Synthetic Wave Research Articles

An Attention-Based Deep Learning Model for Phase-Resolved Wave Prediction

Abstract Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters, resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an artificial neural network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and long short-term memory model.

The 2020 Mw 7.0 Samos (Eastern Aegean Sea) Earthquake: joint source inversion of multitype data, and tsunami modelling

SUMMARY We present a kinematic slip model and a simulation of the ensuing tsunami for the 2020 Mw 7.0 Néon Karlovásion (Samos, Eastern Aegean Sea) earthquake, generated from a joint inversion of high-rate GNSS, strong ground motion and InSAR data. From the inversion, we find that the source time function has a total duration of ∼20 s with three peaks at ∼4, 7.5 and 15 s corresponding to the development of three asperities. Most of the slip occurs at the west of the hypocentre and ends at the northwest downdip edge. The peak slip is ∼3.3 m, and the inverted rake angles indicate predominantly normal faulting motion. Compared with previous studies, these slip patterns have essentially similar asperity location, rupture dimension and anticorrelation with aftershocks. Consistent with our study, most published papers show the source duration of ∼20 s with three episodes of increased moment releases. For the ensuing tsunami, the eight available gauge records indicate that the tsunami waves last ∼18–30 hr depending on location, and the response period of tsunami is ∼10–35 min. The initial waves in the observed records and synthetic simulations show good agreement, which indirectly validates the performance of the inverted slip model. However, the synthetic waveforms struggle to generate long-duration tsunami behaviour in simulations. Our tests suggest that the resolution of the bathymetry may be a potential factor affecting the simulated tsunami duration and amplitude. It should be noted that the maximum wave height in the records may occur after the decay of synthetic wave amplitudes. This implies that the inability to model long-duration tsunamis could result in underestimation in future tsunami hazard assessments.

Experimental reproduction of inhomogeneous fjord waves

Waves in coastal areas and fjords can present inhomogeneities that affect the responses of very large floating structures. However, spatial inhomogeneities of waves are usually not included in model testing. In this paper, the feasibility of generating inhomogeneous wave conditions in an Ocean basin is investigated. For that purpose, a fast linear numerical model of a basin of constant depth is applied, and an optimization method is presented, that allows to find the control signal for the multiflap wavemaker. Comparisons with experiments in SINTEF’s Ocean basin demonstrate the validity of the method for generating simple monochromatic synthetic waves, as well as short-crested irregular waves representing realistic conditions in Sulafjorden.

Stochastic Excitation-Incorporated Seismic Fragility Assessment Using Monte Carlo Simulations

Structures are susceptible to damage from natural disasters like tornadoes, floods, and seismic events. A resilient structure can withstand such damage and ensure cost-effectiveness. Therefore, accurately assessing the probability of structural failure is imperative for structural designers. This study employs an innovative fragility methodology to assess seismic damage in structures. In comparison to conventional fragility approaches, this method is cost-effective, universally applicable across various seismic sources, and highly precise. Specifically, this method involves the generation of artificial seismic records using the point-source stochastic seismological model and Monte Carlo simulation method. These synthetic seismic records offer distinct advantages when compared to the seismic data collected from real earthquakes at recording stations, such as the ability to adjust the epicentral distance of the synthetic waves as needed. Therefore, this approach enables a comprehensive analysis of structural responses under seismic waves characterized by different attributes. For example, the influence of different seismological parameters such as wave magnitude and epicentral distance are well studied and compared. The advanced finite element software, OpenSees, serves as the computational platform for examining how structures react to diverse seismic waves. The research findings suggest that, compared to traditional methods, utilizing OpenSees can significantly enhance computational efficiency. Furthermore, the methods employed introduce a novel technique for factoring in wave magnitudes and distances during the calculation of structural seismological responses/fragility. This innovation holds significance for the development of next-generation structural resilience designs.

Identifying and characterising trapped lee waves using deep learning techniques

Trapped lee waves, and resultant turbulent rotors downstream, present a hazard for aviation and land‐based transport. Though high‐resolution numerical weather prediction models can represent such phenomena, there is currently no simple and reliable automated method for detecting the extent and characteristics of these waves in model output. Spectral transform methods have traditionally been used to detect and characterise regions of wave activity in model and observational data; however, these methods can be slow and have their limitations. Machine‐learning (ML) techniques offer a new and potentially fruitful method of tackling this problem. We demonstrate that a deep‐learning model can be trained to accurately recognise and label coherent regions of lee waves from vertical velocity data on a single level from a high‐resolution numerical weather prediction (NWP) model. Using transfer learning, wave characteristics (wavelength, orientation, and amplitude) can be extracted from the trained segmentation model. The use of synthetic wave fields with prescribed wave characteristics makes this transfer learning possible without the need to characterise real complex wave fields. Addition of noise to the synthetic data makes the models more robust when applied to more complex and noisy NWP data. The collection of trained models produced provides a valuable tool to investigate the prevalence and nature of lee wave activity, as well as a new way for forecasters to detect resolved waves. The deep‐learning model was more capable and quicker at detecting and characterising lee waves than a spectral technique was. This work is just one example of how already established ML techniques can be used to detect and characterise complex weather phenomena from NWP model output and observational data, and how the careful use of synthetic data can reduce the requirements for large volumes of hand‐labelled training data for ML models.

