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Python-based tool for calculating slip rates w/ uncertainties from PDFs of age and offset for geologic features cut by faults.
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Slip Rate Calculator


The Slip Rate Calculator is a tool that takes probability distributions for age and offset distance for geologic (or other) features cut by a fault and calculates the probability distribution of the fault's slip rate via Monte Carlo methods. The idea is to use empirical estimates of the probability of the ages and offsets directly from field observations and analytical measurements, rather than canned PDFs (uniform, Gaussian, etc.), when possible. It's not always, though, so some canned PDFs are supported.

Typically, the offset features (called offset markers) show cumulative offset, i.e. all the offset markers that are present on a fault at a given time interval experience the same offset amount over that time.

Sample output from linear slip rate estimation

The Slip Rate Calculator can also calculate slip rates that change through time, either by fitting a piecewise line (with a specified number of segments) or a cubic spline. The piecewise fitting routine also includes a statistical test (based on the Bayesian Information Criterion) to determine whether there was a slip rate change at some point in the past.

Sample output from 2-piece linear slip rate estimation (data from Lifton et al., 2015.)

The Slip Rate Calculator is MIT licensed.


The Slip Rate Calculator is a Python 3 application. It depends heavily on the core Python scientific stack, as well as PyQt4. The easiest way to get everything running is to install the free Anaconda Python 3.5 scientific Python distribution. It doesn't interfere with your system Python, or any other Python versions you may have on your computer.

Then, either download a release if you don't have or don't want to use git, or (if you want easy updates) clone the git repository:

git clone

Note: PyQt is now at version 5 in Anaconda's repository, which is incompatible. The easiest way to install a functioning Python environment is to use Anaconda's environment system. From the slip_rate_calculator directory, with Anaconda or Miniconda installed, type in the terminal:

conda env create -f slip_rate_calculator_env.yml

and follow the directions in the terminal.

To activate the environment to be able to run the Slip Rate Calculator, type:

source activate slip_rate_calculator

if you're on Linux/MacOS, or

activate slip_rate_calculator

if you're on windows.

The Slip Rate Calculator (which does need a more clever name) itself doesn't need to be installed. Instead, go into the slip_rate_calculator/app directory and run python SlipRateGUI.pyw or ./SlipRateGIU.pyw.

Note: Because the Slip Rate Calculator has an embedded IPython console, it can't be run using IPython.


The actual calculator uses numpy, scipy and pandas in addition to the standard Python library. The GUI uses PyQt4 and IPython as well. It may work with Python versions other than 3.4 (e.g. 2.7) but has not been tested.

Basic Theory and Implementation

The Slip Rate Calculator is based on the formulation of fault offset as a function of time:

offset = ƒ(age)

Therefore, the slip rate (i.e. the rate of fault offset) is also a function of time, i.e. the first time derivative of ƒ:

slip rate = ƒ'(age) = ∂ƒ / ∂ age

This fault offset function ƒ can take a variety of forms:

  • linear
  • piecewise linear
  • cubic spline (i.e. continuously-varying)

This formulation of slip rate is both flexible (allowing for the different forms of ƒ) and very tractable computationally, because it makes the calculation into a regression problem. Additionally, the regression framework makes it much more simple to incorporate multiple offset markers than other, convolution-based slip rate calculators (Bird, 2007; Zechar and Frankel, 2009), particularly when considering slip rate changes in time.

Implementation details

PDF sampling

Sampling of the PDFs for age and offset are done through different methods depending on the type of PDF. The canned Gaussian, uniform, etc. PDFs are sampled using NumPy's stock algorithms.

The arbitrary/empirical PDFs are sampled using an inverse transform sampling algorithm, where the points of the PDF are linearly interpolated between, and then are made into a empirical cumulative distribution function, to which the inverse transform sampling algorithm is applied.


Currently, only linear and piecewise-linear fitting is supported. Linear fitting is perfomed as a Linear Least Squares (LLS) regression with the x-intercept fixed at the zero offset age, which is the age at which no offset occurs (i.e. the present for active faults).

