Started:
2021-05-21 15:31:16
Last activity:
2021-05-21 15:31:16
Topics:
Distributed Acoustic Sensing
kasey.aderhold@iris.edu
2021-05-21 15:31:16
The DAS RCN webinar "Distributed Acoustic Sensing and Engineering" was held
on May 19, 2021 and a recording is now available at:
https://youtu.be/vWWVpO47KdI.
Thank you again to our presenters, Dr. Meghan Quinn and Peter Hubbard, and
our moderator, Dr. Dante Fratta. If you are interested in joining the DAS
RCN working group on engineering, please get in touch with Dr. Fratta at
fratta<at>wisc.edu.
Our next webinar in the DAS RCN series will be *Machine Learning for DAS
Data Analysis on June 17, 2021 from 12:30-2pm ET*. This webinar will
feature the following four presentations. Please register for this upcoming
webinar here: https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ.
- *Dr. Whitney Trainor-Guitton* (Colorado School of Mines), “Machine
learning is driving a revolution in seismic ‘listening’ for geoscience”, I
will describe our working group’s synthesis on how DAS combined with
machine learning can lead to transformative subsurface monitoring. A
snapshot of previous work and outstanding challenges will be presented.
- *Dr. Martijn van den Ende* (Université Côte d’Azur), “A
Self-Supervised Deep Learning Approach for Blind Denoising of Distributed
Acoustic Sensing Data”, The spatial density of DAS measurements allows us
to use the full wavefield in our signal processing workflows, rather than
treating each sensor separately. In this talk I will detail a simple
self-supervised Deep Learning technique that leverages the full wavefield
to separate earthquake signals from noise.
- *Dr. Eileen Martin* (Virginia Tech), "Noise Exploration and
Detection," DAS enables us to easily collect more data than we could budget
time to manually explore, especially in populated areas or around
infrastructure. This talk will show simple examples of how to use machine
learning to explore and target noise for removal in two DAS datasets.
- *Fantine Huot* (Stanford), “A deep learning model for microseismic
detection”, Downhole DAS offers a great opportunity to acquire
high-resolution microseismic signals for downhole operation monitoring, but
the large volume of continuous data acquired from the DAS fiber also poses
a huge challenge for data processing and microseismic event detection. In
this talk we will demonstrate a CNN-based deep learning model that tackles
this problem with super-human accuracy on a large-volume downhole DAS
dataset.
Also, please note the upcoming AGU EPSP Connects Panel "Surface processes
applications of environmental seismology and distributed acoustic sensing
(DAS) Q&A" on May 26, 2021 at 11am ET. Register for this event here:
https://wustl.zoom.us/webinar/register/WN_veEEdaYgROWE1ffXtGJg-w.
on May 19, 2021 and a recording is now available at:
https://youtu.be/vWWVpO47KdI.
Thank you again to our presenters, Dr. Meghan Quinn and Peter Hubbard, and
our moderator, Dr. Dante Fratta. If you are interested in joining the DAS
RCN working group on engineering, please get in touch with Dr. Fratta at
fratta<at>wisc.edu.
Our next webinar in the DAS RCN series will be *Machine Learning for DAS
Data Analysis on June 17, 2021 from 12:30-2pm ET*. This webinar will
feature the following four presentations. Please register for this upcoming
webinar here: https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ.
- *Dr. Whitney Trainor-Guitton* (Colorado School of Mines), “Machine
learning is driving a revolution in seismic ‘listening’ for geoscience”, I
will describe our working group’s synthesis on how DAS combined with
machine learning can lead to transformative subsurface monitoring. A
snapshot of previous work and outstanding challenges will be presented.
- *Dr. Martijn van den Ende* (Université Côte d’Azur), “A
Self-Supervised Deep Learning Approach for Blind Denoising of Distributed
Acoustic Sensing Data”, The spatial density of DAS measurements allows us
to use the full wavefield in our signal processing workflows, rather than
treating each sensor separately. In this talk I will detail a simple
self-supervised Deep Learning technique that leverages the full wavefield
to separate earthquake signals from noise.
- *Dr. Eileen Martin* (Virginia Tech), "Noise Exploration and
Detection," DAS enables us to easily collect more data than we could budget
time to manually explore, especially in populated areas or around
infrastructure. This talk will show simple examples of how to use machine
learning to explore and target noise for removal in two DAS datasets.
- *Fantine Huot* (Stanford), “A deep learning model for microseismic
detection”, Downhole DAS offers a great opportunity to acquire
high-resolution microseismic signals for downhole operation monitoring, but
the large volume of continuous data acquired from the DAS fiber also poses
a huge challenge for data processing and microseismic event detection. In
this talk we will demonstrate a CNN-based deep learning model that tackles
this problem with super-human accuracy on a large-volume downhole DAS
dataset.
Also, please note the upcoming AGU EPSP Connects Panel "Surface processes
applications of environmental seismology and distributed acoustic sensing
(DAS) Q&A" on May 26, 2021 at 11am ET. Register for this event here:
https://wustl.zoom.us/webinar/register/WN_veEEdaYgROWE1ffXtGJg-w.
