Land acquisition: sources, receivers, geometry

Part 1 — Acquisition & the data we process

Learning objectives

Name the two main land seismic sources (vibroseis sweep and dynamite impulse) and describe the trace each produces
Explain why vibroseis recordings are useless until you cross-correlate with the pilot, and what the “effective wavelet” becomes after correlation
Read a land acquisition geometry (shot lines, receiver lines, group interval) and recognize common patterns
Distinguish geophones from accelerometers and understand why group arrays attenuate ground roll

Part 0 built the math. Part 1 walks onto the field. Every processing step later depends on what the acquisition crew did, so we spend five sections making sure we understand the data before we try to improve it.

1. Two sources you will meet

Dynamite. A buried explosive. The source wavelet is approximately an impulse in time, short and broadband. The recorded trace looks almost directly like the convolutional model: wavelet ∗ reflectivity + noise. Used in rugged terrain, jungle, swamp — anywhere a vibrator truck cannot drive or would shake things it should not.
Vibroseis. A truck with a baseplate that presses on the ground and sweeps through a range of frequencies over several seconds. The source is a long sinusoidal chirp, not an impulse. The recorded trace looks like a smeared-out mess — because it is the earth’s reflectivity convolved with a multi-second sweep.

Vibroseis is the dominant land source today. Safer, cheaper per shot, far better environmental footprint. But it comes with one catch: the recording is not usable until you correlate it with the pilot sweep.

2. The correlation trick

Cross-correlating the recorded trace r(t) with the pilot sweep p(t) gives

s(t) \;=\; r(t) \star p(t) \;=\; [p(t) \ast h(t)] \star p(t) \;=\; [p \star p](t) \ast h(t)

where h(t) is the earth impulse response and ⋆ denotes cross-correlation. The result is the earth impulse response convolved with the autocorrelation of the sweep. For a clean, long sweep, that autocorrelation is a short Klauder pulse — essentially a broadband wavelet — and the correlated trace looks like an impulsive-source recording after all.

Longer sweep → larger cross-correlation gain → better SNR. Broader sweep bandwidth → shorter Klauder pulse → better temporal resolution. The widget below makes this trade-off tangible.

Move the sliders. Watch how:

Making the sweep longer grows the autocorrelation’s peak — more SNR, same wavelet shape.
Making the sweep broader (bigger f₂ − f₁) sharpens the autocorrelation — shorter wavelet, better resolution.
The amplitude spectrum stays flat inside the sweep band and drops off outside — you cannot see what you did not sweep through.

3. Receiver: the geophone

A geophone is a coil wound around a magnet on a spring. When the ground moves, the coil moves relative to the magnet, inducing a voltage proportional to velocity. Natural frequency typically 4–10 Hz; response is flat above, rolls off below. For frequencies under 5 Hz you need a special low-frequency phone or a broadband accelerometer.

Real surveys group several geophones (6 to 24) at each recording station. The group acts as a spatial array: vertical signal from directly below adds coherently, horizontal ground roll sees a phase gradient across the group and cancels. This is the first noise attenuation in any land data flow — done in hardware, before a single byte is recorded.

4. Survey geometry terms

Shot point (SP). Where a source fires. Usually on a regular grid (shot line + shot interval).
Receiver station (RS). Where a group of geophones records. Also on a regular grid.
Group interval. Spacing between receiver stations. Sets the spatial Nyquist — from §0.5.
Source-receiver offset. Horizontal distance between SP and RS. Decides how wide the move-out hyperbolas stretch.
Spread. All active receivers recording one shot. Modern 3D has 1,000–10,000+ channels per shot.
Template. The relative shot ↔ receiver pattern that “rolls” across the survey as you shoot.

5. What “3D” actually means

A 2D line lays shots and receivers along one profile. You get a 2D section under that line, nothing to the side. Cheap, sparse, lots of assumptions.

A 3D survey lays receivers and shots on a grid, producing a full 3D data cube. You sample cross-line variation, you can image 3D structures, and you can migrate properly. Every capstone in Parts 5–10 of the Interpretation course was 3D.

6. What matters downstream

For a processor opening a new dataset, the acquisition facts that change your workflow are:

Source type. Vibroseis → correlate first, then everything else. Dynamite → skip correlation.
Record length. Vibroseis records are sweep-time longer; processing cuts to “listening time” after correlation.
Geometry regularity. Regular grids make migration straightforward; irregular surveys need interpolation or regularization (covered in Part 4 and Part 9).
Spread pattern. Sets offset and azimuth distributions, which decide AVO, 4D, and anisotropy feasibility.

**The one sentence to remember**

Vibroseis trades an impulsive source for a long sweep plus a correlation step; the effective seismic wavelet everyone else sees downstream is the autocorrelation of that sweep.

Where this goes next

Section §1.2 looks offshore. Airguns, streamers, OBN, ghost notches — a different physics and a different set of processing challenges.

References

Sheriff, R. E., Geldart, L. P. (1995). Exploration Seismology (2nd ed.). Cambridge UP.
Yilmaz, Ö. (2001). Seismic Data Analysis (2 vols.). SEG.
Claerbout, J. F. (1976). Fundamentals of Geophysical Data Processing. McGraw-Hill.