Self-potential observations during hydraulic fracturing

The self-potential (SP) response during hydraulic fracturing of intact Sierra granite was investigated in the laboratory. Excellent correlation of pressure drop and SP suggests that the SP response is created primarily by electrokinetic coupling. For low pressures, the variation of SP with pressure drop is linear, indicating a constant coupling coefficient (Cc) of -200 mV/MPa. However for pressure drops >2 MPa, the magnitude of the Cc increases by 80% in an exponential trend. This increasing Cc is related to increasing permeability at high pore pressures caused by dilatancy of micro-cracks, and is explained by a decrease in the hydraulic tortuosity. Resistivity measurements reveal a decrease of 2% prior to hydraulic fracturing and a decrease of {approx}35% after fracturing. An asymmetric spatial SP response created by injectate diffusion into dilatant zones is observed prior to hydraulic fracturing, and in most cases this SP variation revealed the impending crack geometry seconds before failure. At rupture, injectate rushes into the new fracture area where the zeta potential is different than in the rock porosity, and an anomalous SP spike is observed. After fracturing, the spatial SP distribution reveals the direction of fracture propagation. Finally, during tensile cracking in a point load device with no water flow, a SP spike is observed that is caused by contact electrification. However, the time constant of this event is much less than that for transients observed during hydraulic fracturing, suggesting that SP created solely from material fracture does not contribute to the SP response during hydraulic fracturing

Long-term Monitoring of the Sierra Nevada Snowpack Using Wireless Sensor Networks

International audienc

A Machine-Learning Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments

International audienceWe evaluate the accuracy of a machine-learning-based path loss model trained on 42,157,324 RSSI samples collected over one year from an environmental wireless sensor network using 2.4 GHz radios. The 2218 links in the network span a 2000 km 2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms were evaluated in order to find the one with lowest error: Random Forest, Adaboost, Neural Networks, and K-Neareast-Neighbors. Of the candidate models, Random Forest showed the lowest error. The independent variables used in the model include path distance, canopy coverage, terrain variability, and path angle. We compare the accuracy of this model to several well-known canonical (Free Space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). Unlike canonical models, machine-learning algorithms are not problem-specific: they rely on an extensive dataset and a flexible model architecture to make predictions. We show how this model achieves a 37% reduction in the average prediction error compared to the canonical/empirical model with the best performance. The article presents a in-depth discussion on the strengths and limitations of the proposed approach as well as opportunities for further research

iShake: Mobile phones as seismic sensors, user study findings

ABSTRACT The "iShake" system uses smartphones as seismic sensors to measure and deliver ground motion intensity parameters produced by earthquakes more rapidly and accurately than currently possible. Shaking table tests followed by field trial with approximately 30 iShake users were implemented to evaluate the reliability of the phones as seismic monitoring instruments and the functionality of the iShake system. In addition, user experiences were investigated with 59 iShake users, who provided feedback through a mobile questionnaire. Research included participative planning with a focus group to design and conceptualize how to improve iShake for future use. The shaking table tests demonstrated that cell phones may reliably measure the shaking produced by an earthquake. The performed user studies led to important guidelines for the future development and improvement of the iShake system. User studies also provided understanding of how iShake could best provide value to its users. The iShake system was shown to have great potential in providing critical information and added value for the public and emergency responders during earthquakes. Value creation for other users and first response through user-generated data was seen as a great source of motivation and commitment for active use of the system

Demo: SierraNet: Monitoring the Snowpack in the Sierra Nevada

International audienceNext-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture , air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring

Real-time Alpine Measurement System Using Wireless Sensor Networks

International audienceMonitoring the snow pack is crucial for many stakeholders, whether for hydro-poweroptimization, water management or flood control. Traditional forecasting relies on regressionmethods, which often results in snow melt runoff predictions of low accuracy in non-averageyears. Existing ground-based real-time measurement systems do not cover enough physiographicvariability and are mostly installed at low elevations. We present the hardware and software designof a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurementsystem with real-time remote data transmission that gathers data of snow depth, air temperature,air relative humidity, soil moisture, soil temperature, and solar radiation in physiographicallyrepresentative locations. Elevation, aspect, slope and vegetation are used to select networklocations, and distribute sensors throughout a given network location, since they govern snowpack variability at various scales. Three WSNs were installed in the Sierra Nevada of NorthernCalifornia throughout the North Fork of the Feather River, upstream of the Oroville dam and multiplepowerhouses along the river. The WSNs gathered hydrologic variables and network health statisticsthroughout the 2017 water year, one of northern Sierra’s wettest years on record. These networksleverage an ultra-low-power wireless technology to interconnect their components and offer recoveryfeatures, resilience to data loss due to weather and wildlife disturbances and real-time topologicalvisualizations of the network health. Data show considerable spatial variability of snow depth, evenwithin a 1 km2 network location. Combined with existing systems, these WSNs can better detectprecipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoffduring precipitation or snow melt, and inform hydro power managers about actual ablation andend-of-season date across the landscape

Microseismic source deconvolution: Wiener filter versus minimax, Fourier versus wavelets, and linear versus nonlinear

Deconvolution is commonly performed on microseismic signals to determine the time history of a dislocation source, usually modeled as combinations of forces or couples. This paper presents a new deconvolution method that uses a nonlinear thresholding estimator, which is based on the minimax framework and operates in the wavelet domain. Experiments were performed on a steel plate using artificially generated microseismic signals, which were recorded by high-fidelity displacement sensors at various locations. The source functions were deconvolved from the recorded signals by Wiener filters and the new method. Results were compared and show that the new method outperforms the other methods in terms of reducing noise while keeping the sharp features of the source functions. Other advantages of the nonlinear thresholding estimator include (1) its performance is close to that of a minimax estimator, (2) it is nonlinear and takes advantage of sparse representations under wavelet bases, and (3) its computation is faster than the fast Fourier transforms. (C) 2004 Acoustical Society of America

Pan-viral serology implicates enteroviruses in acute flaccid myelitis.

Since 2012, the United States of America has experienced a biennial spike in pediatric acute flaccid myelitis (AFM)1-6. Epidemiologic evidence suggests non-polio enteroviruses (EVs) are a potential etiology, yet EV RNA is rarely detected in cerebrospinal fluid (CSF)2. CSF from children with AFM (n = 42) and other pediatric neurologic disease controls (n = 58) were investigated for intrathecal antiviral antibodies, using a phage display library expressing 481,966 overlapping peptides derived from all known vertebrate and arboviruses (VirScan). Metagenomic next-generation sequencing (mNGS) of AFM CSF RNA (n = 20 cases) was also performed, both unbiased sequencing and with targeted enrichment for EVs. Using VirScan, the viral family significantly enriched by the CSF of AFM cases relative to controls was Picornaviridae, with the most enriched Picornaviridae peptides belonging to the genus Enterovirus (n = 29/42 cases versus 4/58 controls). EV VP1 ELISA confirmed this finding (n = 22/26 cases versus 7/50 controls). mNGS did not detect additional EV RNA. Despite rare detection of EV RNA, pan-viral serology frequently identified high levels of CSF EV-specific antibodies in AFM compared with controls, providing further evidence for a causal role of non-polio EVs in AFM

University of California Research Seminar Network: A Prospectus

By webcasting the hundreds of seminars presented in the University of California system each week, UC educators hope to enhance the exchange of scientific information for their campuses and create the foundation for an international research seminar network