Numerical Wave Model Research Articles

Multi layers THz duplexer based on combined graphene patterns

Conducting incident waves to transmission and reflection channels via artificial graphene patterns make it possible to realize a wave duplexer. In this way, a multi layers device is introduced and simulated. The presented device includes two layers, each layer consists of graphene pattern on a dielectric. The exploited patterns are periodic arrays of graphene disks, ribbons and continues graphene sheet. Each consisting part is modeled by passive circuit elements and an equivalent impedance is calculated for proposed device. The device is simulated via full wave numerical approach and equivalent circuit model representation. The target frequency operational range is THz while CST and MATLAB software's are involved in simulations.

Wave climate projection in the Persian Gulf: An ensemble of statistically downscaled CMIP6-GCMs

Wave climate projection can provide useful information for climate change adaptation and mitigation. This study explores the future variability of wave conditions in the Persian Gulf under three different shared socio-economic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Near-surface wind speed simulations of the CMIP6 (Coupled Model Intercomparison Project Phase 6) models are evaluated against the ERA5 reanalysis outputs to find the general circulation models (GCMs) with relatively better performance. The outputs related to the selected GCMs are regionalized through a Weibull-based bias-correction technique, applied to the interpolated grid points. A third-generation numerical wave model is employed to simulate wave characteristics over the computational domain using the modified wind field of each CMIP6 GCM. An ensemble model is obtained by applying the arithmetic mean on the wave model's output of the selected GCMs. The ensemble model projects a mild decrease in the future significant wave height, 95th wave height, and peak wave period to the corresponding values in the historical period. The mean wave direction shows partial variations intensifying for the higher emission scenarios in the southern part of the gulf. Projections of extreme waves for the return periods of 2, 5, 10, 50, and 100 years demonstrate insignificant variation in the future extreme conditions. Moreover, the frequency of extreme events under different SSPs is expected to decrease slightly. Finally, wave steepness and wave age calculations for a location in the middle of the gulf show a mixed condition of swells and sea waves with insignificant variability under climate change scenarios.

Minkowski-like fractal seismic metamaterial with wide low-frequency band gaps on single and layered soil

In this paper, we propose a resource-efficient Minkowski-like fractal seismic metamaterial by hybridizing U-shaped and cross-shaped pillars, providing wide complete band gaps (BGs) due to its multi-scale self-similar property. Numerical modeling of seismic surface waves is used to study the band structure and transmission of our seismic metamaterial. A comparison of the band structures of the developed seismic metamaterial with various levels reveals that the iteration order itself is responsible for the increase in the number of frequency bands and the decrease in the center frequencies of the BGs. Furthermore, the vibrational modes are calculated and examined to understand the mechanism of BG generation. To demonstrate the efficiency of earthquake shielding in multiple complete BGs, investigations of seismic surface wave propagation on a 1D array of Minkowski-like fractal structure units on the surface of single and layered semi-infinite substrates are employed. The results show that the layered soil has the function of widening BGs by itself. Our proposed Minkowski-like fractal structure due to its multi-scale self-similarity mitigates the intrinsic drawback of the narrow BG of resonant metamaterial, providing a superior alternative in seismology and related areas of multi-frequency band vibration reduction. Moreover, its green design and manufacturing due to low filling rates, strong wear resistance, and ductility can realize the low-carbon and sustainable development of the construction industry.

The Multi-Channel Maximum-Likelihood (MCML) method for infrasound detection and wave parameter estimation: Evaluation, implementation, and first applications

The detection capability of the International Monitoring System (IMS) infrasound network is a key concern since ubiquitous coherent noise interfere with the identification of explosive events. The need to distinguish between incoherent wind noise and real coherent infrasonic wave signals has motivated the development of a new parametric stochastic model. We establish the expressions of both the Generalized Likelihood Ratio Test (GLRT) and the Maximum Likelihood Estimator (MLE). The major results are the expression of the asymptotic distribution of the GLRT under null hypothesis, leading to the p-value computation, and the expression of the asymptotic covariance. The Multi-Channel Maximum-Likelihood (MCML) detection is implemented in the time-frequency domain in order to discriminate between interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms in poor signal-to-noise ratio scenarios. We illustrate the performance of MCML on real IMS data, highlighting its capability to characterize the coherent ocean ambient infrasound noise in high resolution through comparisons with theoretical state-of-the-art numerical wave models as implemented at the LOPS research unit of IFREMER in the DATARMOR HPC center.

