Background

The Willamette Valley of the Terminal Pleistocene (10,000+ years ago) was a wildly different place than the valley we are used to seeing today. There were massive animals roaming the valley floor, which was a lot wetter, with more marshes, bogs, and lakes. Oak savannah and forested swaths of land made their way towards the Willamette River… This wonderful landscape was likely also inhabited by humans, though that is a very difficult question to explore.

In order to find out where in the Pleistocene Willamette Valley people might have lived, we must first understand the sediments that lay under our feet here in the valley, and the best way to do that is by extracting it from the ground and analyzing it.

This project is a part of a bigger picture study that seeks to use sediment cores extracted from a buried peat bog found at Woodburn High School in Woodburn, Oregon and identify the sediments buried within that are of the appropriate age and environment to find both potential Pleistocene aged archaeological sites as well as more evidence of Pleistocene megafauna.

 

Research Question

For the purposes of this project, I am seeking to find a different method for identifying stratigraphic breaks in a sediment profile. The most popular methods for identifying stratigraphy is through visual examination, multivariate statistical analysis, or by texturing the sediment.

Using x-ray fluorescence geochemical data, and Wavelet Analysis, a method typically used for studying time-series data, is it possible to determine the site stratigraphy using one or two different variables?

 

The Data

The dataset consists of XRF data taken at 2mm intervals, from 65 1.5-meter core samples. These cores come from 14 different boreholes covering the majority of the defined study area which is approximately a 200×50-meter area. The cores were extracted using a Geoprobe direct-push coring rig.

 

The Site:

The Geoprobe in action at the site:

The core samples were halved and run through an iTrax core scanning unit. The iTrax scans the cores using an optical camera, a radiograph (similar to medical x-ray), and an x-ray fluorescence scanner, which collects geochemical data consisting of 35 different element counts at 2mm intervals. The data is organized into 14 CSV files containing the XRF results.

 

The iTrax:

Hypothesis

Using wavelet analysis, significant increases and decreases of geochemical properties can indicate where stratigraphic breaks in the sediment occur. This pattern should be repeatable across all of the gathered cores.

 

Approach

The method I chose to analyze my core data and attempt to break apart the stratigraphy was Wavelet Analysis using an R package called “WaveletComp”.

‘WaveletComp” takes any form of continuous data, typically time-series data, spatial data in this case, and uses a small waveform called a wavelet to run variance calculations along the dataset. The resulting output is a power diagram, which shows (in red) the locations along the dataset where there is a great change in variance. A cross-wavelet power diagram can also be generated. This can indicate when two different variables are experiencing rises and/or drops at the same time.

 

Example of a wavelet.

There are two equations used when generating a wavelet power diagram…

The above equation uses the dataset to calculate the appropriate size of the wavelet according to the number of points in the dataset.

The above equation uses the wavelet to run variance calculations across the dataset and output the power diagram.

 

Using the ‘WaveletComp” package in R, I processed 5 different core scans. In order to properly conduct the analysis, elements had to be selected in order to do both univariate and bivariate analysis. There were a variety of ways that I could have selected the data, but ultimately, in the test sample, I chose to look at the elements that had the most obvious changes, aluminum and iron.

The details on how to actually run the “WaveletComp” package can be found in my wavelet analysis tutorial.

 

Results

 

After all of the tests were run, the resulting wavelet power diagrams were placed alongside line graphs of the element that was run, as well as a cross-wavelet power diagram, which indicates when the two selected elements change at the same time.

The wavelet power diagrams show significant changes in the waveform, as indicated by the red (high variance). The blue in the power diagram shows low variance. In the cross-wavelet power diagram (the one on the far right), the arrows indicate if the waveforms of the two elements are in-phase (pointing left) or out of phase (pointing right) with each other.

In order to identify stratigraphic breaks, I looked at the “taller” red plumes in the wavelet power diagrams, which indicated a significant change in the amount of an element present, either a great increase or a great decrease. This result was compared to the graph, as well as the image of the core (for visual identification of changes in color or texture). The spots that have the high plumes, and correspond with a significant color or texture change in the image are presumably the stratigraphic breaks.

Each of the five cores showed promise that we can identify stratigraphy using wavelet analysis. The two most significant cores are discussed below:

The results for the first core segment shows various distinct changes as indicated by the tall red plumes in the power diagrams, but there is one major problem with the results. The graphed data shows areas where there is zero data at points, as indicated by the drops to the very bottom of the graph. These spots also have the most distinct plumes (for the most part), but about 2/3 of the way down there is a distinct plume that, while contains zero values, also is a spot with a distinct texture change in the sediment. At this point the sediment changes texture and feel entirely.

The results of the second core sample were more interesting, as there were no zero values. The Al+Fe cross-wavelet diagram shows significant plumes where almost all of the significant looking color changes in the stratigraphy are in the profile.

The second core was the most interesting of the analysis, due to the lack of zero/null data. With a little bit of data management, the zero data can be reduced by interpolating some of the values and re-running the data with the interpolated data, That should help reduce the effects of the zero data as demonstrated in the above results.

 

Significance

Wavelet analysis is an excellent tool for observing patterns in spatio-temporal data that is sequential. As for the significance of this project…

Once all of the observed kinks are fixed in the data, this could serve as a new method for identifying changes in stratigraphy. This could be a good method to identify stratigraphy in sediment cores that are extremely similar in color or texture.

 

What I learned about the software

R is an excellent tool for conducting many different kinds of spatio-temporal analysis. From running wavelet analysis to regression, and really any other tool that ArcGIS has to offer.

ArcGIS is an excellent tool for data visualization, but it is a very finicky program that has to

 

What I learned about statistics.

Statistical applications in geospatial analysis are very important to understanding even the smallest of changes, such as an increase or decrease in iron in a sediment core.