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
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. Magnetic or Gravity profile • Noise can be removed using mathematical techniques – Stacking – Fourier Analysis – Signal Processing
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 Analog Model • 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 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. From Wells & Coppersmith 1994
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
Geologic Interpretation • After data is collected and modeling is complete the results must be interpreted into the geological context. • Use all available data. Density (gm/cm 3 ) Material Don’t only look, when you can hear – Air ~0 and touch! Water 1 Interpretations are also typically • non-unique Sediments 1.7-2.3 Many geologic materials have similar Sandstone 2.0-2.6 – properties. Shale 2.0-2.7 Best interpretations use all available – Limestone 2.5-2.8 data, geologic, geophysical, chemical, etc… Granite 2.5-2.8 Basalts 2.7-3.1 Metamorphic 2.6-3.0 Rocks
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