Search for: All records

Small‐scale convection beneath the oceanic plates has been invoked to explain off‐axis nonplume volcanism, departure from simple seafloor depth‐age relationships, and intraplate gravity lineations. We deployed 30 broadband ocean bottom seismometer stations on ∼40 Ma Pacific seafloor in a region notable for gravity anomalies, measured by satellite altimetry, elongated parallel to plate motion.P‐wave teleseismic tomography reveals alternating upper mantle velocity anomalies on the order of ±2%, aligned with the gravity lineations. These features, which correspond to ∼300°–500°K lateral temperature contrast, and possible hydrous or carbonatitic partial melt, are—surprisingly—strongest between 150 and 260 km depth, indicating rapid vertical motions through a low‐viscosity asthenospheric channel. Coherence and admittance analysis of gravity and topography using new multibeam bathymetry soundings substantiates the presence of mantle density variations, and forward modeling predicts gravity anomalies that qualitatively match observed lineations. This study provides observational support for small‐scale convective rolls beneath the oceanic plates.

 

Abstract The Pacific ocean-bottom seismometer (OBS) Research into Convecting Asthenosphere (ORCA) experiment deployed two 30-station seismic arrays between 2018 and 2020—a US contribution to the international PacificArray project. The “Young ORCA” array deployed on ∼40 Ma central Pacific seafloor had a ∼68% data recovery rate, whereas the “Old ORCA” array deployed on ∼120 Ma southwest Pacific seafloor had a ∼80% recovery rate. We detail here the seismic data quality, spectral characteristics, and engineering challenges of this experiment. We provide information to assist users of this dataset, including OBS orientations and tables of daily data quality for all channels. Preliminary analysis illustrates the utility of these data for surface- and body-wave seismic imaging. 

Since the seminal 1961 paper of Monod and Jacob, mathematical models of biomolecular circuits have guided our understanding of cell regulation. Model-based exploration of the functional capabilities of any given circuit requires systematic mapping of multidimensional spaces of model parameters. Despite significant advances in computational dynamical systems approaches, this analysis remains a nontrivial task. Here, we use a nonlinear system of ordinary differential equations to model oocyte selection in Drosophila , a robust symmetry-breaking event that relies on autoregulatory localization of oocyte-specification factors. By applying an algorithmic approach that implements symbolic computation and topological methods, we enumerate all phase portraits of stable steady states in the limit when nonlinear regulatory interactions become discrete switches. Leveraging this initial exact partitioning and further using numerical exploration, we locate parameter regions that are dense in purely asymmetric steady states when the nonlinearities are not infinitely sharp, enabling systematic identification of parameter regions that correspond to robust oocyte selection. This framework can be generalized to map the full parameter spaces in a broad class of models involving biological switches.