1. Active Source Seismology

2. Signal Enhancement through Data Collection Techniques

3. Migration: Recreating a true reflector

4. Examples of migrated sections

5. Useful Signal Processing Concepts
Digital Signals and the Nyquist Frequency:
For any bandlimited time series (and all real signals are bandlimited), ALL the information in that time series can be represented EXACTLY as long as we sample at a rate that is greater than or equal to 2 x the minimum period.
Stacking:
Sum all records that have something in common. The coherent part will gain over the noise like sqrt(N).
Convolution:
A way to calculate an output of certain types of filters (LTI filters) from a given input, assuming you know the impulse response of that filter:

6. * = “stick-gram” r1V1 r2V2 r3V3 r4V4 r5V5 Source wavelet (Ricker)
Synthetic seismogram =
-0.25
0.25
Reflectivity series
wavelet
[0,0,0,0.15,0,0,0,0.3,0,0,0,0.1,0,0,0,-0.1] * [0,1,0,-2,0,1,0] = […..]

7. Deconvolution:
Removing the effects of convolution. Try turning an arbitrary input signal into a spike. Sometimes this works, but sometimes you try to get something for nothing.
Correlation:
A way to quantify “similarity” but also a cool way to mimic a spike, as we will see:
Chirp Signal:
A clever way to capture the essence of a why a spike is so great – it’s got a very wide bandwidth! The seismic technique that uses a chirp is called VIBROSEIS.
Minisosie:
Uses the same idea as a chirp but uses a random signal instead.

8. Vibroseis chirp A Vibroseis Truck Minisosie in action
Vibroseis creates a chirp signal
Must be removed to yield data
Creates a “Klauder” wavelet
Vibroseis chirp
A Vibroseis Truck
Minisosie in action