Evaluation of nearshore bathymetric inversion algorithms using camera observations and synthetic numerical input of surface waves during storms

Nearshore bathymetry is difficult to measure using survey methods when wave heights approach the breaking limit. Remote sensing using digital cameras offers a way to observe the surf zone and calculate water depths based on phase speed but comes with its challenges of potentially noisy data that can introduce error into estimates of frequency and wavenumber used in phase speed calculation. This study investigates the robustness of a new version of a bathymetric inversion algorithm (cBathy, version 2.0) in moderate to energetic wave conditions by comparing depth estimates from timeseries’ of pixel intensity with depth estimates from synthetic water level data. The synthetic data are generated by the phase-resolving numerical model, SWASH, and optical data were collected during a field experiment in 2015. Model results from SWASH computed with known bathymetry are used as input to cBathy, and depth estimates are compared to nearshore surveys. Argus camera observations are also used as input to cBathy for the same times as the SWASH simulations. The SWASH simulations resolve breaking waves and do not include (optical) changes to the relation between water surface slope and pixel intensity, termed modulation transfer function, that occur during wave breaking, enabling better estimates from bathymetric inversion algorithms near morphologic features like sand bars. The results indicate that improvements result from eliminating of disruptions to the modulation transfer function caused by wave breaking and residual foam. We show that the use of synthetic wave data is a valuable means of isolating errors in bathymetric inversion algorithms.

Overcoming losses in superlenses with synthetic waves of complex frequency.

Superlenses made of plasmonic materials and metamaterials can image features at the subdiffraction scale. However, intrinsic losses impose a serious restriction on imaging resolution, a problem that has hindered widespread applications of superlenses. Optical waves of complex frequency that exhibit a temporally attenuating behavior have been proposed to offset the intrinsic losses in superlenses through the introduction of virtual gain, but experimental realization has been lacking because of the difficulty of imaging measurements with temporal decay. In this work, we present a multifrequency approach to constructing synthetic excitation waves of complex frequency based on measurements at real frequencies. This approach allows us to implement virtual gain experimentally and observe deep-subwavelength images. Our work offers a practical solution to overcome the intrinsic losses of plasmonic systems for imaging and sensing applications.

Testing Resilience Aspects of Operation Options for Offshore Wind Farms beyond the End-of-Life

An anticipated challenge for the offshore wind industry is the legally standardized decommissioning of offshore wind infrastructure after the expiration of the respective approval period. To meet the energy and climate targets set by, e.g., the German Federal Government, this challenge must be mastered in the context of sustainability. Potential concepts are (i) the deconstruction of offshore infrastructure without replacement, (ii) the continued operation of the plants, (iii) partially or even completely replacing them with newer, modernized plants (re-powering). Re-powering could also be a combination of existing infrastructures with other innovative technologies, such as hydrogen. In this work, the three concepts are analyzed along with their risks and additional factors, such as feasibility, cost-effectiveness, predictability of technological progress, and, planning security, are discussed. A quantitative risk and resilience analysis is conceptually demonstrated for the specific risk of extreme weather and wave conditions caused by climate change. Synthetic wave height data are generated and the corresponding load changes are applied to example offshore wind farms. The three end-of-life options are compared using resilience indicators that serve as exemplary measures for the energy output, which serves as the key performance indicator.

