Geophysical Open Seismic Hardware: Design of a Vertical Seismic Profiling Instrument
Bibliographic record
Abstract
Geophysics can help provide guidance as we adapt to our changing environment. However, with advent of microelectronics, embedded systems and field programmable gate arrays, geophysical instruments have largely become a black box for most users: experiments are limited by the budgets that are available rather than the imagination of the geoscientific community. The solution we propose is to introduce affordable, modular and lightweight multi-component seismic instruments that can be deployed easily by researchers and explorers alike. We have developed a system that allows seismic data acquisition using very sensitive and compact accelerometers. These sensors are coupled to high-speed, multi-channel 24-bit downhole acquisition modules that were developed for this project. The control and synchronization of the system is engineered around microcontrollers that are compatible with the Arduino ecosystem. Communication between the parts of the system is done via a novel frequency modulated RS-485 communication protocol. This protocol makes it possible to send power and data over a wireline with only two conductors. The small diameter and the low cost of this system facilitates the deployment of a large number of channels or in configurations that may not be feasible with commercial equipment. The modular nature of the system makes it easy to adapt to other downhole applications or for draggable sensor arrays on surface. We consider that these efforts will contribute to the democratization of seismic survey in exploration, civil engineering and water prospecting to help reduce the global environmental impacts of human activities. Metadata Overview Main design files: Geophysical Open Seismic Hardware, in Hardware and Firmware directories. Target group: Geoscientists and engineers. Skills required: 3D printing – easy; electronics – intermediate; Programming – intermediate. Replication: No builds known to the authors so far. See section “Build Details” for more detail.
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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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".