Can we predict Earthquake?

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Presented for an IRIS (Incorporated Research Institutions for Seismology) webinar on 9 April 2014. Webinar can be viewed here: https://www.youtube.com/watch?v=MNcl5AZlX3k Abstract: In February 2010, a magnitude 8.8 megathrust earthquake struck the Maule region of Central Chile - the sixth largest earthquake ever recorded. It is fast becoming one of the best-studied megathrust ruptures, allowing us a unique insight into the inner workings of subduction zone earthquakes. In the earthquake’s immediate aftermath, an international group of research institutions deployed geophysical instruments in the rupture area. A network of ~160 seismic stations on the forearc recorded over 50,000 aftershocks in the first 10 months following the earthquake. I have used observations of P- and S-waves from aftershocks to derive a high-resolution seismic travel-time tomography of the rupture zone. Observations from ocean-bottom seismometers further improve image sharpness in the offshore portion of the seismogenic zone, where most slip occurred during the earthquake. The tomographic images reveal the distribution of P-wave velocity and Poisson’s Ratio within the earthquake rupture zone. Based on accurate aftershock locations and moment tensors, I have defined a new 3-D plate interface geometry to infer the physical structure and composition along the plate interface. I compare these velocities with the mainly geodetically observed behaviour of the fault throughout a cycle of seismic behaviour (preseismic locking, coseismic slip, postseismic deformation). This comparison allows us to understand some of the physical properties that may govern seismogenesis along the megathrust. I will reveal how both the long-lived geological structure of the forearc and the composition of the subducting oceanic plate may influence the rupture behaviour of large megathrust earthquakes. An understanding of seismic velocities along the megathrust may therefore be used to constrain the seismogenic potential of subduction zones worldwide.

Anatomy of a Megathrust Earthquake Rupture - The 2010 M8.8 Chile Quake

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A sustained ocean observing system forms a basis, along with capacity development, enabling societal benefit from ocean information. The Global Ocean Observing System (GOOS) is driven by societal requirements, including: - adapting to and mitigating climate variability and change - preparing for ocean-related hazards and supporting development of the ocean economy, and - monitoring and promoting stewardship of ocean health. GOOS is a key contributor to the GEO Blue Planet task. We are a collaborative programme, connecting a community and organizations working on many aspects of a Framework for Ocean Observations: identifying requirements focused on Essential Ocean Variables, coordinating observing networks and monitoring progress towards targets, and connecting to data and information generation activities that create scientific and societal value. At the global level, these processes are led by three GOOS Panels focused on physics, biogeochemistry, and biology. The panels evaluate the readiness of the observing system, promoting strategic investment by identifying what is essential, and encouraging the development of new capabilities. They work closely with the ocean observing community. A Strategic Mapping is helping to identify how elements integrate into the system. GOOS development projects are evaluating and where necessary will improve and change parts of the sustained ocean observing system. The combined satellite and in situ observing networks contributing to GOOS have strengthened in recent years. At the regional level, GOOS Regional Alliances individually focus on local priorities and requirements. Collectively, they work to develop institutional and human capacity to make and benefit from sustained ocean observations. GOOS both supports and relies on many partners, including other contributors to the GEO Blue Planet, in seeking to sustain present observations, while integrating new essential ocean observations into a sustained observing system.

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In this webinar sponsored by Neurotar, experts present their research on 2-photon imaging of hippocampal place cells and on stress monitoring in head-fixed awake behaving mice. Dr. Konrad Juczewski from the National Institutes of Health (NIH)/National Institute on Alcohol Abuse and Alcoholism (NIAAA) discusses the impact of head fixation on animal’s stress, locomotion and performance in classical behavioral paradigms. Dr. Mary Ann Go from the Laboratory of Neural Coding and Neurodegenerative Disease at Imperial College London led by Prof. Simon Schultz presents her research using 2-photon microscopy aimed at place cell mapping in the hippocampus during exploration and navigation of a circular linear track. Key Discussion Topics Include: - Stress reduction in head-fixed rodents - Improving data reproducibility and translational value of the data acquired from head-fixed rodents - Effects of head fixation on blood corticosterone concentration, locomotion patterns and performance in stress-associated behavioral tests - Optimizing habituation protocol for head-fixed mice - Monitoring neural activity and mapping of place cells using 2-photon microscopy during navigation and exploration behavior - Automating the experiments using a closed-loop approach and behavior-triggered reward systems

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Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework. Links: http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814

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