Influence of low-velocity superficial layer on long-period basin-induced surface waves in eastern Osaka basin

The long-period strong ground motions with periods above 1 s have, in the case of farther or deeper earthquakes, potential to cause serious damage to structures with low eigen frequency, such as long bridges, oil tanks, or artificially damped structures, such as high-rise buildings. This work focuses on wave propagation due to the deep large earthquake representing rare deep damaging events of the region, with relatively sparse data coverage, studied for simple geological models computed by 2D finite differences. We model the wave propagation by finite differences using up-to-date 3D structural model of the Osaka basin. The strong surface waves in the region are not directly generated by these deep sources, but they originate by refraction mostly at the edges of the bedrock–sediments interface. The objective of this research is to model observed surface Love wave generated in the eastern part of the basin that propagates approximately westwards and is recorded by several surface stations. At these stations, the 3D finite-difference modeling provides a good fit with the observed surface wave in terms of waveform, amplitude, and arrival time for the most detailed 3D velocity model that contains topmost 50–250 m structure with the lowest S-wave velocities of 250 m/s. The semblance analysis of the synthetic wave field reveals that the respective synthetic surface wave is a result of interfering waves arriving in OSA and WOS stations from NE and SE directions. Performed tests reveal that such a synthetic wave field is extremely sensitive to the presence of the superficial 50—250 m thick low-velocity structure which is only a small fraction of the propagating surface wave length and occupies only part of the surface area. The ability to model the surface wave in terms of amplitude and time arrival validates the 3D structural model for long-period Osaka Bay earthquake scenario computations.Graphical

A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex

BackgroundIn the cerebral cortex, disinhibited activity is characterized by propagating waves that spread across neural tissue. In this pathological state, a widely reported form of activity are spiral waves that travel in a circular pattern around a fixed spatial locus termed the center of mass. Spiral waves exhibit stereotypical activity and involve broad patterns of co-fluctuations, suggesting that they may be of lower complexity than healthy activity.ResultsTo evaluate this hypothesis, we performed dense multi-electrode recordings of cortical networks where disinhibition was induced by perfusing a pro-epileptiform solution containing 4-Aminopyridine as well as increased potassium and decreased magnesium. Spiral waves were identified based on a spatially delimited center of mass and a broad distribution of instantaneous phases across electrodes. Individual waves were decomposed into “snapshots” that captured instantaneous neural activation across the entire network. The complexity of these snapshots was examined using a measure termed the participation ratio. Contrary to our expectations, an eigenspectrum analysis of these snapshots revealed a broad distribution of eigenvalues and an increase in complexity compared to baseline networks. A deep generative adversarial network was trained to generate novel exemplars of snapshots that closely captured cortical spiral waves. These synthetic waves replicated key features of experimental data including a tight center of mass, a broad eigenvalue distribution, spatially-dependent correlations, and a high complexity. By adjusting the input to the model, new samples were generated that deviated in systematic ways from the experimental data, thus allowing the exploration of a broad range of states from healthy to pathologically disinhibited neural networks.ConclusionsTogether, results show that the complexity of population activity serves as a marker along a continuum from healthy to disinhibited brain states. The proposed generative adversarial network opens avenues for replicating the dynamics of cortical seizures and accelerating the design of optimal neurostimulation aimed at suppressing pathological brain activity.

Wave-By-Wave Prediction in Narrowly Spread Seas Using Fixed- and Drifting-Point Wave Records: Validation Using Physical Measurements

Abstract Accurate and reliable phase-resolved prediction of ocean surface waves is crucial for many offshore operations in ocean engineering and marine science. One important application is in optimal control of a power take-off in a wave energy converter, leading to significantly higher power production. Our interest is forecasting wave fields based on measurements obtained from multiple upwave locations in moderate seas with small directional spreading angles, as occurs along the south coast of Australia. The prediction model, based on FFTs and propagation of waves according to the linear dispersion relation, is applied to both wave groups and irregular wave fields generated in a wave basin and, additionally, to ocean waves measured with drifting wave buoys. To account for spreading, the model numerically advances linear, plane (i.e., long-crested) waves in space at an optimum offset angle equal to the underlying sea-state root mean square spreading angle. Averaging predictions from a few slightly separated measurement locations, each weighted according to its estimated variance, results in more accurate predictions than from any single location. We also assess in detail the effect of drifting buoy measurements in both long-crested and short-crested seas using synthetic wave records and show that it is possible to satisfactorily reconstruct the signal at fixed points based on the Doppler shift felt by the drifting buoy. The reconstructed signals give much better predictions compared to those completely neglecting the effect of even rather slow drift due to current.

Elastic immersive wave experimentation

SUMMARY We describe an elastic wave propagation laboratory that enables a solid object to be artificially immersed within an extended (numerical) environment such that a physical wave propagation experiment carried out in the solid drives the propagation in the extended (numerical) environment and vice versa. The underlying method of elastic immersive wave experimentation for such a laboratory involves deploying arrays of active multicomponent sources at the traction-free surface of the solid (e.g. a cube of granitic rock). These sources are used to accomplish two tasks: (1) cancel outgoing waves and (2) emit ingoing waves representing the first-order interactions between the physical and extended domains, computed using, for example, a finite-difference (FD) method. Higher-order interactions can be built by alternately carrying out the processes for cancelling the outgoing waves and the FD simulations for generating the ingoing waves. We validate the proposed iterative scheme for realizing elastic immersive wave experimentation using 2-D synthetic wave experiments.

