Distributed Acoustic Sensing (DAS) technology enhances seismic monitoring by providing dense, array-like observations near earthquake sources. However, the resulting data volumes, typically on the order of thousands of channels, often limit real-time processing capabilities, with most seismological applications focusing on retrospective analysis of seismic sequences. To address this challenge, we introduce ORION (autOmatic near Real-time channel selectION), a near real-time selector of high-quality DAS channels that reduces the amount of data to process while maintaining key array-like features of the subsampled fiber-optic sensor. The method first adopts spatial clustering to identify cable segments with similar geometrical attributes (e.g., azimuth), and then performs channel selection within each segment using waveform attributes (e.g., signal-to-noise ratio). This approach enables spatial subsampling while preserving azimuthal and spatial coverage. We demonstrate the flexibility of ORION across several cable geometries. Finally, we analyze a seismic sequence recorded with a DAS system using ORION-selected channels and compare the resulting source locations with those obtained using a conventional spatially uniform subset of channels along the cable. The results show significant improvements in the accuracy of the estimated hypocenters.

Given the scarcity of seismometers in marine environments, traditional seismology has limited effectiveness in oceanic regions. Submarine Distributed Acoustic Sensing (DAS) systems offer a promising alternative for seismic monitoring in these areas. However, the existing machine learning model trained on land-based DAS data does not perform well with submarine DAS due to differences in noise characteristics, deployment conditions, and environmental factors. This study presents a machine learning approach tailored specifically to submarine DAS data to enable automated seismic event detection and P and S wave identification. Leveraging DeepLab~v3, a neural network architecture optimized for semantic segmentation, we developed a specialized model to handle the unique challenges of submarine DAS data. Our model was trained and validated on a dataset comprising nearly 57 million manually and semi-automatically labeled seismic records from multiple globally distributed submarine sites, providing a robust basis for accurate seismic detection. The model adapts to a variety of deployment scenarios and can process DAS data from cables with different lengths, configurations, and channel spacings, making it versatile for various ocean environments. We thus provide an adaptable and efficient tool for automated earthquake analysis of DAS data, which has the potential to enhance real-time earthquake monitoring and tsunami early warning in submarine environments.

Continuous, high-density strain and strain-rate distributed acoustic sensing (DAS) recordings are valuable for resolving the shallow Earth's structure at a low cost, especially in environments that are otherwise difficult to access, such as continental shelves and near-coastal oceanic crust. In this study, we apply seismic ambient-noise methods to extract high-quality empirical Green's functions (EGFs) from natural noise sources and model the velocity structure along a 30-km-long dark fibre-optic cable connecting the offshore CASTOR gas storage field in the Gulf of Valencia (Spain) with the associated land facility. We extract broad-band EGFs containing a rich variety of seismic waves using wavelet phase cross-correlation and time-scale phase-weighted stacking methods. In the common-source EGF gathers, clean fundamental and first-overtone Scholte waves dominate the marine channel pairs, while the fundamental Rayleigh mode appears in the land channel pairs. In addition, weak wavefields reflected from the basin edge follow the main surface waves. We then construct a 2-D Vs model from local phase-velocity observations of the fundamental and first-overtone Scholte waves by solving pointwise depth inversions using Markov chain Monte Carlo methods. The model resolves the marine sedimentary basin from very shallow water-saturated sediments to depths exceeding 1 km, identifying the Amposta Central Fault and the basement bedrock west of this fault at roughly 1 km depth. These results help refine the offshore velocity model along the cable in a region where induced seismic activity has been observed, improving the accuracy of seismic monitoring and seismic hazard characterization.

