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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Big Data Seismology

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Currently, Big Data Seismology is an emergent revolution involving the use of large, data-dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data-dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long-duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up-to-speed with how the Big Data revolution is advancing the field of seismology.

58 GEOSCIENCES↗
DOE OSTI2018

Machine Learning in Seismology: Turning Data into Insights

In this article, we provide an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground-motion prediction, seismic tomography, and earthquake geodesy. Lastly, we conclude by discussing the need for a hybrid approach combining data-driven ML with traditional physical modeling.

58 GEOSCIENCES↗
DOE OSTI2022

Editorial: Advances in Ocean Bottom Seismology

Among the physical sciences, seismology is relatively young; with the continued advances in instrumental technologies and computer capacity, ours has to be one of the most rapidly evolving subject areas within the Earth Sciences writ large. Exploration of the Earth by way of seismic observation and modeling is expanding not only in the range of capabilities - from reservoir scale to whole-Earth sensing - but in recent decades seismology has resolutely marched from the Earth’s continents and sea shores into the oceans, which cover nearly 75% of the Earth and have until recently hindered our ability to more fully explore the planet.

58 GEOSCIENCES↗
DOE OSTI2017

Reconsidering seismological constraints on the available parameter space of macroscopic dark matter

Using lunar seismological data, constraints are proposed on the available parameter space of macroscopic dark matter (macros). Here we show that actual limits are considerably weaker by considering in greater detail the mechanism through which macro impacts generate detectable seismic waves, which have wavelengths considerably longer than the diameter of the macro. We show that the portion of the macro parameter space that can be ruled out by current seismological evidence is considerably smaller than previously reported, and specifically that candidates with greater than or equal to nuclear density are not excluded by lunar seismology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Aerial Seismology Using Balloon-Based Barometers

Seismology on Venus has long eluded planetary scientists due to extreme temperature and pressure conditions on its surface, which most electronics cannot withstand for mission durations required for ground-based seismic studies. Here, we show that infrasonic (low-frequency) pressure fluctuations, generated as a result of ground motion, produced by an artificial seismic source known as a seismic hammer, and recorded using sensitive microbarometers deployed on a tethered balloon, are able to replicate the frequency content of ground motion. We also show that weak, artificial seismic activity thus produced may be geolocated by using multiple airborne barometers. The success of this technique paves the way for balloon-based aero-seismology, leading to a potentially revolutionary method to perform seismic studies from a remote airborne station on the earth and solar system objects with substantial atmospheres such as Venus and Titan.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Deep convolutional autoencoders as generic feature extractors in seismological applications

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and phase picking. These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only outperform the baseline under certain conditions, such as when the target problems require features that are similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

58 GEOSCIENCES↗
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗
DOE OSTI2020

Seismology on Venus with infrasound observations from balloon and orbit

The study of Venus' evolution is inexorably linked with studying its interior properties, which can be investigated by performing seismic studies on the planet. However, seismology on Venus has long eluded planetary scientists due to technological challenges presented by high surface temperature and pressure, which limit lifetimes of surface-based instrumentation. In this white paper, we present two complementary techniques for performing seismology on Venus by measuring the low-frequency acoustic signature (infrasound) produced by seismic activity through coupling between the solid planet and the atmosphere. These techniques may be implemented with technology available today, without the use of high-temperature electronics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A high-damping magnetorheological elastomer with bi-directional magnetic-control modulus for potential application in seismology

A high-damping magnetorheological elastomer (MRE) with bi-directional magnetic-control modulus is developed. This MRE was synthesized by filling NdFeB particles into polyurethane (PU)/ epoxy (EP) interpenetrating network (IPN) structure. The anisotropic samples were prepared in a permanent magnetic field and magnetized in an electromagnetic field of 1 T. Dynamic mechanical responses of the MRE to applied magnetic fields are investigated through magneto-rheometer, and morphology of MREs is observed via scanning electron microscope (SEM). Test result indicates that when the test field orientation is parallel to that of the sample's magnetization, the shear modulus of sample increases. On the other hand, when the orientation is opposite to that of the sample's magnetization, shear modulus decreases. In addition, this PU/EP IPN matrix based MRE has a high-damping property, with high loss factor and can be controlled by applying magnetic field. It is expected that the high damping property and the ability of bi-directional magnetic-control modulus of this MRE offer promising advantages in seismologic application.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

MAGNETOHYDRODYNAMIC SEISMOLOGY OF A CORONAL LOOP SYSTEM BY THE FIRST TWO MODES OF STANDING KINK WAVES

We report the observation of the first two harmonics of the horizontally polarized kink waves excited in a coronal loop system lying southeast of AR 11719 on 2013 April 11. The detected periods of the fundamental mode (P {sub 1}), its first overtone (P {sub 2}) in the northern half, and that in the southern one are 530.2 ± 13.3, 300.4 ± 27.7, and 334.7 ± 22.1 s, respectively. The periods of the first overtone in the two halves are the same considering uncertainties in the measurement. We estimate the average electron density, temperature, and length of the loop system as (5.1 ± 0.8) × 10{sup 8} cm{sup –3}, 0.65 ± 0.06 MK, and 203.8 ± 13.8 Mm, respectively. As a zeroth-order estimation, the magnetic field strength, B = 8.2 ± 1.0 G, derived by the coronal seismology using the fundamental kink mode matches with that derived by a potential field model. The extrapolation model also shows the asymmetric and nonuniform distribution of the magnetic field along the coronal loop. Using the amplitude profile distributions of both the fundamental mode and its first overtone, we observe that the antinode positions of both the fundamental mode and its first overtone shift toward the weak field region along the coronal loop. The results indicate that the density stratification and the temperature difference effects are larger than the magnetic field variation effect on the period ratio. On the other hand, the magnetic field variation has a greater effect on the eigenfunction of the first overtone than the density stratification does for this case.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

