Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Fault detection and characterization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Liveness as a factor to evaluate memory vulnerability to soft errors

Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.

Bramley, Richard Gavin↗
DOE OSTI2021

SIR Glass Test Vehicle Designed to Characterize Process Materials

The exposure to harsh environments can result in reliability issues on highly dense electronic packages and circuit assemblies. One area of concern is electrochemical failure mechanisms caused by electrolytic corrosion, electrochemical migration (dendrites), and leakage currents under humid conditions. These no-fault and no-trouble found failure points are challenging to detect and to perform process control during the assembly process. The focus of this research centers on metallizing glass surface insulation resistance test vehicles to characterize materials on electronic packages and components that exhibit the highest risk of failure due to material interactions and process residues. Glass test vehicles provide a visual representation of materials when they are exposed to moisture content in the air. Harsh environments can cause material interactions resulting in morphology changes, opening, and release of active components. A transparent test vehicle is highly functional for environmental stress testing under temperature, humidity, and bias. The designed experiment will evaluate the effects of material and process residues that can undergo changes in behavior during application, which leads to local failures.

Capen, Bill↗
DOE OSTI2020

Data analytics for leak detection in a subcritical boiler

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis, Canonical Variate, and Fisher Discriminant Analysis were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. Furthermore, about 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Automatic Fault Detection & Diagnostics: Residential Market Analysis

This report provides an overview of the market potential for automated fault detection and diagnostics (AFDD) in the residential sector, focusing on embedded AFDD in central air conditioners (CAC) and air-source heat pumps (ASHP) as well as the use of smart diagnostic tools to ensure quality installation of CAC/ASHP equipment. The contents include background on AFDD, stakeholder engagement efforts, a technology assessment of AFDD and smart diagnostic tools, residential CAC and ASHP market trends, a characterization of the CAC and ASHP installed base, a synopsis of utility provider programs, and market barriers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

02 PETROLEUM↗
DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗
DOE OSTI2023

Quaternary slip rates and most recent surface rupture of the Bitterroot fault, western Montana

The Bitterroot fault is a ~100-km-long Quaternary active normal fault that bounds the eastern margin of the north–south-trending Bitterroot Mountains and accommodates extension near the Intermountain Seismic Belt. Here, new detailed mapping using high-resolution topographic data derived from light detection and ranging (lidar) along the southern Bitterroot Range documents multiple generations of fault scarps in Holocene–Pleistocene deposits with vertical off sets that increase in magnitude with age. Fault mapping indicates a complex fault geometry characterized by an en echelon pattern of discontinuous segments of 41–78° east-dipping normal faults that appear to cut the older Eocene detachment fault, and locally 70–88° west-dipping antithetic normal faults. 10 Be cosmogenic exposure dating provides in situ age control for 32 surface boulders (>1 m) sampled in glacial deposits. Near Como Dam, two Pinedale-age glacial moraine sequences yield peak age distributions of 15.0 ± 0.4 ka and 16.4 ± 0.6 ka as apparent exposure ages (e = 0), and 15.4 ± 0.4 ka and 16.8 ± 0.6 ka based on the maximum allowed boulder surface erosion rate (e = 2 mm/ka). Vertical separation of 3.5 ± 0.2 m across the ~16–17 ka glacial moraine off set by the Bitterroot fault scarp yields a fault slip rate of 0.2–0.3 mm/yr. Glacial Lake Missoula highstand shorelines, inset into the ~15 ka glacial moraine and vertically off set 4.6 ± 1.6 m by an antithetic strand of the Bitterroot fault, yield fault slip rates of 0.2–0.5 mm/yr that overlap with fault slip rates on the main strand near Lake Como (0.2–0.3 mm/yr). At the Ward Creek fan located ~15 km to the north of Lake Como, two glacial debris fan sequences yield peak age distributions of 16.6 ± 0.4 ka and 62.8 ± 1.7 ka (e = 0), and 17.0 ± 0.4 ka and 69.9 ± 2.2 ka (e = 2 mm/ka). Vertical separations of 2.4 ± 0.3 m and 4.5 ± 0.2 m on the ~17 ka and ~63–70 ka fan surfaces off set by the Bitterroot fault yield fault slip rates of 0.2–0.3 mm/yr and 0.1 mm/yr, respectively. Our results indicate broadly consistent fault slip rates for the main fault segments at Lake Como (0.2–0.3 mm/yr) and the Ward Creek fan (0.1–0.3 mm/yr) with an along-strike range of 0.1–0.3 mm/yr for the southern Bitterroot fault. Fault scaling relations and evidence of multiple late Quaternary fault surface ruptures suggest the Bitterroot fault could produce a Mw ~7.2 earthquake. Structural model constraints and our slip rate results indicate both high-angle or low-angle fault geometries are possible at depth. A seismogenic low-angle fault model could generate a larger earthquake of Mw >7.2. Earthquake history is unknown for the Bitterroot fault, but fault scarps in young glacial deposits demonstrate its seismogenic potential. Data from this study suggest seismic hazards from the Bitterroot fault may pose a high level of risk to the Missoula metropolitan area, the State’s second most populous region, and major infrastructures across the Missoula and Bitterroot Valleys.

