Data Acquisition
Chapter 2
Data Acquisition
• 1st step: get data
– Usually data gathered by some
geophysical device
– Most surveys are comprised of
linear traverses or transects
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Typically constant data spacing
Perpendicular to target
Resolution based on target
Best for elongated targets
– When the data is plotted (after
various calculations have been
made): Profile
Grids
• When transects are combined
a grid can be formed.
– Good for round or blob-shaped
targets
• Or if target geometry is unknown
– Useful for making contour
maps
– Allows transects to be created
in multiple directions
Data Reduction
• Often the raw data collected is
not useful.
– Data must be converted to a useful
form
• Removing the unwanted signals in
data: Reduction
• Targets are often recognized by
an “anomaly” in the data
– Values are above or below the
surrounding data averages.
• Not all geophysical targets
produce spatial anomalies.
– E.g. seismic refraction produces
travel time curves  depth to
interfaces
• Also a type of reduction.
Signal and Noise
• Even after data is reduced, a
profile may not reveal a clear
anomaly due to noise.
– Noise: Unwanted fluctuations in
measured data.
• May be spatial or temporal
• What causes noise?
– Signal: The data you want, i.e. no
noise.
• Noise can be removed using
mathematical techniques
– Stacking
– Fourier Analysis
– Signal Processing
Magnetic or Gravity profile
Stacking
• Stacking is useful when:
– Noise is random
– Signal is weak
– Instrument is not sensitive
• If noise is random
– Take multiple readings
– Sum the readings
– Noise cancels out
• Destructive Interference
– Signal should add
• Constructive Interference
• Stacking improves signal to
noise ratio
– Commonly used with numerous
techniques.
Resolution
• Even if you have a good
signal to noise ratio,
detection of your target
depends on your
resolution.
– Know what you are looking
for before you begin
– Know the limits of your
data resolution
Modeling
• Most geophysical data is
twice removed from
actual geological
information
– Reduced data is modeled
• Models
– Aim to describe a specific
behavior or process
– Are only as complex as the
data allows
• Occam’s Razor: “Entities
should not be multiplied
unnecessarily”
Model Types
GPS Station Motions
Depth = D
Fault Slip
• In the most basic sense models come in two flavors:
– Forward model
• Given some set of variables, what is the result.
• I.e. you input the “cause” and some “effect” is produced
– Inverse model
• Given some measurements, what caused them
• You know the “effect”, try to determine the “cause”
• Often involves mathematical versions of “guess and check”
Model Types
• Models also come in several flavors
based on technique
Analog Model
– Conceptual Model
• Models an idea…no math/physical parts
– Analog Model
• A tangible model “scaled” to reproduce
geologic phenomena
– Empirical Model
• Based on trends in data
Empirical Model
– Analytical Model
• Solves an equation
• Usually deals with simple systems
– Numerical Model
• Computer-based approximations to an
equation.
– Thousands, millions, or billions of
calculations
• Can handle complex systems.
From Wells & Coppersmith 1994
Non-Uniqueness of Models
• Typically, multiple models
can fit data
– So any given model is nonunique
– Distinguish between models
based on
• Match with geologic data
• Model with least
parameters (most simple)
• Data has limited resolution
– Surveys must be finite
– “Blurs the picture”
– Omission of detail
emphasizes key features
Geologic Interpretation
• After data is collected and
modeling is complete the
results must be interpreted
into the geological context.
• Use all available data.
– Don’t only look, when you can hear
and touch!
• Interpretations are also typically
non-unique
– Many geologic materials have similar
properties.
– Best interpretations use all available
data, geologic, geophysical, chemical,
etc…
Material
Density (gm/cm3)
Air
~0
Water
1
Sediments
1.7-2.3
Sandstone
2.0-2.6
Shale
2.0-2.7
Limestone
2.5-2.8
Granite
2.5-2.8
Basalts
2.7-3.1
Metamorphic
Rocks
2.6-3.0