Abstract

The seismic reflection method is one of the most important methods in geophysical exploration. There are three stages in a seismic exploration survey: acquisition, processing, and interpretation. This paper focuses on a pre-processing tool, the Non-Local Means (NLM) filter algorithm, which is a powerful technique that can significantly suppress noise in seismic data. However, the domain of the NLM algorithm is the whole dataset and 3D seismic data being very large, often exceeding one terabyte (TB), it is impossible to store all the data in Random Access Memory (RAM). Furthermore, the NLM filter would require a considerably long runtime. These factors make a straightforward implementation of the NLM algorithm on real geophysical exploration data infeasible. This paper redesigned and implemented the NLM filter algorithm to fit the challenges of seismic exploration. The optimized implementation of the NLM filter is capable of processing production-size seismic data on modern clusters and is 87 times faster than the straightforward implementation of NLM.

Highlights

  • Seismic exploration involves data acquisition, processing, and interpretation

  • This paper focuses on a pre-processing tool, the Non-Local Means (NLM) filter algorithm, which is a powerful technique that can significantly suppress noise in seismic data

  • This paper proposed applying the Non-Local Means Filter on seismic exploration data

Read more

Summary

Introduction

Seismic exploration involves data acquisition, processing, and interpretation. This paper focuses on the seismic data processing stage, which is an analysis of recorded seismic signals to reduce noise and create an image of the subsurface to enable geological interpretation, and eventually to obtain an estimate of the distribution of material properties in the subsurface. There are three major challenges in applying NLM filter to seismic exploration data: 1. This paper discusses the redesign and implementation of the Non-Local Means filter algorithm using parallel programming to increase its feasibility for processing production-size seismic data, the optimized implementation has good performance and is capable of processing production-size 3D seismic data on modern clusters

Background
Noise Filtering with Seismic Data
Edge Detection
Non-Local Means Filter
Non-Local Means Algorithm
Noise Filtering Algorithm Comparison
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call