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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Specific conductivity, pH, dissolved oxygen, water temperature, alkalinity and sulfide in-situ data; Slate River floodplain, Crested Butte, CO; May 2020-October 2020

This data package includes a time-series of field measurements from May to October 2020 in groundwater and surface water from the Slate River floodplain in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data packade includes 6 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature, alkalinity and sulfide. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used.All files are in csv format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Specific conductivity, pH, dissolved oxygen, water temperature and alkalinity in-situ data; Slate River floodplain, Crested Butte, CO; March 2021-October 2021

This data package includes a time-series of field measurements from March to October 2021 in groundwater and surface water from the Slate River floodplain in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used.All files are in csv format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

In-situ electrochemical and water quality data; Slate River and East River floodplains, Crested Butte, CO; May 2022-September 2022

This data package includes a time-series of field measurements from May to September 2022 in groundwater and surface water from the Slate River and East River floodplains in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used.All files are in csv format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Controlled Fabrication of Quality ZnO NWs/CNTs and ZnO NWs/Gr Heterostructures via Direct Two-Step CVD Method

A novel and advanced approach of growing zinc oxide nanowires (ZnO NWs) directly on single-walled carbon nanotubes (SWCNTs) and graphene (Gr) surfaces has been demonstrated through the successful formation of 1D–1D and 1D–2D heterostructure interfaces. The direct two-step chemical vapor deposition (CVD) method was utilized to ensure high-quality materials’ synthesis and scalable production of different architectures. Iron-based universal compound molecular ink was used as a catalyst in both processes (a) to form a monolayer of horizontally defined networks of SWCNTs interfaced with vertically oriented ZnO NWs and (b) to grow densely packed ZnO NWs directly on a graphene surface. We show here that our universal compound molecular ink is efficient and selective in the direct synthesis of ZnO NWs/CNTs and ZnO NWs/Gr heterostructures. Heterostructures were also selectively patterned through different fabrication techniques and grown in predefined locations, demonstrating an ability to control materials’ placement and morphology. Several characterization tools were employed to interrogate the prepared heterostructures. ZnO NWs were shown to grow uniformly over the network of SWCNTs, and much denser packed vertically oriented ZnO NWs were produced on graphene thin films. Such heterostructures can be used widely in many potential applications, such as photocatalysts, supercapacitors, solar cells, piezoelectric or thermal actuators, as well as chemical or biological sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1994

Mission planning and scheduling concept for the Advanced X-ray Astrophysics Facility (AXAF)

Projected for launch in the latter part of 1998, the Advanced X-ray Astrophysics Facility (AXAF), the third satellite in the Great Observatory series, promises to dramatically open the x-ray sky as the Hubble and Compton observatories have done in their respective realms. Unlike its companions, however, AXAF will be placed in a high altitude, highly elliptical orbit (10,000 x 100,000 km), and will therefore be subject to its own unique environment, spacecraft and science instrument constraints and communication network interactions. In support of this mission, ground operations personnel have embarked on the development of the AXAF Offline System (OFLS), a body of software divided into four basic functional elements: (1) Mission Planning and Scheduling, (2) Command Management, (3) Altitude Determination and Sensor Calibration and (4) Spacecraft Support and Engineering Analysis. This paper presents an overview concept for one of these major elements, the Mission Planning and Scheduling subsystem (MPS). The derivation of this concept is described in terms of requirements driven by spacecraft and science instrument characteristics, orbital environment and ground system capabilities. The flowdown of these requirements through the systems analysis process and the definition of MPS interfaces has resulted in the modular grouping of functional subelements depicted in the design implementation approach. The rationale for this design solution is explained and capabilities for the initial prototype system are proposed from the user perspective.

Newhouse, M.↗
NASA NTRS2001

SIMBIOS Sun Photometer Program

Aerosol optical thickness (AOT) values determined from the satellite ocean color data provide useful information on the spatial and temporal distributions of aerosols and are by-products of the atmospheric corrections required to estimation of water-leaving radiances. The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) project is using in situ atmospheric data, primarily from sun photometers, for several purposes including: (1) validation the SeaWiFS and other ocean color mission aerosol optical products, e.g., AOT and Angstrom exponent; (2) evaluation of the aerosol models currently used for atmospheric corrections; and (3) development of vicarious sensor calibration methodologies, especially for the near-infrared bands where in situ water-leaving radiance data in the visible from sites like the Marine Optical Buoy cannot be used. The principal source of in situ aerosol observations was the Aerosol Robotic Network (AERONET).

