Nadège Langet

Ensemble and Self-supervised Learning for Improved Classification of Seismic Signals from the Åknes Rockslope (2022)

Ensemble and Self-supervised Learning for Improved Classification of Seismic Signals from the Åknes Rockslope (2022)

Mathematical Geosciences

Daesoo Lee, Erlend Aune, Nadège Langet, Jo Eidsvik
Abstract A case study with seismic geophone data from the unstable Åknes rock slope in Norway is considered. This rock slope is monitored because there is a risk of severe flooding if the massive-size rock falls into the fjord. The geophone data is highly valuable because it provides 1000 Hz sampling rates data which are streamed to a web resource for real-time analysis. The focus here is on building a classifier for these data to distinguish different types of microseismic events which are in turn indicative of the various processes occurring on the slope.
Vnibcreg: Vicreg with neighboring-invariance and better-covariance evaluated on non-stationary seismic signal time series (2022)

Vnibcreg: Vicreg with neighboring-invariance and better-covariance evaluated on non-stationary seismic signal time series (2022)

arXiv

Daesoo Lee, Erlend Aune, Nadège Langet, Jo Eidsvik
Abstract This paper presents a novel sampling scheme for masked non-autoregressive generative modeling. We identify the limitations of TimeVQVAE, MaskGIT, and Token-Critic in their sampling processes, and propose Enhanced Sampling Scheme (ESS) to overcome these limitations. ESS explicitly ensures both sample diversity and fidelity, and consists of three stages: Naive Iterative Decoding, Critical Reverse Sampling, and Critical Resampling. ESS starts by sampling a token set using the naive iterative decoding as proposed in MaskGIT, ensuring sample diversity.