New Insights on Ground Deformation at Campi Flegrei Caldera Inferred From Kinematics and Dynamics Investigation of Borehole Tilt

Falanga, M.; Aquino, I.; Lauro, E. De; Petrosino, Simona; Ricco, C.

doi:10.1029/2022ea002702

Cited by 4 publications

(1 citation statement)

References 64 publications

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“…Phlegrean Fields caldera has experienced ground uplift of up to 15 mm/month, and an increase in magnitude and extent 150 of seismicity, especially in 2021-2023 (Falanga et al, 2023).…”

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confidence: 99%

A field-based thickness measurement dataset of fallout pyroclastic deposits in the peri-volcanic areas of Campania region (Italy): Statistical combination of different predictions for spatial thickness estimation

Ebrahimi,

Matano,

Amato

et al. 2024

Preprint

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Abstract. Determining spatial thickness (z) of fallout pyroclastic deposits plays a key role in volcanological studies and shedding light on geomorphological and hydrogeological processes. However, this is a challenging line of research because: (1) field-based measurements are expensive and time-consuming; (2) the ash might have been dispersed in the atmosphere by several volcanic eruptions; and (3) wind characteristics during an eruptive event and soil-forming/denudation processes after ash deposition on the ground surface affect the expected spatial distribution of the fallout pyroclastic deposits. This article tries to bridge this knowledge gap by applying statistical techniques for making representative predictions. First, we compiled a field-based thickness measurement dataset (https://doi.org/10.5281/zenodo.8399487; Matano et al., 2023) of fallout pyroclastic deposits in several municipalities of Campania region, southern Italy. Second, 18 predictor variables were derived mainly from digital elevation models and satellite imageries and assigned to each measurement point. Third, the stepwise regression (STPW) model and random forest (RF) machine learning technique are used for thickness modeling. Fourth, the estimations are compared with those of three models that already exist in the literature. Finally, the statistical combination of different predictions is implemented to develop a less biased model for estimating pyroclastic thickness. The results show that prediction accuracy of RF (RMSE < 82.46 and MAE < 48.36) is better than that existing literature models. Moreover, statistical combination of the predictions obtained from the above-mentioned models through Least Absolute Deviation (LAD) combination approach leads to the most representative thickness estimation (MAE < 45.12) in the study area. The maps for the values estimated by RF and LAD (as the best single model and combination approach, respectively) illustrate that the spatial patterns did not alter significantly, but the estimations by LAD are smaller. This combined approach can help in estimating thickness of fallout pyroclastic deposits in other volcanic regions and in managing geohazards in areas covered with loose pyroclastic materials.

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“…Phlegrean Fields caldera has experienced ground uplift of up to 15 mm/month, and an increase in magnitude and extent 150 of seismicity, especially in 2021-2023 (Falanga et al, 2023).…”

mentioning

confidence: 99%