A Deep Learning Model for Improved Wind and Consequent Wave Forecasts

Abstract The paper presents a combined numerical–deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. The correction to the wind forecast resulted in ∼10% RMSE improvement in both wind velocity and wave height over reanalysis data. This significant improvement is even more substantial at the Aegean Sea when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either the atmospheric or wave numerical model by itself. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs. Significance Statement Wind and wave forecasting models solve a set of complicated physical equations. Improving forecasting accuracy is usually achieved by using a higher-resolution, empirical coefficients calibration or better physical formulations. However, measurements are rarely used directly to achieve better forecasts, as their assimilation can prove difficult. The presented work bridges this gap by using a data-driven deep learning model to improve wind forecasting accuracy, and the resulting wave forecasting. Testing over the Mediterranean Sea resulted in ∼10% RMSE improvement. Inspecting the Aegean Sea when the Etesian wind is dominant shows an outstanding 35% improvement. This approach has the potential to improve the operational atmospheric and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible computational costs.

A WAVEWATCH III® model approach to investigating ocean wave source terms for West Africa: Non-linear wave-wave interaction source terms

• Investigation of ocean wave model parameterization schemes for West Africa. • Selection of appropriate non-linear source terms for ocean wave projections. • DIA term best estimates wave heights for sub-sections of West Africa. • Application of no non-linear term best estimates wave heights for the entire region. Ocean wave forecasting for West Africa has mostly relied on global parameterization schemes and data outputs. Investigating non-linear ocean wave-wave interaction source terms ( S nl ) for the region, that forms part of the wave model parameterization schemes, is a contribution of this study towards developing a regional wave modelling scheme for West Africa. The study evaluates five non-linear source term configurations with the WAVEWATCH III® (WW3) numerical ocean wave model version 5.16; (i) No source term applied - NL0, (ii) Discrete Interaction Approximation (DIA) – NL1, (iii) Exact Interaction Approximation – NL2, (iv) Generalized Multiple DIA – NL3, and (v) Two-Scale Approximation – NL4, in order to determine best performing S nl for projecting significant wave heights and directions for the region. The wave simulations were run on three separate grids comprising a low resolution West Africa grid (latitudes 10 o S – 30 o N; longitudes 35 o W – 15 o E), high resolution south-eastern (latitudes 2 o S – 8 o N; longitudes 10 o W – 10 o E) and north-western (latitudes 10 o N – 25 o N; longitudes 30 o W – 10 o W) sub-grids. Simulations for the entire West Africa grid produced higher accuracies for the wave parameters compared to the higher resolution sub-grids. NL0 best estimates significant wave height whereas NL3 best estimates wave directions for the West Africa grid. For combined stations of the south-eastern sub-grid, while NL1 best estimates wave heights, NL3 best estimates the wave directions with possible alternation with NL1 due to insignificant differences. Similarly, for the north-western sub-grid, while NL1 best estimates significant wave heights, NL0 best estimates wave directions with similar ability to alternate with NL2 in projecting wave directions. Generally, insignificant differences exist between the source terms in projecting wave directions, especially for the north-western sub-region, implying that any of the source terms may be used in projecting wave directions without significantly compromising on accuracy. Thus, NL1 which best estimates significant wave heights for both south-eastern and north-western sub-regions can also be used in estimating the wave directions. Similarly, NL0 would be appropriate for the entire West Africa region.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast

