked on the active fault zone provides a good theoretical basis and a new perspective of precise quantification for earthquake disaster prevention and mitigation research. Although the marking results of the potential earthquake disaster area in this embodiment are different from those of Ju et al. (2023), the quantification marking method of this study can better standardize the practice and application in strong earthquake potential risk area prediction.

[0095] The embodiment identifies 1 area with high confidence in earthquake disaster prediction and 5 areas with medium confidence in earthquake disaster prediction. The large-scale disaster relief material reserve in the study area is currently in a low confidence area in earthquake disaster prediction, and the earthquake protection construction of the reserve and the surrounding area should be further strengthened to avoid disaster failure. The 8 strong earthquake potential risk areas identified are within the maximum safe flight distance of helicopter disaster relief material transportation.

[0096] Embodiment two

[0097] In order to perform the method corresponding to the above-mentioned embodiment one, to realize the corresponding function and technical effect, the following provides a strong earthquake disaster area prediction system based on intraplate and seismic frequency analysis.

[0098] The strong earthquake disaster area prediction system based on intraplate and seismic frequency analysis provided by the embodiment comprises:

[0099] The land inside depth model determining module is configured to determine a land inside depth model of the study area according to seismic literature data of the study area, wherein the seismic literature data at least includes airborne magnetic survey data.

[0100] The seismic frequency distribution map determining module is configured to determine a seismic frequency distribution map of the study area according to historical earthquake catalog data.

[0101] The seismic frequency superposition inside depth map determining module is configured to determine a seismic frequency superposition inside depth map of the study area according to the land inside depth model of the study area and the seismic frequency distribution map of the study area.

[0102] The strong earthquake potential risk area determining module is configured to determine an Ms≥7 strong earthquake potential risk area in the study area based on the seismic frequency superposition inside depth map of the study area.

[0103] The strong earthquake potential disaster area marking map determining module is configured to determine an Ms≥7 strong earthquake potential disaster area marking map of the study area according to the Ms≥7 strong earthquake potential risk area in the study area, wherein the Ms≥7 strong earthquake potential disaster area marking map is used to determine a strong earthquake potential disaster area in the study area.

[0104] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts of each embodiment can be referred to each other.

[0105] The principles and implementation manners of the present application are described by using specific examples in the present application. The above description of the embodiments is only used to help understand the method of the present application and its core idea. Meanwhile, for the general skilled in the art, the specific implementation manners and application range of the present application can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A strong earthquake disaster area prediction method based on residence surface and frequency analysis, characterized in that, The application relates to a method for determining a Ms>=7 strong earthquake potential disaster area marking map of a research region. The method comprises the following steps: determining a land surface depth model of the research region according to seismic literature data of the research region; the seismic literature data at least comprises aviation magnetic survey data; determining a seismic frequency distribution map of the research region according to historical earthquake catalog data; determining a seismic frequency superimposed land surface depth map of the research region according to the land surface depth model of the research region and the seismic frequency distribution map of the research region; determining a Ms>=7 strong earthquake potential risk area in the research region based on the seismic frequency superimposed land surface depth map of the research region; 2. The method according to claim 1, wherein the method is characterized by, determining the Ms>=7 strong earthquake potential disaster area marking map of the research region according to the Ms>=7 strong earthquake potential risk area in the research region; the Ms>=7 strong earthquake potential disaster area marking map is used for determining a strong earthquake potential disaster area of the research region. The method for determining the seismic frequency distribution map of the research region comprises the following steps: dividing the research region based on the historical earthquake catalog data to obtain a 42*62 unit grid region; determining Ms data of each unit grid region according to the seismic literature data of the research region; 3. The method according to claim 1, wherein the method is characterized by, counting the number of Ms>1 earthquakes in each unit grid region to obtain the seismic frequency distribution map of the research region. The method for determining the Ms>=7 strong earthquake potential risk area in the research region based on the seismic frequency superimposed land surface depth map of the research region comprises the following steps: determining a strong earthquake potential occurrence area based on the seismic frequency superimposed land surface depth map of the research region and a first constraint condition; the first constraint condition is that a region with a seismic frequency of more than 9 times is determined as the strong earthquake potential occurrence area; 4. The method according to claim 3, wherein the method is characterized by, determining the Ms>=7 strong earthquake potential risk area according to the strong earthquake potential occurrence area. The method for determining the Ms>=7 strong earthquake potential risk area according to the strong earthquake potential occurrence area comprises the following steps: determining the Ms>=7 strong earthquake potential risk area according to the strong earthquake potential occurrence area and a second constraint condition; 5. The method according to claim 1, wherein the method is characterized by, the second constraint condition is that the Ms>=7 strong earthquake potential risk area should be located at the periphery of an active fault zone, the Ms>=7 strong earthquake potential risk area should be located in a region with a relatively shallow land surface gradient, and the Ms>=7 strong earthquake potential risk area should be located in a region with a very high historical earthquake activity frequency.

6. The method according to claim 1 or 5, characterized in that, The research region is a western Yunnan region in the southeast edge of the Qinghai-Tibet Plateau. The method for determining the Ms>=7 strong earthquake potential disaster area marking map of the research region according to the Ms>=7 strong earthquake potential risk area in the research region comprises the following steps: calculating a region with an intensity of IX and above caused by a 7-level earthquake based on a research region seismic intensity attenuation empirical model of an 8-level earthquake; 7. The method according to claim 1, wherein the method is characterized by, determining the Ms>=7 strong earthquake potential disaster area marking map of the research region according to the region with an intensity of IX and above caused by the 7-level earthquake and satellite observation data. The method further comprises the following steps: obtaining three Ms>=7 strong earthquake potential disaster areas according to the Ms>=7 strong earthquake potential disaster area marking map of the research region and an existing strong earthquake potential disaster area demarcation map. The three Ms≥7 strong earthquake potential disaster areas are: the first, the Ms≥7 strong earthquake potential disaster area determined according to the Ms≥7 strong earthquake potential disaster area marking map of the research area and the existing strong earthquake potential disaster area demarcation map, and the potential disaster confidence is high; the second, the Ms≥7 strong earthquake potential disaster area determined according to the Ms≥7 strong earthquake potential disaster area marking map of the research area or the existing strong earthquake potential disaster area demarcation map, and the potential disaster confidence is medium; and the third, the Ms≥7 strong earthquake potential disaster area predicted by the corrosion expansion operation and the eight connected method, and the potential disaster confidence is low.

8. A strong earthquake disaster area prediction system based on residence surface and frequency analysis, characterized in that, Comprise: A land surface depth model determination module, configured to determine a land surface depth model according to seismic literature data of a research area; the seismic literature data at least includes airborne magnetic survey data; A seismic frequency distribution map determination module, configured to determine a seismic frequency distribution map of the research area according to historical earthquake catalog data; A seismic frequency superimposed land surface depth map determination module, configured to determine a seismic frequency superimposed land surface depth map of the research area according to the land surface depth model of the research area and the seismic frequency distribution map; A strong earthquake potential risk area determination module, configured to determine an Ms≥7 strong earthquake potential risk area in the research area based on the seismic frequency superimposed land surface depth map of the research area; A strong earthquake potential disaster area marking map determination module, configured to determine a Ms≥7 strong earthquake potential disaster area marking map of the research area according to the Ms≥7 strong earthquake potential risk area in the research area; the Ms≥7 strong earthquake potential disaster area marking map is used to determine a strong earthquake potential disaster area of the research area.

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