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A two-part package that incorporates and propagates uncertainties in the calculations of(1) decompaction and backstripping, and (2) thermal subsidence age-depth modeling in post-rift sedimentary basins. The first step uses Monte Carlo (MC) methods, and the second step uses Markov chain Monte Carlo (MCMC) methods.
The decompaction and backstripping MC model (part 1) is based on the workflow demonstrated in Allen and Allen (2005). Users first input stratigraphic information (lithologies and thicknesses of measured section) and, optionally, water depth estimations (as distributions). Then the users need to define the resolution (that decompaction and backstripping calculations perform at) and the number of MC simulations. For each MC simulation, lithology-dependent parameters and water depth estimations are drawn from their respective distributions. The output of this first MC model describes how tectonic subsidence (with uncertainties) changes throughout deposition of the target sedimentary succession.
The second part of this package is an MCMC model that probes the age-depth relationship for strata deposited in a post-rift thermally subsiding basin. It builds upon the stratigraphic MCMC model of Chron.jl, which only uses stratigraphic superposition as the underlying principle (i.e. without making any assumptions about how sedimentation rate changes). Because this package is specifically developed for the thermal subsidence phase of the Mckenzie-type basins, our MCMC model adds two extra log-likelihood terms to examine 1) the shape of the proposed tectonic subsidence-age curve, and 2) the rifting conditions (stretching factor β and onset time for thermal subsidence t0). Inputs for this MCMC model include: 1) results propagated from the first MC model (i.e. tectonic subsidence through time), 2) existing age constraints, 3) prior distributions of β and t0, and 4) user-defined MCMC model configurations. The outputs of this second MCMC model include both age estimations at all model horizons throughout the section, and updated distributions for β and t0.
SubsidenceChron.jl is written in the Julia programming language and registered on the General registry. To use this package, first download and install Julia. Then, SubsidenceChron.jl can be installed by typing ] in the REPL to enter the Julia package manager and typing:
pkg> add SubsidenceChron
To see the standard useage of this program, run example/SubsidenceChron1.0Synthetic.jl, which should follow the following steps:
using SubsidenceChron
using StatGeochem, Distributions, Plots, Statistics, StatsBaseHere we use a synthetic stratigraphic succession as an example. This synthetic succession consists of, from the youngest to the oldest:
| Thickness (m) | Lithology | Water depth |
|---|---|---|
| 1000 | Shale | Between storm wave base and fair weather wave base |
| 600 | Sandstone | Above fair weather wave base |
| 400 | Limestone | Above fair weather wave base |
Stratigraphic information and (optional) water depth information should be stored in .csv files following the same format as those provided in the examples/ folder. For example, the stratigraphic information .csv file should look like examples/Test_DB_PerfectSubsidence.csv:
layer,Thickness,Lithology
3,1000,Shale
2,600,Sandstone
1,400,Limestone
while the water depth information .csv file should look like examples/PerfectSubsidence_SequenceStrat.csv:
layer,Thickness,Type,Notes
3,1000,SWB,
2,600,FWWB,
1,400,FWWB,
Below is a list of currently acceptable lithology and water depth identifiers:
| Lithology | Water depth | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
Once the .csv files is in the correct format, the user can import them into the model:
# # # # # # # # # # # Enter stratigraphic information here! # # # # # # # # # # # #
# Import the data file (.csv)
data_csv = importdataset("examples/Test_DB_PerfectSubsidence.csv",',')
# Obtain stratigraphic info from the data file
nLayers = length(data_csv["Thickness"])
strat = NewStratData(nLayers)
strat.Lithology = data_csv["Lithology"]
strat.Thickness .= data_csv["Thickness"]
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # OPTIONAL - Enter paleo water depth information here! # # # # # # # #
# Import the data file (.csv)
wd_csv = importdataset("examples/PerfectSubsidence_SequenceStrat.csv", ',')
# Obtain paleo water depth info from the data file
wd_nLayers = length(wd_csv["Thickness"])
wd = NewWaterDepth(wd_nLayers)
wd.DepthID = wd_csv["Type"]
wd.Thickness .= wd_csv["Thickness"]
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #If water depth information is not applicable/does not exist for the user's case study, the code block for entering water depth information should be commented out.
