Probabilistic, Multi‐Sensor Eruption Forecasting
Abstract We developed an eruption forecasting model using data from multiple sensors or data streams with the Bayesian network method. The model generates probabilistic forecasts that are interpretable and resilient against sensor outage. We applied the model at Whakaari/White Island, an andesite is...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
|
| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL112029 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849243686943064064 |
|---|---|
| author | Y. Behr A. Christophersen C. Miller |
| author_facet | Y. Behr A. Christophersen C. Miller |
| author_sort | Y. Behr |
| collection | DOAJ |
| description | Abstract We developed an eruption forecasting model using data from multiple sensors or data streams with the Bayesian network method. The model generates probabilistic forecasts that are interpretable and resilient against sensor outage. We applied the model at Whakaari/White Island, an andesite island volcano off the coast of New Zealand, using seismic tremor recordings, earthquake rate, and CO2, SO2, and H2S emission rates. At Whakaari/White Island, our model shows increases in eruption probability months to weeks prior to the three explosive eruptions that were recorded between 2013 and 2019. Our model outperforms the use of any of the data sets alone as an indicator for impending eruptions. Although developed for Whakaari/White Island, our model can be easily adapted to other volcanoes, complementing existing forecasting methods that rely on single data streams. |
| format | Article |
| id | doaj-art-af5018ddfe5f4400a11332d39b4fc0a4 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-af5018ddfe5f4400a11332d39b4fc0a42025-08-20T03:59:22ZengWileyGeophysical Research Letters0094-82761944-80072025-04-01528n/an/a10.1029/2024GL112029Probabilistic, Multi‐Sensor Eruption ForecastingY. Behr0A. Christophersen1C. Miller2Wairakei Research Center GNS Science Taupo New ZealandGNS Science Lower Hutt New ZealandWairakei Research Center GNS Science Taupo New ZealandAbstract We developed an eruption forecasting model using data from multiple sensors or data streams with the Bayesian network method. The model generates probabilistic forecasts that are interpretable and resilient against sensor outage. We applied the model at Whakaari/White Island, an andesite island volcano off the coast of New Zealand, using seismic tremor recordings, earthquake rate, and CO2, SO2, and H2S emission rates. At Whakaari/White Island, our model shows increases in eruption probability months to weeks prior to the three explosive eruptions that were recorded between 2013 and 2019. Our model outperforms the use of any of the data sets alone as an indicator for impending eruptions. Although developed for Whakaari/White Island, our model can be easily adapted to other volcanoes, complementing existing forecasting methods that rely on single data streams.https://doi.org/10.1029/2024GL112029Bayesian networkeruption forecastingWhakaariWhite Island |
| spellingShingle | Y. Behr A. Christophersen C. Miller Probabilistic, Multi‐Sensor Eruption Forecasting Geophysical Research Letters Bayesian network eruption forecasting Whakaari White Island |
| title | Probabilistic, Multi‐Sensor Eruption Forecasting |
| title_full | Probabilistic, Multi‐Sensor Eruption Forecasting |
| title_fullStr | Probabilistic, Multi‐Sensor Eruption Forecasting |
| title_full_unstemmed | Probabilistic, Multi‐Sensor Eruption Forecasting |
| title_short | Probabilistic, Multi‐Sensor Eruption Forecasting |
| title_sort | probabilistic multi sensor eruption forecasting |
| topic | Bayesian network eruption forecasting Whakaari White Island |
| url | https://doi.org/10.1029/2024GL112029 |
| work_keys_str_mv | AT ybehr probabilisticmultisensoreruptionforecasting AT achristophersen probabilisticmultisensoreruptionforecasting AT cmiller probabilisticmultisensoreruptionforecasting |