Data Acquisition. Chapter 2

Transcription

1 Data Acquisition Chapter 2

2 1 st step: get data Data Acquisition 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

3 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

4 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.

5 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

6 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.

7 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

8 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

9 GPS Station Motions Model Types 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

10 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 1994

11 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

12 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/cm 3 ) Air ~0 Water 1 Sediments Sandstone Shale Limestone Granite Basalts Metamorphic Rocks