After importing data, the user need to configure the MC model (i.e. specify the number of MC simulations and the resolution):
# # # # # # # # # # Configure MC model here! # # # # # # # # # #
# Number of MC simulations
nsims = 1000
# Resolution for model horizons (in meters)
res = 20.0
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Run the decompaction and backstripping MC model
# (wd input is optional)
@time (Sₜ, Sμ, Sσ, model_strat_heights) = DecompactBackstrip(strat, wd, nsims, res)If water depth information is not applicable/does not exist for the user's case study, the user should exclude the input wd when calling the DecompactBackstrip function:
@time (Sₜ, Sμ, Sσ, model_strat_heights) = DecompactBackstrip(strat, nsims, res)The outputs of this first MC model are
- tectonic subsidence (throughout the deposition of the target succession) from all MC simulations (
Sₜ); - tectonic subsidence mean, averaging across all MC simulations (
Sμ); - tectonic subsidence standard deviation (
Sσ); - model stratigraphic heights (spacing =
res) (model_strat_heights).
For the synthetic test case, we used the "real" ages for four randomly-picked horizons as age constraints. The "real" ages are calculated using β = 1.85 and t0 = 400 Ma, and assuming that this succession was deposited in a perfect McKenzie-type thermally subsiding basin.
# # # # # # # # # # # Enter age constraint (sample) information here! # # # # # # # # # # # #
# Input the number of samples we wish to model (must match below)
nSamples = 4
# Make an instance of a Chron Section object for nSamples
smpl = NewChronAgeData(nSamples)
smpl.Name = ("Sample 1", "Sample 2", "Sample 3", "Sample 4") # Et cetera
smpl.Age .= [ 388.67, 362.63, 328.16, 220.08] # Measured ages
smpl.Age_sigma .= [ 2, 2, 2, 2] # Measured 1-σ uncertainties
smpl.Height .= [ -1700, -1100, -700, -100] # Depths below surface should be negative
smpl.Height_sigma .= [ 0.01, 0.01, 0.01, 0.01] # Usually assume little or no sample height uncertainty
smpl.Age_Sidedness .= [ 0, 0, 0, 0] # Sidedness (zeros by default: geochron constraints are two-sided). Use -1 for a maximum age and +1 for a minimum age, 0 for two-sided
smpl.Age_Unit = "Ma" # Unit of measurement for ages
smpl.Height_Unit = "m" # Unit of measurement for Height and Height_sigma
# # # # # # # # # # Enter thermal subsidence parameter priors here! # # # # # # # # # #
# Enter initial guesses for the beta factor and thermal subsidence onset age and their uncertainties
Beta = 1.8
Beta_sigma = 0.2
T0 = 410
T0_sigma = 50
therm = NewThermalSubsidenceParameters()
therm.Param = [Beta, T0]
therm.Sigma = [Beta_sigma, T0_sigma]Note that when entering the age constraints, smpl.Height must increase with increasing stratigraphic height. Also, users must make sure to change the thermal subsidence parameter priors (Beta, Beta_sigma, T0, T0_sigma) accordingly so that the prior conditions are appropiate for the specific basin/succession that the user would like to model. If the user has no previous information about the stretching factor β, we suggest setting Beta = 2 and Beta_sigma = 1 to begin with.
To run the MCMC model, the user should first configure the model by setting appropiate values for config.bounding, config.nsteps, and config.burnin.
# # # # # # # # # # Configure MCMC model here! # # # # # # # # # #
# Configure the stratigraphic MCMC model
config = NewStratAgeModelConfiguration()
# If you in doubt, you can probably leave these parameters as-is
config.resolution = res # Same units as sample height. Smaller is slower!
config.bounding = 1.0 # how far away do we place runaway bounds, as a fraction of total section height. Larger is slower.
(bottom, top) = extrema(smpl.Height)
npoints_approx = round(Int,length(bottom:config.resolution:top) * (1 + 2*config.bounding))
config.nsteps = 50000 # Number of steps to run in distribution MCMC
config.burnin = 10000*npoints_approx # Number to discard
config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #Then we call the SubsidenceStratMetropolis function. The user is welcomed to change the last two inputs of this function, which represents the perturbation to β and t0 for the initial proposal - changing these two values shouldn't affect the posterior distribution.
