How to find the geomagnetic parameters of major cities in China?

Earthquake geomagnetic observation and research

Seismology and geomagnetism

Observation and research

1999 Volume 20, Issue 6

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Application of geomagnetic method in earthquake prediction

Lin Zeng Zhao Chun Rong Qi

Since 1989, we have put forward three geomagnetic methods: transfer function method, spatial correlation and weighted difference method, and loading-unloading response ratio method, which have been applied to the analysis of more than 200 earthquake cases, among which 106 earthquake is officially predicted every year, and 15 earthquake is successfully predicted in 97 sub-dangerous areas.

Geomagnetic method; Earthquake prediction; Predictive evaluation

Application of geomagnetic method in earthquake prediction

Lin Yunfang 1), Zeng Xiaoping 1), Xu Chunrong 2), Zhao Ming 1), Li Qi 1)

Institute of Geophysics, Seismological Bureau of China, Beijing 10008 1

2) Institute of Crustal Dynamics, Seismological Bureau of China, Beijing 100085.

Abstract: Since 1989, we have studied more than 200 earthquake cases by three geomagnetic methods: transfer function method, spatial correlation method, weighted difference method and loading-unloading response ratio method. In 97 earthquake predictions, three elements of earthquakes have been successfully predicted.

Keywords: geomagnetic method, earthquake prediction, prediction effect evaluation

introduce

Geomagnetic phenomena can reflect various electromagnetic-related physical processes from solar-terrestrial space to atmosphere and solid earth. The study of the main magnetic field and its variation is an important way to understand the physical process of the deep mantle and core of the earth. It is a good method to study the electrical structure and its change in the earth by using the electromagnetic induction phenomenon of changing magnetic field.

The observation and research of seismic magnetic phenomena have a history of nearly a hundred years. At the end of 1950s, the advent and widespread use of proton precession magnetometer (also known as "nuclear magnetometer") with high accuracy and good stability changed the observation situation of seismic magnetic phenomena, and obtained more accurate observation data of seismic magnetic phenomena and more reliable results of earthquake cases.

In recent years, some progress has been made in the study of seismomagnetic relationship. Since 1987, we have studied the local anomalies of geomagnetic field in China, explored the seismomagnetic relationship, and made experimental earthquake prediction. Since 1989, we have chosen three geomagnetic methods: transfer function method, spatial correlation and weighted difference method, and loading-unloading response ratio method, and analyzed more than 200 earthquake cases since 1966. Among them, earthquakes are predicted every year 106 times. In the trend prediction of 97 sub-dangerous areas, 15 earthquakes are close to the actual situation.

1 geomagnetic transfer function method

1955 riki take &； Hengshan (1955) and Parkinson (1959) confirmed that the vertical component Δ z, horizontal component Δ h and declination Δ d of geomagnetic short-period variation have the following stable linear relationships.

( 1)

Where a and b are transfer functions.

Using natural magnetic field as incident signal has the characteristics of wide frequency band, wide distribution, approximate plane wave and strong penetration. The transfer functions A and B obtained from analyzing the natural magnetic field signals recorded at various measuring points on the ground are functions of position (λ,), depth (h), frequency (ω) or period (t) and time (t), namely

(2)

According to the skin effect of electromagnetic wave theory, the magnetic field penetration depth f (ωj) at a certain frequency ω j at a certain measuring point (λ 0,0) is

(3)

Here μ and σ are the permeability and conductivity of the medium, respectively. Electromagnetic waves with different frequencies have different penetration depths into the earth, so we can obtain the conductivity information of different depths underground through electromagnetic induction of different frequency changing fields. By analyzing and studying the changes of A, B and their parameters with time t, we can understand and monitor the changes of underground electrical structure and environment in the seismic activity area, so as to capture these changes before the earthquake and predict the seismic risk area.

After analyzing more than 100 earthquake cases since 1987, we think that the abnormal changes of several parameters such as the modulus | a | and | b | of the complex transfer function, the total variance σz of the solution matrix equation (1), the magnetic azimuth αp of Parkinson's vector and its changes are all related to the earthquake near the measuring point. These parameters are defined as follows

A=Ar+iAi

B=Br+iBi (4)

The lower angles r and I represent the real part and the imaginary part respectively.

|A|=(A2r+A2i) 1/2

|B|=(B2r+B2i) 1/2 (5)

αp = arc tangent (Br/Ar) (6)

(7)

(8)

Where m is the number of events in the j-th cycle.

