Method and equipment for pre-judging strong earthquake risk area

Abstract

The invention discloses a method and equipment for pre-judging a strong earthquake risk area, and the method comprises the steps: obtaining an inversion inhabitation depth result of a target area, and searching an area meeting a strong earthquake characteristic index in the target area as a potential strong earthquake occurrence area; longitude and latitude unit gridding processing is carried out on the potential strong earthquake occurrence area, and earthquake generation units are determined; marking the earthquake generation unit, and drawing a potential earthquake generation danger area of the strong earthquake; and obtaining a strong earthquake danger area in combination with the intensity attenuation model, and carrying out range marking on the range of the strong earthquake danger area. According to the method, the result of the living depth is combined with the strong earthquake generation characteristic to pre-judge the strong earthquake danger area, the whole process has high operability by quantifying the pre-judgment process of the strong earthquake danger area, the strong earthquake danger area is obtained on the basis of the development danger area by using the intensity attenuation model, and the method has high practicability. And the intensity is integrated into pre-judgment work, so that practical application is facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of earth observation, and more specifically, relates to a method and device for predicting strong earthquake risk areas. Background Art

[0002] The earthquake source layer is mainly located near the brittle-plastic transition zone of the earth's crust. The bottom temperature of the earthquake source layer is an important factor controlling the brittle-plastic deformation of rocks. It is not only an important parameter for studying the mechanical structure and temperature structure of the earth's crust, but also an important parameter for earthquake activity analysis. Therefore, studying the correlation between the crustal thermal structure and earthquake activity has become an effective means for predicting earthquake disasters. In early studies, scholars carried out empirical research in a large number of target areas by obtaining surface heat flow. The results showed that the temperature of the brittle-plastic deformation transition zone of rocks obtained during the empirical process was relatively consistent with the results of laboratory rock physical measurements. Through the analysis and research of earthquake activity, it can be found that there is an obvious negative correlation between surface heat flow (or geothermal gradient) and earthquake activity. Terrestrial heat flow is the heat transferred from the interior of the earth to the surface and released into the air, and is an important geophysical parameter reflecting the thermal state of the earth, known as the window to "peek" into the thermal state of the earth's interior. The most direct and effective method for obtaining heat flow data is to use drilling technology to measure the geothermal gradient of deep strata, and then calculate the heat flow value at this depth by taking cores, sampling and measuring the rock thermal conductivity in the laboratory. However, globally, high-quality heat flow measurements still face challenges. Surface heat flow is easily disturbed by local thermal anomalies, and the measurement depth that can be achieved by regional overall measurement is relatively shallow, and the measured value will be affected by surface hydrothermal circulation and erosion. Therefore, to obtain accurate heat flow data with wide coverage, a large number of deep drillings are required, which requires high technical and financial requirements. Facing this restriction, scholars have turned their attention to using gravity, magnetic, electromagnetic and seismological methods, hoping that these studies can contribute to the research of the earthquake source layer. In the first embodiment of the present invention, the study on predicting strong earthquake risk areas is carried out by using the Curie depth, and the application research is carried out by superimposing the Curie depth and the characteristics of strong earthquake occurrence.

[0003] During the process of strong earthquake tracking, when the future epicenter is unknown, the credibility of using seismological anomaly methods to track dangerous areas is relatively high. Combining earthquake activities with geophysical measurement data is an effective means for obtaining strong earthquake prediction criteria in the future. Currently, it is difficult to achieve a wide range of applications in earthquake dangerous areas in a short time because fixed observation points need to be arranged densely enough.

[0004] In view of this, overcoming the technical defects of the above-mentioned existing technologies is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] In view of the above deficiencies or improvement requirements of the prior art, the present invention provides a method and device for predicting strong earthquake risk areas, aiming to use a quantitative method in the process of predicting strong earthquake danger areas to solve the problem that it is currently difficult to quickly realize the large-scale prediction application of earthquake danger areas.

[0006] To achieve the above object, according to one aspect of the present invention, there is provided a method for predicting strong earthquake risk areas, the method comprising:

[0007] Obtaining the inversion Moho depth result of the target area, and finding the area that meets the strong earthquake characteristic index in the target area as the potential strong earthquake occurrence area;

[0008] Performing longitude and latitude unit grid processing on the potential strong earthquake occurrence area to determine the seismogenic unit;

[0009] Labeling the seismogenic unit and drawing the potential strong earthquake occurrence danger area;

[0010] Combining with the intensity attenuation model to obtain the strong earthquake danger area, and marking the range of the strong earthquake danger area.

[0011] Preferably, the strong earthquake characteristic index includes:

[0012] An area where moderate earthquakes of MS5.0 - MS6.5 with a radius of 150 km centered on the historical epicenter have a quiet phenomenon for 7 - 14 years, and before a historical MS≥6.8 earthquake, there is moderate earthquake activity of magnitude 5 in the historical epicenter area, showing a strip - distributed clustering phenomenon in time and space.

