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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2021

Tonlé Sap Food Security & Agriculture III: Evaluating Changes in Ecosystem Vitality and Freshwater Health in the Tonlé Sap Basin using Remotely Sensed Data

Tonlé Sap Lake, located in Cambodia, Southeast Asia, is one of the most productive inland fisheries in the world. With the unique reverse flow hydrology of the Tonlé Sap River, this freshwater system contains rich biodiversity and provides critical freshwater resources for the local community. Overfishing, stronger seasonality, drought, dam construction, forest fires, and untreated industrial domestic sewage threaten the ecosystem vitality and economic success of Tonlé Sap. In collaboration with Conservation International and the Cambodian Ministry of Water Resources and Meteorology’s Tonlé Sap Authority, we calibrated and finalized remotely-sensed proxies for sub-indicators of the Freshwater Health Index (FHI). We used NASA Earth observation data from Moderate Resolution Imaging Spectroradiometer (MODIS), and the Gravity Recovery and Climate Experiment (GRACE). These datasets were used in RS proxies and a Soil and Water Assessment Tool (SWAT) model that previous teams developed for sub-indicators of FHI. They included landcover, bank modification, and water quality metrics. It was determined that the ground water storage in Tonlé Sap Lake exhibited a slight declining trend over from April 1st, 2002 to February 3rd, 2017. To calculate chlorophyll-a concentration in the Tonlé Sap Lake, we used Copernicus Global Land Service mission data which provided a Trophic State Index of the lake. Between January 1st, 2000 and December 31st, 2020, the lake and surrounding region within the study area boundary displayed a slight decrease in vegetation density and consistently high chlorophyll-a concentrations. The SWAT model calculated nitrogen and phosphorus content measured in outlet points of the lake. We demonstrated that remotely sensed data is valuable for providing additional information for the FHI, but is not fully capable of replacing its in situ counterpart. We also concluded the water quantity is on a slowly declining trend within the basin.

Joey Lindsay↗
DOE OSTI2019

Challenges and perspectives for large-scale temperature reconstructions of the past two millennia

Knowledge of the temperature variability during the last one to two millennia is important for providing a perspective to present-day climate excursions, for assessing the sensitivity of the climate to different forcings, and for providing a test bed for climate models. Since systematic instrumental temperature records only extend back to the nineteenth century, such knowledge mainly relies on climate-sensitive proxy data. Here we critically assess some of the many challenges related to large-scale multiproxy temperature reconstructions. We begin with a review of available large-scale temperature reconstructions, focusing on the differences in low-frequency variability and the response to natural forcings such as major volcanic eruptions and changes in total solar irradiance. Then, we discuss different proxy selection strategies, review previously used reconstruction methods, and discuss their ability to reconstruct the amplitude of the low-frequency variability. To shed additional light on the challenges of large-scale reconstructions, we investigate the spatial and temporal correlation structures in the observed temperature field and discuss the implications of these correlation structures regarding the required number and positions of proxies. We demonstrate how the unavoidable uncertainty related to noisy proxies will show up as bias and variance in the reconstruction and that the partition between these forms of errors depends on the reconstruction method. Pseudo-proxy experiments are conducted to further discuss the influence of noise and the requirements regarding the geographical location and number of proxies necessary for reliably reconstructing the low-frequency variability. Finally, we conclude with recommendations for future large-scale temperature reconstructions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Solar EUV irradiance derived from a sounding rocket experiment on November 10, 1988

Results are presented on the solar EUV irradiance measurements in the range 30-100 nm obtained in a sounding rocket experiment launched from the White Sands Missile Range, New Mexico, on November 10, 1988. The observed solar EUV irradiance was found to be about 20 percent less than the solar EUV flux from a proxy model based on the daily 10.7-cm solar flux and its 81-day mean and the AE-E solar EUV data taken in the 1970s. The November 10 measurement of the solar EUV flux provides a good calibration reference spectrum for the solar EUV instruments on the San Marco satellite.

