Noise is an inherent feature of seismic data, especially in land acquisition. Due to the presence of noise, observations can be treated as random variables with associated uncertainty. An accurate estimate of data uncertainty is important for data interpretation and also for imaging and tomography. For challenging inverse problem, like full waveform inversion, an accurate estimate of data uncertainty can lead to robust qualitative estimates of posterior uncertainty, and also guide the selection of key parameters, e.g. the regularization strength. Uncertainty estimation can be a challenging task for seismic data because not enough repeat measurements are available to estimate reliable statistics. Based on a land seismic field experiment with repeated shots, we quantify seismic data uncertainty (with standard deviation as a proxy) and the short-term repeatability of reflection data. We find that the uncertainty for coherent seismic events in data is proportional to their amplitude while the distributions characterizing the main events differ from Gaussians.

Presentation Date: Monday, October 15, 2018

Start Time: 1:50:00 PM

Location: 210C (Anaheim Convention Center)

Presentation Type: Oral

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