Thread: GAGE/SAGE Plenary Webinar, New approaches to processing big geophysical and geospatial datasets, 10/22 at 2 PM Eastern

Started: 2020-10-19 15:53:52
Last activity: 2020-10-19 15:53:52
Topics: Webinars
Please join us for a virtual GAGE/SAGE Plenary Webinar on *October 22, 2020*
at *2 PM Eastern*. The plenary session is *New approaches to processing
big geophysical and geospatial datasets*. The in-person GAGE/SAGE Science
Workshop was postponed from August 2020 to August 2021. In the meantime,
several of the planned speakers are presenting in this webinar series so we
learn more about these subjects before next year.

Please click the link below and *register* to join the webinar:
https://zoom.us/webinar/register/WN__itxoABtRweTrbIaHtHfXw

*After registering, you will receive a confirmation email containing
information about joining the webinar.*

*Presented by:* Drs. Lindsey Heagy, UC-Berkeley, and Michael Olsen, Oregon
State University

Dr. Heagy will present on *Community driven development of open source
tools for simulations and inversions of geophysical data*

Dr. Olsen will present on *From Big to Small, you can do it all! Efficient
approaches to analyze rockfall activity from point clouds*

*Plenary Abstract: *‘Big data’ is challenging our computational
capabilities in terms of volume, velocity and variety (3Vs). Increases in
the spatial and temporal resolution of our geophysical observations have
produced dramatic increases in the amount of data for geoscientists to
analyze. Each stage of the life cycle of geospatial big data can present
issues for our community: (1) data acquisition, (2) compilation and
management, and (3) data analysis, visualization and distribution. This
session seeks to showcase strategies that are overcoming these obstacles
and providing new insights into Earth behavior. For instance, machine
learning presents a host of new opportunities for our community, with
increasing emphasis on technical rigor, common benchmarks, and
repeatability. In addition, enhanced understanding can also be achieved via
data amalgamation, high-performance computing, data mining, and
dataset-integration strategies. Vast datasets also create opportunities for
improved statistical characterization and improved quantification of
uncertainties. We are particularly interested in highlighting techniques
likely to be scalable and transferable to other problems.

*Dr. Heagy's Abstract:* Open communities in astrophysics, scientific
computing, machine learning, and many other domains demonstrate the power
of collaborative efforts to develop open-source software that facilitates
research in each of their respective domains (e.g. Astropy, SciPy,
Scikit-learn, etc.). Not only do open tools facilitate the reproducibility
of scientific work, but they also streamline the exchange of ideas between
researchers, even across domains. In 2013, we started SimPEG as an effort
to build an open-source framework and community around numerical
simulations and gradient-based inversions in geophysics. The SimPEG
software supports forward simulations and inversions across a range of
geophysical methods including magnetics, gravity, direct current
resistivity, induced polarization, electromagnetics (time domain, frequency
domain and natural source methods) as well as fluid flow. We leverage tools
in the Pangeo ecosystem, including Jupyter and Dask (for parallelization),
to enable scalable, interactive computation (locally, on the cloud, and HPC
centers). The community has grown and brings together researchers working
on advancing inversion methods (e.g. joint inversions, compact
regularizations, large-scale inversions), as well as those focussed on
using geophysical data in applications that span groundwater, mineral
exploration, tectonic studies and near-surface applications such as
agriculture. In this talk, I will share examples from active research
projects led by members of the community and discuss how we have built a
suite of resources that combine SimPEG with tools in the Jupyter ecosystem
to enable interactive exploration of geophysical simulations and inversions.

*Dr. Olsen's Abstract: *Important infrastructure such as highways in the
Pacific Northwest traverse particularly unstable terrain throughout much of
the state, resulting in maintenance, system unreliability due to frequent
closures and restrictions, and safety hazards due to landslides and
rockfalls. Seismic activity significantly amplifies these negative economic
and community impacts. This presentation will discuss efficient data
processing strategies to utilize point cloud data to analyze rockfall
activity at rock outcrops from repeat lidar or Uncrewed Aircraft Systems
(UAS) Structure from Motion/MultiView Stereo (SfM/MVS) photogrammetric
surveys that enable one to simultaneously look closely at detailed features
on the slope as well as from afar to evaluate an entire corridor. These
technologies enable personnel to safely capture data for active rock slopes
that may be otherwise inaccessible. Nevertheless, the “Big Data” generated
through these technologies combined with other data sources provide immense
challenges in terms of acquisition and data processing. This presentation
will discuss a suite of tools enabling efficient analyses of detailed
remote sensing data to quantify and visually communicate rockfall hazards.
These tools include efficient surface modeling algorithms, rockfall cluster
detection to produce magnitude-frequency relationships, the Rockfall
Activity Index-a morphological-based assessment technique, and
seismically-induced rockfall debris estimates. Such approaches yield many
important safety benefits, enable mitigation of geohazards before
catastrophic events occur, and improved responses after major seismic
events.



*PLEASE NOTE: T*he webinar is limited to 500 participants. Please hop on
the webinar early for your best chances to see the webinar live. Remember
that all webinars are archived for later viewing at
https://www.youtube.com/playlist?list=PLD4D607C2FA317E6D

Any questions? Contact us at webinar<at>iris.edu

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