/Exercise-8

Exercise 8: Coding and visualizing geostatistics

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Exercise 8: Coding and visualizing geostatistics

In this week's exercise we will take our first steps toward learning how to convert equations into Python code, and visualizing some geochronological data. We will be using Python tools that are already familiar to us, but applying them in a slightly different way than in the earlier exercises.

This exercise is due by the start of class on 6.11. Late submissions will lose 25% of the total points per day late unless discussed with Dave or Jorina in advance of the due date.

Problem 1: Converting math to Python

One of the goals of this part of the course is to develop your quantitative geoscience skills, including learning how to convert mathematical equations to Python code. Doing this allows you to explore how various equations work and produce useful data plots or predictions, something increasingly done by geoscience professionals.

  • For this problem, you should create Python functions for the following common statistical equations:

    • The mean or average, calculated using a function you should call mean().

      Mean value equation
      Equation 1. The mean value, where xi is a value to be included in the mean calculation and N is the total number of values to average.

    • The standard deviation, calculated using a function you should call stddev().

      Standard deviation equation
      Equation 2. The standard deviation.

    • The standard deviation of the mean or standard error, calculated using a function you should call stderr().

      Standard error
      Equation 3. The standard error.

    • Of course, you should not use existing Python functions in the functions you create, other than perhaps a function for calculating the square root.

  • Each of the functions should be part of a Python script called geo-stats.py.

  • Within the geo-stats.py file, include tests that demonstrate your code is working properly using the ages measured for minerals in the five geochronological samples below.

    Sample Subsample ID Age [Ma]
    F09 F09-1 2.01
    F09-2 1.95
    F09-3 2.38
    F09-4 2.3
    F09-5 2.0
    BH63 BH63-1 4.77
    BH63-2 5.11
    BH63-3 3.30
    BH63-4 3.34
    BH63-5 4.45
    BH161 BH161-1 8.8
    BH161-3 2.15
    BH412 BH412-1 4.74
    BH412-2 5.14
    BH412-3 5.14
    BH412-4 5.5
    BH412-5 5.1
    BHF04 BHF04-1 2.21
    BHF04-3 5.1
    BHF04-4 2.93
    BHF04-5 4.69

Table 1. Apatite (U-Th)/He thermochronometer ages from Coutand et al. (2014)[1].

  • To input the data you can simply create lists of the ages, rather than reading a file.
  • Your tests should use your three functions to calculate the mean, standard deviation, and standard error for each set of ages.
  • In addition, you should use the NumPy functions np.mean(), np.std(), and np.sqrt() to calculate the mean, standard deviation, and standard error values for each age list, and compare those values to the values found using your functions.
  • The calculated values can be output to the screen using the print() function.

Questions for Problem 1

  1. How much do the standard deviation values vary among the samples in Table 1, and what does this tell you about the measured values?
  2. Do you observe a large difference between the standard deviation and standard error values? Is it clear why you should always indicate whether reported values are standard deviations or standard errors?
  3. Did you observe any descrepancy between the values calculated in your functions and those found using NumPy? If so, what is the cause of the difference(s)?

Problem 2: Visualizing uncertainty

In this problem we will continue to develop our Python mathematical and plotting skills by visualizing the calculated sample geochronological ages from Problem 1 using the normal distribution. The normal distribution is a mathematical function with a bell shape, also known as the Gauss function or Gaussian. This function is centered on the mean value of a given set of values, where its value is greatest, and its value decreases away from the mean in what is hopefully a familiar form. Your tasks in the exercise are to:

  1. Use the mean and standard deviation values calculated in Problem 1 to calculate the normal distribution for each of the five samples in Table 1. Note that we are explicitly assuming here that the uncertainty is symmetric and follows a bell-shaped distribution about the mean.

    • Since you will use the values and functions from Problem 1 in this problem, I suggest you create a copy of your code from Problem 1 and save it in your exercise repository as geo-stats-problem2.py.

    • The mathematical definition of the normal distribution function is

      The normal distribution
      Equation 4. The normal distribution, where e is the exponential function and x is the value for which the normal distribution is calculated.

      Note that we are assuming in this case that the value of x in Equation 4 is a geological age, and x-bar would be the mean age of the sample.

    • You should convert this equation to a Python function called Gaussian(), and save it in your geo-stats-problem2.py file.

    • The ages for which the normal distribution should be calculated are from 0-10 Ma in increments of 0.1 Ma. In other words, at each of those ages you will calculate the value of the normal distribution for each sample.

  2. Plot the resulting five normal distributions using Matplotlib.
    • The normal distributions should be plotted using different colored solid lines with the plt.plot() function.
    • The ages of the individual mineral ages should also be plotted as colored points along the bottom of the plot (i.e., at y = 0).
    • The mean age with its standard deviation should also be plotted at the maximum value of each normal distribution using the plt.errorbar() function. The error bars should be symmetric, at the mean value, and with the value of the standard deviation as a horizontal error bar.
    • You should include a plot legend for the normal distribution lines, and use the same colors for each sample.
    • Include axis labels and a title.
    • All plotted values should be on the same plot, a copy of which should be included in your answers below.

Tasks/Questions for Problem 2

  1. Add a copy of the plot your code produces in the answers below, and include a proper figure caption that describes the plot as if it were in a scientific journal article.
  2. In 1-2 paragraphs, discuss the following:
    • How does the Gaussian distribution change for different values of the standard deviation?
    • Using this method of visualization, is it clear which samples have larger or smaller random errors?
    • How much variation in age is observed in ages measured from the same sample? Does this amount of variation concern you?

Hints

If you get stuck, have a look at the hints for this week's exercise.

Footnote(s)

[1]: Coutand, I., Whipp, D.M., Grujic, D., Bernet, M., Fellin, M.G., Bookhagen, B., Landry, K.R., Ghalley, S.K. and Duncan, C., 2014. Geometry and kinematics of the Main Himalayan Thrust and Neogene crustal exhumation in the Bhutanese Himalaya derived from inversion of multithermochronologic data. Journal of Geophysical Research: Solid Earth, 119(2), pp.1446-1481

Answers

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