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NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗

DOE OSTI2021

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 5 Report Field Scale Stress Modeling)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686, for the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage.” The purpose of this DOE funding is to develop non-invasive methods to obtain important information about subsurface stresses. The overall research goal of the project was to develop and improve methods for determining the spatial distribution of stresses, including magnitude and orientation of the three principal stress components in the subsurface, based on new methods that extract stress information from seismic data combined with well measurements, extended with well tests and logs, and unified in a numerical model that permits computation of the full stress tensor throughout the domains under investigation.

58 GEOSCIENCES↗

DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗

NASA NTRS2012

The Namibia Early Flood Warning System, A CEOS Pilot Project

Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide early warning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood early warning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated under the Open Cloud Consortium (OGC) Sensor Web Enablement (SWE) set of web services was good [1][2]. However, it was discovered that in order to make a system like this functional, there were many performance issues. Data sets were large and located in a variety of location behind firewalls and had to be accessed across open networks, so security was an issue. Furthermore, the network access acted as bottleneck to transfer map products to where they are needed. Finally, during disasters, many users and computer processes act in parallel and thus it was very easy to overload the single string of computers stitched together in a virtual system that was initially developed. To address some of these performance issues, the team partnered with the Open Cloud Consortium (OCC) who supplied a Computation Cloud located at the University of Illinois at Chicago and some manpower to administer this Cloud. The Flood SensorWeb [3] system was interfaced to the Cloud to provide a high performance user interface and product development engine. Figure 1 shows the functional diagram of the Flood SensorWeb. Figure 2 shows some of the functionality of the Computation Cloud that was integrated. A significant portion of the original system was ported to the Cloud and during the past year, technical issues were resolved which included web access to the Cloud, security over the open Internet, beginning experiments on how to handle surge capacity by using the virtual machines in the cloud in parallel, using tiling techniques to render large data sets as layers on map, interfaces to allow user to customize the data processing/product chain and other performance enhancing techniques. The conclusion reached from the effort and this presentation is that defining the interoperability standards in a small fraction of the work. For example, once open web service standards were defined, many users could not make use of the standards due to security restrictions. Furthermore, once an interoperable sysm is functional, then a surge of users can render a system unusable, especially in the disaster domain.

Mandl, Daniel↗

NASA NTRS1978

Compatibility of seasonal variations in mid-latitude thermospheric models at solar maximum and low geomagnetic activity

Satellite drag data, in situ mass spectrometer data, Fabry Perot interferometer data, and incoherent scatter data were used to produce models of the concentration of atomic oxygen and molecular nitrogen in the thermosphere and of the exospheric temperature. Incoherent scatter data from two stations near 45 deg N were used. The three techniques provide similar patterns for the annual variations of the atomic oxygen concentration at 400 km, and the mean annual temperatures are within 20 K of each other for five of the six models involved. Four of the models are in good agreement concerning the annual temperature variation. There is fair agreement between incoherent scatter models and mass spectrometer models for the N2 concentrations at 400 km but disagreement between these models and the satellite drag model. It is shown that most disagreements can be resolved by changing both the vertical temperature profile and the lower-thermosphere oxygen concentration in the direction indicated by previous incoherent scatter measurements.

Alcayde, D.↗

NASA NTRS1990

Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images

The maximum cross correlation (MCC) method of Leese et al. (1971) for estimating sea surface velocities from sequences of satellite images is evaluated by comparing the MCC fields obtained from sequences of AVHRR and CZCS with in situ data and velocity fields calculated with a high-resolution quasi-geostrophic model. The rms differences and vector correlations between the velocity field produced by the MCC method and the model's field are presented. It is shown that much of the difference between the MCC fields and either the in situ data or the model velocity fields can be accounted for by considering physical and biological processes not included in the MCC method. The conditions under which the method is likely to be most successful are discussed.