[0013] Preferably, the method for obtaining the inversion Moho depth result of the target area includes:

[0014] Using the aeromagnetic data of the target area and inversing the Moho depth of the target area by the fractal method.

[0015] Preferably, the method for performing longitude and latitude unit grid processing on the potential strong earthquake occurrence area includes:

[0016] Dividing the potential strong earthquake occurrence area into longitude and latitude unit grids with 0.1°×0.1° as the unit, and counting the number of historical earthquakes occurring in the cell according to the principle from left to right and from bottom to top in the cell.

[0017] Preferably, the method for determining the seismogenic unit includes:

[0018] In history, a 0.1°×0.1° cell with 0 historical earthquakes occurring in the cell is the seismogenic unit.

[0019] Preferably, the method for obtaining the strong earthquake danger area by using the combined intensity attenuation model includes:

[0020] The strong earthquake danger area is the severely affected area of earthquakes with an intensity of magnitude 9 or above.

[0021] Preferably, the method for inversely obtaining the Curie depth of the target area by using the aeromagnetic data of the target area and adopting the fractal method includes:

[0022] When calculating the Curie depth, the size of the sliding window is set to 100km×100km.

[0023] Preferably, after obtaining the inversion result of the Curie depth of the target area, the method further includes:

[0024] Comparing the inversion Curie depth map of the target area with the shallow geothermal field map of the target area and the hot spring distribution map of the target area respectively;

[0025] If the geothermal heat flow activity state represented by the inversion Curie depth map of the target area in the target area is consistent with the shallow geothermal field map of the target area and the hot spring distribution Figure 1 in the target area, then continue with the method for predicting the strong earthquake risk area.

[0026] Preferably, the method for marking the earthquake origin unit includes:

[0027] Mark the earthquake origin unit on the cell in the shape of an elliptical isoseismal line;

[0028] The long axis direction of the ellipse is the same as the strike of the geological active fault closest to the cell;

[0029] The ellipse is tangent to at least two corners of the 0.1°×0.1° longitude and latitude unit grid;

[0030] The ellipse is marked by expanding at a fixed ratio based on the way of coinciding with the center of the longitude and latitude unit grid and connecting with the four corners.

[0031] As a further improvement and supplement to the above solution, the present invention further includes the following additional technical features.

[0032] According to another aspect of the present invention, there is provided a device for predicting the strong earthquake risk area, the device includes:

[0033] One or more processors;

[0034] A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method for predicting the strong earthquake risk area as described in the first aspect.

[0035] Generally speaking, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0036] A method and device for predicting strong earthquake risk areas provided by the present invention not only realizes the combination of the results of the Curie surface depth and the characteristics of strong earthquake occurrence to predict strong earthquake danger areas, but also quantifies the process of predicting strong earthquake danger areas, making the whole process highly operable. Moreover, the present invention obtains strong earthquake danger areas based on the developed danger areas by using the intensity attenuation model, incorporates intensity into the prediction work, and is more conducive to practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments of the present invention. Obviously, the following described drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0038] Figure 1 is a schematic flow chart of a method for predicting strong earthquake risk areas provided in Embodiment 1;

[0039] Figure 2 A is the magnetic anomaly map after reduction to the pole corresponding to Figure 2 B provided in Embodiment 1;

[0040] Figure 2 B is a large-scale high-precision magnetic anomaly map provided in Embodiment 1;

[0041] Figure 3 is the Curie surface inversion depth map of the target area provided in Embodiment 1;

[0042] Figure 4 A is the Curie surface depth of the target area inversed by using the aeromagnetic data of the target area and adopting the fractal method provided in Embodiment 1;

[0043] Figure 4 B is the Curie surface depth of the target area inversed by using the aeromagnetic data of the target area and adopting the linear method provided in the prior art;

[0044] Figure 4 C is the Curie surface depth of the target area inversed by using the satellite magnetic data of the target area and adopting the fractal method provided in the prior art;

[0045] Figure 4 D is the Curie surface depth of the target area inversed by using the satellite magnetic data of the target area and adopting the linear method provided in the prior art;

[0046] Figure 4 E is the Curie depth of the target area obtained by using the aeromagnetic data of the target area in the prior art and performing linear inversion.

[0047] Figure 4 F is the Curie depth of the target area obtained by using the satellite magnetic data of the target area in the prior art and performing linear inversion.

[0048] Figure 5 A is the Curie depth inversion depth map of the target area in the first embodiment of the present invention, same as Figure 3 ;

[0049] Figure 5 B is the shallow geothermal field map of the target area in the first embodiment of the present invention.