Submarine fiber-optic cables instrumented with distributed acoustic sensing (DAS) provide an effective approach for large-scale monitoring of fin whales. We present an end-to-end workflow for detecting, characterizing, and localizing fin whale notes, tested on two submarine telecom cables in the Strait of Gibraltar and western Alboran Sea. The workflow applies a kurtosis-value picker adapted to narrow-band fin whale notes. Channel-wise detections are grouped into individual notes using density-based spatio-temporal clustering, cluster agglomeration, and hyperbolic fitting to reject incoherent picks. The retained clusters are characterized through temporal, spectral, and energy-related descriptors that support note-type discrimination and estimation of inter-note intervals. Relative arrival times across DAS channels are then used in a grid-search procedure to estimate candidate source locations. Evaluation against manually annotated detections from six fin whale songs yielded median pick-level precision of 0.990 and recall of 0.744, and median cluster-level precision of 0.880 and recall of 0.806. Representative applications demonstrate separation of overlapping vocalizations, characterization of type-A and type-B notes, and the inference of apparent source movement. By transforming dense DAS recordings into compact note-level bioacoustic information, the workflow provides an integrated framework for fin whale monitoring and a basis for adaptation to other synchronized acoustic receiver arrays.

Offshore decommissioning of oil and gas platforms, once fields have completed their productive life, presents several impacts—primarily economic and environmental. However, these impacts can be mitigated, offering both challenges and opportunities (win-win). It is worth noting that offshore platforms, close to the coast, concentrate dense maritime traffic, are prone to environmental, geohazards & subsurface monitoring; not forgetting that they are usually located in strategic areas.Casablanca platform (offshore Mediterranean, REPSOL operator & CLMV-MOEVE-NATURGY partners) has been operated for more than 40 years until 2021. Located in the continent shelf off Tarragona (Spain), its area is nearby a major Mediterranean port, active fishing grounds, intense surface-wave & storms and presence of a known migration corridor for marine mammals. This infrastructure represents a real opportunity for long-term observations, specifically in an offshore region (> 160 meters water-depth) were natural, anthropogenic, geophysical and biological processes converge periodically.Last year a submarine fiber-optics cable (Distributed Acoustic Sensing DAS) was deployed in Casablanca platform; with real time data since Q4-2025. This project was carried out under the European Union Next Generation EU in a public-private collaboration between ICM-CSIC, REPSOL, Alcalá University and Aragon Photonics. By transforming submarine optical fiber cable into dense arrays of virtual sensors, this pilot project enables continuous monitoring of physical processes across solid earth, water column not forgetting atmosphere -ocean interface over displayed cable length of seafloor. But this is not just data acquisition, there is a further paramount computing potential ahead. Artificial Intelligence (AI) has been implemented to tailor DAS data to detect and also classify, almost automatically existing signals from several physical domains. In this case after conditioning & denoising it is possible to differentiate seismic events, vessel activity, marine mammals, ocean-wave and infrastructure related noise; among others. Data Analysis supported by artificial intelligence has proved quite useful, at first sight, for continuous offshore monitoring, detection of low magnitude seismic events.First results, even still provisional, are quite promising and reveal further potential of fiber-optics sensing based on additional cable deployment and focused seafloor design. These real capabilities, not just hypothetical studies, would visualize Casablanca platform as a host scientific observatory for offshore seismicity, ocean noise maritime traffic and possible geohazards, among others. This is a potential step (with real insights) to visualize sustainable & useful future for a legacy asset. A second phase study is already in motion, through a scalable pathway; so, this project just moved from geoscience to real time offshore monitoring. More results to come, stay tuned.

hvarma is a Python software for estimating the horizontal-to-vertical (H/V) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the H/V transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating H/V transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.

Ocean mixing plays a crucial role in the Earth’s climate, however quantifying it is challenging because energy enters the ocean at basin-scale but it dissipates at cm-scale over the vast ocean. Internal waves play an important role in the cascade of energy toward dissipative scales. Energy transfers to the internal wave field are greatly enhanced through flow interactions with topography. Observations of wave-topography interactions are, however, scarce. Distributed Acoustic Sensing (DAS) has recently opened a new door for exploring near-bottom wave dynamics using fibre-optic cables at unprecedented spatio-temporal resolution (meters and seconds) over long spatio-temporal scales (kilometers and months). DAS is particularly attractive as it can use telecommunication cables already in place so that it could potentially be implemented at global scale. Here we present repeated DAS observations on the continental slope east of Gran Canaria island complemented with contemporaneous hydrographic and velocity data collected with bottom moorings for the first time. Results show that upslope propagation of internal tides is a permanent feature at this site. DAS-inferred tidal temperature oscillations of 2 K magnitude agree with direct temperature observations. Preliminary results showing spectral peaks at the M2 tidal frequency and its harmonics is suggestive of wave-wave interactions. Finally, the potential of DAS to estimate lateral diffusion coefficients is considered.

Automatic event detection and phase picking are critical for processing the large volumes of data produced by modern seismological instrumentation. Accurate picking is especially challenging in Distributed Acoustic Sensing (DAS) recordings, where data quality can significantly vary along segments of the fibre due to localized environmental noise and coupling issues, reducing signal-to-noise ratios (SNR). Similarly, Ocean Bottom Seismometer (OBS) data quality also suffers from these issues. To improve accuracy under diverse conditions, we developed a novel multiband kurtosis-based picking algorithm, Kurtosis-Value-Picker (KVP), that enhances phase picking for both impulsive and emergent seismic signals. Our approach uses characteristic functions (CFs) calculated with sliding windows across multiple frequency bands. Triggers are identified based on localized kurtosis jumps over a few samples, providing greater sensitivity to emergent signals than traditional finite-difference methods. Each individual CF has its own set of triggers, adding flexibility to phase picking and retaining spectral information. We validate the KVP algorithm using earthquake data recorded with DAS on two land and submarine fibre-optic cables, as well as OBS data. We also compare its performance with a widely cited, kurtosis-based algorithm, the widely used FilterPicker algorithm and the well-known PhaseNet model, using impulsive signals on nearby DAS channels as a ground truth for emergent arrivals. Our results demonstrate that KVP provides accurate picks and is suitable for complex seismic data sets.

The J magnetic anomaly in the Central Atlantic has 10 times larger amplitude than surrounding seafloor-spreading magnetic lineations and is often associated with thick oceanic crust formed by excess magma. The anomaly has also been identified in the southern North Atlantic, where it has been linked to the onset of seafloor spreading, challenging traditional 2D models of lithospheric break-up offshore Iberia. These findings underscore the importance of constraining the crustal structure along the J anomaly to fully understand its genetic processes and geodynamic significance. Yet, the crustal structure in the Central Atlantic, where the anomaly is strongest, remains poorly defined by low-resolution legacy seismic data. We present wide-angle and multichannel seismic data from the 2022 ATLANTIS survey across the J anomaly at similar to 31 degrees N in the Central Atlantic. We use 2D seismic tomography to invert for P-wave (Vp) and S-wave (Vs) velocities. Results reveal significant lateral variations in igneous basement thickness and seismic velocities, contradicting the idea of uniformly thick crust. The peak of the anomaly is not aligned with the thickest segment (10 km) but with a region where oceanic layer 3 exhibits the fastest Vp and Vs. Variations in basement thickness are accompanied by lateral differences in Vp/Vs, reflecting changes in composition and/or basement fracturing. These findings suggest diverse crustal accretion processes, influenced by a fertile mantle source and short-term mantle temperature differences during the formation of the J anomaly.