Tokmakian, Robin↗

DOE OSTI2021

Evaluating Diurnal and Semi-Diurnal Cycle of Precipitation in CMIP6 Models Using Satellite- and Ground-Based Observations

We report the diurnal and semi-diurnal cycle of precipitation simulated from CMIP6 models during 1996-2005 are evaluated globally between 60°S and 60°N, as well as at ten selected locations representing three categories of diurnal cycle of precipitation: (1) afternoon precipitation over land, (2) early morning precipitation over ocean, and (3) nocturnal precipitation over land. Three satellite-based and two ground-based rainfall products are used to evaluate the climate models. Globally, the ensemble mean of CMIP6 models shows a diurnal phase of 3 to 4 hours earlier over land and 1 to 2 hours earlier over ocean, when compared with the latest satellite products. These biases are in line with what were found in previous versions of climate models but reduced compared to the CMIP5 ensemble mean. Analysis at the selected locations complimented with in-situ measurements further reinforces these results. Several CMIP6 models have shown a significant improvement in the diurnal cycle of precipitation compared to their CMIP5 counterparts, notably on delaying afternoon precipitation over land. This can be attributed to the use of more sophisticated convective parameterizations. Most models are still unable to capture the nocturnal peak associated with elevated convection and propagating mesoscale convective systems, with a few exceptions that allow convection to be initiated above the boundary layer to capture nocturnal elevated convection. We also quantify an encouraging consistency between the satellite- and ground-based precipitation measurements despite differing spatiotemporal resolutions and sampling periods, which provides confidence in using them to evaluate the diurnal and semi-diurnal cycle of precipitation in climate models.

58 GEOSCIENCES↗

NASA NTRS2015

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

Monitoring groundwater drought using land surface models is a valuable alternative given the current lack of systematic in situ measurements at continental and global scales and the low resolution of current remote sensing based groundwater data. However, uncertainties inherent to land surface models may impede drought detection, and thus should be assessed using independent data sources. In this study, we evaluated a groundwater drought index (GWI) derived from monthly groundwater storage output from the Catchment Land Surface Model (CLSM) using a GWI similarly derived from in situ groundwater observations. Groundwater observations were obtained from unconfined or semi-confined aquifers in eight regions of the central and northeastern U.S. Regional average GWI derived from CLSM exhibited strong correlation with that from observation wells, with correlation coefficients between 0.43 and 0.92. GWI from both in situ data and CLSM was generally better correlated with the Standard Precipitation Index (SPI) at 12 and 24 month timescales than at shorter timescales, but it varied depending on climate conditions. The correlation between CLSM derived GWI and SPI generally decreases with increasing depth to the water table, which in turn depends on both bedrock depth (a CLSM parameter) and mean annual precipitation. The persistence of CLSM derived GWI is spatially varied and again shows a strong influence of depth to groundwater. CLSM derived GWI generally persists longer than GWI derived from in situ data, due at least in part to the inability of coarse model inputs to capture high frequency meteorological variability at local scales. The study also showed that groundwater can have a significant impact on soil moisture persistence where the water table is shallow. Soil moisture persistence was estimated to be longer in the eastern U.S. than in the west, in contrast to previous findings that were based on models that did not represent groundwater. Assimilation of terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission improved the correlation between CLSM based regional average GWI and that based on in situ data in six of the eight regions. Practical issues regarding the application of GRACE assimilated groundwater storage for drought detection are discussed. An important conclusion of this study is that model parameters that control the depth to the water table, including bedrock depth, strongly influence the evolution and persistence of simulated groundwater and require careful configuration for drought monitoring.

Evaluation↗

NASA NTRS2014

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

hydrology↗

DOE OSTI2023

Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.

