A wavelet transform based application for seismic waves. Analysis of the performance.
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The document presents a wavelet transform based application for analyzing seismic waves. It discusses analyzing the performance of an algorithm for seismic signal processing using continuous wavelet transforms. The algorithm is simulated and tested on seismic data with conclusions drawn regarding the results. Key steps of the algorithm include preprocessing the data, applying a multiresolution wavelet filter, detecting signal onsets, and analyzing body and surface wave dispersion and polarization.
![Some geophysical issues
•
3 components:
EW, NS, Z (transverse)
•
Body Waves (P and S
waves) and Surphase
Waves (Rayleigh and
Love).
•
Seismic Spectrum:
0.001-10hz [1].
•
Frequency
characterization:
Spectrum overlaping of
Body and Surphase
Waves .
Image taken from Dr. José Ignacio Badal Nicolás (Faculty
of Geologics, Zaragoza University). Shared resource.
[1] “Fundamentals of Geophysics” Agustín Udías & Julio
Mezcua. Chap.13](https://image.slidesharecdn.com/awavelettransformbasedapplicationforseismicwaves-analysisoftheperformance-131210033802-phpapp02/85/A-wavelet-transform-based-application-for-seismic-waves-Analysis-of-the-performance-4-320.jpg)
A wavelet transform based application for seismic waves. Analysis of the performance.
- 1. A Wavelet Transform based application for seismic waves. Analysis of the performance. Telecommunication Engineering Thesis Author: Pedro Cerón Colás Fraunhofer IIS, Erlangen December 9th 2013
- 2. General outline of the presentation Introduction Method and Process Simulation of the algorithm Conclusions
- 3. Overview of the problem Geophysics field Design of Matlab algorithms Complex Continuous Wavelet Transform Bio _QRS Complex, “Biomedical Signal Processing”, Sorno & Laguna. _Circular buffer 3rd FIR filter. “Sound digital processing”, Rocchesso. Sound Procces ing But… Where can we apply the Wavelet Transform?
- 4. Some geophysical issues • 3 components: EW, NS, Z (transverse) • Body Waves (P and S waves) and Surphase Waves (Rayleigh and Love). • Seismic Spectrum: 0.001-10hz [1]. • Frequency characterization: Spectrum overlaping of Body and Surphase Waves . Image taken from Dr. José Ignacio Badal Nicolás (Faculty of Geologics, Zaragoza University). Shared resource. [1] “Fundamentals of Geophysics” Agustín Udías & Julio Mezcua. Chap.13
- 5. General Outline of the presentation Introduction Method and Process Simulation of the algorithm Conclusions
- 6. Method and process D A T A B A S E S Conversion of the signals • • • Preprocessing: Correction Data format? SAC or Mseed Compressed Info? STEIM1, STEIM2 Not compressed Info? ASCII, float, integer… Processing step: Filtering Multiresolution filter (WT) Matlab .mat Onset detection Body Waves Surphase Waves Polarization analysis
- 7. Seismic formats: SAC and MiniSEED Word Type NAMES o 0 F DELTA DEPMIN DEPMAX SCALE ODELTA 5 F B E O A INTERN AL 10 F T0 T1 T2 T3 T4 15 F T5 T6 T7 T8 T9 20 F F RESP0 RESP1 RESP2 RESP3 25 F RESP4 RESP5 RESP6 RESP7 RESP8 30 F RESP9 STLA STLO STEL STDP 35 F EVLA EVLO EVEL EVDP MAG 40 F USER0 USER1 USER2 USER3 USER4 45 F USER5 USER6 USER7 USER8 USER9 50 F DIST AZ BAZ GCARC INTERN AL 55 F INTERN AL DEPME N CMPAZ CMPINC XMINIM UM 60 F XMAXIM YMINIM YMAXIM UNUSED UNUSED UM UM UM 65 F UNUSED UNUSED UNUSED UNUSED UNUSED 70 I NZYEAR NZHOUR NZMIN NZSEC 75 I NZMSEC NVHDR NORID NEVID NPTS 80 I INTERN AL NWFID NXSIZE NYSIZE UNUSED 85 I IFTYPE IDEP IZTYPE UNUSED IINST 90 I ISTREG IEVREG IEVTYP IQUAL 95 I IMAGTY P IMAGSR UNUSED UNUSED UNUSED C 100 I UNUSED UNUSED UNUSED UNUSED UNUSED 105 L LEVEN LPSPOL 110 K KSTNM KEVNM* 116 K KHOLE KO KA 122 K KT0 KT1 KT2 128 K KT3 KT4 KT5 134 K KT6 KT7 KT8 140 K KT9 KF KUSER0 146 K KUSER1 KUSER2 KCMPN M 152 K KNETWK KDATRD KINST NZJDAY o o LOVROK LCALDA o ISYNTH UNUSED Algorithms to decode the information. Tables taken from: http://www.iris.edu/software/sac/manual/file_format.html, november 2013. SEED manual v.2.4, B appendix.
- 8. Compressional techniques: STEIM 1 and STEIM 2 STEIM 2:More number of possibilities (8) with dnib. Algorithms to decompress the information. Tables taken from: SEED reference manual (version 2.4). B appendix. November 2013.
- 9. Response for channel correction • .PAZ .RESPONSE
- 10. Multiresolution filtering using the Wavelet Transform Amplitude Mathematical tool Phase Inst. Freq. Freq? Input (Div.) Multiresolution filter: www.sciencedirect.com, nov. 2013. Plot of a .cwt matrix in Matlab.
- 11. Prepocessing stage: Filtering How? Computations are done directly to the .cwt matrix • Band pass filtering. • Once we have seen in the .cwt plot where we can locate the parts of the signal with higher energetic contributions, we can remove the unnecesary bands (coefficients). • Remove DC level and high frequency seismic noise.
- 12. Onset detector (body waves) What’s the concept? Body Waves tend to be at higher frequencies in the octaves (higher divisions) than Surface waves. Energetic Criteria: Mk1 Mk2 Variability Criteria: Finer adjustment Low frequency envelope High Frequency envelope
- 13. Onset detector (surphase waves) What’s the concept? Surphase Waves tend to be at lower frequencies every octaves Derivative Derivative + envolope We can roughly locate where it’s located the onset of the Surphase waves.
- 14. Surphase wave: Dispersion What is the distinctive element that define the Surphase Waves? How can be use the wavelet coefficients to analyse this phenomenon? .cwt matrix Dispersion
- 15. Arrival times Polarization analysis P wave onset S wave onset Surphase wave onset Transformation of 3 axis into 2: http://www.motionscript.com/mastering-expressions/randomsphere.html, november 2013 • Polarization of P, S, Love and Rayleigh waves?
- 16. General Outline of the presentation Introduction Method and Process Simulation of the algorithm Conclusions
- 17. Time errors: First onset Inner structure problem 3.5 3 2.5 Low SNR 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12
- 18. Time errors: Second onset Inner structure problem 4 3.5 Low SNR 3 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12
- 19. General Outline of the presentation Introduction Method and Process Simulation of the algorithm Conclusions
- 20. Conclusions • Algoritms easy to apply (engineering principles: energy, variability, derivatives…) • Very satisfactory results. • Automatic algorithm: Input (signal). • Outputs are specially interesting in terms of the signal processing and geophysic field: Time-Frequency analysis, onsets, analysis of the dispersion phenomena, polarization. • Formats (SAC and Miniseed) and compressional techniques. • The multiresolution analysis is specially appropiate for the nonstationary signals where we don’t know (in advance) where are the frequency bands of interest. FIR of how many coefficients and what are the frequencies of the design?