[0050] Figure 5 C is the shallow geothermal temperature field of the target area in the first embodiment of the present invention.

[0051] Figure 6 A is the Curie depth inversion depth map of the target area in the first embodiment of the present invention, same as Figure 3 ;

[0052] Figure 6 B is the earthquake distribution map of the target area during the quiet period from 2015 to 2024 provided in the first embodiment of the present invention.

[0053] Figure 6 C is the earthquake distribution map of the target area during the quiet period from 2010 to 2024 provided in the first embodiment of the present invention.

[0054] Figure 6 D is the earthquake distribution map of the target area during the quiet period from 2016 to 2024 provided in the first embodiment of the present invention.

[0055] Figure 7 A is the earthquake statistics table of MS≥4 within the range of 98°E - 100°E, 25°N - 27°N provided in the first embodiment of the present invention.

[0056] Figure 7 B is the earthquake statistics table of MS≥4 within the range of 99°E - 101°E, 24°N - 26°N provided in the first embodiment of the present invention.

[0057] Figure 8 A is the Curie depth inversion depth map of the target area in the second embodiment of the present invention, same as Figure 3 ;

[0058] Figure 8 B is the earthquake frequency grid statistical chart of the target area in the second embodiment of the present invention.

[0059] Figure 8 C is the earthquake - generating unit within the target area in the second embodiment of the present invention.

[0060] Figure 9 It is a schematic diagram of the potentially seismogenic dangerous area in the target area of the second embodiment;

[0061] Figure 10 It is a schematic diagram of marking the specific range values of the potentially seismogenic dangerous area in the target area of the second embodiment;

[0062] Figure 11 It is a schematic diagram of a device for predicting strong earthquake risk areas provided in the third embodiment. Specific implementation manners

[0063] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] Embodiment 1

[0065] Embodiment 1 of the present invention provides a method for predicting strong earthquake risk areas. The method includes the following steps, as Figure 1 shown:

[0066] S101: Obtain the inversion Moho depth result of the target area, and find the area that meets the strong earthquake characteristic index in the target area as the potentially seismogenic area.

[0067] In Embodiment 1, the inversion area of the Moho is determined as the western part of Yunnan (21.8°N - 28.5°N, 97.8°E - 102°E). The total magnetic field anomaly data used in Embodiment 1 is from the compilation data of the Aero Geophysical and Remote Sensing Center of the China Geological Survey. The data covers the area from 97.8°E to 102°E and from 21.8°N to 28.4°N on the southeastern margin of the Qinghai-Tibet Plateau. The data comes from 65 different airborne magnetic measurement flights during 1959 and 2004. The data contains various parameter information such as line spacing, altitude, reference field, and data collection year. Each airborne magnetic measurement data is corrected to a height of 1 km from the Earth's surface and is mosaicked into a continuous grid data body of 1 km. The global geomagnetic anomaly grid (EMAG2) with a 3 km horizontal resolution is used to fill the data blank areas and expand the magnetic data range. However, the southwestern corner of the target area is a data blank area (lacking aeromagnetic data or EMAG2 data). In Embodiment 1, the magnetic data sets are mosaicked together in the OasisMontaj software, and finally a large-scale high-precision magnetic anomaly map is created, as Figure 2As shown in Figure B. The combined aeromagnetic data is projected onto the ellipsoidal transverse Mercator coordinate system with a central meridian of 81°, and reduction to the pole (RTP) processing is carried out in Geoprobe software. The processed magnetic anomaly shows a slight westward shift in the image after reduction to the pole, as Figure 2 shown in Figure A, Figure 2 where the blue arrow in Figure A represents the GPS velocity relative to Siberia.

[0068] To calculate the Curie depth and obtain a regional plan view of the Curie depth, the reduced-to-pole magnetic anomalies of the entire target area (with an east-west span of approximately 350 km and a north-south span of approximately 330 km) are subdivided into a series of square sub-regions corresponding to overlapping windows. A single Curie depth point is calculated from the spectral window, and the window center is moved a specific distance each time until the entire area is covered by repeated calculations. In this Example 1, a window size of 100 km × 100 km is selected to calculate the depth of the bottom boundary of the magnetic layer.

[0069] To select the optimal wavenumber range for recovering the depths of the intermediate layer (Z0) and the top boundary (Zt), various tests are also carried out in this Example 1. Considering the grid size used and the expected basement depth of the magnetic layer, the two ranges between the upper and lower limits of the wavenumber for linear fitting to calculate Zt and Z0 are respectively determined to be approximately 0.0237 - 0.0334 and 0.015 - 0.023 km-1. Finally, contour interpolation of the Curie depth calculated by the moving window method is carried out by the minimum curvature method to obtain the Curie depth value, as Figure 3 shown. It should be noted that the Curie depth value at the southwestern corner of the map must be clipped because there is missing data. If continuous Curie depth interpolation is carried out for the data blank area, it will reflect the characteristics of unrealistic geological units.

