Seismic response to evolving injection at the Rotokawa geothermal field, New Zealand

Catalogs of microseismicity are routinely compiled at geothermal reservoirs and provide valuable insights into reservoir structure and fluid movement. Hypocentral locations are typically used to infer the orientations of structures and constrain the extent of the permeable reservoir. However, frequency-magnitude distributions may contain additional, and underused, information about the distribution of pressure. Here, we present a four-year catalog of seismicity for the Rotokawa geothermal field in the central Taupō Volcanic Zone, New Zealand starting two years after the commissioning of the 140 MWe Nga Awa Purua power station. Using waveform-correlation-based signal detection we double the size of the previous earthquake catalog, refine the location and orientation of two reservoir faults and identify a new structure. We find the rate of seismicity to be insensitive to major changes in injection strategy during the study period, including the injectivity decline and shift of injection away from the dominant injector, RK24. We also map the spatial distribution of the earthquake frequency-magnitude distribution, or b-value, and show that it increases from ∼1.0 to ∼1.5 with increasing depth below the reservoir. As has been proposed at other reservoirs, we infer that these spatial variations reflect the distribution of pressure in the reservoir, where areas of high b-value correspond to areas of high pore-fluid pressure and a broad distribution of activated fractures. This analysis is not routinely conducted by geothermal operators but shows promise for using earthquake b-value as an additional tool for reservoir monitoring and management

An Approach for Modeling and Ranking Node-level Stragglers in Cloud Datacenters

The ability of servers to effectively execute tasks within Cloud datacenters varies due to heterogeneous CPU and memory capacities, resource contention situations, network configurations and operational age. Unexpectedly slow server nodes (node-level stragglers) result in assigned tasks becoming task-level stragglers, which dramatically impede parallel job execution. However, it is currently unknown how slow nodes directly correlate to task straggler manifestation. To address this knowledge gap, we propose a method for node performance modeling and ranking in Cloud datacenters based on analyzing parallel job execution tracelog data. By using a production Cloud system as a case study, we demonstrate how node execution performance is driven by temporal changes in node operation as opposed to node hardware capacity. Different sample sets have been filtered in order to evaluate the generality of our framework, and the analytic results demonstrate that node abilities of executing parallel tasks tend to follow a 3-parameter-loglogistic distribution. Further statistical attribute values such as confidence interval, quantile value, extreme case possibility, etc. can also be used for ranking and identifying potential straggler nodes within the cluster. We exploit a graph-based algorithm for partitioning server nodes into five levels, with 0.83% of node-level stragglers identified. Our work lays the foundation towards enhancing scheduling algorithms by avoiding slow nodes, reducing task straggler occurrence, and improving parallel job performance

Human Mission to Mars: Designing a Crew Expert Tool for a Safety Critical Environment

On a mission to other planets, the crew would come across situations and challenges that have not been foreseen even by experienced engineers, designers, scientists and previous explorers. This paper considers existing problem solving approaches that can help structure the development of ‘troubleshooting support tools’ for autonomous crews during long-duration missions. It also considers the suitability of these problem solving techniques for crew autonomous operations

A Structured Approach to Scenario Generation for the Design of Crew Expert Tool

It is often difficult to identify the ways in which innovative systems can be used to support the crews on long duration space missions over the coming decades. This paper presents a structured approach towards scenario generation for crew autonomous operations during these future missions. The proposed approach will help to systematically generate scenarios that help define the design requirements for mission related equipment. A crew expert tool is used to illustrate our approach. This system is intended to help crewmembers identify and then resolve complex system failures in situations where it may not be possible to call upon immediate technical assistance from ground support staff. Our approach to scenario design helps to identify ways in which such an application may support crew tasks during the initial development of the application

Seismic Response to Injection Well Stimulation in a High-Temperature, High-Permeability Reservoir

Fluid injection into the Earth's crust can induce seismic events that cause damage to local infrastructure but also offer valuable insight into seismogenesis. The factors that influence the magnitude, location, and number of induced events remain poorly understood but include injection flow rate and pressure as well as reservoir temperature and permeability. The relationship between injection parameters and injection-induced seismicity in high-temperature, high-permeability reservoirs has not been extensively studied. Here we focus on the Ngatamariki geothermal field in the central Taupō Volcanic Zone, New Zealand, where three stimulation/injection tests have occurred since 2012. We present a catalog of seismicity from 2012 to 2015 created using a matched-filter detection technique. We analyze the stress state in the reservoir during the injection tests from first motion-derived focal mechanisms, yielding an average direction of maximum horizontal compressive stress (SHmax) consistent with the regional NE-SW trend. However, there is significant variation in the direction of maximum compressive stress (σ1), which may reflect geological differences between wells. We use the ratio of injection flow rate to overpressure, referred to as injectivity index, as a proxy for near-well permeability and compare changes in injectivity index to spatiotemporal characteristics of seismicity accompanying each test. Observed increases in injectivity index are generally poorly correlated with seismicity, suggesting that the locations of microearthquakes are not coincident with the zone of stimulation (i.e., increased permeability). Our findings augment a growing body of work suggesting that aseismic opening or slip, rather than seismic shear, is the active process driving well stimulation in many environments

Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit

ML-NA: A Machine Learning Based Node Performance Analyzer Utilizing Straggler Statistics

Current Cloud clusters often consist of heterogeneous machine nodes, which can trigger performance challenges such as the task straggler problem, whereby a small subset of parallel tasks running abnormally slower than the other sibling ones. The straggler problem leads to extended job response and deteriorates system throughput. Poor performance nodes are more likely to engender stragglers, and can undermine straggler mitigation effectiveness. For example, as the dominant mechanism for straggler alleviation, speculative execution functions by creating redundant task replicas on other machine nodes as soon as a straggler is detected. When speculative copies are assigned onto the poor performance nodes, it is hard for them to catch up with the stragglers compared to replicas run on fast nodes. And due to the fact that the performance heterogeneity is caused not only by static attribute variations such as physical capacity, but also dynamic characteristic uctuations such as contention level, analyzing node performance is important yet challenging. In this paper we develop ML-NA, a Machine Learning based Node performance Analyzer. By leveraging historical parallel tasks execution log data, ML-NA classies cluster nodes into different categories and predicts their performance in the near future as a scheduling guide to improve speculation effectiveness and minimize task straggler generation. We consider MapReduce as a representative framework to perform our analysis, and use the published OpenCloud trace as a case study to train and to evaluate our model. Results show that ML-NA can predict node performance categories with an average accuracy up to 92.86%

Aortic calcification and femoral bone density are independently associated with left ventricular mass in patients with chronic kidney disease

Background Vascular calcification and reduced bone density are prevalent in chronic kidney disease and linked to increased cardiovascular risk. The mechanism is unknown. We assessed the relationship between vascular calcification, femoral bone density and left ventricular mass in patients with stage 3 non-diabetic chronic kidney disease in a cross-sectional observational study. Methodology and Principal Findings A total of 120 patients were recruited (54% male, mean age 55±14 years, mean glomerular filtration rate 50±13 ml/min/1.73 m2). Abdominal aortic calcification was assessed using lateral lumbar spine radiography and was present in 48%. Mean femoral Z-score measured using dual energy x-ray absorptiometry was 0.60±1.06. Cardiovascular magnetic resonance imaging was used to determine left ventricular mass. One patient had left ventricular hypertrophy. Subjects with aortic calcification had higher left ventricular mass compared to those without (56±16 vs. 48±12 g/m2, P = 0.002), as did patients with femoral Z-scores below zero (56±15 vs. 49±13 g/m2, P = 0.01). In univariate analysis presence of aortic calcification correlated with left ventricular mass (r = 0.32, P = 0.001); mean femoral Z-score inversely correlated with left ventricular mass (r = −0.28, P = 0.004). In a multivariate regression model that included presence of aortic calcification, mean femoral Z-score, gender and 24-hour systolic blood pressure, 46% of the variability in left ventricular mass was explained (P<0.001). Conclusions In patients with stage 3 non-diabetic chronic kidney disease, lower mean femoral Z-score and presence of aortic calcification are independently associated with increased left ventricular mass. Further research exploring the pathophysiology that underlies these relationships is warranted

Development of an international scale of socio-economic position based on household assets.

The importance of studying associations between socio-economic position and health has often been highlighted. Previous studies have linked the prevalence and severity of lung disease with national wealth and with socio-economic position within some countries but there has been no systematic evaluation of the association between lung function and poverty at the individual level on a global scale. The BOLD study has collected data on lung function for individuals in a wide range of countries, however a barrier to relating this to personal socio-economic position is the need for a suitable measure to compare individuals within and between countries. In this paper we test a method for assessing socio-economic position based on the scalability of a set of durable assets (Mokken scaling), and compare its usefulness across countries of varying gross national income per capita.Ten out of 15 candidate asset questions included in the questionnaire were found to form a Mokken type scale closely associated with GNI per capita (Spearmans rank rs = 0.91, p = 0.002). The same set of assets conformed to a scale in 7 out of the 8 countries, the remaining country being Saudi Arabia where most respondents owned most of the assets. There was good consistency in the rank ordering of ownership of the assets in the different countries (Cronbachs alpha = 0.96). Scores on the Mokken scale were highly correlated with scores developed using principal component analysis (rs = 0.977).Mokken scaling is a potentially valuable tool for uncovering links between disease and socio-economic position within and between countries. It provides an alternative to currently used methods such as principal component analysis for combining personal asset data to give an indication of individuals relative wealth. Relative strengths of the Mokken scale method were considered to be ease of interpretation, adaptability for comparison with other datasets, and reliability of imputation for even quite large proportions of missing values