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Found 6 papers, 0 papers with code
Joint Microseismic Event Detection and Location with a Detection Transformer
Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution.
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising
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.
Transfer learning for self-supervised, blind-spot seismic denoising
To preempt the network's ability to predict the signal and reduce its opportunity to learn the noise properties, we propose an initial, supervised training of the network on a frugally-generated synthetic dataset prior to fine-tuning in a self-supervised manner on the field dataset of interest.
The potential of self-supervised networks for random noise suppression in seismic data
Illustrated on synthetic examples, the blind-spot network is shown to be an efficient denoiser of seismic data contaminated by random noise with minimal damage to the signal; therefore, providing improvements in both the image domain and down-the-line tasks, such as inversion.
An introduction to distributed training of deep neural networks for segmentation tasks with large seismic datasets
Typically, training data is preloaded into memory prior to training, a particular challenge for seismic applications where data is typically four times larger than that used for standard image processing tasks (float32 vs. uint8).
Bidirectional recurrent neural networks for seismic event detection
The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters.
