Mapping the Alaskan Moho: An Exercise in Reproducible Science

Date: 4/26/2019

Time: 06:00 PM

Room: Grand Ballroom

In Miller & Moresi (2018), we presented a series of Moho depth maps for the Alaskan region based on P receiver function estimates using data from all available broadband instrumentation from 1999 to April 2018 including the USArray Transportable Array. The average Moho depths beneath individual broadband stations were presented first as spot measurements and then used to produce a series of interpolated smooth surfaces by an adaptive triangulation process followed by the fitting of a bicubic spline. The interpolated surfaces included a measure of confidence in the interpolation to help etermine a preferred model. The resulting Moho depth map (single continuous surface) provided a reasonable estimate of the Earth’s outermost layer thickness beneath Alaska as constrained by receiver functions for use in applications such as tomography, regional‐scale interpretations, or simulations of seismic waves.

REPRODUCIBILITY & REUSE

Making research reproducible means providing the entire workflow from data, through software and post-processing freely available. Not only can somebody repeat experiments and verify them, they can build upon them. In lab-based disciplines, there are many further challenges, but in research that is predominantly based on data processing, this is an achievable goal today.

We have released all of the background for our paper on the Alaska Moho to make it transparent and reproducible. Open source software is one thing (the software and raw moho picks are available through pip install miller_alaskamoho_srl2018), but to manage versions and operating system changes, we have packaged everything in a docker container that can be launched here: https://mybinder.org/v2/gh/lmoresi/miller-moho-binder/publication

NOTES & REFERENCE

Meghan S. Miller, Louis Moresi; Mapping the Alaskan Moho. Seismological Research Letters ; 89 (6): 2430–2436. doi: https://doi.org/10.1785/0220180222

The software and data for this model is available via github accessed via the following doi:10.5281/zenodo.1459110

Presenting Author: Louis Moresi

Authors