McClain, Charles↗
NASA NTRS2005

Tumbleweed Rovers

Tumbleweed rovers, now undergoing development, are lightweight, inflatable, approximately spherical exploratory robotic vehicles designed to roll across terrain, using only wind for propulsion. Tumbleweed rovers share many features with beach-ball rovers, which were discussed in several prior NASA Tech Briefs articles. Conceived for use in exploring remote planets, tumbleweed rovers could also be used for exploring relatively inaccessible terrain on Earth. A fully developed tumbleweed rover would consist of an instrumentation package suspended in an inflated twolayer (nylon/polypropylene) ball. The total mass of the rover would be of the order of 10 kg, the diameter of the ball when inflated would be 2 meters, and the minimum wind speed needed for propulsion would be about 5 m/s. The instrumentation package would contain a battery power supply, sensors, a Global Positioning System (GPS) receiver, and a radio transmitter that would send the sensor readings and the GPS position and time readings to a monitoring station via a satellite communication system. Depending on the specific exploratory mission, the sensors could include a thermometer, a barometer, a magnetometer (for studying the terrestrial magnetic field and/or detecting buried meteorites), a subsurface radar system (for measuring ice thickness and/or detecting buried meteorites), and/or one or two diametrally opposed cameras that would take the part of sending two side-looking images out. In the planned Antarctic field test, a prototype tumbleweed rover was released at a location near the South Pole. Using the global Iridium satellite network to send information about its position, the rover transmitted temperature, pressure, humidity, and light intensity data to NASA s Jet Propulsion Laboratory. The rover reached speeds of 30 km per hour over the Antarctic ice cap, and traveled at an average speed of about 6 km per hour. The test was designed to confirm the rover s long-term durability in an extremely cold environment, with the goal being eventual use of the device to explore the Martian polar caps and other planets in the solar system. On future Antarctic exploratory missions, tumbleweed rovers might be used to acquire sensor data for studies of global warming, ozone depletion, and impacts of meteorites.

Behar, Alberto↗
NASA NTRS2018

USA Space Debris Environment, Operations, and Research Updates

Space Missions in 2017 Earth Satellite Population Collision Avoidance Maneuvers Post mission Disposal of U.S.A. Spacecraft Space Situational Awareness (SSA) and the Space Debris Sensor (SDS) A total of 86 space launches placed more than 400 spacecraft into Earth orbits during 2017, following the trend of increase over the past decade NASA has established conjunction assessment processes for its human spaceflight and uncrewed spacecraft to avoid accidental collisions with objects tracked by the U.S. Space Surveillance Network - NASA also assists other U.S. government spacecraft owners with conjunction assessments and subsequent maneuvers The ISS has conducted 25 debris collision avoidance maneuvers since 1999 - None in 2016-2017, but an ISS visiting vehicle had one collision avoidance maneuver in 2017 During 2017 NASA executed or assisted in the execution of 21 collision avoidance maneuvers by uncrewed spacecraft - Four maneuvers were conducted to avoid debris from Fengyun-1C - Two maneuvers were conducted to avoid debris from the collision of Cosmos 2251 and Iridium 33 - One maneuver was conducted to avoid the ISS NASA has established conjunction assessment processes for its human spaceflight and uncrewed spacecraft to avoid accidental collisions with objects tracked by the U.S. Space Surveillance Network - NASA also assists other U.S. government spacecraft owners with conjunction assessments and subsequent maneuvers The ISS has conducted 25 debris collision avoidance maneuvers since 1999 - None in 2016-2017, but an ISS visiting vehicle had one collision avoidance maneuver in 2017 During 2017 NASA executed or assisted in the execution of 21 collision avoidance maneuvers by uncrewed spacecraft - Four maneuvers were conducted to avoid debris from Fengyun-1C - Two maneuvers were conducted to avoid debris from the collision of Cosmos 2251 and Iridium 33 The 2014-15 NASA Engineering and Safety Center (NESC) study on the micrometeoroid and orbital debris (MMOD) assessment for the Joint Polar Satellite System (JPSS) provided the following findings - Millimeter-sized orbital debris pose the highest penetration risk to most operational spacecraft in LEO - The most effective means to collect direct measurement data on millimetersized debris above 600 km altitude is to conduct in situ measurements - There is currently no in situ data on such small debris above 600 km altitude Since the orbital debris population follows a power-law size distribution, there are many more millimeter-sized debris than the large tracked objects - Current conjunction assessments and collision avoidance maneuvers against the tracked objects (which are typically 10 cm and larger) only address a small fraction (<1%) of the mission-ending risk from orbital debris To address the millimeter-sized debris data gap above 600 km, NASA has recently developed an innovative in situ measurement instrument - the Space Debris Sensor (SDS) - One maneuver was conducted to avoid the ISS

Liou, J.-C.↗
DOE OSTI2019

Asset Monitoring using Smart Sensing and Advanced Analytics for the Distribution Network

As the electric grid transitions into a highly distributed and intelligent grid, electric utilities are becoming increasingly concerned with monitoring the health and performance of their distribution assets. As Internet of Things technologies and data analytics become inexpensive and ubiquitous, low-cost asset monitoring becomes more feasible. Moreover, using edge analytics, computations can be off-loaded to these intelligent sensors to report only actionable information to the cloud. This needs to be done in a highly distributed manner using non-intrusive sensors and device physics that seamlessly integrates with legacy infrastructure. Here, this paper reviews existing sensor based solutions, identifies t heir issues, proposes a novel solution by exploring the required attributes and proposes an approach for low-cost distribution transformer health monitoring.

