1. 1 Geophysical Methods Data Acquisition, Analysis, Processing, Modelling, Interpretation

2. Geophysical Analysis Geophysical surveys measure the variation of some physical quantity with respect to position or to time. The geophysicist’s task is to separate the ‘signal’ from the ‘noise’ and interpret the signal in terms of ground structure. 2

3. Geophysical Analysis Data acquisition. Data reduction. Signal and noise. Data processing. Modelling. Visualization. Geological interpretation. 3

4. Data Acquisition A truck for Vibroseis surveying. 4

5. Data Acquisition Schematic illustration of a Vibroseis survey. 5 In the Vibroseis system, the source generates a continuous series, or train of waves but with changing frequency. Vibroseis is the most used source on land.

6. Data Acquisition Most geophysical measurements are made at the surface of the Earth. Instrumental readings are taken along a line or traverse. Each place were readings are taken is called a station. The measurements of a physical quantity along a traverse form a profile. The subsurface feature to be detected is called a target. 6 Mussett and Khan, 2000

7. Data Acquisition For irregular targets with broad features several profiles are taken. The traverses can be also close to one another. The stations then form an array or grid. The results are often contoured. 7

8. Data Reduction Direct readings from instruments are not always useful. Corrections to different factors has to be made. Conversion of readings to more useful form is called data reduction. 8 Mussett and Khan, 2000

9. Data Reduction The presence of a target is usually revealed by a geophysical anomaly in the profile. Usually an anomaly is a value which is below or above some normal background value. Reduction of the data for topography. 9 Mussett and Khan, 2000

10. Signal and Noise The signal is usually a wanted part of the measurement. Noise is unwanted variations or fluctuations in the quantities being measured. Signal and noise can be temporal or spatial. Distinction between noise and signal depends on the purpose of the survey. 10 Mussett and Khan, 2000

11. Signal and Noise A weak magnetic anomaly from a granite intrusion. Dyke intrusions would be noise. If looking for dykes those anomalies will define a signal. 11 Mussett and Khan, 2000

12. Signal and Noise In seismology the signal is the ground motion from the energy source (earthquake). Noise can be due to vibrations from traffic, tides, instruments, etc. 12 Mussett and Khan, 2000

13. Signal and Noise Noise can be due to vibrations from traffic, tides, instruments, etc. 13 Reynolds, 2011

14. Signal and Noise To improve the signal-to-noise ratio is to repeat measurements and take their average. The signal parts of each reading add. The noise being random tends to cancel out. The procedure is called stacking. More sophisticated methods use signal processing techniques. 14 Mussett and Khan, 2000

15. Signal and Noise Stacking – the addition together of traces to improve the signal-to-noise ratio (SNR). 15 Reynolds, 2011

16. Signal and Noise Signal-to-noise ratio (SNR): 16 Reynolds, 2011

17. Modelling Modelling usually involves interpretation of the reduced data in physical terms. Model (common language) – a small version of the real system. Model (in geophysics) – a structure defined by its physical properties such as depth, size, density, magnetic properties, etc. that could account for the data measured. The model is always a simplified version of reality. 17

18. Modelling A section that could account for the observed negative gravity anomaly. Values calculated from the model are compared with the actual measured values to test the model. 18 Mussett and Khan, 2000

19. Modelling Modelling complications:  The observed signal is usually ‘blurred’ compared with the target.  This makes it difficult to locate the exact position of the target. 19 Mussett and Khan, 2000

20. Modelling Modelling complications:  Due to noise and sensitivity of instrumentation it is not always possible to deduce the exact shape and size of the anomaly. 20 Mussett and Khan, 2000

21. Modelling Modelling complications:  The resolution of the grid of stations is not sufficient to reveal all the details of the signal.  There are theoretical and practical limits on resolution. 21 Mussett and Khan, 2000

22. Resolution The resolution and configuration of the grid of stations play an important role to reveal all the details of the signal: 22 Reynolds, 2011

23. Inverse and Forward Modelling Inverse modelling – deduction of the form or properties of a target or structure from measured data or anomaly. Forward modelling – the model is guessed usually using a computer:  The readings are produced from simulations.  They are compared with observations.  The model is adjusted until it matches the results sufficiently well. 23

24. Forward Modelling A structure is guessed based on different methods. The paths of rays leaving the source are traced through the structure. The synthetic travel-time diagrams and seismograms are compared with observed ones. 24

25. Geological Interpretation The major goal is to translate the physical model into geological terms. At this stage all available information has to be taken into account:  Geological context;  Outcrop information;  Boreholes;  Geophysical surveys; 25

26. The Kenya Rift The Kenya Rift International Seismic Project (KRISP). 7 profiles (A-G) were used to obtain the data. 200 portable recorders were used. 26 Mussett and Khan, 2000

27. The Kenya Rift Reduced travel-times for line G. 27

28. The Kenya Rift Refraction seismology studies of Kenya Rift zone. (a) Ray traces according to the velocity model. (b) Travel times calculated from the model and observed ones. (c) The final model taking into account all the arrivals from all shots. 28 Mussett and Khan, 2000

29. The Kenya Rift Crustal structure of the Kenya Rift zone. 29 Mussett and Khan, 2000

30. Visualization Results of the analysis can be presented in different forms:  2-D plots and graphs; Contour plots; etc.  3-D plots; Isometric projections; Grid meshes; Density plots; etc. 30 Sigma software provides an integrated platform for seismic interpretation and geologic modeling.

31. Visualization To visualize spatial data contouring can be employed. To enhance contouring shading, color, and false illumination is used. Isometric projection is used when presenting 3-D data. 31

32. Visualization A block of subsurface from the Forties oil field in the North Sea. The layers are revealed by their porosity. The low porosity layers act as barriers to upward migration of oil. Any oil will be trapped below those layers. 32 Mussett and Khan, 2000

33. Visualization A single layer from the previous figure. Its highest-porosity parts are due to sandy channels. 33 Mussett and Khan, 2000

34. Visualization 3-D surveying is also can be done by repeating at different times to monitor the oil extraction. This is called time-lapse modelling. 34

35. Data Processing Data processing is used to emphasize and enhance wanted anomalies or signals. Data processing uses mathematical methods such as:  Fourier analysis;  Wavelet analysis;  Digital filtering;  etc. 35 Promax software

36. Data Processing Time section before and after migration. Elimination of the diffraction produces a better image of structures at depth. 36

37. Data Processing The re-processed seismic data for the Vlaming Sub-basin in the offshore southern Perth Basin. The re-processed seismic data has significantly improved the quality of seismic data through providing a higher frequency content, enhanced structural features, enhanced stratigraphic detail, etc. 37

38. Reading for the next Lecture Mussett, A.E. and Khan, M.A., pages 24-28. 38