Woods, Thomas N.↗
NASA NTRS2009

Developing ISM Dust Grain Models with Precision Elemental Abundances from IXO

The exact nature of interstellar dust grains in the Galaxy remains mysterious, despite their ubiquity. Many viable models exist, based on available IR-UV data and assumed elemental abundances. However, the abundances, which are perhaps the most stringent constraint, are not well known: modelers must use proxies in the absence of direct measurements for the diffuse interstellar medium (ISM). Recent revisions of these proxy values have only added to confusion over which is the best representative for the diffuse ISM, and highlighted the need for direct, high signal-to-noise measurements from the ISM itself. The International X-ray Observatory's superior facilities will enable high-precision elemental abundance measurements. We ill show how these results will measure both the overall ISM abundances and challenge dust models, allowing us to construct a more realistic picture of the ISM.

Valencic, L. A.↗
NASA NTRS2020

Results of the Deep Space Atomic Clock Deep Space Navigation Analog Experiment

The timing and frequency stability provided by the Deep Space Atomic Clock (DSAC) is almost comparable with the Deep Space Network’s ground clocks, and will enable one-way radiometric measurements with accuracy equivalent to cur- rent two-way tracking data. A demonstration unit of the clock was launched into low Earth orbit on June 25, 2019, for the purpose of validating DSAC’s perfor- mance in the space environment. GPS data collected throughout the mission was utilized not only for precise clock estimation, but also as a proxy for deep space tracking data to conduct the Deep Space Navigation Analog Experiment. Through careful processing of GPS Doppler data and limited modeling fidelity representa- tive of deep space navigation capabilities, the analog orbit solutions are compared to higher-fidelity solutions, demonstrating DSAC’s viability as a navigation instru- ment in conditions typical for a low altitude Mars orbiter.

Stuart, Jeffrey↗
NASA NTRSDate not supplied

NASA GEOS Composition Forecast System: GEOS-CF

NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution analysis and forecasts for weather, aerosols, and air quality. Since 2019, the NASA Global Earth Observing System (GEOS) model provides global near-real-time historical estimates and daily 5-day forecasts of atmospheric composition to the public at unprecedented horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). The GEOS-CF is a tool for scientists and the public health community. This presentation will cover 1) an overview of the GEOS-CF modeling framework compared to the GEOS-5 Nature Run with Chemistry (used in the post-processing to make the TEMPO Proxy Data), 2) description of the file used to support the TEMPO retrieval team, and 3) research and development activities as the GEOS-CF system continues to evolve to include multi-constituent data assimilation.

K. Emma Knowland↗
NASA NTRS2011

NASA Satellite Data for Seagrass Health Modeling and Monitoring

Time series derived information for coastal waters will be used to provide input data for the Fong and Harwell model. The current MODIS land mask limits where the model can be applied; this project will: a) Apply MODIS data with resolution higher than the standard products (250-m vs. 1-km). b) Seek to refine the land mask. c) Explore nearby areas to use as proxies for time series directly over the beds. Novel processing approaches will be leveraged from other NASA projects and customized as inputs for seagrass productivity modeling

Spiering, Bruce A.↗
DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [U. Chicago (main)] (ORCID:00090002189↗
DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗
DOE OSTI2024

Greater aperture counteracts effects of reduced stomatal density on WUE: a case study on sugarcane and meta-analysis

Stomata regulate CO 2 and water vapor exchange between leaves and the atmosphere. Stomata are a target for engineering to improve crop intrinsic water use efficiency (iWUE). One example is by expressing genes that lower stomatal density (SD) and reduce stomatal conductance (g sw ). However, the quantitative relationship between reduced SD, g sw , and the mechanisms underlying it is poorly understood. We addressed this knowledge gap using low-SD sugarcane (Saccharum spp. hybrid) as a case study alongside a meta-analysis of data from 10 species. Transgenic expression of EPIDERMAL PATTERNING FACTOR 2 from Sorghum bicolor (SbEFP2) in sugarcane reduced SD by 26-38% but did not affect gsw compared to wildtype. Further, no changes occurred in stomatal complex size or proxies for photosynthetic capacity. Measurements of gas exchange at low CO 2 concentrations that promote complete stomatal opening to normalize aperture size between genotypes were combined with modeling of maximum gsw from anatomical data. These data suggest that increased stomatal aperture is the only possible explanation for maintaining gsw when SD is reduced. Meta-analysis across C 3 dicots, C 3 monocots, and C 4 monocots revealed engineered reductions in SD are strongly correlated with lower gsw (r 2 =0.60-0.98), but this response is damped relative to the change in anatomy.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