Loring, Burlen↗

NASA NTRS1993

Ionospheric calibration for single frequency altimeter measurements

This report investigates the potential of using Global Positioning System (GPS) data and a model of the ionosphere to supply a measure of the sub-satellite Total Electron Current (TEC) of the required accuracy (10 TECU rms) for the purpose of calibrating single frequency radar altimeter measurements. Since climatological (monthly mean) models are known to be in error by as much as 50 percent, this work focused on the Parameterized Real-Time Ionospheric Specification Model (PRISM) which has the capability to improve model accuracy by ingesting (adjusting to) in situ ionospheric measurements. A set of globally distributed TEC measurements were generated using GPS data and were used as input to improve the accuracy of the PRISM model. The adjusted PRISM TEC values were compared to TOPEX dual frequency TEC measurements (which are considered truth) for a number of TOPEX sub-satellite tracks. The adjusted PRISM values generally compared to the TOPEX measurements within the 10 TECU accuracy requirements when the sub-satellite track passed within 300 to 400 km of the GPS TEC data or when the track passed through a night time ionosphere. However, when the sub-satellite points were greater than 300 to 400 km away from the GPS TEC data or when a local noon ionosphere was sampled, the adjusted PRISM values generally differed by greater than 10 TECU rms with data excursions from the TOPEX TEC measurements of as much as 40 TECU (an 8 cm path delay error at K band). Therefore, it can be concluded from this analysis that an unrealistically large number of GPS stations would be needed to predict sub-satellite TEC at the 10 TECU level in the day time ionosphere using a model such as PRISM. However, a technique currently being studied at the Jet Propulsion Laboratory (JPL) may provide a means of supplying adequate TEC data to meet the 10 TECU ionospheric correction accuracy when using a realistic number of ionospheric stations. This method involves using global GPS TEC data to estimate a global grid of vertical ionospheric TEC as a function of time (i.e. every one half hour) in a sun-fixed longitude frame. Working in a sun-fixed longitude frame, one is not limited by the spatial decorrelation distance of the ionosphere, but instead is limited more by the temporal correlations of the ionosphere in the sun-fixed frame which are a smaller effect. It is the opinion of the authors that using the global sun-fixed TEC grid data, in particular, ingesting it into PRISM, offers the best possibility of meeting the 10 TECU ionospheric correction accuracy requirement, and should be the subject of further study.

Schreiner, William S.↗

NASA NTRS2014

The Role of Phytoplankton Dynamics in the Seasonal and Interannual Variability of Carbon in the Subpolar North Atlantic - a Modeling Study

We developed an ecosystem/biogeochemical model system, which includes multiple phytoplankton functional groups and carbon cycle dynamics, and applied it to investigate physical-biological interactions in Icelandic waters. Satellite and in situ data were used to evaluate the model. Surface seasonal cycle amplitudes and biases of key parameters (DIC, TA, pCO2, air-sea CO2 flux, and nutrients) are significantly improved when compared to surface observations by prescribing deep water values and trends, based on available data. The seasonality of the coccolithophore and "other phytoplankton" (diatoms and dinoflagellates) blooms is in general agreement with satellite ocean color products. Nutrient supply, biomass and calcite concentrations are modulated by light and mixed layer depth seasonal cycles. Diatoms are the most abundant phytoplankton, with a large bloom in early spring and a secondary bloom in fall. The diatom bloom is followed by blooms of dinoflagellates and coccolithophores. The effect of biological changes on the seasonal variability of the surface ocean pCO2 is nearly twice the temperature effect, in agreement with previous studies. The inclusion of multiple phytoplankton functional groups in the model played a major role in the accurate representation of CO2 uptake by biology. For instance, at the peak of the bloom, the exclusion of coccolithophores causes an increase in alkalinity of up to 4 μmol kg(sup −1) with a corresponding increase in DIC of up to 16 μmol kg(sup −1). During the peak of the bloom in summer, the net effect of the absence of the coccolithophores bloom is an increase in pCO2 of more than 20 μatm and a reduction of atmospheric CO2 uptake of more than 6 mmolm(sup −2) d(sup −1). On average, the impact of coccolithophores is an increase of air-sea CO2 flux of about 27 %. Considering the areal extent of the bloom from satellite images within the Irminger and Icelandic Basins, this reduction translates into an annual mean of nearly 1500 tonnes C yr(sup −1).

