Data Acquisition - Seismology - Lecture Slides, Slides of Geology

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Data Acquisition

Chapter 2

Data Acquisition

• 1 st^ step: get data
  • Usually data gathered by some

geophysical device

• Most surveys are comprised of

linear traverses or transects

• 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

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

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

• Models also come in several flavors

based on technique

• 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
• 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.

Analog Model

Empirical Model

From Wells & Coppersmith 1994Docsity.com

Non-Uniqueness of Models

• Typically, multiple models

can fit data

• So any given model is non- unique
• 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