Shallow Water Bathymetry Inversion Using Hybrid Multi-band Composites Resulted from Integration of Drone-based RGB and Multispectral Imagery

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

Shallow water bathymetry inversion using hybrid multi-band composites resulted from integration of drone-based RGB and multispectral imagery

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

Distance Protection of Series Capacitor Compensated Lines: Practical Considerations, Industrial Status and Development

The introduction of series capacitors in transmission lines causes problems in terms of reliability and the security of distance protection relays. As distance protection is widely used in the transmission network, the challenge of applying it to series compensated lines has been taken up by utilities and relay manufacturers in various ways. In the field of power system protection, developments are largely driven by relay manufacturers, and are often not published in the academic literature; the status and trend of the relay manufacturer’s development are better found in their product manuals and patent activity. Further insight into specific implementations by transmission utilities can be found from publications in industry-led forums and some academic journals. This article surveys the status and development of distance protection for series compensated lines, with a focus on industrial implementation and practical considerations. Factors that influence the protection of series compensated lines are presented. Implementation examples reported by utilities are summarized as examples of the different situations encountered and the methods used to deal with them. It is observed that many utilities use communication-aided protection in series compensated lines, and distance protection is used with reduced reach. Solutions described in relay manuals are presented to demonstrate the manufacturers’ approaches to problems associated with series capacitor protection. While there are methods to counter voltage inversion, current inversion seems to represent a more serious challenge. A patent overview indicates the trends in this domain to be moving towards time-domain-based faster protection methods.

Towards Real-Time Moment Tensor Inversions in a Data Rich Micro-Seismic Environment using Deep Learning

<p>Despite advanced seismological methods, source characterization for micro-seismic events remains challenging since current inversion and modelling of high-frequency waveforms are complex and time consuming. For a real-time application like induced-seismicity monitoring, these methods are slow for true real-time information because they require repeated evaluation of the often computationally expensive forward operation. Moreover, because of the low amplitude and high-frequency content of the recorded micro-seismic signals, routine inversion procedure can become unstable and manual parameter tuning is often required. Therefore, real-time and automatic source inversion procedures are difficult and not standard. A more promising alternative to the current inversion methods for rapid source parameter inversion is to use a deep-learning neural network model that is calibrated on a data set of past and/or possible future observations. Such data-driven model, once trained, offers the potential for rapid real-time information on seismic sources in a monitoring context.</p><p>In this study, we investigate how a supervised deep-learning model trained on a data set of synthetic seismograms can be used to rapidly invert for source parameters. The inversion is represented in compact form by a convolutional neural network which yields seismic moment tensor. In other words, a neural-network algorithm is trained to encapsulate the information about the relationship between observations and underlying point-source models. The learning-based model allows rapid inversion once seismic waveforms are available. Moreover, we find that the method is robust with respect to perturbations such as observational noise and missing data. In this study, we seek to demonstrate that this approach is viable for micro-seismicity real-time estimation of source parameters. As a demonstration test, we plan to apply the new approach to data collected at the geothermal field system in the Hengill area, Iceland, within the framework of the COSEISMIQ project funded through the EU GEOTHERMICA programme.</p>

Multiphase tectonic interaction of Tyrrhenian - Tunisia Margin - Ionian systems: Implications for regional seismogenesis

<p>The region at the transition from the west to the east Mediterranean is a complex puzzle of terrains spanning in age from the Mesozoic Ionian lithosphere to the Pleistocene arc and back arc domains of the Tyrrhenian system. Although the region has had a complicated evolutionary history, the current configuration of terrains fundamentally denotes Miocene to recent kinematics.</p><p>In this contribution we present new data from Tunisia Margin showing the evolution from its formation in early Miocene to recent, the tectonic interaction with the opening of the Tyrrhenian system and its current inversion, and discuss the implications for the regional kinematics evolution.  </p><p>The Tyrrhenian is no longer extending, but all basin borders indicate currently active large-scale thrusting  to strike slip tectonics. Tunisia margins formed by a well-know contractional tectonic phase in early Miocene expressed in large-scale tectonics with a clearly imaged thrust  and fold belt, cut by Messinian to Pliocene extensional faulting. However, high resolution multibeam bathymetry and images of the shallowest layers indicates ongoing inversion tectonics.</p><p>We compare the tectonic evolution of north Tunisia and Tyrrhenian with the patterns of deformation of the Ionian tectonic wedge observed in new and reprocessed seismic images. We interpret the current deformation of the Ionian tectonic wedge based on the integration of evolution of the kinematics from the data sets of observations from the three systems.</p><p>We conclude that the entire region is currently under collision of the Africa Plate with the Adria Plate and the Neogene terrains of the Tyrrhenian Domain.  The corollary is the subduction of the Ionian lithosphere is fundamentally stalled so that the megathrust fault is possibly not any longer accumulating significant shortening and most deformation is currently occurring in steeper faults re-activation or cutting the previous structural framework.</p>