Comparing the low-frequency content of exploration seismic source - receiver combinations using surface waves: a field study in Hussar, Alberta
Bibliographic record
Abstract
The low-frequency content of seismic waves in exploration is of substantial value as it can benefit imaging and inversion by providing deeper penetration, broader-band energy, and wavelet stability. However, characterizing the factors contributing to low frequencies (the seismic source, response of the receiver, and spectral signal-to-noise) and their effects may be complicated. The Hussar, Alberta survey, conducted by the CREWES Project at the University of Calgary and used here, addresses this challenge with a range of sources and receiver types. We further analyze the low-frequency content of the Hussar data using surface waves - because of their significant coherent low-frequency energy. The multichannel analysis of surface waves (MASW) method is used. To improve accuracy, a nonlinear approach is applied to extract dispersion properties, overcoming the limitations of conventional methods at low frequencies. This allows for precise phase velocity measurements across frequencies and assesses the frequency content of different source-receiver type combinations based on surface-wave coherence. The extracted dispersion properties were validated by comparing the dispersion curves estimated using Vs from traveltime tomography and well-logging data. The survey tested 2 kg dynamite in addition to vibroseis sources with low-dwell and linear sweeps. The receivers evaluated included Vectorseis accelerometers, as well as 10 and 4.5 Hz geophones. Our dispersion results indicate that all source-receiver combinations contain considerable surface-wave energy down to about 2 Hz. The inverted 1-D Vs models provide Vs estimates to about 800 m, consistent with results from S-wave tomography and shear logging. Dynamite produced more low-frequency energy in surface waves than vibroseis sources, extending below 1.5 Hz. Low-dwell sweeps showed clearer coherence in surface waves at low frequencies than linear sweeps. Of the receivers tested, the 4.5 Hz geophone showed higher sensitivity to low frequencies than both the Vectorseis accelerometer and the 10 Hz geophone. Although the Vectorseis accelerometer recorded more coherent low-frequency surface waves than the 10 Hz geophone, its signals were affected by some instrument noise. Analyzing surface-wave energy and coherency to assess low-frequency content can complement other types of spectral analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".