We would like to bring to your attention the following upcoming events within the community that may be of interest to you and your colleagues. Additional details are below.

• UBC will be hosting the 7th International Symposium on Three-Dimensional Electromagnetics (3DEM-7) from Nov 13 to 17th. DETAILS BELOW
• MDRU & DMEC are organizing a one day short course titled “EM for Geologists” to be held on Nov 17. DETAILS BELOW
• BCGS Monthly talk – next item.

November 23, 2023 – Monthly Talk

Speaker: Jorge Lopez-Alvis, University of British Columbia

Title: Using convolutional neural networks to classify UXO with multi-component electromagnetic induction data

Date: Thurs, Nov 23, 2023

Time: 4:30pm to 5:30pm PDT

Location: In-person: Room 111 – 409 Granville Street, Vancouver, BC, V6C 1T2

Virtual: via Zoom, to be announced.

Note: This will be an in-person meeting

Abstract

Electromagnetic induction (EMI) methods are commonly used to classify unexploded ordnance (UXO) in both terrestrial and marine settings. Modern time-domain systems used for classification are multicomponent which means they acquire many transmitter-receiver pairs at multiple time-channels. Traditionally, classification is done using a physics-based inversion approach where polarizability curves are estimated from the EMI data. We developed a convolutional neural network (CNN) that classifies UXO directly from EMI data. The architecture of our CNN produces high-resolution results and can handle the multiple transmitter-receiver pairs and the per-line acquisition of multicomponent systems. The CNN is trained using synthetic data generated with a dipole forward model considering all possible UXO and clutter objects. In this presentation, we will discuss how we structure the data to be input into a neural network, how we design the network, and how training is performed. Our approach was tested on data acquired with the UltraTEMA system in the Sequim Bay marine test site. For this test dataset, our CNN-based approach detects all UXOs and classifies more than 90% as the correct type while also discriminating ~70% of the clutter. An overview of our workflow applied to this dataset and some discussion of the classification results will be presented.

Bio:

Jorge is a Postdoctoral Fellow from the Geophysical Inversion Facility (GIF) group at UBC. He has a background in near-surface geophysics and using machine learning in geophysics. His current research involves developing an approach for using neural networks to classify UXO directly from electromagnetic data. He completed a joint PhD program at Ghent University and University of Liège where he explored the use of deep generative models (or generative A.I.) to produce geologically realistic images from geophysical data.

3DEM-7 Symposium: Nov 13-17, 2023,   https://3dem-7.geosci.xyz/

The 7th International Symposium on Three-Dimensional Electromagnetic (3DEM-7) will be held on Nov 13-15, 2023 and we are looking forward to welcoming the EM community to Vancouver!

Registration is open at https://3dem-7.geosci.xyz/#registration and the abstract submission deadline has been extended to Oct 20: https://3dem-7.geosci.xyz/#abstracts-call. If you plan to attend, we appreciate early registration so that we can orchestrate the catering.

We are still seeking sponsors, so if your organization is interested in sponsoring 3DEM-7, please reach out to Lindsey Heagy (lheagy@eoas.ubc.ca) and Eldad Haber (ehaber@eoas.ubc.ca).

If you are in Vancouver and are willing to billet students / postdocs for the event, please contact Lindsey (lheagy@eoas.ubc.ca). There are some folks with limited travel funds who would like to attend, but require additional support.

Workshop: EM for Geologists (MDRU/DMEC): Nov 17, 2023

A one day short course covering electromagnetic methods, case studies, etc. See below link for the schedule and to register.

https://www.mdru.ubc.ca/training/sc125-em-for-geologists-workshop/

STUDENTS NOTE: Condor Consulting will cover the registration fee ($60) for any student who wants to attend. DMEC will cover $200 for any out-of-town student who attends to defray expenses (receipts required).