Geophysical assessment on how near surface sediments impact seismic groundmotion due to induced seismicity
Bibliographic record
Abstract
In Union City, Oklahoma, hydraulic fracturing and wastewater injection are a root cause \nof increased induced seismicity that is felt by nearby homeowners who report damage to the \nOklahoma geological survey (OGS). To assess the source of the small magnitude earthquake \ndamage, we deployed over 60 continuously recording nodes and conducted nearly two \nkilometers of electrical resistivity tomography (ERT). From the nodal data, we performed \nhorizontal-vertical spectral ratio (HVSR) to achieve the resonance frequency information from \neach nodal location. Pairing the resonance frequency information with the high resolution ERT \nallows the resonating body to be identified with the aid of in-situ sampling and grain size \nanalysis. Our results show that the northern portion of the study area contains the highest \nresonance frequencies, which correlate to terrace deposits of the same depth and thickness \naccording to the USGS. In the southern nodes, the resonance frequencies are suggested to be \ntrapped in thick clays that are deeper than the alluvium from the Canadian river. Our findings \nsuggest that near surface sediments, particularly terrace deposits in Union City, OK, may \ncontribute to heightened property damage when high-frequency seismic waves resonate with \nnear-surface materials' resonance frequency due to the trapping mechanisms of the Canadian \nriver layered sediments. This study provides a detailed spatial image of the subsurface, \ndescribing near-surface material impact on ground motion sourced from induced seismic energy. \nThe insights gained can potentially aid in creating more accurate risk analysis maps, benefiting \nnearby suburbs in understanding and mitigating the impact of induced seismic energy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".