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NASA NTRS2020

The Zwicky Transient Facility Bright Transient Survey. I. Spectroscopic Classification and the Redshift Completeness of Local Galaxy Catalogs

The Zwicky Transient Facility (ZTF) is performing a three-day cadence survey of the visible northern sky (∼3π) with newly found transient candidates announced via public alerts. The ZTF Bright Transient Survey (BTS) is a large spectroscopic campaign to complement the photometric survey. BTS endeavors to spectroscopically classify all extragalactic transients with m(peak) ≤ 18.5 mag in either the g(ZTF) or r(ZTF) filters, and publicly announce said classifications. BTS discoveries are predominantly supernovae (SNe), making this the largest flux-limited SN survey to date. Here we present a catalog of 761 SNe, classified during the first nine months of ZTF (2018 April 1–2018 December 31). We report BTS SN redshifts from SN template matching and spectroscopic host-galaxy redshifts when available. We analyze the redshift completeness of local galaxy catalogs, the redshift completeness fraction (RCF; the ratio of SN host galaxies with known spectroscopic redshift prior to SN discovery to the total number of SN hosts). Of the 512 host galaxies with SNe Ia, 227 had previously known spectroscopic redshifts, yielding an RCF estimate of 44% ± 4%. The RCF decreases with increasing distance and decreasing galaxy luminosity (for z < 0.05, or ∼200 Mpc, RCF ≈ 0.6). Prospects for dramatically increasing the RCF are limited to new multifiber spectroscopic instruments or wide-field narrowband surveys. Existing galaxy redshift catalogs are only ∼50% complete at r ≈ 16.9 mag. Pushing this limit several magnitudes deeper will pay huge dividends when searching for electromagnetic counterparts to gravitational wave events or sources of ultra-high-energy cosmic rays or neutrinos.

C. Fremling↗

NASA NTRSDate not supplied

Watching Without Seeing a Tool to Surveil Astronaut Health Outcomes While Maintaining Astronaut Medical Privacy

BACKGROUND The Privacy Act of 1974 regulates the use a nd disclosure of personally identifiable information by US Federal agencies. The Act applies to biographical, financial, a nd other identity-linked information, a s well a s personal health information (PHI). As such, the use of astronaut PHI is limited to authorized personnel for preapproved uses, with data reporting often limited to aggregated information about groups. These limitations on the use a nd reporting of astronaut PHI complicates surveillance efforts, wherein epidemiologists a t the National Aeronautics and Space Administration (NASA)monitor the incidence of targeted health conditions in the astronaut population, or to discover emerging trends of aging and disease. Stratification on one or more covariates –particularly time-period, sex, a nd mission participation –can lead to extremely small datasets such that the reporting of results is potentially attributable to individuals. An additional challenge is the small size of the astronaut population, both in terms of numbers of individuals a s well a s in terms of density of exposure time. Such small datasets yield volatile rate estimates that are difficult to interpret. To a id the epidemiological surveillance efforts, a surveillance tool is required that can (a) satisfy the need for rapid computation of condition-specific incidence and mortality rates; (b) improve the statistical estimates of these estimated rates; and (c) maintain astronaut privacy. Here we describe a nd demonstrate such a tool. METHODS We devised a system that models incidence a nd mortality rates rather than calculating them directly. This ha s the advantage of using all the available data to derive the estimates, lea ding to rates that a re not attributable to any one individual, a nd a re a s numerically stable a s they can be given the extremely limited data. The system models disease endpoints using a Poisson regression model with exposure density (measured in person-years) a s a n offset term. By doing so the model is estimating event counts per person-year, equivalent to modeling the rates directly. It uses a standard (pre-specified)set of covariates; the system does not engage in “model-building” as model parsimony is not the goa l. Instead, it is explicitly recognized that if a covariate is not statistically significant a nd not a confounder then it will likely have very little effect on the estimate of the incidence a nd mortality rates. Users are able to specify the disease endpoint of interest and the covariates over which they would like to stratify. The system then uses the resulting model to compute the estimated rates for the user-chosen configuration of variables as visualizes those either over an age range within a specified time-period, or over time for astronauts with a specified age range. RESULTS The first iteration of the tool computes incidence a nd mortality rates for cardiovascular conditions and cancers. Code ha s been developed to retrieve the appropriate data from the IMPALA analysis platform, compute the models for incidence a nd mortality, a nd then use those models to generate the corresponding rate curves. A companion graphical user interface allows the user to specify the curves and visualize the results. CONCLUSIONS It is important to note that the rapid surveillance tool described here is neither meant to be a definitive assessment of the incidence or mortality of any particular disease or condition in the astronaut population, nor is it meant to be used for research purposes. Rather, it is meant as an early indicator that in-depth investigation may be warranted. By automating a repetitive process and leveraging carefully curated astronaut health outcomes, the tool makes possible a rapid “first look” into known areas of concern, and, if used judiciously, may surface new areas of concern for long-term astronaut health. This work is supported in part by the Translational Research Institute for Space Health (TRISH) through NASA Cooperative Agreement NNX16AO69A.

