禁核试核查地震数据处理系统中关键技术研究/Research of the Technologies in the Seismic Data Processing
The Comprehensive Nuclear-Test-Ban Treaty (CTBT) was opened for signature at United Nations Headquarters in September 1996. To verify compliance, a verification regime has been established consisting of International monitoring system, Global Communication Infrastructure, On-site Inspection and International Data Center, et al. In order to enhance our verification abilities and protect our privileges, the verification technology must be studied before the Treaty entering into force.
Some important technologies in data processing system for nuclear test ban verification were studied in the dissertation. Coopertated with the construct of test system of national data center, the study work analyzed the operation mode and data processing methods, resolved some important problems for testing continuous data subsystem CDS, analyzed these main algorithms of IDC automatic data processing. In the study of event identification the method of time-frequency analyse for discriminating ripple fired explosions were analyzed.
The main contribution of this dissertation is summarized as follows.
(1) The monitoring data of IMS stations are transmitted based on protocol CD1.0, and data
are received and forwarded by continuous data subsystem CDS in data processing center. In order to testing CDS without real monitoring data, a set of data transmitting system simulating IMS seismic stations were designed, a kind of data uncompressing algorithm based on protocol CD1.0 was developed, which could read and transmit continuous seismic data in format of CD1.0.
(2) In automatic processing the first step is data quality check, which be used to find or
repair problem data that may lead to wrong results for rest processing. Spikes, repeated amplitude values and dropouts are considered to be problem data, in which the detection of spikes is more complex. Amplitude comparison is the main method used in IDC to detect spikes, the drawback of the method is that small spikes could not be detected efficiently. Therefore, A novel algorithm based on stationary wavelet transform and nonlinear energy operator was proposed, which could detected spikes in various amplitude accurately.
(3) STA/LTA is a traditional method in automatic signal processing, in which the threshold is
range from 0 to , the optimal threshold must be experiment with lots of data to get a balance between false detection ratio and miss detection ratio. In order to resolve the deficiency, a novel algorithm based on support vector machine was proposed, in addition, the way of preprocessing and feature extracting, the selection of kernels function for support vector machine were also discussed. Experiments with real records showed that the novel algorithm could detect signal accurately with lower false alarm even in the records of lower signal to noise ratio.
(4) Three different BP neural networks are used in IDC to identify seismic phases in three
component seismograms, which increase computation load of data processing in real applications due to each seimic station must use different neural networks. Based on wavelet transform and polarization analyse, a novel algorithm of multiple scales and multiple parameters for phase identification was proposed, which could identify seismic phases efficiently in automatic processing.
(5) The algorithm AIC is the main method for estimating first break of seismic signals, but
the estimation results is too sensitive for signal-to-noise of seismograms, especially for onset estimation of later signals. Two modified algorithms, i.e. a hybrid method based on AIC and signal amplitude variety, an arrival time estimation based on generalized fractal, were proposed. Experiments showed that performance of the first modified method was same as that of AIC algorithm in three component records. The arrival times for 23 seismic P-phases and later phases recorded at WMQ station in CDSN are picked by this new method. In comparison of manual results, the root mean square error of P-phase is 0.71 second, and the root mean square error of later phase is 1.64 second, which is better than the result obtained by traditional AIC algorithm.
While considering some inherent deficiencies of AIC algorithm, such as phase shift for frequency band filter and computation complexity for construction of AR model, the second modified method are present. The change of intercept of Mandelbrot-Richardson curve can be used to estimate arrival time of seismic signal accurately. The second method may save computation resources for real data processing.
(6) STFT spectrogram is the main time-frequency analysis method used in identification of
ripple fired explosions. However, for the time-frequency resolution of STFT Spectrogram is very poor, some important features of ripple fired explosions can not be extracted directly from STFT spectrogram. A novel method of time-frequency analysis based on Auto-regressive model (AR) is presented, which inherits merits of STFT Spectrogram and has very good time-frequency resolution. In this paper we analyzed the reasons why the discrete Wigner-Ville-Distribution (WVD) of real-valued signal sampled at the Nyquist rate has spectral aliasing, whereas the new method has no such problems. When data for processing are very large, the new method may has excellent performance for promoting velocity of calculating, saving storage and keeping high time-frequency resolution. In addition, the applications of the new method were also illustrated for identifying ripple-fired explosions.