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Optical interferometry can be used to measure the propagation time of the light along fibre optic cables, enabling the detection of environmental perturbations such as earthquake waves as they interact with the fibre [Marra et al., 2018]. However, at any point in time, the measured signal is an integration of the optical path length changes along the entire fibre. This can result in a high measurement noise floor since the ambient noise along the entire length of the seafloor cable is integrated. Over the lengths of thousands of kilometres of transoceanic seafloor cables, this can prevent the detection of smaller magnitude events.We present a novel technique that allows many discrete spans along a single fibre optic cable to be used as individual ocean bottom seismometers. These spans are tens of kilometres long and this reduction in length of each line-receiver allows us to monitor specific locations along the cable path. From an observation window of four months, we show that earthquakes, tides, wave heights and ocean microseisms can all be recordedon individual spans between repeaters on a 5,860 km-long transatlantic cable, in addition to monitoring along the whole cable length.On the continental shelf we observe strong correlations between periodic signals at different frequencies with tides, wave heights and microseisms. In the comparatively quieter deep ocean we have detected two teleseismic earthquakes greater than Mw7. Other enigmatic signals related to ocean tides and the solid Earth’s microseisms are also evident., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge for a period of time. However, the physical designs of the charging unit coils depends on many physical parameters, which leads to complexity when determining how to design the unit. A design then needs to be judged for its quality and performance, for which there are eight proposed objective functions. These objective functions represent performance metrics but are conflicting. Some metrics, such as output power are to be maximized, while others such as stray magnetic field and volume of windings and magnetic cores are to be minimized. Different unit designs will trade off performance for these objectives. In order to address the complex issue of finding near-optimal designs, a machine-learning, Generative Neural Network (GNN) approach is presented for the rapid development of near-optimal DIPT systems. GNNs generate new examples from a trained neural network and have demonstrated remarkable power in creating novel graphic design images from text-to-image training. This stems from their ability to be creative yet constrained in a regular domain. In this case, a simulator network and evaluator generator network are implemented. The simulator is a pre-trained neural network that maps from the physical designs to the objective functions. The generator network is trained to generate the near-optimal physical designs. A design is considered successful if it passes given thresholds for each of the eight objective functions, which evaluate the quality of a design. Before training, the rate of finding successful designs is 0.005%, but within 500 training epochs the rate becomes 98% (about 30 seconds total GPU run time). Engineers and production professionals are interested in both the best performing designs as well as a diversity of configurations to build. In order to improve on these criteria, several different loss functions were developed that incorporate the objective functions. Loss functions are what the neural networks use to determine how to adjust parameters and produce a better design. The various loss functions presented greatly influence the ability of the GNN to produce diverse and high-performance design solutions.

This paper introduces variational design methods that are novel to Geophysics, and discusses their benefits and limitations in the context of geophysical applications and more established design methods. Variational methods rely on functional approximations to probability distributions and model-data relationships. They can be used to design experiments that best resolve either all model parameters, or the answer to specific questions about the system to be interrogated. The methods are tested in three schematic geophysical applications: (i) estimating a source location given arrival times at sensor locations, and (ii) estimating the contrast in seismic wavefield velocity across a stratal interface given measurements of the amplitudes of seismic wavefield reflections from that interface, and (iii) designing a survey to best constrain CO2 saturation in a subsurface storage scenario. Variational methods allow the value of an experiment to be calculated and optimised simultaneously, which results in substantial savings in computational cost. In the context of designing a survey to best constrain CO2 in a subsurface storage scenario, we show that optimal designs may change substantially depending on the questions of interest. Overall, this work indicates that optimal design methods should be used more widely in Geophysics, as they are in other scientifically advanced fields., Comment: Submitted to Geophysical Journal International in June 2023