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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

A broad-band microseismometer for planetary operations

There has recently been renewed interest in the development of instrumentation for making measurements on the surface of Mars. This is due to the Mars Environmental Survey (MESUR) Mission, for which approximately 16 small, long-lived (2-10 years), relatively inexpensive surface stations will be deployed in a planet-wide network. This will allow the investigation of processes (such as seismology and meteorology) which require the simultaneous measurement of phenomena at many widely spaced locations on the surface over a considerable length of time. Due to the large number of vehicles involved, the mass, power, and cost of the payload will be severely constrained. A seismometer has been identified as one of the highest priority instruments in the MESUR straw-man payload. The requirements for an effective seismic experiment on Mars place a number of constraints on any viable sensor design. First, a large number of sensors must be deployed in a long-lived global network in order to be able to locate many events reliably, provide good spatial sampling of the interior, and increase the probability of seismic detection in the event of localized seismicity and/or high attenuation. From a practical standpoint, this means that individual surface stations will necessarily be constrained in terms of cost, mass, and power. Landing and thermal control systems will probably be simple, in order to minimize cost, resulting in large impact accelerations and wide daily and seasonal thermal swings. The level of seismic noise will determine the maximum usable sensitivity for seismometer. Unfortunately, the ambient seismic noise level for Mars is not well known. However lunar seismic noise levels are several orders of magnitude below that of the Earth. Sensitivities on the order of 10(exp -11)g over a bandwidth of .04 to 20 Hz are thought to be necessary to fulfill the science objectives for a seimometer placed on the Martian surface. Silicon micromachined sensor technology offers techniques for the fabrication of monolithic, robust, compact, lower power and mass accelerometers. Conventional micro-machined accelerometers have been developed and are commercially available for high frequency and large acceleration measurements. The new seismometer we are developing incorporates certain principles of conventional silicon micromachined accelerometer technology. However, currently available silicon micromachined sensors offer inadequate sensitivity and bandwidth for the Mars seismometer application. Our implementation of an advanced silicon micromachined seismometer is based on principles recently developed at JPL for high-sensitivity position sensor technology.

Banerdt, W. B.↗
DOE OSTI2023

Mapping Glacier Basal Sliding Applying Machine Learning

During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

58 GEOSCIENCES↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
NASA NTRS2016

Seismic Exploration of Small Bodies

As a result of the Phase I study, we have demonstrated that a mission to a smallbody (asteroid, comet) whose objective is to conduct a seismic experiment to understandthe interior structure, can be accomplished with small (<200 kg) spacecraft launched on asmall launch vehicle (Athena IIc).We modeled the seismic response of a small body and calculated that the energynecessary to propagate through the body and be detected by a seismometer. Thesecalculations provide guidance as to the type of energy source that is required. A simpleenergy source similar to a NASA standard initiator (NSI) can be used, although a singleNSI is insufficient. An NSI is an explosive pyrotechnic that is used to sever connectionson spacecraft. Use of such an energy source has illustrated two additional areas of study -anchoring of the source and sensor to the surface and understanding the efficiency ofenergy propagation from the source into the surface.The spacecraft has the ability to carry and deploy a series of source/sensors toconduct the experiment by placing them on the surface. We identified a suite of candidatenear-Earth asteroids as targets and used one 1991VG as the target to calculate the missiontrajectory and ∆V requirements. Spacecraft and launch vehicle performance are launchthat any of the candidates could be reached with appropriate mass and launch margins.Sources and sensors are deployed from arm attached to the spacecraft. Thespacecraft maneuvers next to the target body and presses the sensor against the surfaceand releases it. After emplacing all of the surface packages, the sensors are monitored fora period of time to measure the seismic noise. Finally, the active seismic experiment isconducted.The work conducted during Phase I demonstrates that a small mission can be designed to conduct an active seismic experiment on a small near-Earth body. Whileother targets may require more performance, the basic architecture is viable for anytarget.We have identified a number of specific technical areas that require more detailed study. Those areas are largely focused on the detailed analysis and design of the source mechanism and anchoring it to the surface. Such topics will be part of a Phase IIproposal.

Spacecraft↗
DOE OSTI2021

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗
DOE OSTI2022

Ambient Noise Attenuation Tomography Reveals an Asymmetric Damage Zone Across San Jacinto Fault Near Anza, California

Here, we perform seismic attenuation tomography of the shallow structure for the San Jacinto Fault in the Ramona Reservation of southern California. The study uses ambient seismic noise recorded by a linear array of 65 3-C sensors across the fault. We extract amplitude decay information, with uncertainty, from noise interferometry functions. To account for strong heterogeneities in the complex shallow fault zone structure, we apply a frequency-dependent amplitude correction for focusing/defocusing effects using inverted phase velocity maps obtained by solving the transport equation. We then estimate the attenuation structure based on the linear station-triplet method for both Love and Rayleigh waves. The attenuation tomography indicates strong attenuation correlated with known San Jacinto Fault surface traces. The Love wave attenuation tomography reveals an asymmetric damage zone that exists primarily on the fault side with faster seismic velocity, consistent with earthquake ruptures on a fault bimaterial interface with preferred propagation direction to the northwest.