EVIDENCE OF THERMAL CONDUCTION SUPPRESSION IN A SOLAR FLARING LOOP BY CORONAL SEISMOLOGY OF SLOW-MODE WAVES

Analysis of a longitudinal wave event observed by the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory is presented. A time sequence of 131 Å images reveals that a C-class flare occurred at one footpoint of a large loop and triggered an intensity disturbance (enhancement) propagating along it. The spatial features and temporal evolution suggest that a fundamental standing slow-mode wave could be set up quickly after meeting of two initial disturbances from the opposite footpoints. The oscillations have a period of ∼12 minutes and a decay time of ∼9 minutes. The measured phase speed of 500 ± 50 km s{sup −1} matches the sound speed in the heated loop of ∼10 MK, confirming that the observed waves are of slow mode. We derive the time-dependent temperature and electron density wave signals from six AIA extreme-ultraviolet channels, and find that they are nearly in phase. The measured polytropic index from the temperature and density perturbations is 1.64 ± 0.08 close to the adiabatic index of 5/3 for an ideal monatomic gas. The interpretation based on a 1D linear MHD model suggests that the thermal conductivity is suppressed by at least a factor of 3 in the hot flare loop at 9 MK and above. The viscosity coefficient is determined by coronal seismology from the observed wave when only considering the compressive viscosity dissipation. We find that to interpret the rapid wave damping, the classical compressive viscosity coefficient needs to be enhanced by a factor of 15 as the upper limit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The Scaling and Units of the Elastic Response Term for Rayleigh Waves that is Output by Computer Programs in Seismology (CPS)

We report on the scaling and units of the Rayleigh wave elastic response function A R (ω) that is output from the widely used Computer Programs in Seismology (CPS) to aid in the modeling of ground motion sourced by atmospheric explosions. The program uses mixed units (km, second, km/s, gm/cc) to keep A R (ω) near 10 0 and prevent any numerical underflow or overflow. We compare two models for the response of an elastic half space to the output from CPS. Our application inputs the recommended, mixed unit geological models to determine how researchers must scale this output to obtain physical units for A R (ω) that represents the amplitude scaling for the minimum group velocity (Airy phase) contribution to Rayleigh waves. We determine that a CPS user must scale the output for A R (ω) by 10 -12 (m/km) 2 (g/cc/m 3 /kg) to obtain MKS (meter, kg, second) units and then must multiply this result by the vertical component eigenfunction squared, that is evaluated at the free surface.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2017

Determination of the Alfvén Speed and Plasma-beta Using the Seismology of Sunspot Umbra

For 478 centrally located sunspots observed in the optical continuum with Solar Dynamics Observatory /Helioseismic Magnetic Imager, we perform seismological diagnostics of the physical parameters of umbral photospheres. The new technique is based on the theory of slow magnetoacoustic waves in a non-isothermally stratified photosphere with a uniform vertical magnetic field. We construct a map of the weighted frequency of three-minute oscillations inside the umbra and use it for the estimation of the Alfvén speed, plasma-beta, and mass density within the umbra. We find the umbral mean Alfvén speed ranges between 10.5 and 7.5 km s{sup −1} and is negatively correlated with magnetic field strength. The umbral mean plasma-beta is found to range approximately between 0.65 and 1.15 and does not vary significantly from pores to mature sunspots. The mean density ranges between (1–6) × 10{sup −4} kg m{sup −3} and shows a strong positive correlation with magnetic field strength.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Ambient field seismology in critical zone hydrological sciences

Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.

58 GEOSCIENCES↗
DOE OSTI2018

Computational Seismology Workshop Trains Early-Career Scientists

The Computational Infrastructure for Geodynamics (CIG), in collaboration with Lawrence Livermore National Laboratory (LLNL), held a workshop last year that focused on computational seismology. The workshop provided training in seismic waveform processing, visualization, and high-performance computing (HPC) waveform simulation. Fifty-five predominantly early-career participants—graduate students and postdocs—from the United States and 16 other countries attended. The workshop was the first of its kind to feature full access to HPC resources for research-grade example problems.

58 GEOSCIENCES↗
DOE OSTI2024

Utah FORGE 6-3629: Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation - 2024 Annual Workshop Presentation

This is a presentation on the Cutting Edge Application of Machine Learning, Geomechanics, and Seismology for Real-Time Decision Making Tools During Stimulation by the University of Utah, presented by No'am Zach Dvory. This video slide presentation, by the University of Utah, discussed the technical objectives of developing a real-time decision-making platform to enhance seismic monitoring and risk management during stimulation activities. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Using Passive Seismology to Map the Water within Critical Glacial Ice Sheets

Predictions of the long term stability of the major ice sheets depend on the ability to identify where melt is occurring and to monitor changes over time. Particularly important is the detection of melting along the grounding line, where the glacier is in contact with the solid earth. Melting along the grounding line significantly increases the speed at which glaciers flow, increasing the risk of ice sheet collapse. Passive seismology offers an effective, relatively low cost and precise means of measuring the dynamic changes in the ice. If we can demonstrate its effectiveness, it would allow continuous monitoring of the critical contact between the ice-sheet and the subsurface. The purpose of this study was to determine whether this method can be applied to detect liquid water within the glacial ice system using currently available data. The objectives would be: (1) To identify the attenuation characteristics of glacial ice and the contrast within fractured and fluid-filled zones. (2) To map fluid-solid contacts within the ice sheets and particularly between ice and the underlying solid earth. (3) To identify changes in the system over time. (4) To identify an optimal network configuration for the purposes of monitoring critical points within the glacier

54 ENVIRONMENTAL SCIENCES↗