The monthly variations of transfer functions and parameters of more than 30 geomagnetic stations in China during the period t = 9～200min are calculated. Zeng Xiaoping and Lin (1995) preliminarily summarized the following points through the analysis and prediction of more than 100 earthquake cases.

The frequency responses of | a | and | b | of (1) transfer function tend to increase from two years before the earthquake to the earthquake month. There are obvious deviations between individual time periods and | a | or | b |.

(2~3)S | a | and/or | b | with t = 9 ~ 200min obviously increased or fluctuated before the earthquake, and their abnormal values exceeded (2~3)S, and s was the mean square deviation (Figure 1).

(3) The total variance σz with 3)t = 9 ~ 200min fluctuates more than (2 ~ 3s) from 3 years before the earthquake to the earthquake month (Figure 1).

(1)

(2)

Fig.11982 ~1998 abnormal change of transfer function of Chongming geomagnetic station and earthquake (unit km).

(a)|B|(T=20 minutes); (b)| A |(T = 150min)； σ z (t =150min)

(4) At the structural faults (such as the coast, riverbed and the vicinity of the north-south fault zone), the Parkinson's vector points to the epicenter before the earthquake, and most of them return to normal orientation after the earthquake.

(5) Before most earthquakes, the abnormal fluctuations of | a |, | b | and σz started from the deep mantle and gradually "spread" to the shallow crust (Zeng Xiaoping, Lin et al., 1999).

(6) The epicenter of the earthquake in the next 6 ~ 12 months will be located at the "singularity" of the spatial distribution of the abnormal variation of geomagnetic transfer function, or at the center of the dense equivariant line.

Figure 1 shows the relationship between the anomalies with the variation range exceeding (2 ~ 3) s and earthquakes after the transformation functions | a |, | b | and σz of Chongming Geomagnetic Station have eliminated the influence of long-term variation and annual variation. Where p is the score of the total score of prediction effect.

2 geomagnetic loading and unloading response ratio method

The sun affects the earth's magnetic field in two ways: ultraviolet radiation and particle flow radiation, forming a changing magnetic field. The former mainly forms Sq field, and the latter is magnetic storm field. D. Zeng Xiaoping et al. (1996) proposed that magnetic storm and general magnetic disturbance should be regarded as the loading and unloading response of the earth's magnetic field to the solar wind. The magnetic storm field can be divided into the following parts.

D=Dst+Ds+Dp (9)

Among them, Dst is a violent time variation, Ds is a diurnal variation, and Dp(B) is a polar substorm.

Taking the vertical component Z as an example, the response ratio is defined by taking the Ds field of Z component as a parameter to calculate the response ratio of loading and unloading.

P(Z)=Ds(Z)+/D(Z)- ( 10)

If the daily amplitude Δ z is used, the formula (10) is

p(Z)=δZ+/δZ-( 1 1)

The lower corner marks "+"and "-"indicate loading and unloading respectively.

In general, the loading-unloading response ratio P(Z) should be mainly a function of the measuring point position (λ,) and the underground conductivity (σ). Under normal circumstances, p (z) = 1 ~ 2 in the middle and low latitudes. If a measuring point is located in the earthquake-prone area, the underground conductivity will inevitably increase due to the movement of underground substances (mostly hot fluids), which will lead to the increase of P(Z) value and the occurrence of precursor anomalies.

The complexity of underground structure and the inhomogeneity of underground medium result in the inhomogeneity of the spatial and temporal distribution of various physical and chemical parameters during the earthquake preparation process, which inevitably leads to the occurrence of P(Z) anomalies in a certain spatial range many times before each earthquake event, and the spatial distribution images of P(Z) are different (Figure 2).

Fig.2 Spatial distribution of P(Z) before June1998+1October 10 Zhangbei M6.2 earthquake (prediction score P=84.3).

(1) Normal distribution P (Z A) P (Z 65438+September 23rd to September 25th 0996); (b)199611.05 to1.1.09;

(3) 1997 65438+ 101to 65438+ 10/3; (d)1May 1996 15 to May 17; (e) 1 997 65438+February 30th1998 65438+1October1.

