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 mass flows on volcanoes can be challenging due to the ever-changing landscape along the flow path, which can drastically transform the properties and dynamics of the flow. These changes to the flows require the need for detection strategies and risk assessments 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 volcanic mass flows 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 Aotearoa / New Zealand in over 100 years. At 23:18 UTC the tephra dam holding the Crater Lake water back collapsed causing 1.3×106 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 83 km along the Whangaehu River system using three three-component broadband seismometers installed <10 m from the channel at 7.4, 28, and 83 km from the Crater Lake source. Examination of three-component seismic amplitudes, frequency content, and directionality, combined with video imagery and sediment concentration data, was carried out. The seismic data show 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 the channel, where each recording station depicts 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".