Distributed Acoustic Sensing (DAS) offers unprecedented meter-scale spatial sampling of strain/strain-rate wavefields, enabling unaliased seismic event observations. DAS technology utilizes fiber optic cables (FOCs), extending seismological observations to extreme environments, including ocean floors. Given the logistical difficulties in deploying and maintaining traditional seismic stations in these contexts, seismological data near oceanic earthquake sources remain limited. On a positive note, telecommunication FOCs are often deployed on the ocean bottoms to connect urban areas on land, potentially bridging this observational gap. Traditional seismological monitoring, which aims to locate earthquake sources, typically relies on phase picking and subsequent data inversion. In a standard seismometer network, automatically-retrieved arrival times can be manually validated by expert operators; however, this task becomes practically impossible with DAS due to the unprecedented data density it offers, which can easily reach tens of thousands channels, considering the current capabilities of interrogating cables up to 100 km. For the purpose of leveraging both data measurements close to the source (DAS) and the improved azimuthal coverage by land stations, DAS data flows must be automated. Potential solutions include accurately tuning automatic pickers for the specific FOC and/or employing data selection and weighing procedures. Recently, pickers based on machine learning have been tested for DAS as substitutes for standard pickers, offering promising results and efficient arrival time measurements. Despite these advancements, challenges persist in accurately estimating onsets due to spatial variability in DAS waveforms, arising from a) uniaxial signal polarization, b) sensitivity to site conditions, and c) heterogeneities in FOC coupling. These data uncertainties, in turn, affect event location accuracy.We address this problem by conducting a preliminary comparison of two standard pickers (based on the actual amplitude-frequency content of each channel) with a machine-learning-derived picker. We focus on DAS recordings of six local earthquakes located on the ocean bottom between Fuerteventura and Gran Canaria islands during an experiment from November 2022 to April 2023. Each earthquake is provided with a reference location from the regional network of seismometers. Kurtosis and FilterPicker (standard pickers) and Phasenet-DAS (machine-learning picker) onsets are inverted for event location, with a focus on statistically comparing the solutions' uncertainty (scattering). To achieve this, we employed a Markov chain Monte Carlo method to estimate the Posterior Probability Densities (PPDs) of hypocentral parameters.In a second stage, we test a data-weighing approach on absolute arrival times based on specific channel properties. The aim is to assess its effects on location PPDs, in comparison to the “not-weighed” inversion. We repurpose the same algorithm, previously used for the location comparison, to modify each entry of the covariance matrix in the Bayesian inversion scheme, thus enabling a differential weighting of the arrival times. These preliminary comparisons of the efficiency of automatic pickers and data weighting procedures are commonly employed for the evaluation of standard seismological networks. With DAS arrays, these approaches become even more crucial, given the reduced space for manual validation by experts.

During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

Resultados de la aplicación de geodesia satelital en las campañas CASA de 1991, 1994 y 1996, y análisis de 96 mecanismos focales obtenidos en Harvard (Mw > 5), para el período 1976-2000, a los cuales se les aplicaron técnicas de inversión de esfuerzos, permiten establecer coherencia en los métodos empleados y una clara tendencia compresiva en el sentido oeste-este a la altura del Bloque Panamá-Costa Rica (BPC), colisionando con el Bloque Norte de los Andes (BNA). Sin embargo, algunas evidencias sísmicas permitirían confirmar la existencia del propuesto Bloque Chocó (BC), con límite oeste a la altura de la cordillera del Darién, y límite este en la zona de Murindó, evidenciado por las inversiones de los esfuerzos en dichas dos zonas, con tendencia general subparalela norte-sur. El límite sur, a la altura de los 4° N, muestra también esfuerzos con tendencia norte-sur. Algunos sismos superficiales localizados en la Costa Pacífica, aparentemente límite oeste del Bloque Chocó, muestran esfuerzos con tendencia NW-SE. Adicionalmente, los vectores de desplazamiento y los esfuerzos en el suroeste colombiano, a la altura de la zona de Tumaco, arrojan claras tendencias oeste-este. Finalmente, la sismicidad profunda en la zona de Bucaramanga presenta tendencia de esfuerzos NNW-SSE, coherente con la subducción de la Placa Caribe debajo de Sudamérica.