58 GEOSCIENCES↗
DOE OSTI2022

Validation of seismic velocity models in southern California with full-waveform simulations

SUMMARY Crustal seismic velocity models provide essential information for many applications including earthquake source properties, simulations of ground motion and related derivative products. We present a systematic workflow for assessing the accuracy of velocity models with full-waveform simulations. The framework is applied to four regional seismic velocity models for southern California: CVM-H15.11, CVM-S4.26, CVM-S4.26.M01 that includes a shallow geotechnical layer, and the model of Berg et al. For each model, we perform 3-D viscoelastic wave propagation simulations for 48 virtual seismic noise sources (down to 2 s) and 44 moderate-magnitude earthquakes (down to 2 s generally and 0.5 s for some cases) assuming a minimum shear wave velocity of 200 m s–1. The synthetic waveforms are compared with observations associated with both earthquake records and noise cross-correlation data sets. We measure, at multiple period bands for well-isolated seismic phases, traveltime delays and normalized zero-lag cross-correlation coefficients between the synthetic and observed data. The obtained measurements are summarized using the mean absolute derivation of time delay and the mean correlation coefficient. These two metrics provide reliable statistical representations of model quality with consistent results in all data sets. In addition to assessing the overall (average) performance of different models in the entire study area, we examine spatial variations of the models’ quality. All examined models show good phase and waveform agreements for surface waves at periods longer than 5 s, and discrepancies at shorter periods reflecting small-scale heterogeneities and near-surface structures. The model performing best overall is CVM-S4.26.M01. The largest misfits for both body and surface waves are in basin structures and around large fault zones. Inaccuracies generated in these areas may affect tomography and model simulation results at other regions. The seismic velocity models for southern California can be improved by adding better resolved structural representations of the shallow crust and volumes around the main faults.

58 GEOSCIENCES↗
DOE OSTI2025

Hayward Fault Creep From Coda Wave Interferometry

Understanding creep is essential for assessing seismic hazards along the creeping Hayward Fault, which threatens the San Francisco area. Current 1-D monitoring methods, like creepmeters and theodolites, provide limited measurements with low spatial resolution. InSAR offers higher 2-D spatial sampling but lower temporal resolution. We introduce a novel method using coda wave interferometry (CWI) to monitor creep in 2-D with high resolution. By calculating changes in ambient seismic noise travel times, we relate these measurements to creep offsets, bridging the gap between existing methods. Applied to seismic data from 2021 around the Hayward Fault, our CWI-derived offsets aligned with creepmeter measurements, revealing continuous and episodic creep. A 2-D regionalization identified a spatial boundary between these regimes, corroborating previous findings. This approach could enhance seismic monitoring and improve understanding of fault mechanics and earthquake risks.

Luckie, Thomas William [Sandia National Laboratori↗
DOE OSTI2021

The Silencing of U.S. Campuses Following the COVID-19 Response: Evaluating Root Mean Square Seismic Amplitudes Using Power Spectral Density Data

In response to the COVID-19 global pandemic, many populated and active regions have become deserted and show significant reductions in their background seismicity, especially campuses across the United States (U.S.). Seismic sensors located in the vicinity of or within U.S. campuses show that anthropogenic seismic noise remains elevated during the ordinary, nonpandemic, academic year, only subduing during periods of recess (e.g., winter break). Here, we use power spectral density (PSD) data computed by the Incorporated Research Institutions for Seismology Data Management Center for quality assessment to calculate root mean square (rms) amplitude and analyze the effects of the COVID-19 school closures. We processed and analyzed PSD data for 46 seismic stations located within 50 m of a U.S. university or college. Results show that 42 campus stations show an overall rms drop following a statewide school closure.

58 GEOSCIENCES↗
NASA NTRS2019

Space Based Gravitational Wave Astronomy Beyond LISA

The Laser Interferometer Space Antenna (LISA) will open three decades of gravitational wave(GW) spectrum between 0.1 and 100 mHz, the mHz band [1]. This band is expected to be the richest part of the GW spectrum, in types of sources, numbers of sources, signal-to-noise ratios and discovery potential. When LISA opens the low-frequency window of the gravitational wave spectrum,around 2034, the surge of gravitational-wave astronomy will strongly compel a subsequent mission to further explore the frequency bands of the GW spectrum that can only be accessed from space. The 2020's is the time to start developing technology and studying mission concepts for a large-scale mission to be launched in the 2040's. The mission concept would then be proposed to Astro2030. Only space-based missions can access the GW spectrum between 108 and 1 Hz because of the Earth's seismic noise. This white paper surveys the science in this band and mission concepts that could accomplish that science. The proposed small scale activity is a technology development program that would support a range of concepts and a mission concept study to choose a specific mission concept for Astro2030. In this white paper, we will refer to a generic GW mission beyond LISA as bLISA.

