IEEE Signal Processing - March 2018 - 51

Subsurface Exploration:
Recent Advances in Geo-signal Processing,
Interpretation, and Learning

Ali Payani, Afshin Abdi, Xin Tian,
Faramarz Fekri, and Mohamed Mohandes

Advances in Seismic Data Compression
via Learning from Data
Compression for seismic data acquisition

T

he next generation of oil and gas exploration technology is
moving toward large-scale seismic acquisition, automation,
and flexibility. This phenomenon has accelerated the interest in moving away from traditional seismic acquisition systems
that are heavily mechanical. Currently, on a daily basis, a seismic survey may require 800 or more crew members to place
more than 200,000 prewired geophones over a field of several
square miles. As such, the cost of cabling accounts for up to 50%
of the total operating cost of a typical land survey, and up to 75%
of the total equipment weight. This labor-intensive deployment
of the prewired geophones, in addition to cost, prolongs the survey time and places a huge barrier on scaling the seismic acquisition and its adaptation/automation. Therefore, there has been a
growing interest to switch from prewired geophones to wireless
seismic acquisition. On the other hand, a typical seismic survey may generate tens of terabytes of raw seismic data per day.
Hence, wireless communication faces great challenges in light
of the enormous amounts of data that must be transmitted from
geophones to on-site data collection centers.

Introduction

©istockphoto.com/khadi Ganiev

Digital Object Identifier 10.1109/MSP.2017.2784458
Date of publication: 7 March 2018

1053-5888/18©2018IEEE

As the industry makes the transition toward wireless data
gathering, the bandwidth limitation in wireless communication will become the bottleneck on the density and scale of
sensors to be deployed, consequently limiting the sampling
density of Earth's subsurface. To overcome these challenges, we advocate placing some intelligence (i.e., processing
power) at the sensors in the field. This is a departure from
the traditional seismic acquisition systems that assume
passive geophones. In particular, we propose in-field compression in which we leverage the processing power at the
sensors to remove redundancy from the traces, compress
them and, hence, reduce the amount of in-field data transfer.
Further, as the density of source shots increases, the cross
correlation among traces recorded by the same sensor increases, allowing higher compression gains to be achieved.
Source shots are controlled seismic energy signals that are
used in seismic surveys as the inputs to generate the data

IEEE Signal Processing Magazine

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March 2018

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51



http://www.istockphoto.com/khadi

Table of Contents for the Digital Edition of IEEE Signal Processing - March 2018

Contents
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