Piecewise-linear fitting is a nonlinear regression problem if the x coordinates (the ages) of the slip rate changes, called breakpoints, are not known. If they are known, it is a LLS problem. The Slip Rate Calculator employs a Monte Carlo method to deal with the nonlinearity: Some number (e.g. 20) of breakpoints are randomly sampled from a uniform distribution between 0 years and the age of the oldest offset marker. Then, a piecewise fit is performed given each of the breakpoints, and the best least squares fit is chosen. Tests have shown this is fast and yields better results than typical nonlinear inversion routines, because the sparsity of data yields a very ill-posed problem for e.g. gradient descent methods. Additionally, some rate change penalization can be applied, because for certain situations the goodness of fit between two fits can be identical even though one calls for a much more radical change in slip rate, like in the following situation (forgive the bad art):

  | \
  |  \

(o = slip rate change
 x = data point)

Here, the diagonal fit produces a much more geologically reasonable slip rate change, because the other fit (basically vertical) is like going from 0 to infinite slip rate, even though both fit the data points perfectly. Having some slip rate change penalization helps here.

Note that the Monte Carlo iterations described in the above paragraph are not the same as used for the whole slip rate calculation. They're more like an 'inner loop'.

Handling of multiple offset markers

Because the Slip Rate Calculator is based on regression, multiple offset markers are treated very naturally, as multiple data points. However, there are a two different ways the data may be handled, which are appropriate for different geological scenarios:

  1. The data are from the same location and show cumulative offset, i.e. the slip rate at any time is the same for all offset markers that exist at that time. In this instance, barring slip changes on the fault, older markers should always be more offset than younger markers. In this case, Force increasing should be set in the GUI. When this option is set, for each Monte Carlo iteration, it is ensured that the older data point is more offset than the younger data point, but the PDFs themselves are not 'trimmed' pre-sampling (which is statistically inappropriate, as that would force the PDFs into two separate, independent, non-overlapping PDFs instead of the actual overlapping, but mutually conditional PDFs).

  2. The data are from the same fault or fault zone, but may be from different places along strike, where it is possible that slip rate may be spatially variable. In this case, there is no a-priori requirement that older samples must be more offset than younger samples, so Force increasing does not have to be set. The Slip Rate Calculator would then calculate a best-fit slip rate considering all of these data.

Basic Usage

Using the Slip Rate Calculator is fairly simple, at least relative to collecting the data. The GUI has three components: An interactive table for data entry, a box with various field and buttons and stuff for configuring and running the calculations, and an IPython console at the bottom.


Basically, the data and metadata for each offset marker should be added to the table, then the options for the run configuration should be set, and then the Run button should be pressed. Once the run is finished, some results and statistic will be displayed in the IPython console at the bottom. At this time, the user can hit the Plot button and plots of the results will appear.

All data and run configuration settings can be imported, modified (in or out of the GUI) and exported; settings are stored internally as Python dicts and externally as JSON files. This is really helpful for reproducing the runs, as well as entering long lists of numbers (for arbitrary PDFs of age or offset).

Though the IPython console is interactive and fully featured, nothing needs to be typed into it.

Data Entry

This is a table where the offset markers are added. Each row is an offset marker. There are 11 fields for each offset marker: 1 for the name, and then 5 for the age data and 5 for the offset data. The fields for age and offset are very similar, differing mainly in their units.

The fields can be added or deleted with the + and - buttons. The data should be layed out in youngest (top) to oldest (bottom) order. Eventually the ^ and v buttons will allow the rows to be moved around but that isn't implemented yet.

Field Data type Units Options Example
Name String (none) (none) T1
Age Numeric time Number or list of numbers 10 or [23.,27.5,29.2]
Age_Type String (none) mean, median, list median
Age_Err Numeric time Number or list of numbers 7 or [0., 0.53, 0.2]
Age_Err_Type String (none) sd, mad, minmax, probs minmax
Age_Units Numeric time ka (for now) ka
Offset Numeric distance Number or list of numbers 10 or [23.,27.5,29.2]
Offset_Type String (none) mean, median, list list
Offset_Err Numeric distance Number or list of numbers 7 or [0., 0.53, 0.2]
Offset_Err_Type String (none) sd, mad, minmax, probs probs
Offset_Units String distance m (for now) m


Name : The name of the offset marker (typically the geologic unit or other descriptor)

Age : The age of of the offset marker. If this is a single value, it is the 'central' or most-likely age (mean or median). If this is a range of values, it needs to be of Age_Type==list and the Age_Err also needs to be a list of the same length.

