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SUMMARY Differences between P- and S-wave models have been frequently used as evidence for the presence of large-scale compositional heterogeneity in the Earth's mantle. Our two-step machine learning (ML) analysis of 28 P- and S-wave global tomographic models reveals that, on a global scale, such differences are for the most part not intrinsic and could be reduced by changing the models in their respective null spaces. In other words, P- and S-wave images of mantle structure are not necessarily distinct from each other. Thus, a purely thermal explanation for large-scale seismic structure is sufficient at present; significant mantle compositional heterogeneities do not need to be invoked. We analyse 28 widely used tomographic models based on various theoretical approximations ranging from ray theory (e.g. UU-P07 and MIT-P08), Born scattering (e.g. DETOX) and full-waveform techniques (e.g. CSEM and GLAD). We apply Varimax principal component analysis to reduce tomography model dimensionality by 83 percent, while preserving relevant information (94 percent of the original variance), followed by hierarchical clustering (HC) analysis using Ward's method to quantitatively categorize all models into hierarchical groups based on similarities. We found two main tomography model clusters: Cluster 1, which we called ‘Pure P wave’, is composed of six P-wave models that only use longitudinal body wave phases (e.g. P, PP and Pdiff); and Cluster 2, which we called ‘Mixed’, includes both P- and S-wave models. P-wave models in the ‘Mixed’ cluster use inversion methods that include inputs from other geophysical and geological data sources, and this causes them to be more similar to S-wave models than Pure P-wave models without significant loss of fitness to P-wave data. Given that inclusion of new data classes and seismic phases in more recent tomographic models significantly changes imaged seismic structure, our ML assessment of global tomography model similarity may improve selection of appropriate P- and S-wave models for future global tomography comparative studies.
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GLAD-M35: a joint P and S global tomographic model with uncertainty quantification
SUMMARY We present our third and final generation joint P and S global adjoint tomography (GLAD) model, GLAD-M35, and quantify its uncertainty based on a low-rank approximation of the inverse Hessian. Starting from our second-generation model, GLAD-M25, we added 680 new earthquakes to the database for a total of 2160 events. New P-wave categories are included to compensate for the imbalance between P- and S-wave measurements, and we enhanced the window selection algorithm to include more major-arc phases, providing better constraints on the structure of the deep mantle and more than doubling the number of measurement windows to 40 million. Two stages of a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton inversion were performed, each comprising five iterations. With this BFGS update history, we determine the model’s standard deviation and resolution length through randomized singular value decomposition.
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- PAR ID:
- 10536661
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 239
- Issue:
- 1
- ISSN:
- 0956-540X
- Format(s):
- Medium: X Size: p. 478-502
- Size(s):
- p. 478-502
- Sponsoring Org:
- National Science Foundation
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