Joumana Ghosn

A deep learning benchmark for first break detection from hardrock seismic reflection data

Gilles Bellefleur

Ernst Schetselaar

2023-12-13

GEOPHYSICS (publié)

doi.org

Deep Learning Benchmark for First Break Detection from Hardrock Seismic Reflection Data

Pierre-Luc St-Charles

Bruno Rousseau

Joumana Ghosn

Gilles Bellefleur

E. Schetselaar

Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. While many works have … (voir plus)shown the benefits of deep learning, assessing the generalization capabilities of proposed methods to data acquired in different conditions and geological environments remains challenging. This is especially true for applications in hardrock environments where seismic surveys are still relatively rare. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled datasets, and the use of inadequate evaluation methodologies. Since machine learning models are prone to overfit and underperform when the data used to train them is site-specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we propose a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey dataset acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons, and discuss potential improvements to this approach.

2023-10-10

Geophysics (published)

doi.org

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Eilif Benjamin Muller

Meng Qu

Victor Schmidt

Pierre-Luc St-Charles

Hannah Alsdurf

gaetan caron

satya ortiz gagne

Bernhard Schölkopf … (voir 3 de plus)

Abhinav Sharma

Jian Tang

Andrew Robert Williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-01-12

ICLR.cc/2021/Conference (spotlight)

openreview.net