Experiments at SRT Using the NOAA CrIS/ATMS Proxy Data Set

The objectives of the talk are: (1) Assess the performance of NGAS Version-1.5.03.00 CrIS/ATMS retrieval algorithm as delivered by LaRC, modified to include the MW and IR tuning coefficients and new CrIS noise model (a) Percent acceptance (b) RMS and mean differences of T(p) vs. ECMWF truth as a function of % yield (2) Compare performance of NGAS retrieval algorithm with an AIRS Science Team Version-6 like retrieval algorithm modified at Sounder Research Team (SRT) for CrIS/ATMS

Susskind, Joel↗
DOE OSTI2023

The intrinsic alignment of red galaxies in DES Y1 redMaPPer galaxy clusters

ABSTRACT Clusters of galaxies trace the most non-linear peaks in the cosmic density field. The weak gravitational lensing of background galaxies by clusters can allow us to infer their masses. However, galaxies associated with the local environment of the cluster can also be intrinsically aligned due to the local tidal gradient, contaminating any cosmology derived from the lensing signal. We measure this intrinsic alignment in Dark Energy Survey (DES) Year 1 redMaPPer clusters. We find evidence of a non-zero mean radial alignment of galaxies within clusters between redshifts 0.1–0.7. We find a significant systematic in the measured ellipticities of cluster satellite galaxies that we attribute to the central galaxy flux and other intracluster light. We attempt to correct this signal, and fit a simple model for intrinsic alignment amplitude (AIA) to the measurement, finding AIA = 0.15 ± 0.04, when excluding data near the edge of the cluster. We find a significantly stronger alignment of the central galaxy with the cluster dark matter halo at low redshift and with higher richness and central galaxy absolute magnitude (proxies for cluster mass). This is an important demonstration of the ability of large photometric data sets like DES to provide direct constraints on the intrinsic alignment of galaxies within clusters. These measurements can inform improvements to small-scale modelling and simulation of the intrinsic alignment of galaxies to help improve the separation of the intrinsic alignment signal in weak lensing studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Demonstrating the Operational Value of Thermodynamic Hyperspectral Profiles in the Pre-Convective Environment

The Short-term Prediction Research and Transition Center (SPoRT) is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service (NWS) Weather Forecasting Offices (WFO). As a part of the transition to operations process, SPoRT attempts to identify possible limitations in satellite observations and provide operational forecasters a product that will result in the most impact on their forecasts. One operational forecast challenge that some NWS offices face, is forecasting convection in data-void regions such as large bodies of water. The Atmospheric Infrared Sounder (AIRS) is a sounding instrument aboard NASA's Aqua satellite that provides temperature and moisture profiles of the atmosphere. This paper will demonstrate an approach to assimilate AIRS profile data into a regional configuration of the WRF model using its three-dimensional variational (3DVAR) assimilation component to be used as a proxy for the individual profiles.

Kozlowski, Danielle↗
NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗
NASA NTRSDate not supplied

Exploring the Landscape of Earth and Space Science Informatics using Latent Topic Modeling

AGU Earth and Space Science Informatics (ESSI) is at the forefront of data management, analysis, large scale experimentation, and infrastructure development pertaining to Earth and Space Science interests. The key topics of interest within ESSI are also evolving and diversifying over time. We aim to observe and quantify the various topics covered in ESSI, analyze their trends over time, and identify the contributors’ affiliations to gain an understanding of the landscape of ESSI and the direction of the research and management. The data for this work are abstracts submitted to AGU’s ESSI Fall meeting; They serve as a proxy for key research and development areas within ESSI. We use an unsupervised topic modeling technique called Latent Dirichlet Allocation to observe the underlying topics covered in ESSI and their trends over time. With this presentation, we showcase our results from the analysis and insights gained.

Muthukumaran Ramasubramanian↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