Machine-learning techniques have been applied to wave forecasting to address the implementational and computational challenges of conventional numerical wave models. Most previous studies emphasized the accuracy of approaches based on in situ wave measurements, but such approaches are site-specific and lack the spatial and temporal fidelity required by regional wave climate forecasts. This study presents a novel systematization of forecasting regional wave heights at sufficient spatial resolution and coverage, using long-term wave hindcasts generated by numerical wave models as a training dataset for a long short-term memory (LSTM). This systematization not only ensures the accuracy of the model's forecasts but demonstrates the applicability of long-term wave hindcasts as input data. It involves thoroughly investigating the dependence of model accuracy on multi-task LSTM structures (training variables, input data record periods, and forecast lead times), and on regional wave climates (different wave sites and sea states). The resulting model performance is spatially heterogeneous in global wave forecasts, underlining the need for validation of wave forecasting models to global wave climates. This study reveals that machine-learning techniques that leverage validated long-term wave hindcasts can successfully forecast regional wave climates. As access to high-resolution regional wave hindcast data improves, accuracy will also increase.

Investigation of locked operation in a system of two passively mode-locked semiconductor lasers under weak lateral coupling

We study theoretically the operation of two semiconductor passively mode-locked lasers laterally coupled through evanescent fields. The investigation is carried out using a time domain traveling wave numerical model for two-section lasers (gain and absorption) enhanced to include weak mutual coupling. The study of the coupled system shows that although the operation is dominated mostly by instabilities and amplitude modulated pulse trains, under specific conditions, stable mode locking with low intensity noise and time-synchronized pulses emerges. The influence of operational and geometrical parameters on the coupled dynamics is identified and discussed.

Constraints on hydraulic properties from free pressure oscillations recorded during pumping operations in boreholes

SUMMARY Free pressure oscillations during pumping operations in boreholes may potentially constrain hydraulic characteristics of the surrounding material. These damped oscillations occur when flow rate is suddenly changed, and their period and decay rate depend on the hydraulic properties of the entire hydraulic system: the porous medium, a section of the borehole, and/or the injection line, depending on test set-up. There have been previous attempts to estimate transmissivity values from free pressure oscillations that occurred during slug tests in open boreholes. The analysis used did not account for viscous losses due to the fluid interacting with the borehole wall. In contrast, dispersion relations of flow waves in a tight borehole (i.e. a cylindrical hole in an impermeable medium) account for wall friction. We extend a previous analytical treatment of flow waves by changing the boundary condition of the fluid velocity at the borehole wall to include fluid exchange between borehole and porous medium. In addition, we performed numerical modelling of waves propagating in boreholes with impermeable and permeable walls to assess the effect of the assumptions behind the analytical solution. We established how to distinguish cases in which the flow into the porous medium affects the oscillation characteristics (suitable for a hydraulic analysis) from those in which the equipment properties dominate the observations. Applying our methods to a range of field observations yielded plausible hydraulic property values of the rock volume surrounding the borehole.

Numerical modeling of combined wave and current-induced residual liquefaction around twin pipelines

With the operation of offshore oil & gas fields, offshore pipeline has been widely used in offshore engineering. So far, most studies have focused on exploring the seabed responses around a single pipeline, and ignored the effect of multiple pipelines on the physical process of the surrounding seabed. In this study, to investigate the residual seabed dynamic response around twin pipelines, a two-dimensional numerical model, combining the Shear-Stress Transport (SST) turbulence model with the porous seabed model, is developed. Unlike previous studies, this study focuses on three aspects: (1) the physical process of seabed around twin pipelines; (2) the wave and current-induced residual soil response; and (3) the effect of upstream pipeline on the liquefaction of downstream pipeline. Numerical results demonstrate the significant effect of current and twin pipelines’ arrangement on the residual pore pressure and liquefaction zone around the twin pipelines. Results show that the pressure fluctuation caused by the vortex between the twin pipelines deepens the residual liquefaction depth of the seabed.

