27 citations · 48 across the 11 of their papers we have counts for
11 papers
A self-supervised scheme for ground roll suppression
Sixiu Liu, Claire Birnie, Andrey Bakulin +3
In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage,…
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising
Claire Birnie, Matteo Ravasi
The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self-supervise…
Joint Microseismic Event Detection and Location with a Detection Transformer
Yuanyuan Yang, Claire Birnie, Tariq Alkhalifah
Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulatio…
Deep Learning-based extraction of surface wave dispersion curves from seismic shot gathers
Danilo Chamorro, Jiahua Zhao, Claire Birnie +3
Multi-channel Analysis of Surface Waves (MASW) is a seismic method employed to obtain useful information about shear-wave velocities in the near surface. A fundamental step in this…
Transfer learning for self-supervised, blind-spot seismic denoising
Claire Birnie, Tariq Alkhalifah
Noise in seismic data arises from numerous sources and is continually evolving. The use of supervised deep learning procedures for denoising of seismic datasets often results in po…
Coherent noise suppression via a self-supervised blind-trace deep learning scheme
Sixiu Liu, Claire Birnie, Tariq Alkhalifah
Coherent noise regularly plagues seismic recordings, causing artefacts and uncertainties in products derived from down-the-line processing and imaging tasks. The outstanding capabi…
A hybrid approach to seismic deblending: when physics meets self-supervision
Nick Luiken, Matteo Ravasi, Claire E. Birnie
To limit the time, cost, and environmental impact associated with the acquisition of seismic data, in recent decades considerable effort has been put into so-called simultaneous sh…
The potential of self-supervised networks for random noise suppression in seismic data
Claire Birnie, Matteo Ravasi, Tariq Alkhalifah +1
Noise suppression is an essential step in any seismic processing workflow. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years,…
An introduction to distributed training of deep neural networks for segmentation tasks with large seismic datasets
Claire Birnie, Haithem Jarraya, Fredrik Hansteen
Deep learning applications are drastically progressing in seismic processing and interpretation tasks. However, the majority of approaches subsample data volumes and restrict model…
A Joint Inversion-Segmentation approach to Assisted Seismic Interpretation
Matteo Ravasi, Claire Emma Birnie
Structural seismic interpretation and quantitative characterization are historically intertwined processes. The latter provides estimates of properties of the subsurface which can…
Bidirectional recurrent neural networks for seismic event detection
Claire Birnie, Fredrik Hansteen
Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tr…