Deep learning of geological structures in seismic reflection data

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Downloads

Download Preprint

Authors

Thilo Wrona , Indranil Pan , Rebecca E. Bell, Robert Gawthorpe, Haakon Fossen, Sascha Brune

Abstract

Understanding the internal structure of our planet is a fundamental goal of the Earth Sciences. As direct observations are restricted to surface outcrops and borehole cores, we rely on geophysical data to study the Earth’s interior. Especially, seismic reflection data showing acoustic images of the subsurface, provide us with critical insights into sedimentary, tectonic and magmatic systems. The interpretation of these large, 2-D grids or 3-D seismic volumes is however time-consuming even for a well-trained person or team of people. Here we demonstrate how to automate and accelerate the analysis of these increasingly large seismic datasets with machine learning. We are able to perform typical seismic interpretation tasks, such as the mapping of (1) tectonic faults, (2) salt bodies and (3) sedimentary horizons at high accuracy using deep convolutional neural networks. We share our workflows and scripts, encouraging users to apply our methods to similar problems. Our methodology is generic and flexible allowing an easy adaptation without major changes. Once trained, these models can analyze large volumes of data within seconds; opening a new exciting pathway to study the internal structure and the processes shaping our planet.

DOI

https://doi.org/10.31223/X5S88B

Subjects

Physical Sciences and Mathematics

Keywords

Geology, Deep learning, Geophysics

Dates

Published: 2020-10-21 09:02

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
Data is confidential

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.