58 GEOSCIENCES↗
DOE OSTI2020

Holistic Measurement Driven Resilience: Combining Operational Fault and Failure Measurements and Fault Injection for Quantifying Fault Detection, Propagation and Impact. Final report

For HPC systems to date, application resilience to faults and failures has been accomplished by the brute- force method of checkpoint/restart, which allows an application to make forward progress in the face of system and application faults, errors, and failures independent of root cause or end result. It has remained the primary resilience mechanism because we lack a way to identify faults and anticipate consequences early enough to take meaningful mitigating action. However, checkpoint/restart implementations put a tremendous burden on system resources and on the applications themselves and is becoming less feasible at scale. Because we have not yet operated at scales at which checkpoint/restart fails to provide forward progress, despite increasing costs, vendors have had little motivation to provide the instrumentation necessary for early identification of faults and failures. However, as we move from petascale to exascale, component mean time to failure (MTTF) will render the existing techniques ineffectual and/or too expensive. Furthermore, fault recovery mechanisms such as failover and/or error correction introduce performance inconsistency. Instrumentation allowing early indication of problems and tools to enable use of such information by systems, operating systems, and applications offer an alternative, more scalable and less costly solution. In the HMDR project, we built on our experience and expertise developed and accumulated over years of research on design, monitoring, measurement, and assessment of resilient computing systems. Analysis of field data on the current and past generations of extreme-scale systems revealed several challenges that, if not addressed in increasingly larger and more complex systems, may hinder the effectiveness of future exascale computing systems. Specifically, i) file systems and interconnects in current-generation large-scale systems already operate at the margins of resiliency, including consistent performance, and may not scale to larger deployments; ii) automated, software-based failover mechanisms are frequently inadequate and can introduce wider failures, such that failures during recovery may lead to system/application failures, including system-wide outages; and iii) silent data corruption represents a critical fault mode and will require efficient detection mechanisms if next-generation applications are to take full advantage of exascale hardware. To address the above challenges, we assembled a team of world-renowned experts in resilient extreme- scale computing from the University of Illinois (Electrical and Computer Engineering, Computer Science, and NCSA), SNL, LANL, NERSC, and Cray. Our team includes representatives from centers that house many of the largest HPC resources in the world, both today and over the coming years. The team has a unique track record of research in i) system and application failure characterization based on the analysis of field data, ii) data-driven design of fault/error detection mechanisms, and iii) experimental characterization of system/application resiliency. The team includes system owners/operators who provide continuous data collection and access and ensure installation of appropriate analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Single-Particle Studies Reveal a Nanoscale Mechanism for Elastic, Bright, and Repeatable ZnS:Mn Mechanoluminescence in a Low-Pressure Regime

Mechanoluminescent materials, which emit light in response to elastic deformation, are demanded for use as in situ stress sensors. ZnS doped with Mn is known to exhibit one of the lowest reported thresholds for appearance of mechanoluminescence, with repeatable light emission under contact pressure <10 MPa. Here, the physical basis for such behavior remains as yet unclear. Here, reliable microscopic detection of mechanoluminescence of single ZnS:Mn microparticles, in combination with nanoscale structural characterization, provides evidence that the mechanoluminescent properties of these particles result from interplay between a non-centrosymmetric crystal lattice and its defects, viz., dislocations and stacking faults. Statistical analysis of the distributions of mechanoluminescence energy release trajectories reveals two distinct mechanisms of excitation: one attributable to a piezo-phototronic effect and the other due to dislocation motion. At pressures below 8.1 MPa, both mechanisms contribute to mechanoluminescent output, with a dominant contribution from the piezo-phototronic mechanism. In contrast, above 8.1 MPa, dislocation motion is the primary excitation source. For the piezo-phototronic mechanism, we propose a specific model that accounts for elastic ZnS:Mn mechanoluminescence under very low pressure. The charged interfaces in stacking faults lead to the presence of filled traps, which otherwise would be empty in the absence of the built-in electric field. Upon application of external stress, local enhancement of the piezoelectric field at the stacking faults’ interfaces facilitates release of the trapped carriers and subsequent luminescence. This field enhancement explains how <10 MPa pressure produces thousands of photons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A fault-tolerant neutral-atom architecture for universal quantum computation