[0070] The Curie surface, as the 580 °C temperature interface within the crust, reflects the distribution state of the deep geothermal field in the crust. The spatial distribution of its burial depth depicts the spatial thermal difference in the deep crust of the target area and may be the most intuitive deep thermal field structure of the target area. Figure 3The black dotted line shows the situation of the target area range inside the crust, with the variation range between 15 and 35 km. Overall, it shows the distribution characteristics of shallower in the north and deeper in the south, and shallower in the east and deeper in the west. The buried depth uplift area of the Moho presents a "T" shape wedging in from north to south. Roughly bounded by 25°N, different crustal thermal structures are shown on both sides of the north and south. The north side of the target area is adjacent to the southeastern margin of the Qinghai-Tibet Plateau, with the Moho buried depth of 15-25 km, showing a "1+1" Moho uplift structure. The south side is adjacent to the Shan State Plateau, with the Moho buried depth of 22-35 km, presenting an east-west Moho uplift-sag alternating structure. There is a significant Moho uplift center on the north side of the Baoshan block, located at 26°N 99.8°E, with a buried depth less than 17 km. In addition, there is a local Moho uplift area in the Tengchong volcanic area, located at 25.2°N 98.2°E, with a buried depth less than 23 km. The Moho on the south side of the Sichuan-Yunnan rhombic block is one concave and one convex. The concave area is located on the south side of the Tengchong block and the Baoshan block. The estimated center is located at 24°N 98.3°, and the Moho depth is about 25-30 km. The third uplift area is located at 24°N 100°E, in a nearly north-south trending strip, which is in line with the Moho uplift trap on the north side and basically coincides with the Lincang block, with the Moho buried depth of 22-25 km.

[0071] The Tengchong volcanic area is located in the central and western regions of this target area, on the eastern edge of the Yunnan-Burma arc tectonic belt at the collision boundary of the Indian-Eurasian plates. In recent years, it has obvious activity signs, and many studies believe that it may have the risk of re-eruption. At the same time, there are also studies showing that there are signs of strong earthquake-volcano interaction in the Tengchong volcanic area. In addition, obvious hydrothermal eruption activities are also a major feature of the Tengchong volcanic area. As early as 1965, there was a record of hydrothermal explosion in the Tengchong volcanic area. Subsequently, the frequent periods of hydrothermal explosion activities monitored were from 1993 to 2003 and from 2008 to 2017. A total of nearly 50 hydrothermal explosion events were monitored and recorded. Among them, the largest-scale hydrothermal explosion monitored was in 2001, with high-temperature water vapor jetting obliquely up to more than 30 meters and forming a series of hot springs with a temperature of nearly 100°C. The above results and the detected data can all prove that the surface heat flow in the Tengchong volcanic area forms a relatively obvious high-temperature area compared with the surrounding areas. Based on this situation, in the first embodiment, the method of digitizing the Moho results related to the target area and unifying the color scale is used. The inversion results of the first embodiment are compared with the existing results, as Figure 4 shown. The Moho results inverted by 6 methods are respectively marked with the Tengchong volcanic area. The results of the first embodiment show that the Moho in the Tengchong volcanic area presents an inversion result of central depression and peripheral uplift. This result is consistent with the analysis of the Tengchong volcanic area in the first embodiment above. It can be found that the Moho inversion result obtained in the first embodiment can better reflect the heat flow activity state in the region, and is an ideal choice for carrying out strong earthquake research work based on the Moho.

[0072] In the first embodiment, the distribution of the Curie surface is compared with key parameters of the crustal thermal structure such as the surface heat flow, shallow thermal field, and crustal temperature field to confirm the important position of the Curie surface in constructing the deep thermal field of the target area. In fact, the distribution of the surface heat flow data in the Western Yunnan Tethys domain is extremely sparse and the coverage is extremely limited. There are almost no measured heat flow data in the Baoshan block, and the statistical significance of the heat flow data in the target area is limited. Therefore, the high-value characteristic distribution is used in the first embodiment to explore the coincidence between the Curie surface and the surface heat flow. There are a total of 8 high-quality surface heat flow values in the target area, with a variation range from 65.6 - 18.1 mW / m 3 , and the high values are mainly distributed near the convex position of the Tengchong volcanic area, with a heat flow value as high as 118.1 mW / m 3 , and near the depression area at the intersection of the Red River Fault and the Lijiang - Xiaojinhe Fault, with a heat flow value as high as 103 mW / m 3 . Although there is no one-to-one correspondence between the burial depth of the Curie surface and the measured heat flow value, the relative undulation of the Curie surface has indicative significance, and the distribution ranges of the uplift areas of the Curie surface and the high-value areas of the surface heat flow are consistent.

