[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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, but not to limit the present invention.
[0019] The invention provides a method for predicting the short-term and imminent earthquake based on high-order magnetic anomaly derivatives. Please refer to figure 1 , figure 1 is the flow chart of the inventive method; the inventive method comprises the following steps:
[0020] S101: Select the geomagnetic F component as the earthquake short-term prediction data source;
[0021] It should be noted that in the long-term change curve of the geomagnetic field, the geomagnetic component F is used as the total amount. When the change curve has a "V"-shaped sudden change, it is often accompanied by an earthquake, because there are natural changes in the earth's internal magnetic field. Component F is associated with earthquakes, but the "V"-shaped variation curve of the F component is not enough to prove that it is a precursor to an earthquake.
[0022] The geomagnetic Z component can extract better earthquake precursor information; the geomagnetic Z component is highly correlated with the F component, and the geomagnetic F component is less affected by the magnetic storm. Please refer to figure 2 , figure 2 is a schematic diagram of the correlation between the geomagnetic Z component and other parts of the geomagnetic component;
[0023] figure 2 Among them, the data with positive correlation is positive, and the data with negative correlation is negative; the larger the value, the greater the degree of correlation, and the higher the degree of correlation, the darker the circle in the upper right corner and the larger the area; figure 2 It is not difficult to see that the Z component and the F component are positively correlated and the correlation coefficient is large.
[0024] In view of the above considerations, the present application obtains earthquake precursor information through the geomagnetic F component.
[0025] S102: Fitting the geomagnetic F component to obtain a high-order polynomial;
[0026] It should be noted that the sudden "V"-shaped change of the geomagnetic field curve is often accompanied by earthquakes, because the component of the geomagnetic field can be regarded as a high-order polynomial, and the geomagnetic vibration is defined as the high-order derivative with respect to time in the long-term change of the magnetic field. , so the present invention first uses a sliding window to perform fitting processing on the F component change curve, and then obtains a high-order polynomial.
[0027] Please refer to image 3 , image 3 is a schematic diagram of the sliding window algorithm;
[0028] Sliding window algorithm: Sliding window algorithm is to perform the required operation on an array of a specific window size instead of the entire array, this technique can turn the nested loop into a single loop, so it can reduce the time complexity. The specific algorithm is to start from the left side of a set of data, set the window size, fit the data in the window, and continuously slide to the right to fit the data in the current window. When the window slides to the far right Terminate swiping.
[0029] Steps when the algorithm is specifically applied in this application: Sliding window algorithm fitting polynomial: This patent uses the existing geomagnetic station data and adopts the sliding window algorithm to fit every 5 adjacent data into a high-order polynomial. The order n of the high-order polynomial for predicting moderate and strong earthquakes in this region will be determined by studying the historical data in different regions. If there are m experimental data in total, (m-4) high-order polynomials can be obtained by using the sliding window algorithm.
[0030] S103: Differentiate the high-order polynomial to obtain a high-order magnetic anomaly derivative;
[0031] It should be noted that, based on the high-order polynomial fitted by the above sliding window algorithm, the geomagnetic vibration can be regarded as the change of the high-order coefficient, that is, the high-order polynomial needs to be derived with respect to time, and the high-order derivative is used to represent the original geomagnetic component. The cumulative effect of data changes can more clearly amplify the changes in the curve, so the closer the geomagnetic curve is to the "V"-shaped change, the more accurate the higher-order derivative with respect to time corresponds to the geomagnetic vibration. The formula for obtaining the high-order magnetic anomaly derivative is as follows:
[0032]
[0033] In the above formula, y(t) represents a higher-order polynomial, t represents time, and k i Represents the coefficients of each order after sliding window fitting, n represents the higher order polynomial order, n≥3.
[0034] For example, taking the third-order polynomial as an example, the formula for obtaining the magnetic anomaly derivative is as follows:
[0035]
[0036] In the formula, A, B, C, D correspond to k in the previous i , representing the polynomial coefficients of the different terms.
[0037] S104: Combined with the geomagnetic field itself, use the high-order magnetic anomaly derivative to predict the short-term and imminent earthquake.
[0038] It should be noted that the geomagnetic field itself specifically refers to the extension of the spatial-temporal attributes of the geomagnetic field, so the time and data of earthquakes in the early years can be analyzed and processed to determine the threshold value, and then the current data can be processed for earthquake prediction;
[0039] As an example, Xinjiang is one of the areas where moderate and strong earthquakes occur frequently in China, and the seismic activity has the characteristics of high intensity, high frequency and wide distribution, so the method of the present invention is applied to the Kashgar earthquake in 2017, such as Figure 4 shown. The curve showing a gradual upward trend is the annual change curve of the geomagnetic F component, located at Figure 4 The curve in the center of the distribution is based on the higher-order magnetic anomaly derivatives obtained by the present invention, and the outliers have been marked with circles in this curve. It can be seen that there is a curve with a gradual upward trend near the abnormal value marked by the curve (this curve is the geomagnetic field itself), and the curves show obvious "V"-shaped changes. The earthquake situation, using the time corresponding to the high-order derivative of the magnetic anomaly calculated by the present invention, accurately predicts moderate and strong earthquakes that occur within 30 days.
[0040] The technical key points of the present invention are:
[0041] 1. Using high-order magnetic anomaly derivatives combined with the extension of the temporal and spatial attributes of the geomagnetic field, earthquake prediction can be realized. The prediction period can be less than 30 days, which greatly improves the prediction performance of short-term earthquakes.
[0042] 2. The sliding window algorithm is used to more quickly and accurately fit the change curve of the geomagnetic component into a high-order polynomial, which is convenient for the later extraction of high-order magnetic anomaly derivatives.
[0043] It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
[0044] The beneficial effects of the present invention are:
[0045] 1. The high-order magnetic anomaly derivative is used to predict earthquakes, which greatly shortens the prediction period before the earthquake occurs, and makes it more convenient for relevant institutions to take emergency measures for short-term earthquakes.
[0046] 2. High-order magnetic anomaly derivatives are used to predict earthquakes, which further improves the prediction performance of moderate and strong earthquakes. The invention effectively overcomes the limitation of the current earthquake prediction method under the influence of the magnetic storm, shortens the prediction period, improves the prediction accuracy of medium and strong earthquakes, and at the same time ensures the universality of the method, and can be applied to the earthquake monitoring industry, and furthermore Reduce casualties and property losses caused by earthquakes.
[0047] The specific embodiments of the present invention described above do not limit the protection scope of the present invention. Any other corresponding changes and modifications made according to the technical concept of the present invention shall be included in the protection scope of the claims of the present invention.