Baker, John↗
DOE OSTI2026

Lunar accelerometer network gravitational observatory (LANGO)

With ground-based interferometers detecting hundreds of gravitational-wave (GW) events, GW astronomy has continued to blossom. U.S. and European scientists are developing plans to construct third-generation ground-based interferometers. ESA has proceeded through the mission formulation phase of space-based interferometer in a lower-frequency band, 10 –4 –0.1 Hz. Despite all these exciting developments, there is still a missing frequency band, 0.1–10 Hz. This mid-frequency band is rich with interesting astrophysical events. Coalescence and merger of intermediate-mass black holes (IMBHs) will occur in this frequency band. Coalescing stellar-mass BHs will pass through this frequency band days before they reach the frequency band of LIGO and Virgo. Detection of such signals would enable a mid-frequency detector to issue an advance notice to the high-frequency GW detectors, as well as to optical, x-ray and γ-ray telescopes. We propose Lunar Accelerometer Network Gravitational Observatory (LANGO) to detect GWs in this frequency band. In the first phase (LANGO 1), we propose to deploy four ambient-temperature (250 K) accelerometers in the tetrahedral or in a square configuration on the hemisphere facing the Earth. After successful operation at 250 K, LANGO would be upgraded to a cryogenic (4 K) version with over two orders of magnitude increased sensitivity (LANGO 2), with coherent rejection of seismic noise implemented. LANGO is a full-tensor detector, capable of determining the source direction and wave polarization. Each test mass (TM) is suspended as a pendulum with resonance frequency ∼ 0.01 Hz, thus is only weakly coupled to the lunar surface horizontally. LANGO is designed to detect the relative motion of globally separated, nearly free, TMs by using the Moon as a large quiet platform. LANGO 1 and 2 accelerometers aim at sensitivities ⩽ 10 –11 m s –2 Hz –1/2 and ⩽ 10 –13 m s –2 Hz –1/2 in the horizontal axes over the frequency band of 1 mHz–10 Hz, which yield GW sensitivities 1.1 x 10 -21 Hz -1/2 and 3.8 x 10 -24 Hz -1/2 at 1 Hz, respectively. The LANGO accelerometers will be 10 3 –10 5 times more sensitive than Apollo seismometers. With such sensitivity, LANGO will also make great contribution to the advancement of lunar geophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Crustal and uppermost mantle structure near the Gloria Fault, North Atlantic, from ocean bottom seismometer surface wave observations

SUMMARY In this work, we present both 1-D and 3-D shear wave velocity (Vs) models of the oceanic crust and uppermost mantle below the Deep OCean Test ARray area, located ∼ 70 km north of the central section of the Gloria Fault, in the eastern North-Atlantic Ocean. The velocity models are inferred from the dispersion of surface waves recorded on ocean bottom seismometers. Dispersion measurements are obtained from the analysis of ambient seismic noise at short periods (< 14 s) and teleseismic surface waves at long periods (> 14 s) using the two-station method. The 1-D Vs model is inferred from the joint inversion of Rayleigh wave phase velocities and Love wave group and phase velocities. The 3-D tomographic model is obtained by inversion of 2-D Love wave group velocity maps as a function of depth, further constrained by the average of Love wave phase velocities obtained from ambient noise (4–9 s) and the average Rayleigh and Love wave phase velocities calculated from teleseismic data (14–44 s). The 1-D Vs model shows a sediment layer with a low velocity of 1.05 km s−1, similar to previous studies in the region. Below the sediments, we find an oceanic crust with velocities ranging from 3.3 to 4.5 km s−1. The model reaches an unusually high velocity of 4.9 km s−1 in a 20 km thick layer at depths between 16 and 36 km. We interpret this fast velocity layer as indicative of the presence of harzburgite, a residue of enhanced melting that might have been formed by the proximity between the Mid-Atlantic Ridge and the Azores mantle plume. At greater depths the velocity decreases, forming a low-velocity zone that reaches a minimum at ∼ 70 km depth, which we interpret as the maximum depth for the lithosphere–asthenosphere boundary. The 3-D model shows a structure that is mostly horizontally layered, with Vs isocontours at 3.5–4.5 km s−1 highlighting oscillations of the crustal structure with wavelengths of ∼25–30 km. These oscillations may be due to changes in the rate of mantle upwelling and magma supply rate.