[0073] Considering that there are many hot springs in the Western Yunnan Tethys tectonic belt, with wide distribution and high temperature, which play an important "window" role in the deep thermal state and energy balance of the crust. The spring fluid comes from high-temperature rock layers or fractured zones of rock masses with certain permeability, and the geothermal temperature scale method of its hydrochemical ion components and SiO2 content makes it possible to estimate the temperature of shallow thermal reservoirs. Taking the hydrothermal activity of hot springs as the starting point, the geothermal temperature scale can be used to calculate the thermal reservoir temperatures of hundreds of hot springs in the area, so as to obtain the shallow geothermal thermal field represented by them, as Figure 5 shown in C. In the first embodiment, the deep thermal field represented by the Curie surface is compared with the shallow thermal field represented by the hot spring thermal reservoir. The high-temperature areas of the shallow thermal field are highly consistent with the uplift areas of the crustal thermal field. The high-temperature areas above 100 °C in the shallow thermal field are all within the predicted range of the uplift areas of the Curie surface, including the intersection area of the Red River Fault and the Lijiang - Xiaojinhe Fault, the Tengchong - Yingjiang area of the Tengchong block, and the Baoshan - Lincang belt of the Baoshan block, which are 3 uplift areas of the Curie surface. In addition, in the first embodiment, underground seismic events with a focal depth of more than 1 km between January 1964 and January 2023 are also plotted on the inversion depth map of the Curie surface in the target area, as Figure 5 shown in A and the shallow geothermal thermal field map of the target area, as Figure 5 shown in B, and the results are consistent with previous studies. Based on the above analysis, the first embodiment will select the Curie surface results inverted in the first embodiment for the next step.

[0074] S102: Perform longitude and latitude unit grid processing on the potential strong earthquake occurrence area to determine the earthquake occurrence unit.

[0075] In the first embodiment, according to the inversion results of the Curie depth, two typical characteristics of strong earthquakes (MS≥6.8) concentrated in the convex area of the Curie depth gradient zone and near the fault zone in the southeastern margin of the Qinghai-Tibet Plateau are searched for within the target area, and at the same time, the area that meets the strong earthquake characteristic index is further determined. Based on the theory of seismic energy release, the data of the target area monitored by the International Seismological Centre from 1964 to 2023 is selected and digitized using ArcGIS. The target area is divided into unit grids of 42×62 with 0.1°×0.1° as the unit for the gridding process of longitude and latitude units of 0.1°×0.1°. According to the principle from left to right and from bottom to top, the number of earthquakes occurring within the cell is counted in the cell. Subsequently, based on the way of the potential occurrence area of earthquakes with MS≥7 according to the theory of energy release, and according to the actual situation selected, the earthquake occurrence unit with 0.1°×0.1° as the unit is determined in the area where no earthquake has occurred in history.

[0076] S103: Mark the earthquake occurrence unit and draw the potential earthquake occurrence danger area of strong earthquakes.

[0077] After determining the earthquake occurrence unit with 0.1°×0.1° as the unit, the method of marking the earthquake occurrence unit in the first embodiment includes:

[0078] The marking shape is an isoseismal line style of an ellipse, and the marking is carried out on the cell; the long axis direction of the ellipse is the same as the strike of the geological active fault closest to the cell; the ellipse is tangent to at least two corners of the longitude and latitude unit grid of 0.1°×0.1°; the ellipse is marked by expanding at a fixed ratio based on the way of coinciding with the center of the longitude and latitude unit grid and connecting with the four corners.

[0079] S104: Combine the intensity attenuation model to obtain the strong earthquake danger area and mark the range of the strong earthquake danger area.

[0080] The strong earthquake characteristic index includes:

[0081] An area where moderate earthquakes of MS5.0-MS6.5 show a quiet phenomenon for 7-14 years within a range with a radius of 150 km centered on the historical epicenter, and there is moderate earthquake activity of magnitude 5 in the historical epicenter area before the historical earthquake with MS≥6.8, showing a strip distribution and clustering phenomenon in time and space.

[0082] According to the seismic intensity index and the actual disaster situation, determining the severely affected area after an earthquake is the main means of current post-earthquake emergency work. Therefore, in long-term prediction work, using the high intensity formed after an earthquake as a measure of the potential severely affected area has strong practical significance. The method for judging the strong earthquake danger area in the first embodiment is the earthquake occurrence danger area obtained above. Based on the intensity attenuation model, the intensity attenuation situation of the target area is calculated by a model, and then the strong earthquake danger area is obtained.