Asset monitoring↗
NASA NTRS2011

Sao Paulo Lightning Mapping Array (SP-LMA): Deployment and Plans

An 8-10 station Lightning Mapping Array (LMA) network is being deployed in the vicinity of Sao Paulo to create the SP-LMA for total lightning measurements in association with the international CHUVA [Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)] field campaign. Besides supporting CHUVA science/mission objectives and the Sao Luz Paraitinga intensive operation period (IOP) in December 2011-January 2012, the SP-LMA will support the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), both sensors on the NOAA Geostationary Operational Environmental Satellite-R (GOES-R), presently under development and scheduled for a 2015 launch. The proxy data will be used to develop and validate operational algorithms so that they will be ready for use on "day1" following the launch of GOES-R. A preliminary survey of potential sites in the vicinity of Sao Paulo was conducted in December 2009 and January 2010, followed up by a detailed survey in July 2010, with initial network deployment scheduled for October 2010. However, due to a delay in the Sa Luz Paraitinga IOP, the SP-LMA will now be installed in July 2011 and operated for one year. Spacing between stations is on the order of 15-30 km, with the network "diameter" being on the order of 30-40 km, which provides good 3-D lightning mapping 150 km from the network center. Optionally, 1-3 additional stations may be deployed in the vicinity of Sa Jos dos Campos.

Bailey, J. C.↗
NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Miller, Jerry↗
NASA NTRS2012

Sao Paulo Lightning Mapping Array (SP-LMA): Deployment, Operation and Initial Data Analysis

An 8-10 station Lightning Mapping Array (LMA) network is being deployed in the vicinity of Sao Paulo to create the SP-LMA for total lightning measurements in association with the international CHUVA [Cloud processes of the main precipitation systems in Brazil: A contribution to cloud resolving modeling and to the GPM (Global Precipitation Measurement)] field campaign. Besides supporting CHUVA science/mission objectives and the Sao Luiz do Paraitinga intensive operation period (IOP) in November-December 2011, the SP-LMA will support the generation of unique proxy data for the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI), both sensors on the NOAA Geostationary Operational Environmental Satellite-R (GOES-R), presently under development and scheduled for a 2015 launch. The proxy data will be used to develop and validate operational algorithms so that they will be ready for use on "day1" following the launch of GOES-R. A preliminary survey of potential sites in the vicinity of Sao Paulo was conducted in December 2009 and January 2010, followed up by a detailed survey in July 2010, with initial network deployment scheduled for October 2010. However, due to a delay in the Sao Luiz do Paraitinga IOP, the SP-LMA will now be installed in July 2011 and operated for one year. Spacing between stations is on the order of 15-30 km, with the network "diameter" being on the order of 30-40 km, which provides good 3-D lightning mapping 150 km from the network center. Optionally, 1-3 additional stations may be deployed in the vicinity of Sao Jos dos Campos.

Blakeslee, R.↗
DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Detection of small earthquakes with dense array data: example from the San Jacinto fault zone, southern California

We present a technique to detect small earthquakes not included in standard catalogues using data from a dense seismic array. The technique is illustrated with continuous waveforms recorded in a test day by 1108 vertical geophones in a tight array on the San Jacinto fault zone. Waveforms are first stacked without time-shift in nine non-overlapping subarrays to increase the signal-to-noise ratio. The nine envelope functions of the stacked records are then multiplied with each other to suppress signals associated with sources affecting only some of the nine subarrays. Running a short-term moving average/long-term moving average (STA/LTA) detection algorithm on the product leads to 723 triggers in the test day. Using a local P-wave velocity model derived for the surface layer from Betsy gunshot data, 5 s long waveforms of all sensors around each STA/LTA trigger are beamformed for various incident directions. Of the 723 triggers, 220 are found to have localized energy sources and 103 of these are confirmed as earthquakes by verifying their observation at 4 or more stations of the regional seismic network. This demonstrates the general validity of the method and allows processing further the validated events using standard techniques. The number of validated events in the test day is >5 times larger than that in the standard catalogue. Using these events as templates can lead to additional detections of many more earthquakes.

