Procesamiento sísmico

Convetional Seismic data processing

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Yilmaz (2001)

1 Pre-processing	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.	Démultiplexage
	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.).	Reformatage
	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.	Édition
	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.	Correction de la dispersion géométrique
	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/	Géométrie de terrain
	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.	Corrections statiques
2	Deconvolution and trace balancing	Deconvolution makes the signal look better by increasing the temporal resolution and removing echoes. Trace balancing makes the amplitude uniform.	Déconvolution et normalisation des traces
3	CMP sorting	Sorts the data into CMP gathers, so it can be corrected for the NMO and stacked after that (see step 7).	Triage par CMP
4	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).	Analyse de vitesse
5	Residual statics corrections	Counts for near-surface velocity variations that causes some static and dynamic distortion problems.	Corrections statiques résiduelles
6	Velocity analysis	(see step 4). If we have residual statics problems, we do velocity analysis after we count for them.	Analyse de vitesse
7	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 NMO
			Correction DMO
			Correction NMO inverse
			Analyse de vitesse
			Correction NMO
8	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	Sommation
9			Déconvolution
10			Blanchiment de spectre à temps variable
11	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.	Filtre à temps variable
12	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.	Migration
13	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.	Gain