Pinzón, Juan I. (ORCID:0000000338078068)↗
DOE OSTI2022

Seismic monitoring of well integrity

Seismic tube waves, produced by flexure of the well boundary, pressure changes in the fluid in the well, and deformation of the material immediately surrounding the well, are particularly sensitive to variations in the state of the well. In this work, we evaluate a direct approach for generating and observing tube waves as a means of detecting well damage. While we find that it can be difficult to reliably excite observable tube waves without a very strong surface source, time-frequency techniques can be employed to increase the detectability of tube wave reflections. New technologies, particularly distributed acoustic sensing, hold great promise for evaluating well integrity by monitoring tube waves, temperature changes, and seismic noise due to well deformation and fluid leakage.

58 GEOSCIENCES↗
DOE OSTI2021

Utah FORGE: Seismic Velocity Models, February 2021

This dataset contains a map, showing the Utah FORGE seismic stations, and seismic velocity model data. There are 61 1-D velocity models which are in a compressed TAR file. A paper is referenced at the end of this description which discusses the use of these data in 3D modelling. The paper summary follows: We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2-5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ~22 degrees to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity. Reference: Zhang, H. and K. L. Pankow (2021). High-resolution Bayesian spatial auto-correlation (SPAC) pseudo-3D Vs model of Utah FORGE site with a dense geophone array, Geophys. Res. Int, https://doi.org/10.1093/gji/ggab049

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Imperial Valley Dark Fiber Project Continuous DAS Data

The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecommunications networks and is repurposed in this project for DAS applications. The objective, which is further detailed in the attached journal article from Ajo-Franklin et al., is to demonstrate dark fiber DAS as a tool for basin-scale geothermal exploration and monitoring. The included DAS data were recorded during two days at the beginning the project. Data is stored in the .h5 (HDF5) file format, readable using various software tools, including the 'h5read' and 'h5info' functions in Matlab. Provided here are examples of methods to read and use the data with the 'h5py' package in Python.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Assessing the Expansion of Ground-Motion Sensing Capability in Smart Cities via Internet Fiber-Optic Infrastructure

Monitoring ground motion in smart cities can improve the public safety by providing critical insights on natural and anthropogenic hazards, for example, earthquakes, landslides, explosions, infrastructure failures, and so forth. Although seismic activity is typically measured using dedicated point sensors (e.g., geophones and accelerometers), techniques such as distributed acoustic sensing have demonstrated the utility of using fiber-optic cable to detect seismic activity over comparable distances. In this article, we present the results of a study that quantifies the expansion in an area monitored for low-amplitude ground-motion events by augmenting existing point sensors with the internet fiber-optic cable infrastructure. Here we begin by describing our methodology, which utilizes geospatial data on point sensors and internet optical fiber deployed in metropolitan statistical areas (MSAs) in the United States. We extend these data to identify the area that can be monitored by (1) considering the observed seismic noise data in target locations, (2) applying the model from Wilson et al. (2021) to understand the potential coverage area gains using optical fiber sensing, and (3) optimizing the selection of fiber segments to maximize coverage and minimize deployment costs. We implement our methodology in ArcGIS to assess the additional area that can be monitored for low-amplitude ground-motion events (i.e., magnitude >0.5) by utilizing internet fiber-optic cables in the 100 most populous MSAs in the United States. We find that the addition of internet fiber-based sensors in MSAs would increase the area monitored on average by over an order of magnitude from 1% to 12%, if the subset of fiber cable segments that maximize coverage and minimize deployment costs is chosen even if only 20% of all fibers are used.

58 GEOSCIENCES↗
DOE OSTI2021

Aftershock Identification Using a Paired Neural Network Applied to Constructed Data

This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Detection of high-frequency gravitational waves using SRF cavities

Today, apart from some isolated R\&D efforts, there are no GW experiments, yet which explore a large part of the vast frequency range above the LIGO/Virgo band. It is planned to establish an experiment at DESY and Fermilab to search for high-frequency GWs in the frequency range of 10\,kHz to 100\,MHz. The basic idea is to use superconducting radiofrequency (SRF) cavities to detect tiny harmonic deformations induced by GWs which change the boundary conditions of the oscillating electromagnetic field. This paper summarizes the challenging environmental boundary requirements, and the R\&D to operate a cavity using a LLRF system which pushes beyond state-of-the-art accuracy and resolutions and a seismic noise mitigated cryostat at 1.8\,K. The focus of this paper is the warm and cold commissioning of a prototype cavity, built 20 years ago during the MAGO collaboration, and its first measurement in our collaborative research project.

Giaccone, Bianca [Fermilab]↗