Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes

We present a new method of data clustering applied to earthquake catalogs, with the goal of reconstructing the seismically active part of fault networks. We first use an original method to separate clustered events from uncorrelated seismicity using the distribution of volumes of tetrahedra defined by closest neighbor events in the original and randomized seismic catalogs. The spatial disorder of the complex geometry of fault networks is then taken into account by defining faults as probabilistic anisotropic kernels, whose structures are motivated by properties of discontinuous tectonic deformation and previous empirical observations of the geometry of faults and of earthquake clusters at many spatial and temporal scales. Combining this a priori knowledge with information theoretical arguments, we propose the Gaussian mixture approach implemented in an Expectation-Maximization (EM) procedure. A cross-validation scheme is then used and allows the determination of the number of kernels that should be used to provide an optimal data clustering of the catalog. This three-steps approach is applied to a high quality relocated catalog of the seismicity following the 1986 Mount Lewis ($M_l=5.7$) event in California and reveals that events cluster along planar patches of about 2 km$^2$, i.e. comparable to the size of the main event. The finite thickness of those clusters (about 290 m) suggests that events do not occur on well-defined euclidean fault core surfaces, but rather that the damage zone surrounding faults may be seismically active at depth. Finally, we propose a connection between our methodology and multi-scale spatial analysis, based on the derivation of spatial fractal dimension of about 1.8 for the set of hypocenters in the Mnt Lewis area, consistent with recent observations on relocated catalogs

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

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Master of Science

thesisOn August 6, 2007, the Crandall Canyon Mine in central Utah experienced a major collapse that was recorded as an Mw 4.1 seismic event. Application of waveform cross-correlation detection techniques to data recorded at permanent seismic stations located within ~30 km of the mine has resulted in the discovery of 1,494 previously unknown microseismic events related to the collapse. These events occurred between July 26, 2007, and August 19, 2007, and were detected with a magnitude threshold of completeness of 0.0, about 1.6 magnitude units smaller than the threshold associated with conventional techniques. Relative locations for the events were determined using a double-difference approach that incorporated absolute and differential arrival times. Absolute locations were determined using ground-truth reported in mine logbooks. Lineations apparent in the newly detected events have strikes similar to those of known vertical joints in the mine region, which may have played a role in the collapse. Prior to the collapse, seismicity occurred mostly in close proximity to active mining, though several distinct seismogenic hotspots within the mine were also apparent. In the 48 hours before the collapse, changes in b-value and event locations were observed. The collapse appears to have occurred when the migrating seismicity associated with direct mining activity intersected one of the areas identified as a seismic hotspot. Following the collapse, b-values decreased and seismicity clustered farther to the east

Understanding error log event sequence for failure analysis

Due to the evolvement of large-scale parallel systems, they are mostly employed for mission critical applications. The anticipation and accommodation of failure occurrences is crucial to the design. A commonplace feature of these large-scale systems is failure, and they cannot be treated as exception. The system state is mostly captured through the logs. The need for proper understanding of these error logs for failure analysis is extremely important. This is because the logs contain the “health” information of the system. In this paper we design an approach that seeks to find similarities in patterns of these logs events that leads to failures. Our experiment shows that several root causes of soft lockup failures could be traced through the logs. We capture the behavior of failure inducing patterns and realized that the logs pattern of failure and non-failure patterns are dissimilar.Keywords: Failure Sequences; Cluster; Error Logs; HPC; Similarit

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care