Geometry-independent realistic noise models for synthetic data generation

Abstract

Synthetic datasets are vital for the development and benchmarking of new processing and imaging algorithms as well as in the training of machine learning models. It is therefore important that such datasets are generated with realistic noise conditions making them resemble as much as possible their corresponding field datasets. Building on previously developed covariance-based noise modelling, we propose an extension of such an approach that aims to translate a noise model onto a user-defined geometry by means of Gaussian Process Regression. Starting from a synthetic data, we show that noise models can be generated and transformed into a desired geometry whilst keeping the same underlying statistical properties (i.e., covariance and variogram). The modelling procedure is subsequently applied to the ToC2ME passive noise dataset transforming the actual 69-sensor acquisition geometry into a gridded, 56-sensor array. The ability to generate realistic, geometryindependent noise models opens up a host of new opportunities in the area of survey design. We argue that by coupling the noise generation and monitoring algorithms, the placement of sensors could be further optimised based on the expected microseismic signatures as well as the surrounding noise behaviour.