Quantum error correction (QEC) is essential for the realization of large-scale quantum computers. However, owing to the complexity of operating on the encoded ‘logical’ qubits, understanding the physical principles for building fault-tolerant quantum devices and combining them into efficient architectures is an outstanding scientific challenge. Here we use reconfigurable arrays of up to 448 neutral atoms to implement the key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms. We first use surface codes to study how repeated QEC suppresses errors, demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding. We then investigate logical entanglement using transversal gates and lattice surgery and extend it to universal logic through transversal teleportation with three-dimensional [[15,1,3]] codes, enabling arbitrary-angle synthesis with polylogarithmic overhead. Finally, we develop mid-circuit qubit reuse16, increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations with [[7,1,3]] and high-rate [[16,6,4]] codes while maintaining constant internal entropy. Our experiments show key principles for efficient architecture design, involving the interplay between quantum logic and entropy removal, judiciously using physical entanglement in logic gates and magic state generation, and leveraging teleportations for universality and physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation in neutral atom systems.

atomic and molecular physics↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Small-Magnitude Seismic Swarms in Central Utah (US): Interactions of Regional Tectonics, Local Structures and Hydrothermal Systems

Swarms in Central Utah are situated in the complex transition between the Basin and Range (BR) province and the Colorado Plateau. Transecting transverse structures, volcanic deposits, and hydrothermal systems complicate the extensional BR horst and graben structures and provide a multitude of plausible triggering mechanisms. Revisiting the catalog of the University of Utah Seismograph Stations (1981–2022), we analyze spatio-temporal patterns and characteristic features of seismic sequences. Swarms with alternating seismicity rates, bursts, and longer swarms with persistent moment release exhibit a remarkable diversity in temporal evolution. Swarm durations do not scale with cumulative seismic moment: swarms lasting less than 1 day can have similar cumulative seismic moments as month-long swarms. Here, we observe stationary swarms re-occurring for years (e.g., Mineral Mountains), as well as singular swarms in low-seismicity areas (e.g., activating a local structure). The swarms show a pronounced heterogeneity in triggering and driving mechanisms, observed in the detailed analysis of exemplary sequences (detections, relocations, moment tensors, waveform-based clustering, and repeater analysis). The 2022 Sevier Valley sequence activated a BR-related normal fault, the first resolved fault plane in the valley since 1983. The 2011 Circleville sequence is interpreted as a swarm triggered by mainshock-aftershock activity characterized by increasing magnitudes, changing rupture mechanisms, and a concentration of highly similar events in the second part of the sequence. By jointly discussing exemplary sequences and catalog statistics, we draw a comprehensive picture of swarm activity and its relation to geothermal and tectonic activity.

58 GEOSCIENCES↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Identification of Faults Susceptible to Induced Seismicity (Final Report)

Central to the work documented in this report is the capability of geocellular models to represent the geologic conceptual model updated with fault identification from machine learning and joint inversion modeling of microseismic data measured and recorded as a consequence of CO 2 injection at a field demonstration site: the Illinois Basin - Decatur Project (IBDP). This work required seven unique geocellular models with 100s of simulated variations to gain a very high degree of confidence in the identification of geologic features present that contributed to induced microseismicity at IBDP. All forward modeling: pressure modeling, stress modeling, and seismic modeling used the same geologic conceptual model and representations of that model at different scales. The pressure modeling and poroelastic modeling created “snapshots” of pore pressure and stress field changes at different times during CO 2 injection, in which microseismic events were clustered (in time). These pressure and stress snapshots, within the framework and architecture of the geologic conceptual model via the geocellular model, informed the single fault and fault network models to ascertain the likelihood of fault movement (seismic or aseismic). The outcomes of the pressure, stress, and fault/fault network (seismic) modeling confirmed that the faults in the geologic conceptual model in Task 2 were likely the source of microseismic events measured at IBDP and acted as conduits for pressure to be transmitted from the injection interval into the Precambrian crystalline basement rock. This closely coordinated and integrated unique modeling approach was conducted to prove the viability of our proposed workflow 1) to better resolve crystalline basement faults, 2) detect subseismic faults that could be activated by injection, 3) increase the certainty in fault detection and their susceptibility to release seismic energy, and 4) understand transmission of pressure vertically from the well to the underlying fractured crystalline basement. The proposed methodology was effective in guiding an iterative process of calibrating forward modeling results based on similar geocellular models while honoring the geologic conceptual model (i.e., characterization data and knowledge of regional geology); this led to higher level of certainty in the identification of fault/faults zones to control seismicity and transmission of pressure to the regions of recorded and located injection induced seismicity.

58 GEOSCIENCES↗
DOE OSTI2022

Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview

A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Quantifying rupture characteristics of microearthquakes in the Parkfield Area using a high-resolution borehole network

It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the M w 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions.

58 GEOSCIENCES↗
DOE OSTI2019

Draft Discharge Permit 1132 [Slides]

This presentation covers site hydrogeologic setting (including geologic setting and faults and fractures), groundwater monitoring for RLWTF, monitoring well locations, monitoring suite and frequency, quality of wells, and defense in depth – groundwater monitoring coupled with use of engineering controls and visual inspections. The objectives of groundwater monitoring are early detection of any future noncompliant releases, providing additional safety net to support extensive administrative and engineering controls, and monitoring to characterize extent of groundwater effected by noncompliant discharge.

54 ENVIRONMENTAL SCIENCES↗