Keith Gray, Max Grossman, Anar Yusifov
Challenge Science Domain: Geosciences
Data Set Name: Synthetic Seismic Realizations
Description of the Data Set
In the energy industry, an understanding of subsurface characteristics and structure is crucial to identifying and localizing untapped resources. At a high level, the process of taking an entirely unexplored region of earth and generating an actionable understanding of its structure includes:
With each of these steps comes an amount of uncertainty from various sources of potential error: instrument error, human error, modeling error, and more. Despite this, the output of most seismic processing workflows is a single, gold standard, output image. An image which we know cannot possibly be 100% accurate!
It is crucial that future seismic processing workflows start to incorporate uncertainty when estimating the true subsurface structures. Rather than outputting a single interpretation, we should aim to emit a spectrum of possible realizations and an understanding of where uncertainty is high or low.
The dataset included in this data challenge serves as a starting point in exploring techniques for quantifying uncertainty in seismic processing workflows. In this dataset we are focused on quantifying and visualizing the uncertainty in our estimations of the density of the subsurface based on how varying those estimates impacts our output 3D volume. At a high level, this dataset consists of a set of synthetic but realistic models of the density of the subsurface, randomly generated based on a single, known, synthetic ground truth. This dataset also includes the final 3D realizations generated using those density models (also called velocity models). These files are stored in the industry standard SEGY format, and an example Jupyter notebook is provided to illustrate how to load and visualize them.
The end goal of this data challenge is to construct an uncertainty map for a given seismic survey, labeling each pixel in a final 2D seismic image with a value between 0.0 and 1.0 indicating how volatile the estimate for that pixel is.
However, we also welcome submissions that include any intermediate work towards that end goal or answers to any of the below challenge questions. Even if you are unable to complete the entire challenge, any submissions that show progress towards this end goal and lay out ideas for how the challenge could eventually be completed will be considered.
For more background information, please see the PDF included with the challenge.