IEEE Signal Processing - March 2018 - 110

Seismic anisotropy is the variation of a seismic property
such as velocity with the direction along which it is measured.
The anisotropy type in a medium depends on its symmetry
system, e.g., cubic, hexagonal, etc. The symmetry system of a
medium defines what happens to its properties upon geometrical manipulations such as inversion and rotation. TI is the most
common type of seismic anisotropy encountered in sedimentary rocks. TI involves a property that is the same within a
plane but different along a perpendicular symmetry axis. In
most TI media, velocity is lowest when measured parallel to
the symmetry axis and highest when measured perpendicular
to the symmetry axis.
The authors of [20] use direct P- and S-waves recorded
in typical downhole microseismic geometries to infer seismic anisotropy in narrow angular apertures. Application of
their method on synthetic and real data in a gas-shale field
indicated that seismic anisotropy is important in building
a physically sound velocity model. In most applications
of anisotropic model inversion from real data sets, the data
needs to be explained by temporarily changing anisotropy.
This is interpreted as increased cracked density resulting from
hydraulic fracturing.

Summary
The phenomenon of microseismicity is an active and challenging problem area for signal processing. We have described
many techniques for the situation where the microseismic
events of interest result from injection of fluids in wells to
recover oil or gas resources. Figure 10 summarizes the interrelationships among all of the methods discussed in this article.
Another area of active research is monitoring micro-earthquakes in areas where large quakes are expected. Dense surface arrays (with 3C sensors) are being deployed for continuous
monitoring, so the data collected are noisy and the data sets
are huge. Opportunities abound for new techniques that can
exploit high-speed computation to mine these large data sets
for low SNR events.

Acknowledgments
We are grateful for the support from the Center for Energy and
Geo Processing at both King Fahd University of Petroleum and
Minerals, Saudi Arabia, and the Georgia Institute of Technology. We also acknowledge support from the long-term conceptual development research organization RVO: 67985891.

Authors
James H. McClellan (jim.mcclellan@ece.gatech.edu) received
his B.S. degree in electrical engineering from Louisiana State
University in 1969 and his M.S. and Ph.D. degrees from Rice
University, Houston, Texas, in 1972 and 1973, respectively.
He was a researcher at Lincoln Laboratory, Massachusetts
Institute of Technology from 1973 to 1975, where he later
became a professor from 1975 to 1982. He was with
Schlumberger Well Services, Austin, Texas from 1982 to
1987. Since 1987, he has been a professor in the School of
Electrical and Computer Engineering at the Georgia Institute
110

of Technology, where he holds the John and Marilu McCarty
Chair. In 2004, he was a corecipient of the IEEE Jack S. Kilby
Signal Processing medal for work on finite impulse response
filter design. He is a Life Fellow of the IEEE.
Leo Eisner (leo.eisner@seismik.cz) obtained his M.Sc.
degree in physics from Charles University (Faculty of
Mathematics and Physics), Prague, Czech Republic, in 1994.
He received his Ph.D. degree in geological and planetary sciences from the California Institute of Technology, Pasadena,
in 2001. He was a senior research scientist with Schlumberger
Research, Cambridge, United Kingdom, from 2001 to 2007
and spent six years with MicroSeismic, Inc, Houston, Texas,
from 2008 to 2014. He was awarded the Purkyne Fellowship
from the Institute of Rock Structure and Mechanics, Academy
of Sciences, Prague, Czech Republic, from 2010 to 2014. He
is the founder and, since 2015, the full-time president of the
seismic service company Seismik s.r.o. He is a continuous
education lecturer for the Society of Exploration Geophysics
and European Association of Geoscientists and Engineers. His
papers and extended abstracts cover a broad range of subjects,
including the seismic ray method, finite-difference methods,
seismological investigations of local and regional earthquakes,
and microearthquakes induced by hydraulic fracturing.
Entao Liu (liuentao@gmail.com) received his B.S. degree
from Shandong University, Jinan, China, in 2005 and his
Ph.D. degree from the University of South Carolina in 2011,
both in applied mathematics. Currently, he is a postdoctoral
associate with the Center for Energy and Geo Processing at
the Georgia Institute of Technology. His research interests
include signal processing, seismic imaging, machine learning,
numerical analysis, and inverse problems.
Naveed Iqbal (naveediqbal@kfupm.edu.sa) received his
B.S. and M.S. degrees in electrical engineering from the
University of Engineering and Technology, Peshawar, Pakistan.
He received his Ph.D. degree from King Fahd University of
Petroleum and Minerals, Saudi Arabia, where he is now a postdoctoral fellow. His research interests include adaptive algorithms, compressive sensing, heuristic algorithms, and seismic
signal processing.
Abdullatif A. Al-Shuhail (ashuhail@kfupm.edu.sa) received
his B.S. degree from King Fahd University of Petroleum and
Minerals (KFUPM), Saudi Arabia, in 1988 and his M.S. and
Ph.D. degrees from Texas A&M University in 1993 and 1998,
respectively, all in geophysics. He is an associate professor of
geophysics at KFUPM. He founded and directed the NearSurface Seismic Investigation Consortium at KFUPM in
2006-2008. He holds three U.S. patents. He is a coauthor of
Processing of Seismic Reflection Data Using MATLAB
(Morgan & Claypool, 2011) and Seismic Data Interpretation
Using Digital Image Processing (Wiley, 2017). His interests
include near-surface effects on petroleum seismic data, seismic investigation of fractured reservoirs, and ground penetrating radar.
Sanlinn I. Kaka (skaka@kfupm.edu.sa) received his B.S.
degree in geology and his diploma in applied geophysics from
Rangoon University, Burma, in 1983 and 1985, respectively.

IEEE Signal Processing Magazine

March 2018

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