Validity of the wave stationarity assumption on estimates of wave attenuation in sea ice: toward a method for wave–ice attenuation observations at global scales

AbstractIn situ observations of wave attenuation by sea ice are required to develop and validate wave–ice interaction parameterizations in coupled wave models. To estimate ice-induced wave attenuation in the field, the wave field is typically assumed to be stationary. In this study we investigate the validity of this assumption by creating a synthetic wave field in sea ice for different attenuation rates. We observe that errors in estimates of the wave attenuation rates are largest when attenuation rates are small or temporal averaging periods are short. Moreover, the adoption of the wave stationarity assumption can lead to negative estimates of the instantaneous wave attenuation rate. These apparent negative values should therefore not be attributed to wave growth or erroneous measurements a priori. Surprisingly, we observe that the validity of the wave stationarity assumption is irrelevant to the accuracy of estimates of wave attenuation rates as long as the temporal averaging period is taken sufficiently long. This may provide opportunities in using satellite-derived products to estimate wave attenuation rates in sea ice at global scales.

Development of a finite element-based damage localization technique for concrete by applying coda wave interferometry

Coda wave interferometry is a structural health monitoring technique that allows the detection of cracks in concrete using diffuse ultrasound. This study develops a finite element-based methodology that computes a substitute model referred to as a sensitivity kernel. This kernel simulates the decorrelation development of two compared ultrasonic signals over the signals’ length and allows to locate damage by solving an inverse problem. Results are compared to an established analytical approach. Two different solving techniques namely the LSMR method and one that respects upper and lower bounds on the variables called STIR are successfully applied in this study. The finite element-based methodology is validated with a reference simulation. In addition to the synthetic wave signals, the approach is tested on a real four-point bending test of a reinforced concrete specimen, where multiple cracks were successfully detected.

SEM3D: A 3D High-Fidelity Numerical Earthquake Simulator for Broadband (0–10 Hz) Seismic Response Prediction at a Regional Scale

In this paper, we present SEM3D: a 3D high-fidelity numerical earthquake simulator that is tailored to predict the seismic wave field of complex earthquake scenarios from the fault to the epicenter site. SEM3D solves the wave-propagation problem by means of the spectral element method (SEM). The presented demonstrative test case was a blind MW6.0 earthquake scenario at the European experimental site located in the sedimentary basin of Argostoli on the island of Kefalonia (Western Greece). A well-constrained geological model, obtained via geophysical inversion studies, and seismological model, given the large database of seismic traces recorded by the newly installed ARGONET network, of the site were considered. The domain of interest covered a region of 44 km × 44 km × 63 km, with the smallest grid size of 130 m × 130 m × 35 m. This allowed us to simulate the ground shaking in its entirety, from the seismic source to the epicenter site within a 0–10 Hz frequency band. Owing to the pseudo-spectral nature of the numerical method and given the high polynomial order (i.e., degree nine), the model featured 1.35·1010 DOFs (degrees of freedom). The variability of the synthetic wave field generated within the basin is assessed herein, exploring different random realizations of the mean velocity structure and heterogeneous rupture path.

A data-driven analysis of inhomogeneous wave field based on two-dimensional Hilbert–Huang transform

Quantitative characterization of the wave field nearshore is critical for coastal applications. The spatial inhomogeneity of the coastal wave field poses challenges to conventional Fourier analysis. To address this issue, we propose a data-driven analysis framework based on the adaptive two-dimensional Hilbert–Huang transform, the accuracy of which is first demonstrated using synthetic wave data. We then conduct wave-phase-resolved simulations based on a high-order spectral method, where the initial wave conditions are constructed for sea states with various wave field properties and the bathymetry profile varies continuously from deep water to shallow water. The impact of varying bathymetry is observed on the raw data obtained from the simulation and the large-scale components obtained from the empirical mode decomposition of the raw data. We also calculate the Hilbert spectrum and identify the features of coastal wave processes including refraction, shoaling and breaking. We propose three integral quantities to characterize the spatially-variant wave field, including the direction angle, the characteristic wavenumber, and the wave energy. Further discussions on the limitations of the conventional Fourier analysis and the Hilbert–Huang transform are also provided.

