Active Source Seismology

Signal Enhancement through Data Collection Techniques

Migration: Recreating a true reflector

Examples of migrated sections

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:

* = “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] = […..]

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.

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