Started:
2021-05-25 08:00:00
Last activity:
2021-05-25 08:00:00
Topics:
Distributed Acoustic Sensing
kasey.aderhold@iris.edu
2021-05-25 08:00:00
Please join us on *Thursday, June 17th at 12:30-2:00 PM Eastern* for…
*Machine Learning for DAS Data Analysis*
*Register*: *https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ
https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ*
This webinar will feature the following four presentations:
- *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.
Use of Distributed Acoustic Sensing is rapidly expanding in our community,
prompting the initiation of an Distributed Acoustic Sensing Research
Coordination Network to facilitate workshops, tutorials, and other
opportunities for sharing ideas and resources. This webinar is one in a
planned series on different topics within Distributed Acoustic Sensing.
You can find out more information and sign up for the DAS RCN mailing list
at: https://www.iris.edu/hq/initiatives/das_rcn.
Previous DAS RCN webinars are available on YouTube here:
https://www.youtube.com/playlist?list=PLvd8fqCFf8CUySHtD7omI7kMAxjqyPj9o
Any questions? Contact us at das-rcn<at>iris.edu.
*Machine Learning for DAS Data Analysis*
*Register*: *https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ
https://zoom.us/webinar/register/WN_rrf3Id3ORMWmPRMHI7HXCQ*
This webinar will feature the following four presentations:
- *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.
Use of Distributed Acoustic Sensing is rapidly expanding in our community,
prompting the initiation of an Distributed Acoustic Sensing Research
Coordination Network to facilitate workshops, tutorials, and other
opportunities for sharing ideas and resources. This webinar is one in a
planned series on different topics within Distributed Acoustic Sensing.
You can find out more information and sign up for the DAS RCN mailing list
at: https://www.iris.edu/hq/initiatives/das_rcn.
Previous DAS RCN webinars are available on YouTube here:
https://www.youtube.com/playlist?list=PLvd8fqCFf8CUySHtD7omI7kMAxjqyPj9o
Any questions? Contact us at das-rcn<at>iris.edu.