Three-Dimensional Model of Oil and Gas Reservoirs Based on Gaussian Beam Processing of Scattered Seismic Waves

Abstract —The efficiency of the development of an oil and gas field is largely determined by the knowledge of its geologic structure. In the recent decade, complex fractured carbonate reservoirs have attracted more and more attention. This paper is concerned with a new technology for constructing 3D images of complex reservoirs, based on Gaussian beam processing of scattered seismic waves. This technology was developed at OOO RN-KrasnoyarskNIPIneft’ in cooperation with the Trofimuk Institute of Petroleum Geology and Geophysics. To test it, a special synthetic model was constructed, which is analogous to one of the licensed objects of PAO NK Rosneft’. For this purpose, a full-scale 3D seismic was performed, which provided us with synthetic wave fields and made it possible to carry out well-controlled numerical experiments for reconstructing the geologic structure of the object of study. One of the distinctive features of the constructed digital model (digital twin) is the presentation of faults not as some ideal slip surfaces but as 3D geologic bodies filled with tectonic breccias. A series of numerical experiments was performed to simulate such breccias, the geometry of these bodies, and the geomechanical processes of fault formation. To select the parameters of the used method of discrete elements, we used the information obtained by geophysical studies in horizontal wells crossing the fault within the geologic prototype of the constructed digital model.

Performance Analysis of Multiple Antennas Synthetic False Target Jamming

The radar jamming technology has been studied in this paper mainly, which uses the antenna synthetic wave to generate phase distortion at the signal reception to carry out angle measuring jamming, such as traditional cross-eye jamming. However, cross-eye jamming has strict parameter tolerance. To improve the tolerance range of the phase parameters, a multiple antennas synthetic false target electromagnetic jamming technique is proposed in this paper, which has a wider phase parameter tolerance and jamming range. The jamming method can be regarded as an improved cross eye jamming method. We can conclude that this method can generate a “false target” in space and far away from the carrier which has jamming system, which mislead the radar to point to “false target” rather than to real targets. A jamming system with three jamming antennas was considered as the research object. The position of “false target” can be controlled casually by adjusting the feed amplitude and phase of the three antennas simultaneously. The jamming mathematical model of synthetic false target with three antenna emitters is constructed under the sum and difference channel transceiver mechanism of radar in this paper. The error angle and synthetic gain of the three jamming antennas were derived. The phase parameter tolerance of the proposed method was obtained- and compared with cross-eye jamming. A rigorous mathematical derivation is proposed in this paper, and the superiority of the multiple antennas synthetic false target jamming method is verified.

Annual and seasonal trend detection of significant wave height, energy period and wave power in the Mediterranean Sea

An analysis of a 40-year long wave time series was carried out in the Mediterranean basin to identify ongoing trends of three wave parameters: significant wave height, energy period and wave power. Synthetic wave data were deduced from the global atmospheric reanalysis ERA-Interim by the ECMWF. After a preliminary data examination, a trend analysis, at annual and seasonal scales, was performed on the mean and maximum values through the Mann-Kendall test. Moreover, the application of the Theil-Sen estimator allowed us to quantify the magnitude of the increase/decrease in the wave parameters. Results highlighted different behaviours between the mean and the maximum values. In general, an overall increase in the mean wave quantities was observed, particularly for the energy period, while the increase in the maximum wave parameters was limited to a few grid points. For both the mean and the maximum values, negative trends were identified in specific areas of the Mediterranean Sea, especially for the significant wave height and the wave power. The study findings could be useful for analyses linked to various activities such as coastal inundations and erosions, the design of marine structures and the identification of optimal sites for field installations of Wave Energy Converters.

Estimating ocean wave directional spreading using wave following buoys: a comparison of experimental buoy and gauge data

Directional spreading of ocean waves plays an important role in various aspects of ocean engineering, such as wave-induced loads, nonlinear wave evolution, and wave breaking. Wave following buoys, which are widely deployed across the oceans, offer the potential to measure directional wave properties. To assess the accuracy of directional spreading estimates made using buoy measurements, we compared estimates based on experimentally obtained buoy and wave gauge measurements, first presented in McAllister and van den Bremer (J Phys Oceanogr 50:399–414, 2020) Buoy and gauge measurements were recorded at the same locations and in identical sea states, allowing for a like-for-like comparison. We examine experiments with both following (unimodal in direction) and crossing (bimodal in direction) sea states. In addition to this, we use synthetic wave data to investigate the effects of wave generation and nonlinearity on spreading estimates. Our results show that while directional estimates produced using buoy measurements are reasonably accurate in following sea states, they struggle to identify distinct directional peaks in crossing sea states. We find that spreading estimates made using buoy measurements tend to underestimate the degree of directional spreading by approximately 9-14% in following sea states, which is most apparent in narrowly spread conditions.