[0083] The current seismic intensity standard in China is the "China Seismic Intensity Scale" issued by the State Administration for Market Regulation and the Standardization Administration of China. Since the 20th century, the earthquake with the highest magnitude in Yunnan Province was the 7.7-magnitude Tonghai earthquake on January 5, 1970, with the intensity of the epicentral area being X degrees. The earthquake with the highest magnitude in the target area was the 7.2-magnitude double earthquake in Lancang / Gengma in 1988, and the intensity of the epicentral areas was both X degrees. Therefore, in the first embodiment, it is predicted that the intensity of the epicentral area of an earthquake above magnitude 7 in the target area is X degrees, and the maximum magnitude is 8. According to the intensity scale

[226] and the historical records of the earthquake damage of houses in each intensity and the Tonghai earthquake in 1970 and the Lancang-Gengma earthquake in Yunnan in 1988, the areas with an intensity above VIII degrees belong to severely affected areas. Therefore, the model in the first embodiment predicts that the areas with an intensity above IX degrees in the target area are severely affected areas of an earthquake above magnitude 7.

[0084] Yunnan region is divided into 3 first-level sub-regions, namely western Yunnan, Sichuan-Yunnan, and eastern Sichuan-Yunnan, and the intensities of 146 earthquakes above magnitude 5.0 that occurred in Yunnan region from 1900 to 2014 are statistically analyzed. In the first embodiment, according to the "elliptical" isoseismal pattern in Yunnan, an intensity attenuation model for the 3 sub-regions is fitted. The formula for the western Yunnan region is:

[0085] I a = 6.8053 + 1.2972Ms - 4.7603log(R a + 22)

[0086] I b = 5.3315 + 1.2013Ms - 4.1917log(R b + 10)

[0087] In the formula: I is the seismic intensity, a and b represent the major and minor axes respectively; Ms is the magnitude; R a and R b are the lengths of the major semi-axis and minor semi-axis of the elliptical isoseismal with intensity I respectively.

[0088] Substitute Ms = 9 into the calculation formula, and the results are shown in the following table:

[0089] Table 1 Calculation results of the intensity attenuation model for the western Yunnan sub-region based on a magnitude 9 earthquake

[0090]

[0091] Combined with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, the method for obtaining the inverted Curie depth result of the target area includes:

[0092] Using the aeromagnetic data of the target area and adopting the fractal method to invert the Curie depth of the target area.

[0093] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, the method for performing longitude and latitude unit grid processing on the potential strong earthquake occurrence area includes:

[0094] The potential strong earthquake occurrence area is divided into unit grids of 42×62 with 0.1°×0.1° as the unit for 0.1°×0.1° longitude and latitude unit grid processing. The number of earthquakes occurring within the cell is counted in the cell according to the principle from left to right and from bottom to top.

[0095] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, the method for determining the earthquake - generating unit includes:

[0096] In history, a 0.1°×0.1° cell with the number of earthquakes occurring within the cell being 0 is the earthquake - generating unit.

[0097] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, the method for obtaining the strong earthquake danger area by combining the intensity attenuation model includes:

[0098] The strong earthquake danger area is the severely affected area of earthquakes with an intensity of magnitude 7 or above.

[0099] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, the method for inversely obtaining the Curie depth of the target area by using the aeromagnetic data of the target area and adopting the fractal method includes:

[0100] When calculating the Curie depth, the size of the sliding window is set to 100km×100km.

[0101] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, after obtaining the inversely calculated Curie depth result of the target area, the method further includes:

[0102] Comparing the inversely calculated Curie depth map of the target area with the shallow geothermal field map of the target area and the hot spring distribution map of the target area respectively;

[0103] If the geothermal heat flow activity state represented by the inversely calculated Curie depth map of the target area in the target area is Figure 1 consistent with the shallow geothermal field map of the target area and the hot spring distribution

[0104] In combination with the embodiments of the present invention, there is also a preferred implementation solution. Specifically, the method for marking the earthquake - generating unit includes:

[0105] The earthquake - generating unit is marked on the cell in the form of an elliptical isoseismal line style for marking;

[0106] The major axis direction of the ellipse is the same as the strike of the geological active fault closest to the cell;

[0107] The ellipse is tangent to at least two corners of the 0.1°×0.1° longitude and latitude unit grid;

[0108] Based on the ellipse coinciding with the center of the longitude and latitude unit grid and connecting with its four corners, it is marked by expanding at a fixed ratio.

[0109] Embodiment 2

[0110] In this Embodiment 2, earthquake data with MS≥5 from January 2000 to December 2024 of the China Seismic Network near the target area is extracted, and the data is inverted at the obtained Moho depth. As Figure 6 shown in A, no earthquake disasters with MS≥7 occurred in the target area within a 24-year cycle. The MS≥5 earthquakes in the target area basically conform to the basic characteristics of occurring on the Moho gradient belt and tending towards the uplifted area.

