Least-squares migration imaging of receiver functions
Bibliographic record
Abstract
The growth of data recorded by dense seismic arrays has stimulated the development of new array-based receiver function (RF) imaging techniques. This study examines the feasibility and performance of the least-squares migration (LSM) method, a state-of-art technique used in exploration seismology, to lithospheric imaging using teleseismic RFs. Taking advantage of a pair of forward (de-migration) and adjoint (migration) operators, the LSM casts migration as a regularized least-squares optimization problem. We employ the Split-step Fourier method to design the two operators and conduct wavefield propagation in heterogeneous media. Synthetic tests with models containing various Moho geometries demonstrate that LSM enables resolving interfaces at a higher resolution than conventional migration. Then LSM is applied to teleseismic data recorded by the Hi-CLIMB array deployed on the Tibetan Plateau. Considering the irregular and noisy recordings from field acquisition, we adopt signal processing algorithms, including the Radon transform and Singular Spectrum Analysis filter, to regularize the wavefields and precondition the RFs. The proposed workflow produces a significantly improved subsurface image than conventional methods, revealing new observations of 1) two well-defined interfaces at the base of the crust and 2) gently dipping mantle discontinuities extending continuously from the Lhasa Block to the Qiangtang Block. These structures could represent the imbricated Indian and Tibetan crust underlain by the underthrusting Indian lithosphere, implying that the Indian collisional front extends as far north as the Bangong-Nujiang suture. Overall, our study offers a new high-resolution RF imaging tool and inspires the future development of advanced array processing workflows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".