Age_Type : What the value in the Age field represents.

Age_Err : The error (uncertainty) of the Age measurements. Needs to be the same length as Age; i.e. if Age is a scalar, then Age_Err does too, and if Age is a list, then Age_Err needs to have the same number of elements. If these are lists, Age_Err is the scaled probability of the corresponding element in the Age list. These probabilities are basically on the same arbitrary and don't have to add up to 1 or any other value; they could all be on a 0-10 scale or whatever. 0 always means 0 though.

Age_Err_Type : The type of uncertainty given by the Age_Err field. This can be sd for the standard deviation (to be used with Age_Type==mean), mad for the median absolute deviation (to be used with Age_Type==median), minmax for the half-width of a uniform distribution (where the center of the distribution is specified by the Age, with Age_Type==mean).

Age_Units: This should be self-explanatory. Currently the everything is in thousand years (ka). Later, other units will be allowed.

Offset fields are exactly the same as ages.

Choosing and specifying PDFs

Arbitrary PDFs

This is one of the major reasons for using this tool instead of others. Most of the time, the best estimates for the probablity distributions of the age or offset of an offset marker are not uniform or Gaussian PDFs. The offsets should be empirically specified based on mapping, trenching or remote sensing, using the geologist's knowledge and intuition. The ages should come directly from analytical measurements (or the like) and may also be truncated by cross-cutting relationships or whatever other constraints.

To use an arbitrary PDF, you want ot make a list of x values and a list of y (or p(x)) values. For a trapezoid of offset values, you can do something like this:

Offset = [3., 5., 9., 9.01]

Offset_Err = [0., 4., 4., 0.]

which would correspond to a PMF that looks like this:

4      *-------*
3     /        |
2    /         |
1   /          |
0  *           *
   3 4 5 6 7 8 9

In this case, the Offset_Type should be list and the Offset_Err_Type should be probs.

Standard PDFs

For a uniform PDF, use a scalar Age or Offset value, Age_Type or Offset_Type set to mean, Age_Err or Offset_Err set to the distance between the middle (mean) and either side of the PDF (i.e. half the uniform PDF width), and the Age_Err_Type or Offset_Err_Type to minmax.

For a Gaussian PDF, use a scalar Age|Offset value, Age|Offset_Type==mean, a scalar Age|Offset_Err value, and Age|Offset_Err_Type==sd (standard deviation). Because you do not have a time machine for fieldwork, the Gaussian PDFs for Age will be truncated at 0, meaning no ages from the future will be allowed. Offsets are allowed to have negative tails, as uncertainty in slip direction is somewhat more common than rocks from the future.

For a Laplacian PDF (should you require one), use a scalar Age|Offset value, Age|Offset_Type==median, scalar Age|Offset_Err, and Age|Offset_Err_Type to be mad (Median Absolute Deviation). Trimming of negative ages isn't currently implemented, mostly because of disuse.

Run Configuration

This is pretty straightforward.

Fit Type

Options are Linear, Piecewise Linear, or Cubic Spline.

Linear fitting is simply a least-squares regression line through each set (iteration) of samples from the data.

Piecewise Linear is a continuous set of line segments that fit the data. Each adjacent pair of line segments is separated by a breakpoint, when the slip rate change occurs.

Mathematically, the number of linear segments is constrained to be equal or smaller than the number of data points (not counting the zero_offset point). This number is set in the field to the right of the Piecewise Linear button. Currently, only two points are supported. The fitting algorithm itself doesn't have this constraint, but the plotting and some other statistics functions are not yet as flexible.

The Piecewise Linear fitting is a nonlinear problem and is substantially (~25x) slower than the Linear fitting. Nonetheless, it is still reasonably fast. For example, it should take about 3-4 seconds on a modern laptop to do a fitting with 1000 iterations.