[0111] In this Embodiment 2, first, two conditions are matched: there is a quiet period of 7 - 14 years for moderate earthquakes with MS5.0 - MS6.5 within a range with a radius of 150 km centered on the epicenter, and before an earthquake with MS≥6.8, there is a clustering phenomenon of magnitude 5 moderate earthquakes near the epicenter in terms of time and space, and it shows a strip distribution. By projecting earthquakes with MS≥5 in different cycles, Figure 6 B and Figure 6 C two scenarios are obtained. The two quiet periods obtained are more than 7 years from 2016 to 2024 and more than 11 years from 2010 to 2024. The two clusters obtained are two MS≥5 earthquakes occurring in 2023, and one is one MS≥5 earthquake occurring in 2023 and four MS≥5 earthquakes occurring in 2021. After excluding the constraint condition of the cluster feature, Figure 6 D scenario is obtained, and the quiet period of this area is from 2016 to 2024. According to Figure 6 B, Figure 6 C and 6D three scenarios, in this Embodiment 2, the areas with a unit of 2°×2° are set as (98°E - 100°E, 25°N - 27°N) and (99°E - 101°E, 24°N - 26°N). Through the statistics of the two 2°×2° units, as Figure 7 shown, due to multiple MS≥4 earthquakes occurring in Yangbi County, Dali Prefecture, Yunnan Province in 2021, there has been an obvious increase in magnitude 4 earthquakes in the target area in recent years.

[0112] Based on two typical characteristics that strong earthquakes (MS≥6.8) are concentrated in the convex area of the Moho gradient zone and near the fault zone, the target area is divided into 42×62 unit grids with 0.1°×0.1° as the unit for 0.1°×0.1° longitude and latitude unit grid processing. According to the principle from left to right and from bottom to top, the number of earthquakes occurring in the cell is counted in the cell, as Figure 8 shown in B. Based on the energy release theory, in the second embodiment, the method for the potential occurrence area of MS≥7 earthquakes determines the earthquake occurrence unit with 0.1°×0.1° as the unit according to the selected actual situation in the area where no earthquake has occurred historically. The result is Figure 8 the purple 0.1°×0.1° unit squares in C.

[0113] After determining the earthquake occurrence unit with 0.1°×0.1° as the unit, the earthquake occurrence unit is marked according to the earthquake occurrence danger area marking method, and the earthquake occurrence danger areas of three earthquake occurrence units are obtained, as Figure 9 shown.

[0114] According to the prediction method of strong earthquake danger area, the result of Table 1, the value of intensity X R a is 10.2775 km, the value of R b is 5.0973 km, the value of intensity IX R a is 30.36 km, the value of R b is 16.2542 km. Substituting the values, the result is as Figure 10 shown, where Figure 10 the lower left corner of is the potential earthquake occurrence danger area of the target area, and the specific range values are marked for intensity 9 and above.

[0115] Embodiment 3:

[0116] A device for predicting strong earthquake risk areas, as Figure 11 shown, the device includes:

[0117] One or more processors;

[0118] A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method for predicting strong earthquake risk areas as described in any one of Embodiment 1.

[0119] Figure 11 This is the schematic structural diagram of the device for predicting strong earthquake risk areas provided in Embodiment 3. Figure 11 It shows a block diagram of an exemplary device for predicting strong earthquake risk areas suitable for implementing the embodiments of the present invention. Figure 11 The device for predicting strong earthquake risk areas shown is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0120] As shown Figure 11 in the figure, the devices in the predicted strong earthquake risk area are presented in the form of general-purpose devices. The components of the devices in the predicted strong earthquake risk area may include, but are not limited to: one or more processors or processing units, a memory, and a bus connecting different system components (including the memory and the processing unit).

[0121] The bus represents one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0122] The devices in the predicted strong earthquake risk area typically include a variety of computer system-readable media. These media can be any available media that can be accessed by the devices modified by the intelligent logging interpretation model, including volatile and non-volatile media, removable and non-removable media.

[0123] The memory may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. The devices in the predicted strong earthquake risk area may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system can be used to read and write non-removable, non-volatile magnetic media ( Figure 11 not shown, commonly referred to as a "hard disk drive"). Although Figure 11 not shown in the figure, a disk drive for reading and writing removable non-volatile disks (such as a "floppy disk") and an optical disk drive for reading and writing removable non-volatile optical disks (such as a CD-ROM, DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to the bus through one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present invention.

[0124] A program / utility with a set (at least one) of program modules can be stored, for example, in the memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules generally perform the functions and / or methods in the embodiments described in the present invention.

