united nations educational, scientific and cultural organization...

Available at: http://publications.ictp.it IC/2007/126

United Nations Educational, Scientific and Cultural Organization and

International Atomic Energy Agency

THE ABDUS SALAM INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS

SPECTRAL CHARACTERISTICS OF NATURAL AND ARTIFICIAL

EARTHQUAKES IN THE LOP NOR TEST SITE, CHINA

I.M. Korrat Geology Department, Faculty of Science, Mansoura University, Egypt,

A.A. Gharib, K.A. Abou Elenean, H.M. Hussein National Research Institute of Astronomy and Geophysics, NRIAG, Helwan, Egypt

and

M.N. ElGabry* National Research Institute of Astronomy and Geophysics, NRIAG, Helwan, Egypt

and The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy.

MIRAMARE – TRIESTE

December 2007

___________________ *Junior Associate of ICTP.

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Abstract

A seismic discriminants based on the spectral seismogram and spectral magnitude

techniques has been tested to discriminate between three events; a nuclear explosion which took

place in Lop Nor, China with mb 6.1 and two earthquakes from the closest area with mb 5.5 and 5.3,

respectively. The spectral seismogram of the three events shows that the frequency content of the

nuclear explosion differs from that of the earthquakes where the P-wave is rich with high frequency

content in the nuclear explosion than the corresponding earthquakes. It is also observed that the

energy decays very rapidly for the nuclear explosion than that for the earthquakes. Furthermore, the

spectral magnitudes reveal significant differences in the spectra between the nuclear explosion and

the two earthquakes. These observed differences appear to be quite enough to provide a reliable

discriminant. The estimated stress drop from the magnitude spectra indicates a higher stress drop of

the nuclear explosion relative to the earthquakes of the same tectonic region.

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Introduction

Discrimination between earthquakes and underground nuclear explosions is a difficult task which

has gained considerable attention in the seismological community. Seismic methods provide the

principal means for a verification of a nuclear test ban (Basham and Dahlman, 1988). The

discrepancies in signals from earthquakes and explosions arise from differences in source

mechanisms, source dimensions and duration. An underground nuclear explosion has a small point

source compared to an earthquake. It sends out compressional waves of equal strength in all

directions. An earthquake occurs along a rupture as a result of sliding rupture sides. Due to this

frictional sliding an earthquake emits more shear waves and surface waves than a nuclear explosion.

As the source dimensions of earthquakes tend to be larger than those of nuclear explosions,

wavelengths of the radiated seismic waves emitted are longer. Thus, earthquakes usually produce

signals with lower frequencies than explosions.

Classical Discriminates such as mb:MS, the ratio of body wave magnitude and surface wave

magnitude; M0:ML, the ratio of seismic moment and local magnitude and various spectral ratios

showed promising results in many instances. Denny et al. (1987) and Taylor et al. (1989) show that

mb:MS works well down to mb = 4. Generally, the explosion generates lower-amplitude surface

waves than an earthquake of equal size. However, in some cases these methods failed to

discriminate between natural earthquakes and nuclear explosions. For example, intermediate and

deep earthquakes can cause problems with mb:MS because they can result in relatively high mb:MS

differentials and sampling of Rayleigh waves near radiation nodes can bias the MS estimates

(Dreger and Woods 2002). Additionally, all nuclear explosions produce some nonisotropic radiation

(Wallace, 1991) and the mode of the nonisotropic radiation (strike-slip vs. dip-slip) can have quite

different effects on Rayleigh wave amplitudes and, hence, MS (Patton, 1991). Surface waves also

have source area dependent behavior (Stevens, 1986). As shown by Patton (1991), the degree of

such bias is a strong function of the F-factor, F= (α2M0/2β2MI), where α and β are the compressional

and shear wave velocities at the source, and M0 and MI are the nonisotropic and isotropic scalar

seismic moments, respectively. The ratio of mb:M0 and ML:M0 discriminants are based on the same

principle as the mb:MS method with the exception that M0 is determined by waveform modeling to

account for source depth and radiation pattern influences (Woods et al., 1993).

The seismic waves observed in earthquake records manifest clearly non-stationary

characteristics, as well as wide frequency content. Those characteristics are twofold (Huerta-López,

et al., 2003). The first characteristic involves variations of the intensity of ground motion with the

time. The second characteristic involves variation with the time of the frequency content, with a

tendency to shift to lower frequencies as the time increases. This phenomenon is well known as the

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frequency dependent dispersive effect which is very complex and involves the arrival of different

seismic phases (P, S and surface waves), the intensity of ground motion, the magnitude of

earthquake, source and path effects, and the local soil conditions. Spectral characteristics of

different seismic waves have been used before for the discrimination analysis and source parameter

evaluations of different tectonic origins earthquakes (Hussein et al., 1998, Lyskova et al., 1998 and

Abou Elenean et al., 2000). Moreover, Chernobay and Gabsatarove (1999) applied the spectrogram

method for routine discrimination between regional earthquakes and chemical explosions of

comparable magnitudes in northern Caucasus. Recently, the spectrogram was implemented in the

routine analysis used by Comprehensive Nuclear-Test- Ban Treaty Organization (CTBTO).

The need for a suitable tool for measuring strength of any seismic event, as well as for

discrimination between natural and artificial ones, is very important issue. Our study has been

forward to apply both the spectral seismogram (spectrogram) and spectral magnitudes tools for the

verification of a nuclear explosion at the Lop Nor test site, China and the two natural earthquakes

which occurred closer to the test site. These tools can help in resolving possible biases in the

identification of an explosion.

Data

In this study, we used three events; a known nuclear explosion and two natural earthquakes which

are both located in the China Lop Nor area. The selection is based upon event size (magnitude),

focal depth and location proximity. We search for the available natural earthquakes with relatively

comparable magnitudes to that of the explosion and very close to the test site. This ensures that

dissimilarities observed between both events would originate from the type of the source rather than

from different propagation paths and origin areas. Table 1 shows the parameters of the three tested

events. The broadband records of IRIS data base were utilized. We try to use the same stations with

the same time window during our analysis. Six seismic stations equipped with 3 components

Streckeisen STS-1 broadband seismometers (Fig. 1) which have good signal to noise ratio were

used. The available selected stations have epicentral distances ranging from 20°- 60°.

Spectral seismogram

The Fourier transform decomposes a signal into its constituent frequency components. Looking at

the Fourier spectrum we can identify these frequencies; however, we cannot identify their temporal

localization. Time-frequency distribution map converts a one-dimensional signal into a colored two-

dimensional function of time and frequency, and describes how the spectral content of the signal

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changes with time. The basic idea of the method of analyzing the time-varying nature of the

spectral content is to compute the Fourier transform of the signal using a short sliding time window.

The absolute values of this function yield the spectrogram (Fasthoff and Lucan, 1996). The basis for

this approach has been developed by Gabor (1946). He defined the complex (analytic signal) from a

real one s(t):

z(t) = s(t)+iH[s(t)] (1)

where, H is the Hilbert transform which is defined as:

H[s(t)] = p.v. τ πτ τ dts )( −∫

+∞

∞− (2)

(p.v. stands for principal value of the integral). Moreover, Gabor (1946) demonstrated that the

analytic signal can be calculated as well in the frequency domain by Fourier transforming the signal

s(t), then doubling the amplitude of the positive frequencies and suppressing the amplitude of the

negative frequencies. For obtaining th