Characterizing the evolution of mass flow properties and dynamics through analysis of seismic signals: Insights from the 18 March 2007 Mt. Ruapehu lake-breakout lahar
Bibliographic record
Abstract
Abstract. Monitoring for lahars on volcanoes can be challenging due to the ever-changing landscape which can drastically transform the properties and dynamics of the flow. These changes to the flows require the need for detection strategies and risk assessment that are tailored not only between different volcanoes, but at different distances along flow paths as well. Being able to understand how a flow event may transform in time and space along the channel is of utmost importance for hazard management. While visual observations and simple measuring devices in the past have shown how lahars transform along the flow path, these same features for the most part have not been described using seismological methods. On 18 March 2007 Mt. Ruapehu produced the biggest lahar in New Zealand in over 100 years. At 23:18 UTC the tephra dam holding the Crater Lake water back collapsed causing 1.3 x 106 m3 of water to flow out and rush down the Whangaehu channel. We describe here the seismic signature of a lake-breakout lahar over the course of 85 km along the Whangaehu river system using three 3-component broadband seismometers installed < 10 m from the channel at 7.4, 28, and 83 km from the crater lake source. Examination of 3-componennt seismic amplitudes, peak frequency content, and directionality combined with video imagery and sediment concentration data were used. The seismic data shows the evolution of the lahar as it transformed from a highly turbulent out-burst flood (high peak frequency throughout), to a fully bulked up multi-phase hyperconcentrated flow (varying frequency patterns depending on the lahar phase) to a slurry flow (bedload dominant). Estimated directionality ratios show the elongation of the lahar with distance down channel, where each recording station shows a similar pattern, but for differing lengths of time. Furthermore, using directionality ratios shows extraordinary promise for lahar monitoring and detection systems where streamflow is present in the channel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".