Multi-instrument observations of microseisms generated by typhoon Kalmaegi (2014) over the Northwestern Pacific

The typhoon-generated microseisms, originating from the complex energy coupling and transferring among the Atmosphere-Ocean-Solid Earth spheres, can be detected remotely by seismometers as the strongest ambient seismic noise. The lack of in situ observations during the passage of typhoons has hampered numerical modeling of wind fields and ocean waves, and limited our understanding of the generation mechanisms of microseisms associated with typhoons. Here we present a comprehensive investigation of microseisms generated by typhoon Kalmaegi (September 2014) based on multiple-instrument constraints from observations including terrestrial and ocean-bottom seismic stations as well as ocean buoys. To understand the generation mechanisms, we apply an improved frequency-domain beamforming method to seismic array data leading to successful location of double-frequency (DF) microseism source regions. For comparison, we calculate the typhoon-induced ocean waves and theoretical source regions of the DF microseisms using the coupled ocean–atmosphere–wave–sediment transport modeling system with validation by ocean buoy observations. Both observations and numerical modeling results reveal two different generation mechanisms for typhoon-induced DF microseisms during the lifespan of Kalmaegi. When over the Philippine Sea, the DF microseisms were generated mainly by opposing ocean waves from two distinct storms. After Kalmaegi entered the South China Sea, the DF microseisms were generated mainly by the fast-moving typhoon with source regions just trailing behind, with the minimum frequencies determined by the typhoon translation speed. DF microseisms generated in coastal source regions were not detected by ocean bottom seismometers, suggesting that DF microseisms might not effectively propagate across the ocean-basin seafloor covered by thick sediments, owing to severe seismic attenuation and spreading losses. This information is crucial for the use of DF microseisms for future tracking and monitoring of typhoons.

Surface Wave and Roller Dissipation Observed With Shore‐Based Doppler Marine Radar

AbstractSurface wave energy and dissipation are observed across the surf zone. Utilizing the concept of surface rollers, a new scaling is introduced to obtain the energy flux and dissipation related to rollers from Doppler velocities measured by a shore‐based X‐band marine radar. The dissipation of wave energy and hence the transformation of the incoming wave height (or energy) is derived using the coupled wave and roller energy balance equations. Results are compared to in‐situ wave measurements obtained from a wave rider buoy and two bottom mounted pressure wave gauges. A good performance in reproducing the significant wave height is found yielding an overall root‐mean‐square error of 0.22 m and a bias of −0.12 m. This is comparable to the skill of numerical wave models. In contrast to wave models, however, the radar observations of the wave and roller energy flux and dissipation neither require knowledge of the bathymetry nor the incident wave height. Along a 1.5 km long cross‐shore transect on a double‐barred, sandy beach in the southern North Sea, the highest dissipation rates are observed at the inner bar over a relatively short distance of less than 100 m. During the peak of a medium‐severe storm event with significant wave heights over 3 m, about 50% of the incident wave energy flux is dissipated at the outer bar.

A Novel Deep Learning Model for Sea State Classification Using Visual-Range Sea Images

Wind-waves exhibit variations both in shape and steepness, and their asymmetrical nature is a well-known feature. One of the important characteristics of the sea surface is the front-back asymmetry of wind-wave crests. The wind-wave conditions on the surface of the sea constitute a sea state, which is listed as an essential climate variable by the Global Climate Observing System and is considered a critical factor for structural safety and optimal operations of offshore oil and gas platforms. Methods such as statistical representations of sensor-based wave parameters observations and numerical modeling are used to classify sea states. However, for offshore structures such as oil and gas platforms, these methods induce high capital expenditures (CAPEX) and operating expenses (OPEX), along with extensive computational power and time requirements. To address this issue, in this paper, we propose a novel, low-cost deep learning-based sea state classification model using visual-range sea images. Firstly, a novel visual-range sea state image dataset was designed and developed for this purpose. The dataset consists of 100,800 images covering four sea states. The dataset was then benchmarked on state-of-the-art deep learning image classification models. The highest classification accuracy of 81.8% was yielded by NASNet-Mobile. Secondly, a novel sea state classification model was proposed. The model took design inspiration from GoogLeNet, which was identified as the optimal reference model for sea state classification. Systematic changes in GoogLeNet’s inception block were proposed, which resulted in an 8.5% overall classification accuracy improvement in comparison with NASNet-Mobile and a 7% improvement from the reference model (i.e., GoogLeNet). Additionally, the proposed model took 26% less training time, and its per-image classification time remains competitive.

