Numerical Modeling to Assess the Impact of Positional Errors during the Acquisition of Waterborne Continuous Resistivity Measurements
Bibliographic record
Abstract
Waterborne continuous resistivity profiling (WCRP) permits rapid collection of direct current (DC) resistivity data over water by means of towing a multi-electrode cable streamer behind a moving vessel. During WCRP electrical current is injected using a pair of electrodes, while multiple potential measurements are made using other pairs of electrodes. This acquisition results in a near continuous profile of measured resistances along the survey path. These data are then processed/inverted to produce a 2.5D resistivity model, which can be used to interpret physical subsurface properties (i.e. lithology, and pore fluid conductivity). There are several advantages of WCRP over land based DC resistivity surveying, such as: 1) rapid data collection (e.g. approximately 15 km per day), 2) simplified access to survey areas (e.g. land access not required), and 3) the data are generally of high quality due to near-zero surface contact resistance. However, there are issues with using WCRP, including: 1) understand/incorporation of positional errors (e.g. the cable shape is constantly changing), 2) assessment of data noise (e.g. cannot collect reciprocals), and 3) incorporation of the water-column and water-bottom interface into the model. With this work, we seek to address challenge (1) through the use of numerical simulations of cable deformation. We have developed a heuristic model for cable shape, based on extensive experience with WRCP, that accounts for bends and slack caused by water currents and changing boat velocity. Using this cable model, we simulated, for a range of water currents and boat velocities, the data that would have been measured for a series of earth conductivity models. These data were then compared to data that would have been generated, assuming a straight/taut cable positioned with a random GPS error (this is the assumption that is often made when inverting WCRP data). The differences in these two simulated datasets (i.e. idealized vs. realistic), though specific to the range of parameters considered here, lend insight into the underlying positional errors associated with WRCP and can be used to better inform the error model used during the inversion of these data
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".