Exploration of Data Space Through Trans-Dimensional Sampling: A Case Study of 4D Seismics.

ABSTRACT: We present a novel methodology for exploring 4D seismic data in the context of monitoring subsurface resources. Data-space exploration is a key activity in scientific research, but it has long been overlooked in favor of model-space investigations. Our methodology performs a data-space exploration that aims to define structures in the covariance matrix of the observational errors. It is based on Bayesian inferences, where the posterior probability distribution is reconstructed through trans-dimensional (trans-D) Markov chain Monte Carlo sampling. The trans-D approach applied to data-structures (termed "partitions") of the covariance matrix allows the number of partitions to freely vary in a fixed range during the McMC sampling. Due to the trans-D approach, our methodology retrieves data-st

SUBMITTER: Piana Agostinetti N 

PROVIDER: S-EPMC9287047 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature