Shear Wave Tomography Beneath the United States Using a Joint Inversion of Surface and Body Waves
Bibliographic record
Abstract
Abstract Resolving both crustal and shallow‐mantle heterogeneity, which is needed to study processes in and fluxes between crust and mantle, is still a challenge for seismic tomography. Body wave data can constrain deep features but often produce vertical smearing in the crust and upper mantle; in contrast, surface wave data can provide good vertical resolution of lithospheric structure but may lack lateral resolution and are less sensitive to the deeper Earth. These two data types are usually treated and inverted separately, and tomographic models therefore do not, in general, benefit from the complementary nature of sampling by body and surface waves. As a pragmatic alternative to full waveform inversions, we formulate linear equations for teleseismic S wave traveltimes and surface wave phase velocities and solve them simultaneously for variations in shear wave speed anomalies in the crust and upper mantle. We apply this technique to data from USArray and permanent seismic networks and present a model of seismic shear wave speed anomalies beneath the continental United States. Our joint model fits the individual data sets almost as well as separate inversions but provides a better explanation of the combined data set. It is generally consistent with previous models but shows improvements over both body wave‐only and surface wave‐only tomography and can lead to refinements in interpretation of features on the scale of the lithosphere and mantle transition zone. Plain Language Summary Variations in the speed at which seismic waves travel through the Earth reveal information about the structure and history of the planet. In this study, we investigate seismic velocity variations using two common types of data from seismograms: body waves, which travel through the deep Earth, and surface waves, which provide information about the shallower layers. Commonly, these two waves are studied separately, but we adopt the method of Fang et al. (2016, https://doi.org/10.1002/2015JB012702 ) to produce a model of the crust and mantle of the whole Earth by using both types of data. The goal of this paper is to validate the application of this technique on a large scale, using the continental United States as a test region. We perform qualitative and quantitative tests to show that this method improves upon models made with only body or surface waves while maintaining the best fits of the individual models. We conclude that this technique is a valuable and efficient tool to study the Earth's interior at multiple scales. Key Points Joint model improves resolution compared to either body wave or surface wave models Vertical smearing is prevalent in body wave tomography to depths of 400 km Distinct fast lithospheric and mantle signatures visible beneath central and eastern United States
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".