58 GEOSCIENCES↗
DOE OSTI2018

Monitoring solid metal structures with a nervous system embedded with ultrasonic 3D printing

Active and regular monitoring of structural components is a critical safety precaution, particularly as it concerns infrastructure, aerospace, industry, and many other applications. Solutions for effective structural health monitoring need to be able to probe surface and bulk properties using durable and low-power engineering. Towards these ends it is possible to build smarter, highly networked, and secure materials in order to enable remote real-time material sensing with a lesser-known form of 3D printing, ultrasonic consolidation (UC). This approach harnesses sound waves to weld metal layers in a low temperature process that does not damage sensors and electronics as they are embedded into a solid metal structure. This makes UC perfectly suited for designing solid metal parts with active embedded sensing components. However, the process parameters, material influences and mechanical factors that result in high-quality UC metal components are difficult to control or are loosely understood. The use of trial-and-error optimization during fabrication represents the chief hurdle between its current state of use and its potential to transform rapid prototyping and manufacturing of high-impact technologies (e.g., metal smart structures, wearable sensors, lab-on-a-chip, etc.). In this paper we will describe how embedded sensors may be used for in situ process monitoring and optimization. We will also discuss efforts towards standardizing UC welding for similar and dissimilar metal bonding and for embedding active sensors that can be used to create smart structures that will enable long-term structural health monitoring and other high impact applications.

Suter, Jonathan D.↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2018

Nucleation and Crystal Growth in the Formation of Hierarchical Three-Dimensional Nanoarchitecture

This project is to obtain fundamental understandings of the operation of the Ostwald-Lussac (OL) Law and the oriented attachment (OA) mechanism in nucleation and growth of TiO2 nanorods (NR) via surface-reaction-limited pulsed chemical vapor deposition (SPCVD) process. Three-dimensional (3D) NW networks are a unique type of mesoporous architecture that offers extraordinary surface area density and superior transport properties of electrons, photons, and phonons. It is exceptionally promising for advancing the design and application of functional materials for photovoltaic devices, catalysts beds, hydrogen storage systems, sensors, and battery electrodes. Our group has developed the SPCVD technique by mimicking the mechanism of atomic layer deposition (ALD), which effectively decoupled the crystal growth from precursor concentration while retaining anisotropic 1D growth. For the first time, this technique realized a 3D NW architecture with ultrahigh density and achieved ~4-5 times enhancement on photo-conversion efficiency. Through the support of our current DOE award, we revealed the governing role of the OL Law in the nucleation stage of SPCVD. The formation of NR morphology in SPCVD was identified following the OA mechanism. We also discovered a unique vapor-phase Kirkendall effect in the evolution of tubular or core-shell NR structures. These understandings opened many new opportunities in designing 3D NW architectures with improved properties or new functionalities. Specifically, our accomplishments from this project include five aspects: (1) Observation of the Ostwald-Lussac Law in high-temperature ALD. (2) Observation of vapor-solid Kirkendall effect in ZnO-to-TiO2 nanostructure conversion. (3) Development of highly-efficient capillary photoelectrochemical (PEC) solar-fuel generation. (4) Development of efficient and stable electrochemical protections for black silicon PEC electrodes. (5) Development of doped polymers with tunable electrical properties. This project brings a new level of transformative knowledge on nucleation and crystal growth in the SPCVD NR growth processes. Specifically, quantification of the activation energy landscape guided by the OL law will allow us to establish a critical knowledge base of nucleation kinetics for SPCVD synthesis of NR branches on different material surfaces. Studying the OA kinetics will establish a transformative knowledge base to support this new crystal growth mechanism that can be applied to many functional material systems. This research will pave the road toward a capable and versatile synthesis technology for creating 3D hierarchical mesoscale structures.

36 MATERIALS SCIENCE↗
NASA NTRS1986

A densely spaced array of sea level monitors for the detection of vertical crustal deformation in the Shumagin seismic gap, Alaska

A network of sea level gauges has been installed with about 40 km spacing in the Shumagin Islands in order to detect relative vertical motion, in particular, possible crustal motion precursory to an expected major earthquake. This required the development and deployment of a pressure sensor sea level gauge suitable for installation on remote and harsh coastlines. Data are collected in near-real-time via satellite, both in order to exploit fully any precursors that may be observed and to provide continuous information on the status of the instruments. Using Wiener filtering techniques, it has been determined conservatively that no relative vertical crustal motion greater than 0.1 m between stations has occurred during 1981-1985. This is consistent with independent geodetic leveling and trilateration data, though evidence exists for more rapid deformation during 1978-1980. The sea level data collected so far have an rms noise level of about 0.04 m after processing; this is limited in part by the pressure sensor and associated electronics. Improved prressure gauges with better long-term stability have recently been installed. A short-baseline tiltmeter operating in a tunnel has demonstrated that suitably designed and located land-based tiltmeters have a lower noise level, and hence better precursor detection characteristics than the sea level gauges, at monthly and shorter periods.

Beavan, John↗