In order to understand the temporal and spatial distribution of P(Z) and seek the relationship between P(Z) anomaly and earthquakes, following Lin et al. (1996) and Xu Chunrong et al. (1998), we calculated the P(Z) values of 54 geomagnetic stations in China (table 1).

Table 1 P(Z) calculation statistics

Number of stations in this area during the calculation period of P(Z) value * * * m (year)

Beijing11965 ~1998 34

Shanghai11974 ~199825

North China 20 1974 ~ 1998 25

East China131986 ~199813

Southwest111986 ~199813

Northwest 71986 ~199813

South China11986 ~199813

Taking North China as an example, the 157 earthquakes from 1966 Xingtai earthquake to 1998 North China 2 1 near the geomagnetic station with Ms = 3.4 ~ 7.8 (ML = 4.0 ~ 7.9) were analyzed. The preliminary results are as follows: ① the spatial distribution scale of P2P (z) anomaly; ③ The relationship between the occurrence time of precursory anomalies and magnitude; ④ The "epicenter area" (the epicenter and its vicinity) and "blind area" (the range beyond the epicenter 100 km) of geomagnetic effect were found, and there was no abnormal value of P(Z) in these two areas before the earthquake. Furthermore, it explains why some stations near the epicenter were not abnormal before the earthquake, while some stations in the distance were abnormal and strange. ⑤ It is found that the time scale and spatial scale of P(Z) anomaly have a good correlation with the focal volume scale. These results will be discussed in another paper, and a new understanding will be gained in the study of quantitatively judging the relationship between precursory anomalies and seismomagnetism.

3 spatial correlation and weighted difference method

There are many factors that cause geomagnetic changes, mainly from magnetosphere (P), ionosphere (Q), core (C) and underground local anomalies (N). Therefore, the magnetic field value measured at any measuring point on the ground, taking the vertical component Z as an example, mainly includes the above four source fields.

Z=ZP+ZQ+ZC+ZN ( 12)

Generally speaking, the spatial distribution of the earth's magnetic field in the middle and low latitudes is obviously linear, and the correlation coefficient r = 0.90 ~ 1.00. By calculating the monthly spatial correlation coefficients RF, RZ and RH of the total intensity (F), vertical component (Z) and horizontal component (H) at 2 1 h 00 min every day in Beijing time, the relationship between their changes and earthquakes is studied.

On the one hand, it is assumed that non-underground local anomalies can be considered as spatially uniform, at least between pairs of measurement points that are not too far apart. On the other hand, almost all earthquake cases have confirmed that earthquakes are local geophysical phenomena, so the geomagnetic effect is limited to a certain range, which we call "magnetic anomaly area". The results of a large number of earthquake cases show that the geomagnetic Z component is more sensitive and obvious than other components in the magnetic anomaly area. RZ decreased from 0.90 to 0.7 ~ 0.85 before most earthquakes10/1month (in some areas 14 ~ 16 months). Based on this, we speculate that the increase in the conductivity of underground medium in earthquake-prone areas before the earthquake led to the abnormal change of Z value observed at ground observation points, forming a precursory magnetic anomaly area.

1February 1998,165438+1October, there were 8 strong earthquakes (Ms = 5.7 ~ 7.3 (ML = 6.0 ~ 7.4)) in north China, east China and southwest China, and abnormal Rz values were found in precursory magnetic anomaly areas related to strong earthquakes. Rza in southwest and north China is 0.70 ~ 0.80, and Rza in north China is 0.80 ~ 0.85. The following figure shows the spatial distribution of Rz in the months before the1February 30, 994 Mabian, Sichuan earthquake with MS=5.7(ML=6.0). The predicted score of this earthquake is P=96.5.

Fig. 3 Spatial distribution of Mabian M5.7 earthquake (*) and Rz (prediction score: P=96.5).

