Monitoring slow uplift and subsidence in shallow seafloor environments using bottom pressure measurements

SUMMARY Ultraslow seafloor deformations are frequent in several marine environments like volcanic calderas, offshore oil and gas extraction fields subject to subsidence and river delta regions; they can exhibit unpredictable behaviours, particularly in caldera systems situated along coastlines. However, offshore monitoring of seafloor uplift and subsidence is still very challenging. Here, we present a new method to recover vertical seafloor deformation at Campi Flegrei caldera, Southern Italy, using bottom pressure recorder (BPR) sensors, tide gauges and a barometer. Using data from two BPRs installed on the seabed within the multiparametric elastic-beacon devices and underwater sensors acquisition (MEDUSA) marine infrastructure of the Istituto Nazionale Geofisica e Vulcanologia (INGV) -‘Osservatorio Vesuviano’, we transform pressure measurements into equivalent water level changes to derive the vertical seafloor displacements. We obtain high-accuracy vertical deformation records from the BPR measurements by taking into account the high-resolution mean seawater density variation over time, estimated by applying an innovative procedure to the BPRs data and using additional barometric and sea level data. We obtained for the two BPRs an uplift of 22.8 cm over about 2 yr and 7 cm over about 18 months, respectively. We compare the results with data acquired by GPSs installed on the top of MEDUSA buoys, deployed at the same sites as the BPRs, which recorded the vertical seafloor deformation values of 22 and 6.9 cm, respectively, over the same periods. These independent data sets show a strong correlation, with correlation coefficient values of 0.98 and 0.87, and very good agreement in both the trend and amplitude of vertical motion, proving the reliability of BPRs in accurately measuring vertical seafloor deformation. The methodology we developed allows a cost-effective implementation of high-accuracy seafloor vertical deformation monitoring networks.