Seismological Models and Seismicity Patterns in the Kivu Rift and Virunga Volcanic Province (D.R. Congo / Rwanda)
Bibliographic record
Abstract
The Kivu Rift is located in the bordering region of the Democratic Republic of Congo and Rwanda, in the Western branch of the East African Rift. The active volcanoes Nyamulagira and Nyiragongo in the Virunga Volcanic Province threaten the city of Goma and neighbouring agglomerations. Urbanisation in in the direct vicinity of the volcano undergoes sustained rapid growth, and the region counts 1 million inhabitants today. The successive eruptions of Nyiragongo which occurred in 1977, 2002 and 2021 caused major disasters and casualties. Moreover, destructive earthquakes can also affect the region, as it was the case in 2002 in Kalehe (Mw 6.2) along the western shore of Lake Kivu, or in 2008 in Bukavu (Mw 5.9), south of Lake Kivu. Between 2013 and 2022, the first dense real-time telemetered broadband seismic network in the Kivu Rift region (KivuSNet) was gradually deployed in the frame of several research projects and was fully operational with a sufficient station coverage (>10 stations) since October 2015. Due to the fundamental importance of monitoring the seismicity in this region, substantial efforts were made for setting up this network permanent with real-time data acquisition, which thus rapidly became the main seismic network of the Goma Volcano Observatory for daily routine monitoring work. This contribution will present the lessons learned from more than 6.5 years (October 2015 – June 2022) of continuous seismic monitoring in the the Kivu basin as well as the current status of seismological information derived from these data, including a robust 1D seismic velocity model and calibrated local magnitude scale for the Kivu Rift region. The complete seismicity catalogue (volcanic and tectonic events) has been relocated and the main seismic patterns will be discussed with a special emphasis on how this new knowledge can help the Goma Volcano Observatory in improving its monitoring tasks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".