Seismic Modeling with Half Precision Floating Point Numbers
Bibliographic record
Abstract
Summary Half precision floating point numbers is becoming increasingly supported by new processors, often with a significant throughput gain over single precision operations. In this article, we investigate whether half precision is suitable for finite difference based seismic modeling in the context of imaging and inversion. By scaling the finite difference expression of the isotropic elastic wave equation, we manage to obtain a stable solution despite the very narrow dynamic range of the half-precision format. We present a CUDA implementation of this code, which, on most recent GPUs, is nearly twice as fast and uses half the memory of the equivalent single precision version. The error on seismograms caused by the reduced precision is shown to correspond to a fraction of a percent of the total seismic energy, and is mostly incoherent with seismic phases. Thus, half precision modeling could accelerate full waveform inversion or migration with a negligible impact on the quality of their output.
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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 | 0.001 |
| 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".