Passive seismic surveying for depth to base of paleochannel mapping at Lake Wells, Western Australia
Bibliographic record
Abstract
Goldphyre Resources Limited (Goldphyre) undertook a passive seismic survey over parts of the Lake Wells project using Tromino 3G seismometers. Modelling of the passive seismic survey data has defined the depth to bedrock and location of a deep, E-W trending paleochannel system occurring beneath a broad salt lake playa, where underlying Cainozoic paleochannel deposits likely form a reservoir system for potash brines. Drilling by Goldphyre supports these findings. A total of 180 readings were acquired along six survey line traverses over the salt lake, spaced 1,200 m to 2,100m apart on average, using four Tromino seismometers and 20 minute recording times. The passive seismic data were analysed using the H/V spectral ratio (HVSR) method, whereby the spectral ratio of the horizontal and vertical component data are assessed to determine a resonant frequency of an upper softer layer having low S-wave velocity sitting above a high S-wave velocity bedrock. The depth of the upper layer is related to the resonant frequency and S-wave velocity by a simple formula: f0 = Vs/(4h), where f0 = peak frequency in Hz, Vs = shear wave velocity in m/s and h = thickness in m.The modelled passive seismic survey depth to bedrock estimation has delineated a paleochannel system over 20 km long, up to 4 km wide and up to 170 m deep. In addition, the paleochannel system was found to branch to the northwest and southwest, and remains open in these directions. A paleochannel of these dimensions can potentially provide significant high-grade sulphate of potash (SPO) mineralisation hosted in the paleochannel brines.The passive seismic method is commonly used for seismic hazard mapping, and has not been used extensively for mineral exploration up to now, as very broadband seismometers have typically been used in array configurations that take significant time to setup and record data. The Tromino seismometers are self-contained, lightweight, portable and very simple to use. The HVSR method of analysis allows the user to quickly estimate the thickness of softer sediment cover over hard basement rocks ranging from 0 to 500m. This method has many applications for mineral exploration, and it may become a standard survey tool to predict thickness of cover deposits and reduce drilling costs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".