Ground motion through geophones and MEMS accelerometers: Sensor comparison in theory, modeling, and field data
Bibliographic record
Abstract
Digital sensors based on micro electro mechanical systems (MEMS) accelerometers are one of the newest technologies being used in seismic acquisition. As such, some confusion remains surrounding similarities and differences relative to the coil‐over‐magnet geophone. An understanding of the functioning of these sensors and how to compare them can be facilitated by deriving transfer functions, which relate the data acquired through each sensor to actual ground motion. An equation is then derived to calculate acceleration comparable to unprocessed MEMS data from unprocessed geophone data. The inverse of this equation may be used to calculate geophone data from MEMS data. The effects of sensors on zero and minimum phase wavelets are modeled, demonstrating that the raw output from the sensors should be similar. The minimum phase wavelets are convolved with a random reflectivity series to test deconvolution of impulsive source data. Deconvolution produces geophone and MEMS processed traces that appear similar, and constant phase rotation of MEMS data after deconvolution cannot correct all remaining differences. The geophone‐to‐MEMS transfer equation will exactly transfer between sensors only in the absence of instrument noise. Comparisons between MEMS and geophones recording the same shots, and ground motion domains calculated from those records, show that the data is very similar in frequency content when the same domain is considered, and MEMS records will not necessarily have a larger magnitude contribution from low frequencies than geophones.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".