[0125] The device for predicting strong earthquake risk areas can also communicate with one or more external devices (such as keyboards, pointing devices, displays, etc.), and can also communicate with one or more devices that enable users to interact with the device for predicting strong earthquake risk areas, and / or communicate with any device (such as network cards, modems, etc.) that enables the device for predicting strong earthquake risk areas to communicate with one or more other devices. Such communication can be carried out through an input / output (I / O) interface. Moreover, the device for correcting the intelligent logging interpretation model can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. As Figure 11 shown, the network adapter communicates with other modules of the device for predicting strong earthquake risk areas through a bus. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the device for predicting strong earthquake risk areas, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0126] The processing unit executes various functional applications and data processing by running programs stored in the memory, for example, implementing the method for predicting strong earthquake risk areas provided in any embodiment of the present invention. That is, obtaining the inversion Moho depth result of the target area, searching for the area that meets the strong earthquake characteristic index in the target area as the potential strong earthquake occurrence area; performing grid processing on the potential strong earthquake occurrence area in longitude and latitude units to determine the earthquake occurrence unit; labeling the earthquake occurrence unit and drawing the potential strong earthquake occurrence risk area; combining the intensity attenuation model to obtain the strong earthquake risk area and marking the range of the strong earthquake risk area.

[0127] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting strong earthquake risk areas, characterized in that, The method includes: Obtaining the inversion Moho depth result of the target area, and finding the area that meets the strong earthquake characteristic index within the target area as the potential strong earthquake occurrence area; Performing longitude and latitude unit grid processing on the potential strong earthquake occurrence area to determine the earthquake occurrence unit; Labeling the earthquake occurrence unit and drawing the potential strong earthquake occurrence risk area; Combining the intensity attenuation model to obtain the strong earthquake risk area, and marking the range of the strong earthquake risk area.

2. The method for predicting strong earthquake risk areas according to claim 1, wherein The strong earthquake characteristic index includes: An area where moderate earthquakes of MS5.0 - MS6.5 with a radius of 150 km centered on the historical epicenter have a quiet phenomenon for 7 - 14 years, and there is moderate earthquake activity of magnitude 5 before the historical MS≥6.8 earthquake, showing a strip - distributed clustering phenomenon in terms of time and space.

3. The method for predicting strong earthquake risk areas according to claim 1, wherein, The method for obtaining the inversion Moho depth result of the target area includes: Using the aeromagnetic data of the target area and adopting the fractal method to invert the Moho depth of the target area.

4. The method for predicting strong earthquake risk areas according to claim 1, wherein, The method for performing longitude and latitude unit grid processing on the potential strong earthquake occurrence area includes: Dividing the potential strong earthquake occurrence area into longitude and latitude unit grids with 0.1°×0.1° as the unit, and statistically counting the number of historical earthquakes occurring within the cell according to the principle from left to right and from bottom to top in the cell.

5. The method for predicting strong earthquake risk areas according to claim 4, wherein The method for determining the earthquake occurrence unit includes: Historically, a 0.1°×0.1° cell with the number of historical earthquakes occurring within the cell being 0 is the earthquake occurrence unit.

6. The method for predicting strong earthquake risk areas according to claim 5, wherein The method for combining the intensity attenuation model to obtain the strong earthquake risk area includes: The strong earthquake risk area is the severely affected area of earthquakes with an intensity of magnitude 9 or above.

7. The method for predicting strong earthquake risk areas according to claim 3, wherein The method for using the aeromagnetic data of the target area and adopting the fractal method to invert the Moho depth of the target area includes: When calculating the Moho depth, the size of the sliding window is set to 100 km×100 km.

8. The method for predicting strong earthquake risk areas according to claim 7, wherein After obtaining the inversion Moho depth result of the target area, the method further includes: Comparing the inversion Moho depth map of the target area with the shallow geothermal field map of the target area and the hot spring distribution map of the target area respectively; If the geothermal heat flow activity state represented by the inversion Moho depth map of the target area within the target area is consistent with the shallow geothermal field map of the target area and the hot spring distribution map of the target area, then continue with the method of predicting the strong earthquake risk area.

9. The method for predicting strong earthquake risk areas according to any one of claims 1 to 8, characterized in that, The method for labeling the earthquake occurrence unit includes: Using an elliptical isoseismal line style as the labeling shape for the earthquake occurrence unit to label on the cell; The long axis direction of the ellipse is the same as the strike of the geological active fault closest to the cell; The ellipse is tangent to at least two corners of the 0.1°×0.1° longitude and latitude unit grid; The ellipse is marked by expanding at a fixed ratio based on the way of coinciding with the center of the longitude and latitude unit grid and connecting with the four corners.

10. A device for predicting strong earthquake risk areas, characterized in that the device Includes: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors, enable the one or more processors to implement the method of predicting the strong earthquake risk area as described in any one of claims 1 - 9.

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