(a) 65438+August 0994 (normal); (b) 65438+May 0994; (c) 65438+June 0994; (d) 1994 1 1 month; (e)1994 65438+February

There are many reports of geomagnetic anomalies (Honkura, 198 1) by measuring the difference (simple difference) of geomagnetic total intensity f with a nuclear gyroscope. Because the simple difference method ignores the difference of the effect of the source field in outer space on different measuring points on the ground. Especially, the different effects of the ionospheric current system with an average height of about 1 10 km on two measuring points which are several tens of kilometers to 2,300 kilometers apart can not be ignored in the study of seismomagnetic relationship. For this reason, Rikitake proposed a more reasonable weighted difference method. By analyzing the data of more than 65,438+00 nuclear gyroscopes in China from165,438+00 in 1990 to165,438+09 in 1998, the results show that the weighted difference method is a more reasonable and simple method to extract earthquake precursor information in a local range (Lin et al., 65,

When using the weighted difference method, we should pay attention to two points: ① the choice of weighting factor α; We adopt the linear correlation between two measuring points y = a+the regression coefficient b in BX, and take its average value as α under normal conditions and without earthquakes; ② The applicable time of α value should be 3 years to several decades. This is because Fujita( 1973) and Rikitake( 1985) pointed out that the variation of exogenous field is different from that of core source field, and the time scale of local magnetic anomaly must be smaller than that of long-term geomagnetic variation.

4. Prediction effect evaluation

We adopted a relatively simple and practical method to evaluate and predict the earthquake effect (Zhu Ruomin et al., 1998). This method is recommended by the global planning project of the United Nations Department of Economic and Social Affairs (UNDESA)-short-term and imminent earthquake evaluation criteria and annual prediction opinions, and is referred to as "ESTAPE" for short. According to the "three elements of annual earthquake prediction scoring table" in this method, the error between annual prediction and actual earthquake is calculated and scored.

Calculate the prediction errors of the three elements of the earthquake as required: ① the prediction position error δR: the distance between the geographical position (λ 0,0) of the geometric center of the danger zone delineated in the annual forecast and the actual epicenter position (λ 0,0), in kilometers; ② Earthquake magnitude error: surface wave magnitude MS is adopted. Magnitude error δ ms = | ms-MS0 |, and MS0 is the median value of predicted magnitude; (3) If there are multiple earthquakes in the once-predicted danger zone, the earthquake with the highest score will be scored; ④ Time error: in the annual forecast, all earthquakes that occurred within the forecast year from 65438+ 10/day to 65438+February 3 1 day, the time error δT = 0 month.

The scores obtained from Δ t, Δ r and Δ ms are PT, PR and PM respectively. Considering the different difficulties of earthquake prediction, the difficulty factors αT, αR and αM are introduced as 0.3, 0.4 and 0.3 respectively. The prediction effect is calculated by the total score p.

P=αTPT+αRPR+αMPM ( 13)

From June of 1989 to June of 10, we formally submitted the earthquake trend opinions to the competent departments (Institute of Geophysics, Seismological Bureau of China and Seismological Bureau of China) by the method described in this paper. From 1995, supplementary forecast opinions are put forward in the second half of each year (late June). During the nine years from 1989 to 1998, * * proposed 97 sub-dangerous areas, of which 34 times were false positives, accounting for 34.0% of the total forecast times. According to ESTAPE Table 2, the number of earthquakes with prediction scores P≥60, 85 and 90 are 54 14 and 9 respectively, accounting for 55.4%, 14.4% and 9.3% of the total number of predicted earthquakes respectively. Fig. 4 shows the results of grading the p value by the number in the past 9 years.

Table 2 lists 14 earthquake cases with P≥85 and their prediction effect scores.

Table 2 14 Earthquake Cases with Prediction Score P≥85

Year-month-day φ (n φ( N) λ( E) MS point δt/ month δ r/km δ ms pt pr pm p method

11990-2-1031.71.05.1Changshu, Jiangsu 0390.410090.280.090./

21991-3-26 40.013.9 5.8 Shanxi Datong 0450.210088.7 90.0 92.51.

31991-5-30 39.7118.35.2 Douhe 0 550.3100 86.485.090.11.

41991-6-16 38.9105.75.10 920.4100 77.9 80.085.2/kloc-0 in Azuo Banner, Inner Mongolia.

51992-4-23 22.3 99.16.7 China-Myanmar border 0 950.2100 83.0 92.3 90.913

61993-1-27 23.1.16.3 Yunnan Pu 'er 0.39 0.2100 92.8 92.3 94.81.

71994-1-1239.175.45.6 Kashgar, Xinjiang 0310100 92.295.0 95.4/kloc.