Extensive monitoring initiatives driven by the urgency to address climate change have led to the rise of long-term projects, particularly in offshore environments. These projects are evolving into complex, multi-decadal operations, necessitating comprehensive monitoring. Distributed Acoustic Sensing (DAS) arrays offer unique advantages in long-distance, high-density, real-time monitoring. However, the long-term archiving of DAS data presents significant challenges, due to the need for vast storage capacities (on the order of hundreds of terabytes per year). Innovative data compression techniques are essential to make continuous high-sample-rate DAS data storage feasible. DAS data is composed of multiple channels carrying highly correlated and coherent signals. These characteristics allow us to exploit inter-channel compression techniques, which leverage the signal from consecutive channels for prediction-based compression. Inter-channel compression methods achieve a much higher compression ratio compared to compressing each channel separately and have been little studied. In this work, we present novel inter-channel compression algorithms and demonstrate state-of-the-art lossless compression. For this purpose, a lossless coding scheme was implemented inspired by successful video coding techniques, following a pipeline composed of intra-prediction, inter-prediction, transform, and entropy coding. The implementation is divided into an encoder and a decoder. The encoder uses a bitrate optimization search and can be tuned for either speed or high-compression modes, while the decoder is optimized for quick signal reconstruction. The designed algorithms and the provided implementation facilitate the deployment of long-term DAS recording and archiving.

Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species’ capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.

Nowadays, fiber-optic telecommunication cables are serving a multitude of purposes beyond their conventional role. One such application is earthquake detection, which is particularly advantageous in submarine environments, where data acquisition is inherently more challenging and costly. Leveraging Distributed Acoustic Sensing (DAS) technology, these cables undergo a remarkable metamorphosis, transforming into a network of seismic sensors spaced just meters apart, and spanning remote and inaccessible environments with ease.Nevertheless, the quality of DAS data relies on varios factors that surpass the interrogator’s performance, such as the seafloor topography and cable characteristics and coupling. Equally critical is the development of robust software capable of discerning earthquake waves amidst a cacophony of noise and a broad spectrum of signals in the submarine environment.With this aim in mind, this study utilizes DAS data from a telecommunications fiber-optic cable linking the islands of Tenerife and Gran Canaria within the Canary Islands region. Our dataset spans approximately 2 months in 2020 and encompasses readings from both cable ends, each equipped with an interrogator, providing coverage of approximately 60 kilometers on each side. Situated between these islands lies a submarine volcano, which exhibits seismic activity nearly every day. Additionally, the recent installation of new land stations has enabled to observe an increase in seismicity to the east of Gran Canaria. Hence, this dark-fiber could enhance our ability to monitor the volcano and to accurately locate the source of this newfound seismic activity.Leveraging the pre-trained PhaseNet-DAS model, we detect P and S waves of seismic events and compare our findings with those published by the National Geographic Institute (IGN) earthquake bulletin. Through comparative analysis of both cable ends, we ascertain their earthquake detection capabilities, delineating sensitivity levels and identifying cable segments with optimal event detection and minimal noise interference.

Submarine optical fibers are nowadays the core backbone of international communications, carrying over 99% of the intercontinental data traffic. These critical infrastructures for communications have also recently demonstrated to have strong potential for geophysical monitoring in the bottom of the oceans. In this paper, we show that submarine optical fiber cables can be used to gain knowledge on the planet and its dynamics, including more accurate estimations of currents and other water mixing phenomena that have strong impact in climate change estimations. Among other things, we show that internal waves, a large-scale phenomenon generated by the interaction of barotropic tides with bathymetric changes in the sea-bottom, can be very accurately observed by deploying chirped-pulse Distributed Acoustic Sensing (DAS) technology over these cables.

