Procesamiento sísmico

Convetional Seismic data processing

---

Yilmaz (2001)

1 Pre-processing	Démultiplexage	Demultiplexing: Orders seismic data by seismic trace instead of being ordered by time. Multiplexed recording is still found in older data or in current data shots with old recording instruments.
	Reformatage	Reformatting (SEG-Y/SEG-D) Puts the data in a format that is understandable by computers (e.g. SEG-Y format, SEG-D format, etc.).
	Édition	Seismic data edition Searchin for noisy, monofrequency and incorrect polarities traces. examines seismic data for bad, noisy, and\or monofrequency traces, and deletes them, and also spots any traces with incorrect polarities and corrects them.
	Correction de la dispersion géométrique	Geometrical spreading correction It is also called amplitude correction. Because the signal becomes weaker as it moves away from its source (i.e. its amplitude decreases with time), amplitude correction is needed to count for this lose, and to make amplitudes stronger.
	Géométrie de terrain	Set-up of field geometry – Geometry QC Incorporates field geometry with seismic data processing. Coordinates, shot\ receiver locations and spacing must be entered to the system carefully and precisely because processes like CMP sorting, for example, highly depend on this. http://radexpro.com/scope/qc/
	Corrections statiques	Application of field statics corrections This is needed for land data collected on non-flat areas. It reduces the travel times into a common datum level. The datum could be the sea level or any other local datum.
2	Déconvolution et normalisation des traces	Deconvolution and trace balancing Deconvolution makes the signal look better by increasing the temporal resolution and removing echoes. Trace balancing makes the amplitude uniform.
3	Triage par CMP	CMP sorting Sorts the data into CMP gathers, so it can be corrected for the NMO and stacked after that (see step 7).
4	Analyse de vitesse	Velocity analysis Gives info about velocities in the subsurface layers. It finds the stacking velocity (very close in value to the RMS velocity) that best fits our data (This step may be delayed after step 5).
5	Corrections statiques résiduelles	Residual statics corrections Counts for near-surface velocity variations that causes some static and dynamic distortion problems.
6	Analyse de vitesse	Velocity analysis (see step 4). If we have residual statics problems, we do velocity analysis after we count for them.
7	Correction NMO	NMO Correction Counts for the increase in travel time with increasing offset distance. This increase makes flat reflectors look dipping, and makes dipping reflectors look even more dipping. The amount of correction needed decreases with depth, so that shallower reflectors get more “stretched” than do the deeper ones. Muting: It is just a fancy word for deleting a part of a trace. Muting a whole trace is called “killing”.
		Correction DMO
	Correction NMO inverse
	Analyse de vitesse
	Correction NMO
8	Sommation	Stacking After sorting the data into CMP gathers and applying the NMO correction, reflectors line up nicely and hence their stacking gives a stronger signal. Multiples\ random noises do not line up. Stacking increases S/N ratio by decreasing multiples\ random noise from the data which enhances the overall quality. It also reduces the seismic data volume to the plane of the seismic section
9	Déconvolution
10	Blanchiment de spectre à temps variable
11	Filtre à temps variable	Time- variant band-pass filtering Filters unwanted “signals” based on their frequencies. For example, ground roll has a lower frequency (and higher amplitude) compared to the rest of the section, so we can filter it out based on that fact.
12	Migration	Migration So far, each trace is plotted under its CMP location which puts reflectors in the wrong subsurface location. Migration process moves those reflectors into their true subsurface locations, and that improves lateral resolution. Migration also collapses diffractions into identifiable points on the seismic section.
13	Gain	Gain recovery Seismic energy gets lost in many different ways (e.g. scattering, frication, etc.). Gain is the inverse function of energy loss. It is very hard to predict the attenuation function because primary reflections, multiple reflections, and random noise have different decay functions. Instead, we examine (test) different gain values, and see which value gives a better looking data.