Autonomous underwater vehicles (AUVs) show much promise in environmental sensing, aquaculture, and security applications. Robust and adaptive control strategies can immensely benefit these scenarios by increasing autonomy and endurance. However, AUVs are nonlinear systems whose dynamics are challenging to model, especially during agile maneuvers at high angles of attack. To better capture these nonlinear effects, this paper proposes a physics-informed system identification scheme that combines prior knowledge of the system dynamics with data-driven regression. Strategies including Sparse Identification of Nonlinear Dynamics (SINDy), nonlinear least squares regression, and Gaussian processes (GPs) are used to learn the AUV dynamics online from measured data. These data-driven models are then implemented in an adaptive model predictive controller (MPC) for agile maneuvering that drives the system to a set point while updating the prediction model when new measurements are available. The performance of these three system identification strategies is evaluated on two different 6-DOF AUV platforms. All three strategies show good real-time performance, while the GP model offers the best balance between accuracy, speed and robustness. Field experimental data from the SAM AUV and the MOLA AUV are used for performance evaluation.
Three voluminous inflated lobate lava flow complexes on the distal rifts of Axial Seamount are much larger than other known flows in the global spreading system. Each complex is 65-100 km2, is up to 130 m thick, and is similar to 3.0-4.6 km3, almost 100 times the volumes of historical Axial flows. These extraordinary flows are 5-7 times thicker than typical drained ponds in sheet flows. They thickened as impounded lava accumulated under chilled crusts. As flows expanded, molten interiors partially drained and flow tops collapsed. Levees built around collapses when interiors are repressurized. This formation sequence was preserved when the levee around one deep pond breached and drained the interconnected ponds. The complexes formed during moderately high-rate eruptions. Lavas from the south rift complex are plagioclase phyric mid-ocean ridge basalt (MORB) and those from the north rift complex are nearly aphyric and slightly more evolved. Glass compositions are similar to those of the summit and most rift lavas, implying that they resided in the summit magma reservoir where depleted ridge-derived magma and more enriched hot-spot-derived magma mixed. The distal south rift complex formed similar to 1259 +/- 119 years BP (or similar to 691 CE; based on 14C dating of planktic foraminifera from core bases), a date that is statistically indistinguishable from the dates of phreatomagmatic deposits at the summit and formation of the present-day caldera. The north rift voluminous flows erupted similar to 12,870 +/- 173 years BP. The southwest complex, although partly mapped, remains unsampled, and is still older. Eruptions of these earlier voluminous lava complexes may also have coincided with prior caldera collapses.
A global coordination and continuous synthesis of interoperable data related to biogeochemical Essential Ocean Variables (EOVs) is critically needed to enhance the creation of information products and services to sustainably manage the climate system and ocean health. Among the existing biogeochemical EOVs, data synthesis products—which demonstrate the immense value of data coordination—already exist for carbon-relevant data (e.g. SOCAT, Global Ocean Data Analysis Project), and for methane and nitrous oxide (MEMENTO). The roadmap for building a Global Ocean Oxygen Database and ATlas (GO _2 DAT) (Grégoire et al (2021 Front. Mar. Sci. 1638 )) provides the theoretical basis to increase the interoperability of ocean oxygen data sets, without creating yet another separate repository. The goal is now to advance from the idea of GO _2 DAT to its implementation, building a sustainable, interoperable, and inclusive digital ecosystem for all stakeholders who may use ocean oxygen data. Successful implementation will require (I) the provision of guidance on data acquisition/ocean oxygen measurements, (II) recommended practices for ocean oxygen data management, including metadata requirements, uncertainty and data quality control attribution, (III) development of the ocean oxygen data platform including data flow and application of the recommended practices introduced in I and II, as well as its deep integration with cross-domain data federations such as the Ocean Data and Information System. This document provides an outline of GO _2 DAT’s objective and progress since 2021 and contributes to addressing these three requirements, synthesizing a series of global consultations on recommended practices for marine dissolved oxygen measurements, a working definition of ocean oxygen metadata, proposed data quality control levels and flags, a described novel mechanism for uncertainty attribution to allow the determination of data suitability for different scientific applications, and it concludes with an illustration of the data flow for implementation.
Abyssal marine turbidites provide some of the longest and most spatially extensive records of subduction zone earthquake recurrence globally; however, correlation of these deposits over long distances and interpretation of synchronous emplacement requires both an understanding of the turbidite generating systems and precise dating. Here, we present an integrated suite of high-resolution bathymetry, subbottom profiles, and sediment cores from combined autonomous underwater vehicle, remotely operated vehicle, and ship-based studies at a key paleoseismic site in the southern Cascadia subduction zone. We demonstrate how widespread, earthquake-triggered landslides on the lower slope deposit discrete, proximal mass transport deposits (MTDs) that grade offshore into complex, interfingered abyssal turbidites, which correspond to records of megathrust earthquake history. We propose accretion and oversteepening of thrust folds on the lower slope both preconditions the slope to fail and provides a perpetual source of unstable material to fail during every earthquake cycle. Furthermore, we suggest the periodic and pervasive landsliding indicates coseismic deformation of the outer accretionary wedge during megathrust rupture.
Oceanic microorganisms can rapidly respond to environmental variability. Determining how physical and biological processes control microbial distributions, abundances, and metabolic dynamics is challenging. Here, we used autonomous underwater vehicles capable of Lagrangian feature tracking and in situ sampling, in combination with ship-based measurements, to examine diel to weekly scale changes in microbial transcription and biogeochemistry in the deep chlorophyll maximum (DCM) of a mesoscale cyclonic eddy. Nearly 20% of total transcript expression showed diel periodicity, highlighting the importance of the diurnal cycle on phytoplankton metabolism in the dim waters of the DCM. Eddy-induced isopycnal uplift increased nutrient concentrations and caused upward displacement of the DCM, driving increased picoeukaryotic cell abundances and transcriptional activity of nitrate-incorporating photoautotrophs. As the eddy weakened, the DCM deepened and transcriptional activity shifted towards chemolithoautotrophic ammonia-oxidizing archaea. The temporal dynamics observed demonstrate how plankton communities rapidly respond to both diel variation and stochastic mesoscale disturbances.