#Run the model
(subsmdl, agedist, lldist, beta_t0dist, lldist_burnin) = SubsidenceStratMetropolis(smpl, config, therm, model_strat_heights, Sμ, Sσ, 0.05, 10)The outputs of this second MCMC model are
- summary statistics for the age predictions of all model horizons and for the thermal subsidence parameters (
subsmdl); - age predictions of all model horizons from all recorded MCMC runs (
agedist); - log likelihood from all recorded MCMC runs (
lldist); - subsidence parameters from all recorded MCMC runs (
beta_t0dist); - log likelihood from all runs during the burn-in stage (
lldist_burnin).
- Plot the age-depth model;
# Plot 1: Age-depth model (mean and 95% confidence interval for both model and data)
hdl = plot([subsmdl.Age_025CI; reverse(subsmdl.Age_975CI)],[subsmdl.Height; reverse(subsmdl.Height)], fill=(round(Int,minimum(subsmdl.Height)),0.5,:blue), label="model")
plot!(hdl, subsmdl.Age, subsmdl.Height, linecolor=:blue, label="", fg_color_legend=:white) # Center line
t = smpl.Age_Sidedness .== 0 # Two-sided constraints (plot in black)
any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(smpl.Age[t]-smpl.Age_025CI[t],smpl.Age_975CI[t]-smpl.Age[t]),yerror=(2*smpl.Height_sigma[t]),label="data",seriestype=:scatter,color=:black)
t = smpl.Age_Sidedness .== 1 # Minimum ages (plot in cyan)
any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(smpl.Age[t]-smpl.Age_025CI[t],zeros(count(t))),label="",seriestype=:scatter,color=:cyan,msc=:cyan)
any(t) && zip(smpl.Age[t], smpl.Age[t].+nanmean(smpl.Age_sigma[t])*4, smpl.Height[t]) .|> x-> plot!([x[1],x[2]],[x[3],x[3]], arrow=true, label="", color=:cyan)
t = smpl.Age_Sidedness .== -1 # Maximum ages (plot in orange)
any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(zeros(count(t)),smpl.Age_975CI[t]-smpl.Age[t]),label="",seriestype=:scatter,color=:orange,msc=:orange)
any(t) && zip(smpl.Age[t], smpl.Age[t].-nanmean(smpl.Age_sigma[t])*4, smpl.Height[t]) .|> x-> plot!([x[1],x[2]],[x[3],x[3]], arrow=true, label="", color=:orange)
plot!(hdl, xlabel="Age ($(smpl.Age_Unit))", ylabel="Height ($(smpl.Height_Unit))")
#plot!(hdl, [smpl.Age[1], smpl.Height[1]],[smpl.Age[3], smpl.Height[3]])
savefig(hdl,"Test_AgeModel.pdf")
#display(hdl)- Plot the posterior distribution of β;
# Plot 2: Posterior distributions of beta
post_beta = histogram(beta_t0dist[1,:], color="black", linecolor=nothing, alpha = 0.5, nbins=50)
vline!([subsmdl.Beta_Median], linecolor = "black", linestyle=:dot, linewidth = 3)
savefig(post_beta, "Test_PosteriorBeta.pdf")- Plot the posterior distribution of t0;
# Plot 3: Posterior distributions of t₀
post_t0 = histogram(beta_t0dist[2,:], color="black", linecolor=nothing, alpha = 0.5, nbins=50)
vline!([subsmdl.T0_Median], linecolor = "black", linestyle=:dot, linewidth = 3)
savefig(post_t0, "Test_PosteriorT0.pdf")- Calculate and plot the age predictions (as distributions) for undated horizons of interest.
# Plot 4: Interpolate results for target horizons
target_height = [-1840, -1320, -980, -360]
interpolated_distribution = Array{Float64}(undef, length(target_height), size(agedist,2))
# Interpolate between model horizons
for i=1:size(agedist,2)
interpolated_distribution[:,i] = linterp1s(subsmdl.Height,agedist[:,i],target_height)
end
# Calculate summary statistics
predicted_medians = nanmedian(interpolated_distribution, dims=1)
predicted_025CI = nanpctile(interpolated_distribution, 2.5, dims=1)
predicted_975CI = nanpctile(interpolated_distribution, 97.5, dims=1)
# Plotting (full distribution with mean as a dashed line)
age_interp = histogram(interpolated_distribution[:,2], nbins=50, label="")
vline!([predicted_medians[2]], linecolor = "black", linestyle=:dot, linewidth = 3)
plot!(age_interp, xlabel="Age ($(smpl.Age_Unit))")
savefig(age_interp, "Test_InterpolatedAge.pdf")
#display(age_interp)