R J Reynolds↗

NASA NTRS2021

Spin State and Moment of Inertia of Venus

Fundamental properties of the planet Venus, such as its internal mass dis-tribution and variations in length of day, have remained unknown. We usedEarth-based observations of radar speckles tied to the rotation of Venus ob-tained in 2006–2020 to measure its spin axis orientation, spin precession rate,moment of inertia, and length-of-day variations. Venus is tilted by 2.6392±0.0008 degrees (1σ) with respect to its orbital plane. The spin axis precessesat a rate of 44.58±3.3 arcseconds per year (1σ), which gives a normalized mo-ment of inertia of 0.337±0.024 and yields a rough estimate of the size of thecore. The average sidereal day on Venus in the 2006–2020 interval is 243.0226±0.0013 Earth days (1σ). The spin period of the solid planet exhibits variationsof 61 ppm (∼20 minutes) with a possible diurnal or semidiurnal forcing. Thelength-of-day variations imply that changes in atmospheric angular momentumof at least∼4% are transferred to the solid planet.

Jean-Luc Margot↗

NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗

NASA NTRSDate not supplied

Aero-Propulsive Damping Characterization for eVTOL Aircraft Using Free Motion Wind-Tunnel Testing

This paper describes an electric vertical takeoff and landing (eVTOL) aircraft system identification method applied using three degree-of-freedom (3DOF) free motion wind-tunnel testing. The approach, similar to flight-test system identification, allows for efficient mathematical model development of the aero-propulsive moments applied on an eVTOL vehicle, including aerodynamic damping effects. The approach is demonstrated using a subscale tiltrotor eVTOL aircraft mounted on a new 3DOF wind-tunnel apparatus. To execute the test, a model-based 3DOF control system is designed to track attitude commands and transition the aircraft based on the freestream dynamic pressure. While the flight controller is active, orthogonal phase-optimized multisine inputs are injected into the attitude command and control effector command signals to enable collection of informative data for model identification. Aero-propulsive models are then identified at several reference conditions in the transition flight envelope using the equation-error method in the frequency domain. The identified models are shown to have a good fit to the modeling data and good prediction capability of data not used for model identification. The method yields aerodynamic damping estimates using less wind-tunnel test time compared to traditional forced oscillation experiments and supplements static wind-tunnel testing to produce a comprehensive transition aero-propulsive model suitable for use in flight dynamics simulations.

RAVEN↗

DOE OSTI2021

Distribution, Ecology, Life History, and Conservation Status of the Berry Cave Salamander (Gyrinophilus gulolineatus)

