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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unveiling Spatial and Temporal Dynamics of Plasmon-Enhanced Localized Fields in Metallic Nanoframes through Ultrafast Electron Microscopy

Plasmonic nanomaterials, particularly noble metal nanoframes (NFs), are important for applications such as catalysis, biosensing, and energy harvesting due to their ability to enhance localized electric fields and atomic efficiency via localized surface plasmon resonance (LSPR). Yet the fundamental structure-function relationships and plasmonic dynamics of the NFS are difficult to study experimentally and thus far rely predominately on computational methodologies, limiting their utilization. This study leverages the capabilities of ultrafast electron microscopy (UEM), specifically photon-induced near-field electron microscopy (PINEM), to probe the light-matter interactions within plasmonic NF structures. Here, the effects of shape, size, and plasmonic coupling of Pt@Au core-shell NFs on spatial and temporal characteristics of plasmon-enhanced localized electric fields are explored. Importantly, time-resolved PINEM analysis reveals that the plasmonic fields around hexagonal NF prisms exhibit a spatially dependent excitation and decay rate, indicating a nuanced interplay between the spatial geometry of the NF and the temporal evolution of the localized electric field. These results and observations uncover nanophotonic energy transfer dynamics in NFs and highlight their potential for applications in biosensing and photocatalysis.

Plasmonics↗
DOE OSTI2025

Regional and Temporal Variability of Atmospheric River Seasonality: Influences of Detection Algorithms and Moisture Transport Dynamics

Abstract Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. While ARs were thought to peak in winter, recent research shows they exhibit region‐specific seasonality and are heavily influenced by the chosen detection algorithm. This study examines the link between the year‐to‐year consistency of peak‐AR activity to the presence of a dominant seasonal pattern, considering both location and algorithm choice. Regions are categorized by their temporal characteristics: consistent patterns (e.g., East Asia), patterns with occasional outliers (e.g., British Columbia coast), and regions lacking a clear dominant peak season (e.g., South Atlantic, parts of Australia). Hence, not all regions display a consistent seasonal cycle of AR activity. This study quantifies the extent to which a region experiences a dominant peak season of AR activity (or lacks one) and offers insights to enhance decision‐making in water management, natural hazard preparedness, and forecasting. Furthermore, given our finding that detection algorithms influence the peak season of AR activity, we also examine two diagnostic variables representative of moisture transport to corroborate our results. Integrated vapor transport, which captures meridional and zonal moisture transport, and Moist Wave Activity, representing moisture intrusions from lower to higher latitudes, are examined. Our analysis indicates that inconsistencies in the seasonal cycle of AR activity are not solely due to discrepancies in detection algorithms but also arise from changes in moisture transport. Plain Language Summary Atmospheric rivers (ARs) are critical weather phenomena that can cause extreme events like heavy rainfall and flooding. Understanding when and where ARs are most likely to occur throughout the year is essential for preparing and responding to these events. Traditionally, ARs were thought to peak in winter, but recent studies show this varies by region. Our study helps address the challenge decision‐makers face in anticipating and preparing for AR events by providing insights into the consistency of peak seasonal patterns across different areas. Some regions, like East Asia, and the British Columbia coast, show a consistent peak season, while others, like the South Atlantic and parts of Australia, have significant year‐to‐year variations, making it hard to identify a dominant season. To better understand these changes over time, the study also examines how moisture moves in the atmosphere, using Integrated Vapor Transport (which looks at moisture movement in various directions) and Moist Wave Activity (which tracks moisture shifts from lower to higher latitudes). The findings suggest that inconsistencies in AR patterns are due not only to detection methods but also due to changes in moisture transport. Key Points The peak season of atmospheric river activity can change depending on the year in some areas Interannual variations in the peak season can make identifying a dominant season challenging for some regions Frequent shifts in peak season across years reflect inconsistencies tied to detection algorithms and to underlying dynamics

Kamnani, Diya↗
DOE OSTI2025

Temporal Variability in Reservoir Surface Area Is an Important Source of Uncertainty in GHG Emission Estimates

Ebullitive methane (CH 4 ) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km 2 and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH 4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH 4 emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH 4 emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climatic and Socioeconomic Drivers of Water Use and Their Spatio‐Temporal Patterns for Small and Mid‐Sized Cities in the Contiguous United States