Cubic Spline fitting is not implemented yet, but is basically an exact spline-based fit to the data. Because splines can fit exactly, there is no error associated with the splines. They're perfect! So this will have some different uses, for example tying geodetic, neotectonic and geologic data over different orders of time and displacement magnitude, where the data are sparse and widely-spaced enough that no linear fit is required or expected to fit.

Run Settings

Iterations: This is how many Monte Carlo iterations are done. In each iteration, an age and an offset is sampled from the constructed PDFs, and a line is fit. Depending on the complexity of the system and the desired type of fit, somewhere between 1000 to 10,000 iterations is useful and quick (a second to a minute on a modern computer). For publication, 100,000 iterations will probably be on the overkill side of acceptable. (Note that some of the runs will produce too bad of fits and will be rejected, especially with Piecewise Linear fitting).

Zero offset age: This is the (younger) age at which no offset occurs. I.e. for an active fault, it would be 0. For a Cretaceous fault, it could be 60,000 (data in ka). Currently no uncertainty used in this estimate, although this will change in the future.

Random Seed: This is an option to set a 'random seed', which is a value used to get a predictable sequence of pseudorandom numbers. The reason for doing this is that it makes Monte Carlo simulations and other stochastic or random-number based methods completely reproducible. Note that for our purposes, these pseudorandom numbers are definitely random enough for statistical robustness. This option is set through a check box, and a numerical value to be used is typed in the field.

Force increasing data: This ensures that the ages and offsets for each iteration are monotonically increasing. It does not ensure that the best-fit rates will be everywhere positive, although it strongly increases the likelihood of this.

Allow slip reversals: This box allows the sign of the slip rate to change. It defaults to False.

Importing and exporting

There are two buttons that do this exactly. They open up File Open-type dialongs that allow you to input or export (save) a JSON file that has all of the data and the run configuration.

Intermediate Usage

For users with some knowledge of the Python data stack, once the data are input and run for the first time, the user can type commands to do different analysis, make new plots, and whatnot through the IPython interface.

One feature of the embedded IPython console is that all of the GUI commands for running and plotting are done through the IPython interpreter instead of the Python interpreter in the background that runs the GUI: all of the buttons and so forth directly send text commands to the IPython interpreter. This makes it so that the user can read the exact commands that were sent, modify them, learn from them, etc. Additionally, the slip_rate_tools module that does the real science can be called directly, and has text completion and docstrings exposed.

Advanced Usage

All of the scientific functionality is implemented through the slip_rate_tools module that is packaged with the Slip Rate Calculator. This module is poorly documented at present, but it can be imported and used in other scripts, programs, etc. to do lots of different tasks.

Functionality includes:

  • Creation and sampling of PDFs for age and offset based on the input data fields, including things like multi-dimensional PDFs of offset on multiple features, accounting for conditional probabilities imposed by cross-cutting relationships or monotonically-increasing offset with age requirements.
  • Linear, piecewise-linear, and spline fitting of age/offset pairs (or other xy data).
  • Probably some other stuff.

Most of this functionality is exposed through the GUI but it could be used differently for different use cases. For example, one could use an iterative fitting to refine PDFs of offset on a feature based on how well the fit for the whole system fits that particular offset marker.

Additionally, some slightly different fitting algorithms could be used based on different use of the fitting functions; they are quite modular and could be used creatively based on different needs or information (i.e. breakpoints could be fixed for piecewise regression quite easily).

Forthcoming Features

  • Spline fitting of offset markers
  • KDE estimates of Age or Offset from lists of values (probabilities internally generated).
  • Automatic unit conversion
  • Log/Log fitting (helpful for geodetic through geologic slip histories)
  • Webapp (!!!)
  • Sliders on rate/time and offset/time plots to get PDFs at any time
  • Incorporation of uncertainty in the zero offset age and fitting of this (to determine when slip on an old fault may have stopped, for instance)
  • Excel I/O

See Issues to browse or suggest others. Suggestions are quite welcome!

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