The Berry Cave Salamander (Gyrinophilus gulolineatus) is a neotenic, stygobitic salamander endemic to eight cave systems and an isolated surface record in the Appalachian Valley and Ridge of eastern Tennessee. We conducted surveys for G. gulolineatus from 2017–2019 to assess the status of the species, locate new populations, and address knowledge gaps related to life history and population ecology required for conservation assessment. We confirmed G. gulolineatus presence at four historical sites, but we did not observe the species at any additional caves. At the three known sites with greatest abundance, visual counts per survey ranged 0–19 salamanders in 2017–2019. There was no apparent trend in abundance at Berry Cave. Visual counts declined 65% since the mid-2000s at Meads Quarry Cave and 80% since the early 1980s at Mudflats Cave. Mark-recapture studies in 160-m of cave stream at Berry Cave in 2017–2018 and 900-m of cave stream at Meads Quarry Cave in 2008 yielded population size estimates that ranged from 34–78 and 15–65 individuals, respectively. We identified 13 existing or potential threats to populations. Habitat degradation and groundwater contamination (associated with urbanization of the Knoxville metropolitan area and past mining operations) represent the most evident threats to long-term viability. Based on our conservation assessments, we recommend a rank of Endangered under IUCN Red List criteria and Critically Imperiled–Imperiled (G1G2) under NatureServe criteria. In opposition to the recent U.S. Fish & Wildlife Service decision, we advocate that, at minimum, G. gulolineatus remain a Candidate Species. Given the identified threats and these ranks, we offer several recommendations for research, conservation, and management of this rare salamander.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Photoneutron Production Using an Electron Linear Accelerator for Applications in Neutron Imaging

Photoneutron production is possible using an electron linear accelerator and a target capable of generating photonuclear reactions. A short pulse neutron source can be useful for neutron imaging dynamic experiments. This study is aimed at the feasibility of photoneutron production using a 20 MeV electron linear accelerator and tungsten and depleted uranium targets of various thicknesses. MCNP6 (Monte Carlo N-Particle) code will be used to develop a computational model to estimate total neutron yield, and this will be verified at the Idaho State University’s Accelerator Center. After verification of the neutron yield and energy spectra, an additional MCNP6 model will be developed to analyze the neutron imaging processes. This study will potentially prove it is possible to conduct multi-mode imaging experiments on the anticipated Scorpius electron linear accelerator at the Nevada National Security Sites.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗

NASA NTRS2005

Computational Material Processing in Microgravity

Working with Professor David Matthiesen at Case Western Reserve University (CWRU) a computer model of the DPIMS (Diffusion Processes in Molten Semiconductors) space experiment was developed that is able to predict the thermal field, flow field and concentration profile within a molten germanium capillary under both ground-based and microgravity conditions as illustrated. These models are coupled with a novel nonlinear statistical methodology for estimating the diffusion coefficient from measured concentration values after a given time that yields a more accurate estimate than traditional methods. This code was integrated into a web-based application that has become a standard tool used by engineers in the Materials Science Department at CWRU.

Source record↗

NASA NTRS2014

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

GEOS5↗

DOE OSTI2024

Comparison of source-location algorithms for atmospheric samplers

Numerous algorithms have been developed to determine the source characteristics for an atmospheric release, e.g., (Bieringer et al., 2017). This study compares three models that have been applied to the data collected by the International Monitoring System operated by the Comprehensive Nuclear-Test-Ban Treaty Preparatory Commission to estimate source event parameters. Each model uses a different approach to estimate the parameters. A deterministic model uses a possible source region (PSR) approach (Ringbom et al., 2014) that is based on the correlation between predicted and measured sample values. An unnamed model developed at Pacific Northwest National Laboratory (herein called BAYEST) uses a Bayesian formulation (Eslinger et al., 2019; Eslinger et al., 2020; Eslinger and Schrom, 2016). The FREAR model uses a different Bayesian formulation (De Meutter and Hoffman, 2020; De Meutter et al., 2021; Pieter De Meutter, 2021). The performance of the three source-location models is evaluated with 100 synthetic release cases for the single xenon isotope, 133 Xe. The release cases resulted in detections in a fictitious network with 120 noble gas samplers. All three source-location models use the same sampling data. The two Bayesian models yield more accurate location estimates than the deterministic PSR model, with FREAR having slightly better location performance than BAYEST. Samplers with collection periods of 3, 6, 8, 12, and 24-h were used. Results from BAYEST show that location accuracy improves with each reduction in sample collection length. The BAYEST model is slightly better for estimating the start time of the release. The PSR model has about the same spread in start times as the FREAR model, but the PSR results have a better average start time. The Bayesian source-location algorithms give more accurate results than the PSR approach, and provide release magnitude estimates, while the base PSR model does not estimate the release magnitude. This investigation demonstrates that a reasonably dense sampling grid will sometimes yield poor location and time estimates regardless of the model. The poor estimates generally coincide with cases where there is a much larger distance between the release point and the first detecting sampler than the average sampler spacing.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1981

Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence

A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.

