Chaotic system detection of weak seismic signals

SUMMARY When the signal-to-noise (S/N) ratio is less than −3 dB or even 0 dB, seismic events are generally difficult to identify from a common shot record. To overcome this type of problem we present a method to detect weak seismic signals based on the oscillations described by a chaotic dynamic system in phase space. The basic idea is that a non-linear chaotic oscillator is strongly immune to noise. Such a dynamic system is less influenced by noise, but it is more sensitive to periodic signals, changing from a chaotic state to a large-scale periodic phase state when excited by a weak signal. With the purpose of checking the possible contamination of the signal by noise, we have performed a numerical experiment with an oscillator controlled by the Duffing–Holmes equation, taking a distorted Ricker wavelet sequence as input signal. In doing so, we prove that the oscillator system is able to reach a large-scale periodic phase state in a strong noise environment. In the case of a common shot record with low S/N ratio, the onsets reflected from a same interface are similar to one other and can be put on a single trace with a common reference time and the periodicity of the so-generated signal follows as a consequence of moveout at a particular scanning velocity. This operation, which is called ‘horizontal dynamic correction’ and leads to a nearly periodic signal, is implemented on synthetic wavelet sequences taking various sampling arrival times and scanning velocities. Thereafter, two tests, both in a noisy ambient of −3.7 dB, are done using a chaotic oscillator: the first demonstrates the capability of the method to really detect a weak seismic signal; the second takes care of the fundamental weakness of the dynamic correction coming from the use of a particular scanning velocity, which is investigated from the effect caused by near-surface lateral velocity variation on the periodicity of the reconstructed seismic signal. Finally, we have developed an application of the method to real data acquired in seismic prospecting and then converted into pseudo-periodic signals, which has allowed us to discriminate fuzzy waveforms as multiples, thus illustrating in practice the performance of our working scheme.

[1]  Baojun Yang,et al.  Chaotic system for the detection of periodic signals under the background of strong noise , 2003 .

[2]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[3]  On flat gravitation as a useful model theory. II , 1969 .

[4]  R. May,et al.  Directly transmitted infections diseases: control by vaccination. , 1982, Science.

[5]  Simon Haykin,et al.  Detection of signals in chaos , 1995, Proc. IEEE.

[6]  Carroll,et al.  Driving systems with chaotic signals. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[7]  Shu-Huei Hung,et al.  Can a narrow, melt‐rich, low‐velocity zone of mantle upwelling be hidden beneath the East Pacific Rise? Limits from waveform modeling and the MELT Experiment , 2000 .

[8]  Wooil M. Moon,et al.  Detection of seismic refraction signals using a variance fractal dimension technique , 2000 .

[9]  Hiroaki Niitsuma,et al.  Reflection technique in time‐frequency domain using multicomponent acoustic emission signals and application to geothermal reservoirs , 2002 .

[10]  Ortwin Hess,et al.  Controlling delay-induced chaotic behavior of a semiconductor laser with optical feedback , 1996 .

[11]  Ninos Benyamin,et al.  Key elements of total seismic field design using mathematica: A tutorial , 2002 .

[12]  W. Klische,et al.  Instabilities and chaos of a single mode NH3 ring laser , 1985 .

[13]  R. Westervelt,et al.  Chaos and Broadband Noise in Extrinsic Photoconductors , 1984 .

[14]  R. Westervelt,et al.  Nonlinear Oscillations and Chaos in Electrical Breakdown in Ge , 1983 .

[15]  Mostefa Mesbah,et al.  Signal enhancement by time-frequency peak filtering , 2004, IEEE Transactions on Signal Processing.

[16]  R. May,et al.  Population biology of infectious diseases: Part II , 1979, Nature.

[17]  Mirko van der Baan,et al.  Recognition and reconstruction of coherent energy with application to deep seismic reflection data , 2000 .

[18]  Henry Leung,et al.  Parameter estimation in chaotic noise , 1996, IEEE Trans. Signal Process..

[19]  Yang Baojun Reduction of random noise for seismic data by time-frequency peak filtering , 2005 .

[20]  Michael S. Gaines,et al.  Biological Populations with Nonoverlapping Generations : Stable Points , Stable Cycles , and Chaos , 2007 .

[21]  Yuriy Tyapkin,et al.  Optimum pilot sweep , 2003 .

[22]  Paul A. Bernhardt Communications Using Chaotic Frequency Modulation , 1994 .

[23]  Sverre Brandsberg-Dahl,et al.  Focusing in dip and AVA compensation on scattering‐angle/azimuth common image gathers , 2003 .

[24]  P. Steeghs,et al.  Seismic sequence analysis and attribute extraction using quadratic time‐frequency representations , 2001 .

[25]  R. May,et al.  Population biology of infectious diseases: Part I , 1979, Nature.

[26]  D. L. Birx,et al.  Chaotic oscillators and complex mapping feed forward networks (CMFFNs) for signal detection in noisy environments , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[27]  F. Krüger,et al.  A waveform migration for the investigation of P wave structure at the top of D″ beneath northern Siberia , 2001 .

[28]  Bjørn Ursin,et al.  True-amplitude frequency-wavenumber constant-offset migration , 1999 .

[29]  Kevin M. Short,et al.  Signal Extraction from Chaotic Communications , 1997 .

[30]  D. J. Verschuur,et al.  Surface‐related multiple elimination on land seismic data—Strategies via case studies , 2000 .

[31]  Shuang Qin,et al.  Filtering vibroseis data in the precorrelation domain , 1998 .

[32]  Xing Chen,et al.  The application of chaotic oscillators to weak signal detection , 1999, IEEE Trans. Ind. Electron..

[33]  A note on digital filtering with the second moment norm , 1997 .

[34]  Alan G. Green,et al.  Reducing source-generated noise in shallow seismic data using linear and hyperbolic τ-p transformations , 2001 .

[35]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.

[36]  Yanghua Wang,et al.  Quantifying the effectiveness of stabilized inverse Q filtering , 2003 .

[37]  Prestack Kirchhoff depth migration of shallow seismic data , 1998 .

[38]  Robert E. Sheriff,et al.  Encyclopedic dictionary of exploration geophysics , 1973 .

[39]  M. L. Driesenaar,et al.  Fractal properties and denoising of lidar signals from cirrus clouds , 2000 .

[40]  P. E. Harris,et al.  Improving the performance of f-x prediction filtering at low signal-to-noise ratios , 1997 .