Three-dimensional Reference Earth Model Project: Data, Techniques, Models & Tools

Reconciliation of techniques, models and data has emerged as a frontier area for deep Earth exploration. Past results have proven indispensable for assessing earthquake hazard, characterizing plate tectonics, elucidating material properties under extreme conditions, imaging interior structure, and as a general reference in other fields. We present advancements on a three-dimensional reference Earth model (REM3D) that captures the consensus view of heterogeneity in the mantle.

Progress in modeling the Earth's interior is driven by diverse data, ranging from astronomic-geodetic constraints to full seismic waveforms and derivative measurements of body waves (~ 1 - 20s), surface waves (~ 20 - 300s) and normal modes (~ 250 - 3000s). Reconciliation of data involves retrieving the missing metadata, archiving in scalable storage formats, documenting outliers indicative of the limitations in some techniques, and quantifying summary reference data with uncertainties. Building on our recent work on reference surface-wave dispersion datasets, arrival times of primary, diffracted, and reflected phases from the transition-zone and deeper discontinuities are reconciled for a body-wave reference dataset. This procedure involves revised techniques and archival formats for the processing of frequency-dependent arrival times. A revised dataset of normal-mode eigenfrequencies, quality factors and splitting is reconciled with updated uncertainties based on inter-catalog consistencies.

Full-spectrum tomography uses these diverse observations to constrain physical properties - seismic velocity, anisotropy, density, attenuation and the topography of discontinuities - in variable spatial resolution. This technique is expanded to include long-wavelength geoid as an additional constraint on density variations. All geoscience workflows typically involve querying data or models in order to make inferences. Analysis and Visualization toolkit for plaNetary Inferences (AVNI) is a web-based software environment powered by Python that facilitates these computational workflows. AVNI tools are web-based so that the shared resources are accessed by authenticated users through Application Programming Interfaces (APIs), without the overhead of storing data, compiling and running intensive codes.