<p>With more than 40 years of study, there are still uncertainties about the structure, evolution, and geodynamics of the North African and South Iberian Peninsula lithospheric structure and collision zone. Models of the lithosphere of the region coincide in some anomaly zones, such as the subduction slab under the Gibraltar arc. However, they show discrepancies in the distribution and polarity of the velocity anomalies in the onshore and offshore of most of North Africa.</p> <p>To contribute to the study of the lithospheric structure and to unveil the tectonics in this controversial region, we constructed an ambient noise tomography (ANT) of Love and Rayleigh waves from temporary and permanent broadband stations located in the Iberian Peninsula, North Africa, and Atlantic islands (Madeira, Canarias, Balearic Islands). </p> <p>The methodology employed contemplates phase cross-correlation of 14 months of ambient noise records and the subsequent stacking of the cross-correlograms to obtain the Empirical Green's Function (EGF). To measure the dispersion characteristics of surface wave EGFs present in the ambient noise, we implemented the Frequency-Time Analysis (FTAN). And finally, the inversion of the dispersion measures to get the surface wave tomography.</p> <p>The distribution of the almost 100 broadband stations in North Africa, Portugal, Spain, and the Atlantic islands, results in a broad path coverage in the North African and South Iberian Peninsula lithospheric structure and collision zone, complementing the previous Rayleigh wave velocity models. Furthermore, current studies in this region are Rayleigh-waves based, so the integration of Love waves in this ANT yields new information on the media velocity anisotropy.</p>

We review our work on the use of Distributed Acoustic Sensing for characterizing oceanographic processes. We show that this tool offers new insights on the mechanisms underpinning water mixing which are key in climate regulation.

<p>The Distributed Acoustic Sensing (DAS) method re-purposes fiber optic cables into a very-dense array of strain/strain-rate sensors, <span class="Y2IQFc" lang="en">capable of detecting different types of seismic events. </span>However, DAS data are characterized by lower SNRs compared with standard seismic sensors, mainly because of a) strong directivity effects, 2) ground coupling inhomogeneities, and 3) site effects. Hence, beyond the array geometry, specific noise sources may reduce the potential of DAS for seismic monitoring. Previous research has already shown successful case-studies for event detection/location. Nevertheless, a coherent test on the performances of various arrays of different sizes and geometries is still lacking.</p> <p>In this study, an extensive DAS database is organized for such a goal, including 15 DAS arrays that recorded at least one seismic event (located at a range of distances from the arrays). P wave arrival times are exploited to estimate the epicentral parameters with a Markov Chain Monte Carlo method. Then, to analyze the effects of cable geometry and potential sources of noise/ambiguity on the location uncertainties, a series of synthetic tests are performed, where synthetic traveltimes are modified as follows: a) adding noise with equal variance to all the DAS channels (SYNTH-01), b) adding noise characterized by an increasing variance with the distance from the event (SYNTH-02), c) simulating the mis-pick between P and S phases (SYNTH-03) and d) adding noise with a variance influenced by cable coupling inhomogeneities (SYNTH-04). Results show that the epicentral locations with automatic P wave arrival times have different degrees of uncertainty, given the geometrical relation between the event and the DAS arrays. This behavior is confirmed by the SYNTH-01 test, indicating that specific geometries provide a lower constraint on event location. Moreover, SYNTH-04 shows that simulating cable coupling inhomogeneities primarily reproduces the observed location uncertainties. Finally, some cases are not explained by any of the synthetic tests, stressing the possible presence of more complex noise sources contaminating the signals.</p>

Although typically used to measure dynamic strain from seismic and acoustic waves, Rayleigh-based distributed acoustic sensing (DAS) is also sensitive to temperature, offering longer range and higher sensitivity to small temperature perturbations than conventional Raman-based distributed temperature sensing. Here, we demonstrate that ocean-bottom DAS can be employed to study internal wave and tide dynamics in the bottom boundary layer, a region of enhanced ocean mixing but scarce observations. First, we show temperature transients up to about 4 K from a power cable in the Strait of Gibraltar south of Spain, associated with passing groups of internal solitary waves in water depth <200 m. Second, we show the bore-like propagation of the nonlinear internal tide on the near-critical slope of the island of Gran Canaria, off the coast of west Africa, with perturbations up to about 2 K at 1-km depth and 0.2 K at 2.5-km depth. With spatial averaging, we also recover a signal proportional to the barotropic tidal pressure, including the lunar fortnightly variation. In addition to applications in observational physical oceanography, our results suggest that contemporary chirped-pulse DAS possesses sufficient long-period sensitivity for seafloor geodesy and tsunami monitoring if ocean temperature variations can be separated.