Aux-SI combines these local predictions to obtain the entire subsurface model. This paradigm breaks the SI into local inversion tasks, which predicts each small chunk of subsurface properties using surrounding seismic data. We solve the scalability of SI by proposing a new auxiliary learning paradigm for SI (Aux-SI). In this work, we go beyond convolution and propose a novel SI method.

Seismic data is not naturally an image and need not be processed as images. We argue that this convolution-based way of SI is not only computationally expensive but also conceptually problematic. To extend these approaches to real-scale seismic data, researchers need to process raw nav-merge seismic data into an image and perform convolution. However, these improvements are restricted to data with highly reduced scale and complexity. Recently, data-driven approaches equipped with Deep learning (DL) are envisioned to improve SI efficiency. This inversion process is expensive, requiring over a year of human and computational effort. Abstract: Traditional seismic inversion (SI) maps the hundreds of terabytes of raw-field data to subsurface properties in gigabytes.

Nav mergepdf