81994-12-30 29.0103.65.7 Sichuan Mabian 0 50.2100 98.7 90.096.513.

91995-7-22 36.4103.35.8 Yongdeng Gansu 0 29 0.3100 92.7 85.0 92.613.

101996-2-28 29.0104.65.4 Sichuan Fushun 0 660.4100 83.8 80.087.513.

111996-11-931.8123.16.1Changjiang estuary 0.

121997-1-2139.676.96.2 Jiashi, Xinjiang 0420.210092.292.394.612,3.

131998-4-1439.718.54.7 Hebei guye 0120.8100 96.960.086.8.

141998-3-1940.176.76.0 Xinjiang Artux 01650.010071.8/kloc.

Note: 1, 2 and 3 in the last column of "Methods" respectively represent transfer function method, loading and unloading response ratio method, spatial correlation method and weighted difference method.

Fig. 4 Earthquake prediction effect score of geomagnetic method (1990 ~ 1998)

5 Problems and discussions

Since (1) 1989, we have analyzed and studied more than 200 earthquake cases by three geomagnetic methods: transfer function method, loading-unloading response ratio method, spatial correlation method and weighted difference method. Within 9 years, there were 106 earthquakes in 97 sub-dangerous areas officially submitted, of which 14 earthquakes were successfully predicted. The predicted score P≥85 (Table 2). Due to the inhomogeneity of underground structure and medium distribution, the complexity of earthquake preparation process, the inhomogeneity of ground observation points distribution and the limitation of observation conditions, and our limited understanding of seismomagnetic relationship, the current prediction level is not high, which is manifested in the nine years from 1989 to 1998, with a false alarm rate of 34.0%(33/97) and a pass rate of.

(2) Summing up the experience, let the parameter anomalies of the three geomagnetic methods be a, when a ≥ (2 ~ 3) s, earthquakes may occur near the magnetic anomaly area, and s is the mean square deviation of the parameters under normal conditions. Generally, a≥2 S corresponds to a moderate earthquake with a magnitude of about 6, and a≥3 S corresponds to a strong earthquake with a magnitude of more than 6 or a near earthquake with a magnitude of less than 6.

(3) The abnormal time of each method is different. The transfer function | a | or | b | is from 3 years before the earthquake to the month of the earthquake; σz refers to the period from 2 years before the earthquake to the month when the earthquake occurred. Spatial correlation method is from 14 to 10 months before the earthquake to 1 0 months after the earthquake, and weighted difference method is from 6 months before the earthquake to the day of the earthquake. The loading and unloading response ratio method is 65438+ 0.5 years before the earthquake to 3 days before the earthquake.

(4) The spatial range controlled by each method is related to earthquake intensity and underground structure. For the intensity of ms = 6 ~ 7.8, the farthest control distance is about 550 km. For earthquakes with Ms = 3.5 ~ 5.0, it is about 100 ~ 300 km. For earthquakes with Ms = 5 ~ 6, it is about 300~350 km.

(5) Magnetic anomalies at the spatial scale of103 km are often related to the rare meteorological disasters in this area in the next eight months (Zeng Xiaoping,1992; Zeng Xiaoping, 1996).

(6) We speculate that the movement of mantle material and the tectonic movement in the lithosphere in the seismogenic area make the pores and cracks of underground material increase, move and deform. This creates conditions for the invasion of groundwater and deep hot water vapor, which leads to the increase of the conductivity of underground medium and the precursory anomaly of geomagnetic field in earthquake-prone areas.

According to the electromagnetic induction theory, the depth of underground conductivity is estimated to be about 700 ~1000 km by using the data analysis period of the transfer function and the loading-unloading response ratio method, while the nuclear spinor value of the spatial correlation method and the weighted difference method is the absolute value of the geomagnetic field, and its annual and long-term changes are related to the material movement in the core and mantle. Therefore, it can be inferred that the movement of mantle materials in the earthquake-producing area contributes to the formation and occurrence of earthquakes. In other words, the "source" of the earthquake may come from deep in the mantle.

China Earthquake Administration Institute of Geophysics engineering number 99ac2060.

Authors: Lin, Zeng Xiaoping, Zhao Ming,

Xu Chunrong (Beijing, China 100085 Institute of Geostress, Seismological Bureau of China)

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