Numerical Forecasting of Wind-Waves for Singapore Region

A numerical wave model has been set up and tuned for the Singapore region extending to the parts of the South-China Sea and Malacca Strait. The MIKE 21 spectral wave model was set up using ERA5/ECMWF wind and waves reanalysis data to compute the phase-averaged significant wave heights. The outputs of the calibrated model validates well with the ECMWF data within the domain. The model increases the resolution of available wave data in narrow channelled regions like in the Singapore Straits. When supplied with the wind forecast data the resulting model allows for high resolution forecasting of phase-averaged wave spectra in the Singapore region. The study enables setting up of a wind-wave forecast dissemination framework on a visualization and data-sharing platform or the maritime community.

Error Characterization of Significant Wave Heights in Multidecadal Satellite Altimeter Product, Model Hindcast, and In Situ Measurements Using the Triple Collocation Technique

Abstract Ocean wave measurements are of major importance for a number of applications including climate studies, ship routing, marine engineering, safety at sea, and coastal risk management. Depending on the scales and regions of interest, a variety of data sources may be considered (e.g., in situ data, Voluntary Observing Ship observations, altimeter records, numerical wave models), each one with its own characteristics in terms of sampling frequency, spatial coverage, accuracy, and cost. To combine multiple source of wave information (e.g., for data assimilation scheme in numerical weather prediction models), the error characteristics of each measurement system need to be defined. In this study, we use the triple collocation technique to estimate the random error variance of significant wave heights from a comprehensive collection of collocated in situ, altimeter, and model data. The in situ dataset is a selection of 122 platforms provided by the Copernicus Marine Service In Situ Thematic Center. The altimeter dataset is the ESA Sea State CCI version1 L2P product. The model dataset is the WW3-LOPS hindcast forced with bias-corrected ERA5 winds and an adjusted T475 parameterization of wave generation and dissipation. Compared to previous similar analyses, the extensive (∼250 000 entries) triple collocation dataset generated for this study provides some new insights on the error variability associated to differences in in situ platforms, satellite missions, sea state conditions, and seasonal variability.

Quantum signal processing for simulating cold plasma waves

Numerical modeling of radio-frequency waves in plasma with sufficiently high spatial and temporal resolution remains challenging even with modern computers. However, such simulations can be sped up using quantum computers in the future. Here, we propose how to do such modeling for cold plasma waves, in particular, for an X wave propagating in an inhomogeneous one-dimensional plasma. The wave system is represented in the form of a vector Schr\"odinger equation with a Hermitian Hamiltonian. Block encoding is used to represent the Hamiltonian through unitary operations that can be implemented on a quantum computer. To perform the modeling, we apply the so-called quantum signal processing algorithm and construct the corresponding circuit. Quantum simulations with this circuit are emulated on a classical computer, and the results show agreement with traditional classical calculations. We also discuss how our quantum circuit scales with the resolution.

Performance Evaluation of Seawalls in Mitigating a Real-World Tsunami Wave Using a Nonhydrostatic Numerical Wave Model

In the past few decades, huge surges and waves generated during tsunami events have caused devastating destruction to both onshore and offshore infrastructures, seriously threatening the safety and intactness of coastal communities around the world. As one of the most important coastal protection infrastructures, a seawall can effectively reduce the impact intensity of surges and waves, thus playing a vital role in protecting coastal regions. Most previous studies have systematically investigated the hydrodynamic characteristics of seawalls under the action of regular and irregular waves. Meanwhile, solitary wave models are often used as the wave model for tsunamis. However, vast hydrodynamic differences exist between solitary wave models and real-world tsunamis in terms of both wave profile and wave period. Hence, our understanding of the performance of seawalls in mitigating the damaging power of real-world tsunamis is still insufficient. Hence, it is of great significance to systematically study the performance of seawalls in mitigating the huge surges and waves generated during real-world tsunamis. In comparison to previous research, where the wave profiles of solitary waves were used as tsunami wave models, a parameterized tsunami-like wave based on the recorded wave profile of the 2011 Japan tsunami is applied in this study to evaluate the performance of different seawalls in mitigating tsunami surges and waves by using a nonhydrostatic numerical wave model (NHWAVE). The effects of the prominent factors, such as incident wave height, still water depth, beach slope, and the side slope of the seawall, on the hydrodynamics of the seawall are systematically discussed. It is believed that the research findings drawn from this study can further broaden our understanding of the performance of seawalls in mitigating tsunamis.

