本文選題：地下震動(dòng) + 特征提取�。� 參考：《中北大學(xué)》2015年碩士論文

【摘要】：小區(qū)域淺層地下爆破震動(dòng)信號(hào)特征分析是震源定位技術(shù)的關(guān)鍵，提取混合震動(dòng)信號(hào)中的有用分量是進(jìn)行信號(hào)特征分析的重要基礎(chǔ)。地下固體介質(zhì)的復(fù)雜性導(dǎo)致了爆破震動(dòng)信號(hào)成分多樣，各分量混合模式復(fù)雜，分離難度較高等問(wèn)題，，針對(duì)這些問(wèn)題，本文提出將盲源分離技術(shù)引入震動(dòng)信號(hào)處理過(guò)程，實(shí)現(xiàn)小區(qū)域淺層地下爆破震動(dòng)信號(hào)各分量分離。 首先，利用基于多變量貝葉斯、基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)及基于插值神經(jīng)元網(wǎng)絡(luò)層結(jié)構(gòu)的三種非線性盲源分離算法對(duì)多路模擬混合震動(dòng)信號(hào)進(jìn)行信噪分離，根據(jù)分離結(jié)果中相似系數(shù)及信噪比SNR的值驗(yàn)證基于插值神經(jīng)元網(wǎng)絡(luò)層結(jié)構(gòu)算法在震動(dòng)信號(hào)信噪分離方面的可行性及優(yōu)越性；利用EEMD算法與基于插值神經(jīng)元網(wǎng)絡(luò)層結(jié)構(gòu)算法相結(jié)合的方案對(duì)單路模擬混合震動(dòng)信號(hào)進(jìn)行信噪分離，驗(yàn)證該方案有效性。其次，利用基于擴(kuò)展聯(lián)合對(duì)角化、基于峭度及基于時(shí)間延遲的三種不同盲源分離算法對(duì)多路模擬混合震動(dòng)信號(hào)進(jìn)行橫、縱波的分離，驗(yàn)證基于時(shí)間延遲算法在橫、縱波分離方面的可行性及優(yōu)越性；利用Radon變換與基于時(shí)間延遲算法相結(jié)合的方案對(duì)單路模擬混合震動(dòng)信號(hào)進(jìn)行橫、縱波的分離，驗(yàn)證該方案有效性。 利用以上方案對(duì)實(shí)測(cè)淺層小區(qū)域地下爆破震動(dòng)信號(hào)進(jìn)行分析處理，對(duì)得到的P波信號(hào)進(jìn)行特征分析。結(jié)果表明，最終得到的P波信號(hào)速度特征及頻率特性都與理論值相符，該結(jié)果證明本文方案可以實(shí)現(xiàn)單路小區(qū)域淺層地下爆破震動(dòng)信號(hào)的橫、縱波分離。利用該P(yáng)波信號(hào)進(jìn)行震源定位，定位精度可達(dá)0.5148m。
[Abstract]:The feature analysis of shallow blasting vibration signal in small area is the key of the focal location technology, and the extraction of useful components from the mixed vibration signal is an important basis for signal feature analysis. The complexity of underground solid media leads to various components of blasting vibration signal, complex mixing mode of each component and high difficulty of separation. In view of these problems, the blind source separation technology is introduced into the vibration signal processing process in this paper. Each component of vibration signal of shallow underground blasting in small area can be separated. Firstly, three nonlinear blind source separation algorithms based on multivariable Bayes, radial basis function neural network and interpolated neural network layer are used to separate the signals from multichannel analog mixed vibration signals. The feasibility and superiority of the interpolated neural network layer structure algorithm in vibration signal noise separation are verified according to the values of similar coefficients and SNR in the separation results. The EEMD algorithm and the algorithm based on interpolation neural network layer structure are used to separate the signal and noise of the single channel analog mixed vibration signal, and the validity of the scheme is verified. Secondly, based on extended joint diagonalization, three different blind source separation algorithms based on kurtosis and time delay are used to separate the transverse and longitudinal waves of multi-channel analog mixed vibration signals. The feasibility and superiority of P-wave separation, the combination of Radon transform and time-delay algorithm, is used to separate the transverse and P-wave signals of single channel analog mixed vibration signal, and the validity of the scheme is verified. The vibration signal of underground blasting in shallow and small area is analyzed and processed by the above scheme, and the characteristic of P wave signal is analyzed. The results show that the velocity and frequency characteristics of the P-wave signal obtained are in agreement with the theoretical values. The results show that the proposed scheme can achieve the separation of transverse and longitudinal waves of the vibration signals of shallow underground blasting in a single channel and small area. Using the P-wave signal to locate the source, the positioning accuracy can reach 0.5148m.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P631.4;TN911.7

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