Earth Resources↗

DOE OSTI2021

Grand Challenges of Hydrologic Modeling for Food-Energy-Water Nexus Security in High Mountain Asia

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution (in-situ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

FORGE STRESS annual report

The project's goal is to combine high-fidelity numerical models and true-triaxial block fracturing tests at high temperatures to understand the relationship between in situ stress, thermal effects, wellbore orientations and hydraulic fracture patterns. The numerical models are calibrated against field data, such as well pressures and microseismic data, and employed to estimate the in situ stress at the FORGE site. Laboratory experiments investigate the complex physics driving hydraulic fracture nucleation in EGS, enhancing understanding of the role of parameters like temperature, well orientation, and stress. Additionally, they are employed to validate some of the numerical tools used in the project. The project will have a significant impact by: (1) improving the characterization of the in-situ stress field at FORGE; (2) demonstrating the use of high-fidelity modeling tools for EGS; (3) providing a unique set of high-temperature hydraulic fracturing results to identify key components for in-situ stress estimation, validate current theories, and propose new ones; (4) offering a validated set of numerical tools within an open-source simulation framework, GEOS, that will be available to any future user.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Condition-dependent NO x adsorption/desorption over Pd/BEA: A combined microreactor and in situ DRIFTS study

Pd/BEA is chosen as a model passive NO x adsorber (PNA) to elucidate the effect of the feed gas composition on the NO x adsorption/desorption behavior. The Brønsted acid and the partially hydrolyzed framework Al (P-HAl(OH)) sites in HBEA adsorb NO and NO 2 under dry conditions. Moreover, the performance of HBEA is not affected by CO, while CO inhibits nitrate formation and promotes NO adsorption via the Pd(NO)(CO) complexes formation over Pd/BEA. H 2 O inhibits NO adsorption over the Brønsted acid and P-HAl(OH) sites, and ionic Pd is the only active site for NO x adsorption under wet conditions. Furthermore, NO adsorption over hydrated Pd (Pd 2+ (OH)(NO)(H 2 O) 3 ) is weaker than NO adsorption over bare ionic Pd (Z 2 [Pd 2+ (NO)], Z[Pd 2+ (OH)(NO)]). Dehydration of Pd 2+ (OH)(NO)(H 2 O) 3 forms more stable Z[Pd 2+ (OH)(NO)] during desorption. Here, the NO adsorption capacity of Pd/BEA improves in the presence of CO under both dry and wet conditions by forming a stable carbonyl–nitrosyl complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Advances in in situ/operando techniques for catalysis research: enhancing insights and discoveries

Abstract Catalysis research has witnessed remarkable progress with the advent of in situ and operando techniques. These methods enable the study of catalysts under actual operating conditions, providing unprecedented insights into catalytic mechanisms and dynamic catalyst behavior. This review discusses key in situ techniques and their applications in catalysis research. Advances in in situ electron microscopy allow direct visualization of catalysts at the atomic scale under reaction conditions. In situ spectroscopy techniques like X-ray absorption spectroscopy and nuclear magnetic resonance spectroscopy can track chemical states and reveal transient intermediates. Synchrotron-based techniques offer enhanced capabilities for in situ studies. The integration of in situ methods with machine learning and computational modeling provides a powerful approach to accelerate catalyst optimization. However, challenges remain regarding radiation damage, instrumentation limitations, and data interpretation. Overall, continued development of multi-modal in situ techniques is pivotal for addressing emerging challenges and opportunities in catalysis research and technology.