This study explores the drivers of urban water use and their spatial-temporal patterns in 142 small and mid-sized cities across the Contiguous United States (CONUS) by analyzing the data directly collected from these cities and using advanced machine learning techniques. We identify five distinguished clusters across CONUS, each showing unique trends of the impact of drivers on water use. We find that socioeconomic factors significantly influence water use in eastern and southwestern cities, while climatic variables such as precipitation and temperature range dominate in central and northwestern regions. Temporal analysis reveals the impacts of major socioeconomic and climatic disruptions on urban water use in the period 2011–2021, including the COVID lockdown, the rapid growth of data centers, and the drought of 2012. In addition, our analysis suggests that economic growth in small and mid-sized US cities continues to be accompanied by rising water use, contrasting with the opposite trend observed in large cities in prior studies. This implies that as smaller cities develop, their water use may increase above current levels until incomes reach a higher threshold, highlighting the need to improve water use efficiency. This study also presents useful insights for developing effective water demand management strategies in response to climatic variability and socioeconomic growth in small and mid-sized cities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Investigating the Impact of Temporal and Directional Traffic Distribution on Crash Frequencies

Safety Performance Functions (SPFs) are mathematical models that establish relationships between the frequency of various crash types and site-specific characteristics, serving as essential tools for traffic safety analysis and roadway design. Traditional SPFs, however, often overlook the temporal fluctuations in traffic flow (such as peak-hour surges) and directional imbalances between opposing traffic streams. These traffic patterns can exacerbate congestion, disrupt driver behavior, and create unexpected conflict points, potentially leading to increased crash frequencies and more severe accidents. In light of this gap, this study aims to explore the potential of incorporating K-factors (representing peak-hour traffic proportions) and D-factors (reflecting the imbalance of directional traffic) into the development of SPFs to assess whether these factors can effectively represent the impact of temporal and spatial traffic distribution on roadway safety. Using crash data from Pennsylvania urban-suburban collector roadways, it is found that the D-factor plays a significant role in predicting the frequency of total crashes, fatal + injury crashes, and angle crashes, with positive coefficient signs indicating that higher directional imbalances correspond to increased crash risks. Similarly, the K-factor emerges as a critical predictor for fatal + injury crashes and rear-end crashes, with negative coefficients suggesting that a more pronounced traffic peak is associated with a reduction in expected crash frequencies. These results highlight the importance of accounting for uneven traffic distribution in both time and direction when developing SPFs, offering deeper insights into crash patterns and supporting more effective safety interventions and roadway designs.

Xu, Guanhao [ORNL] (ORCID:0000000214326357)↗
DOE OSTI2025

Comparison of laser-produced plasma spatio-temporal electron density evolution measured using interferometry with simulation results

Due to the difficulties associated with experimental measurements of laser-produced plasma (LPP) properties during the earliest stages of plasma evolution, radiation hydrodynamic codes are often used. However, although these codes have been extensively validated in the higher intensity regimes, validation at low to moderate intensities has been limited. In this study, the spatio-temporal electron density evolution in an LPP generated at moderate laser intensities and at various laser wavelengths was validated against the FLASH code for times up to 20 ns. The LPP was generated by focusing the fundamental and various harmonics radiation (1064, 532, and 266 nm) from a 6 ns full width half maximum Nd:YAG laser, at a laser intensity of 10 GW cm -2 , onto a copper target. The spatio-temporal density evolution of the expanding plasma was analyzed using Nomarski interferometry. Experimental measurements were found to be consistent with FLASH simulations, and the dependence of electron densities on wavelength was found to be in agreement with analytical models, varying as n e ∝ λ -0.7 . However, slight differences were noted in the widths and shape functions of the experimental and simulated electron density profiles.

2D fast imaging↗
DOE OSTI2024

Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio - temporal occupancy modeling

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhaling Coccidioides fungal spores. Incidence has risen in recent years, and it is believed the endemic region for Coccidioides is expanding in response to climate change. While Valley fever case data can help us understand trends in disease risk, using case data as a proxy for Coccidioides endemicity is not ideal because case data suffers from imperfect detection, including false positives (e.g., travel-related cases reported outside of endemic area) and false negatives (e.g., misdiagnosis or underreporting). Here we proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity of Coccidioides, accounting for imperfect detection. We used our model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California, Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeastern Washington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic to Coccidioides. We estimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association with agriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory case reporting.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Investigation of oscillations above the acoustic cut-off frequency for a sample of Kepler stars. II. Identifying temporal frequency shifts