Physical and numerical modelling of representative tsunami waves propagating and overtopping in converging channels

Tsunami waves pose a threat to coastal communities through overland inundation and overtopping of coastal defence structures and river walls. Tsunami wave overtopping in rivers and in straight or converging channels is yet to be studied in detail. The present study evaluates the performance of current numerical models that are employed in various tsunami warning centres across the world, in terms of their accuracy for the prediction of tsunami wave characteristics in converging channels. The study evaluates each model performance against a benchmark dataset obtained from novel physical model experiments of representative tsunami waves (solitary waves and bores) overtopping a river wall in a converging channel. The complex process of simultaneous steepening and overtopping was observed to be different for solitary waves and bores. The experimental data were then used to compare results from a suite of different numerical models to predict wave heights and overtopping volumes in straight and converging channels. Different numerical models ranging from nonlinear/nondispersive in two-dimensions (2D) to fully nonlinear/dispersive in three-dimensions (3D) were used. The widely used models based on the nonlinear shallow water equations ( ANUGA , BASEMENT , and TELEMAC 2D) fail to capture the wave profiles and heights, and consequently do not accurately predict the overtopping volumes in channels for both breaking and non-breaking waves. A Boussinesq model ( TELEMAC 2D) was found to produce results consistent with experimental measurements for non-breaking waves only, with an error of up to 8% and 20%, for wave heights and overtopping volumes, respectively. On the other hand, the wave heights and overtopping volumes are underpredicted and overpredicted, for solitary waves and bores, respectively, using a three-dimensional nonhydrostatic model ( TELEMAC 3D). When the overtopping occurs in the converging channel, reflection of the incident waves is minimal in both the numerical and the physical models. However, in the absence of overtopping, both the numerical models and the classical analytical long-wave model fail to correctly predict the observed minimal reflection of non-breaking solitary waves in the converging channel. The reasons for this remain to be investigated. • Novel experiments of representative tsunami waves overtopping river wall were conducted. • 2D and 3D numerical modelling results were compared with experimental data. • Nonlinear shallow water models fail to predict solitary wave/bore heights, shapes and overtopping volumes in channels. • Boussinesq model produces satisfactory results for non-breaking solitary waves only. • Numerical models and classical analytical long-wave model fail to predict solitary wave reflection in a converging channel.

Quantifying microseismic noise generation from coastal reflection of gravity waves recorded by seafloor DAS

SUMMARY Secondary microseisms are the most energetic noise in continuous seismometer recordings. They are generated by interactions between ocean waves, including between gravity waves incident on and reflected from the coast. Coastal reflections of ocean waves leading to coastal microseismic sources are hard to estimate in various global numerical wave models, and independent quantification of these coastal sources through direct measurements can greatly improve these models. Here, we exploit a 41-km-long submarine optical fibre (OF) cable located offshore Toulon, France, using distributed acoustic sensing (DAS). We record both the amplitude and frequency of seafloor strains induced by ocean surface gravity waves, as well as secondary microseisms caused by the interaction of gravity waves incident and reflected from the coast. By leveraging the spatially distributed nature of DAS measurements, additional fundamental information is recovered such as the velocity and azimuth of the waves. We find that on average 30 per cent of the gravity waves are reflected at the coast generating local sources of secondary microseisms that manifest as Scholte waves. These local sources represent the most energetic contribution to the seismic noise recorded along the OF and by an onshore broad-band station located near the DAS interrogator. Furthermore, we estimate a coastal reflection coefficient of ocean surface gravity waves R2 of about 0.07, which provides improved constraints for seismic noise generation models. In addition, we show that new local sources of microseisms can be generated when gravity waves characteristics (azimuth and frequency content) change and lead to some delays between the OF cable and buoy recordings. These analyses pave the way for a wide use of DAS data to monitor ocean–solid earth interactions as they provide a wealth of information on the reflection of gravity waves, coastal microseismic sources, and new constraints for numerical models of microseismic noise.