Least-Squares Migration Imaging of Receiver Functions
Bibliographic record
Abstract
The growth of seismic data recorded by dense 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-the-art technique used in exploration seismology, to lithospheric imaging using teleseismic RFs. Taking advantage of a pair of forward (demigration) 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 a two-layered crustal model and varying ratio of missing traces demonstrate that LSM is capable of suppressing imaging artifacts and improving imaging resolution compared to conventional migration. Real data application is conducted using teleseismic data recorded by the Himalayan-Tibetan continental lithosphere during mountain building (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 (SSA) filter, to regularize the wavefields and precondition the RFs. The proposed workflow resolves fine-scale crustal structures that are consistent with earlier studies. Overall, our study offers a new high-resolution RF imaging tool and inspires the future development of advanced array processing workflows.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".