Chen, Linfeng↗

DOE OSTI2022

Electro-Optical Characterization of Arsenic-Doped CdSeTe and CdTe Solar Cell Absorbers Doped in-situ During Close Space Sublimation

Most contemporary device models predict that an acceptor concentration of at least 10^16 cm^-3 is required to reach an open circuit voltage of 1 V in polycrystalline CdTe-based solar cells. While copper has traditionally been used as the de facto p-type dopant in polycrystalline cadmium telluride (CdTe) and cadmium selenide telluride (CdSeTe), reaching high acceptor concentrations has proved to be challenging in such devices due to significant dopant compensation. The acceptor concentration in copper-doped CdTe and CdSeTe typically ranges from 10^13 to 10^15 cm^-3 and routinely exhibit low external radiative efficiencies below 0.01%, limiting their implied voltage (i.e., quasi-Fermi level splitting) to approximately 900 mV. As an alternative to copper, this work explores the use of arsenic as a p-type dopant for CdTe and CdSeTe. Using a novel technique in which a thin layer of arsenic-containing material is deposited and used as a reservoir for arsenic to diffuse into a front layer of previously undoped material, this contribution demonstrates that high external radiative efficiencies are achievable, a direct result of combined high acceptor concentrations and long minority-carrier lifetimes in the absorber. This leads to improved implied voltages, and indicates that As-doping represents a promising pathway towards improving the external voltage of CdSeTe/CdTe solar cells.

cadmium telluride↗

DOE OSTI2023

Temperature-induced densification in compressed basaltic glass revealed by in-situ ultrasonic measurements

Abstract Acoustic velocities of a model basalt glass (64 mol% CaMgSi2O6 + 36 mol% CaAl2Si2O8) were measured along different pressure-temperature (P-T) paths. One set of experiments involved isothermal compression-decompression cycles, performed at temperatures of 300, 641, 823, and 1006 K and pressures up to 12.2 GPa. The other set of experiments involved constant-load heating-cooling cycles at temperatures up to 823 K and pressures up to 7.5 GPa. Both sets of experiments were performed in a multi-anvil apparatus using a synchrotron-based ultrasonic technique. Our results show that the glass compressed isothermally at 300 K (cold-compression) displays anomalously decreasing compressional (VP) and shear (VS) wave velocities with increasing pressure until ~8 GPa. Beyond 8 GPa, both VP and VS start to increase sharply with pressure and irreversible densification of the glass occurred, producing large hysteresis loops of velocities upon decompression. However, for the glass compressed isothermally at increasingly higher temperatures (hot-compression), the velocity minima gradually shift to lower pressures. At temperature close to the glass transition temperature Tg, the velocity minima disappear completely, displaying a monotonic increase of velocities during compression and higher VP and VS during decompression. In addition, constant-load heating-cooling experiments show that velocities generally decrease slightly with increasing temperature, but start to increase once heated above a threshold temperature (~650 K). During cooling the velocities increase almost linearly with decreasing temperature, resulting in higher velocities (~1.5–2.5% higher) when returned to 300 K. This implies that a temperature-induced densification may have occurred in the glass at high pressures. Raman spectra on recovered samples show that the hot-compressed and high-P heated glasses contain distinctly densified and depolymerized structural signatures compared to the initial glass and the cold-compressed glass below the velocity transition pressure PT (~8 GPa). Such densification may be attributed to the breaking of bridging oxygen bonds and compaction in the intermediate-range structure. Our results demonstrate that temperature can facilitate glass densification at high pressures and point out the importance of P-T history in understanding the elastic properties of silicate glasses. Comparison with melt velocity suggests that hot-compressed glasses may better resemble the pressure dependence of velocity of silicate melts than cold-compressed glasses, but still show significantly higher velocities than melts. If the abnormal acoustic behaviors of cold-compressed glasses were used to constrain melt fractions in the mantle low-velocity regions, the melt fractions needed to explain a given velocity reduction would be significantly underestimated at high pressures.

Geochemistry & Geophysics↗