ABSTRACT Stellar oscillations with frequencies greater than the acoustic cut-off frequency are not trapped within the star’s interior. Geometric interference between these high-frequency waves produces a peak-like structure in the power spectrum of the star, known as the pseudo-modes. In this study, we aim to analyse 51 stars previously identified (from a sample of 91 Kepler stars) to contain statistically significant evidence for pseudo-modes, to determine if the pseudo-mode frequencies vary in time. We obtained these frequency variations and the uncertainty by the resampled periodogram approach, where periodogram realisations were created from successive, overlapping 90-d time segments, and a cross-correlation function was generated between them. The computed shifts were compared against temporal p-mode frequency shifts and a stellar magnetic activity proxy, $S_{\mathrm{ph}}$. We found that for 16 per cent of the stars pseudo-mode frequency shifts were significantly anticorrelated with p-mode shifts, as is the case for the Sun. However, we also found pseudo-mode and p-mode shifts to be significantly correlated in-phase in 8 per cent of our sample. The remainder showed no significant correlation. We also searched for trends between the maximum variation of pseudo-mode frequency shifts and stellar parameters. Whilst we found no correlation between the maximum variation and stellar photospheric and chromospheric proxies, we found frequency variations to have a greater amplitude for cooler and slower rotating stars, which is in opposition to p-mode frequency shift variation. Temporal changes to pseudo-mode frequencies may be attributable to variations in stellar magnetic activity, and therefore can contribute to our understanding of stellar activity cycles.

Millson, L. J. (ORCID:0009000342542676)↗
DOE OSTI2025

A 291-day Evaluation of the Performance of a Consumer-grade Temporal Radon Detector

Affordable, accurate, and robust temporal measurement devices are desirable for screening and assessment of radon levels in private homes and workplaces. This research expands upon prior research, using the RadonFTlab RadonEye device through a comparison of multiple samples of this instrument with a laboratory-grade instrument, the Saphymo AlphaGUARD, over a more extensive period than reported previously. Data were collected over 291 d in a poorly ventilated basement space in an occupied building. Environmental conditions varied naturally, changing both the radon source term and radon entry into the space approximating typically deployed conditions. The R-squared linear regression correlation coefficient and relative sensitivities of each RadonEye with the AlphaGUARD were computed. Altogether temporal and diurnal variations were also studied. The sensitivities of all RadonEyes and the AlphaGUARD agreed to within 22% throughout the entire deployment period.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance

Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.

Biomolecular & subcellular processes↗
DOE OSTI2024

Greater than Five-Order-of-Magnitude Postcompression Temporal Contrast Improvement with an Ionization Plasma Grating

High-intensity lasers require suppression of prepulses and other nonideal temporal structure to avoid target disruption before the arrival of the main pulse. Here, to address this, we demonstrate that ionization gratings act as a controllable optical switch for high-power light with a temporal contrast improvement of at least 3 ×10 5 and a switching time less than 500 fs. We also show that a grating system can run for hours at 10 Hz without degradation. The contrast improvement from an ionization grating compares favorably to that achievable with plasma mirrors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2025

Rapid Event Detection via Synchro-Waveform Based Temporal Attention Network in Distributed Grid

Compared with the information collected from phasor measurement units, synchro-waveforms contain high-fidelity disturbances of the grid, which can be a granular and authentic representation of measurements in the modern power system. However, the dynamic changing morphology makes it challenging to effectively capture various disturbance information from the synchro-waveforms. To tackle this issue, this paper proposes a Synchro-waveform based Temporal Attention (STA) network to achieve rapid event detection. First, a multi-scenario distributed model with renewable integration is established to generate synchro-waveforms under various uncertainties. Then, three typical temporal features are extracted directly from the synchro-waveform measurements. Additionally, the lightweight STA network is deployed to identify the most common event types in renewable energy systems via the self-attention based vision transformer module. The results from simulated experiments demonstrate that the proposed approach can achieve rapid and real-time detection within 0.81 ms and over 96.27 % accuracy.

Dong, Yuqing [University of Tennessee (UT)]↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2025

Contact patterns in wild pigs ( Sus scrofa ) from GPS tracking reveal spatial and temporal dynamics of social behaviour

Wild pig ( Sus scrofa Linnaeus, 1758) social behaviour affects disease transmission and landscape-level population management. Recent research has incorporated analysis of social structure to better understand the risk of disease transmission in wild pigs, although the relationship between overall social structure of wild pigs remains unclear. Here, we seek to improve understanding of the spatial and temporal dynamics of wild pig social structure and contact rates to better inform management strategies. Using GPS tracking data, we measured home range overlap, and estimated contact rates of wild pigs at four study sites in the southern USA to identify pairwise social associations (i.e., contacts) based on synchronous movement. Contact rate was strongly associated with home range overlap, but exhibited substantial variation, especially at moderate levels of home range overlap. We found that female–female dyads had higher contact rates and longer duration phases of social association compared to female–male and male–male dyads. We found male–male dyads tended to experience social associations farther from their home range centers than female–female or female–male dyads. Social associations between wild pig dyads are highly dynamic in their spatial and temporal structure. Further, dyads with strong social associations still experience substantial time apart. Our findings highlight the challenges of predicting spatial and social associations in wild pig social pairs due to their dynamic social structure over space and time.

McIlraith, Jack R.↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Geochemistry & Geophysics↗