Mark, W. D.↗

NASA NTRS1989

The remote sensing of oceanic primary productivity - A review

The ocean is a major sink for atmospheric carbon dioxide. This paper assesses the correctness of the present estimates of the marine primary productivity, obtained by remote sensing techniques, by modeling the physiological mechanisms of carbon assimilation by phytoplankton. The model uses, as the input, measurements of the ocean-surface pigments and data on the incident solar irradiance, and incorporates a description of the photosynthetically available and usable irradiance in the ocean, to describe the light field required for photosynthesis. A comparison of measured and estimated values of the water-column integrated primary marine productivity over large ocean regions yielded favorable results. These estimates do not appear to depend on regional or seasonal factors.

Collins, Donald J.↗

NASA NTRS2019

A Ground Footprint Eccentricity Model For Asteroid Airbursts

Uncertainties in early observations of potentially hazardous asteroids result in preliminary impact corridors that can stretch across large portions of the Earth’s surface. At this early stage of detection, the corridor width and potential for damage are typically estimated using techniques from nuclear weapons research. These estimates often employ spherical blast assumptions resulting in a constant width impact corridor. In actuality, however, the ground damage footprint of obliquely entering asteroids is generally roughly elliptical or “butterfly” shaped, with the major axis extending in the cross range direction and the minor axis aligned with ground-track of the meteoroid. Since actual ground footprints for oblique entries may have aspect ratios greater than two or three, the assumption of a circular blast may significantly underestimate the area of the impact swath and the at-risk population. This work develops an engineering model that can be used to quickly estimate the eccentricity of the ground footprint as a function of local impact parameters. This yields vastly improved local estimates of the corridor width and can significantly enhance the accuracy of risk analysis.

Aftosmis, Michael J.↗

NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

DOE OSTI2021

Spatial distributions of X CO 2 seasonal cycle amplitude and phase over northern high-latitude regions

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 (X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72° N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1995

Coseismic fault slip associated with the 1992 M(sub w) 6.1 Joshua Tree, California, earthquake: Implications for the Joshua Tree-Landers earthquake sequence

Coseismic surface deformation associated with the M(sub w) 6.1, April 23, 1992, Joshua Tree earthquake is well represented by estimates of geodetic monument displacements at 20 locations independently derived from Global Positioning System and trilateration measurements. The rms signal to noise ratio for these inferred displacements is 1.8 with near-fault displacement estimates exceeding 40 mm. In order to determine the long-wavelength distribution of slip over the plane of rupture, a Tikhonov regularization operator is applied to these estimates which minimizes stress variability subject to purely right-lateral slip and zero surface slip constraints. The resulting slip distribution yields a geodetic moment estimate of 1.7 x 10(exp 18) N m with corresponding maximum slip around 0.8 m and compares well with independent and complementary information including seismic moment and source time function estimates and main shock and aftershock locations. From empirical Green's functions analyses, a rupture duration of 5 s is obtained which implies a rupture radius of 6-8 km. Most of the inferred slip lies to the north of the hypocenter, consistent with northward rupture propagation. Stress drop estimates are in the range of 2-4 MPa. In addition, predicted Coulomb stress increases correlate remarkably well with the distribution of aftershock hypocenters; most of the aftershocks occur in areas for which the mainshock rupture produced stress increases larger than about 0.1 MPa. In contrast, predicted stress changes are near zero at the hypocenter of the M(sub w) 7.3, June 28, 1992, Landers earthquake which nucleated about 20 km beyond the northernmost edge of the Joshua Tree rupture. Based on aftershock migrations and the predicted static stress field, we speculate that redistribution of Joshua Tree-induced stress perturbations played a role in the spatio-temporal development of the earth sequence culminating in the Landers event.

Bennett, Richard A.↗