First_Steps_in_Seismic_Interpretation

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GEOPHYSICAL MONOGRAPH SERIES
NUMBER 16 
FIRST STEPS IN 
SEISMIC INTERPRETATION
Donald A. Herron
Rebecca B. Latimer, managing editor
Tulsa, Oklahoma
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ISBN 978-0-931830-56-3 (Series)
ISBN 978-1-56080-280-8 (Volume)
Society of Exploration Geophysicists
P.O. Box 702740
Tulsa, OK 74170-2740
© 2011 by Society of Exploration Geophysicists
All rights reserved. This book or parts hereof may not be reproduced in any 
form without written permission from the publisher.
Published 2011
Printed in the United States of America
Cover background image courtesy of Thomas H. Wilson
Library of Congress Cataloging-in-Publication Data
Herron, Donald A., 1949-
 First steps in seismic interpretation / Donald A. Herron ; Rebecca B. Latimer, 
managing editor.
 p. cm. -- (Geophysical monograph series ; no. 16)
 Includes bibliographical references and index.
 ISBN 978-1-56080-280-8 (volume : alk. paper) -- ISBN 978-0-931830-56-3 (series : alk. paper)
1. Seismology. 2. Geophysical surveys. I. Latimer, Rebecca B. II. Title. 
 QE534.3.H47 2011
 551.22--dc23
2011047720
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iii
Contents
About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2: Seismic Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Chapter 3: Seismic Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Chapter 4: Velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Sonic logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Well-velocity surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Seismically derived velocities . . . . . . . . . . . . . . . . . . . . . . . . 41
Velocity anisotropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Time-depth conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Chapter 5: Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Chapter 6: Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Chapter 7: Correlation Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
First look . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Horizons versus faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Multiple reflections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Manual versus automatic tracking . . . . . . . . . . . . . . . . . . . . 96
Artifacts and interpretation pitfalls . . . . . . . . . . . . . . . . . . . . 105D
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iv 
Chapter 8: Correlation Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Getting started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Loop tying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Jump correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Correlations in depth-migration projects . . . . . . . . . . . . . . . 140
Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Interpretation processes and work flows . . . . . . . . . . . . . . . . 149
Chapter 9: Data Quality and Management . . . . . . . . . . . . . . . . . . . 153
Data quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Data management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
Nomenclature systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Chapter 10: Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Gridding and contouring . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
4D seismic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Seismic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Interpretive judgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Curiosity and interpretive thinking . . . . . . . . . . . . . . . . . . . . 170
The interpretation paradox . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Uncertainty and risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
The workstation environment . . . . . . . . . . . . . . . . . . . . . . . . 178
Ergonomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Career development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Advanced interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Time spent and value added . . . . . . . . . . . . . . . . . . . . . . . . . 185
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 193
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v
About the Author
Don Herron received a bachelor of sci-
ence degree (with honors) in geological sci-
ences from Brown University in 1971 and a 
master of science degree in geological sci-
ences from the California Institute of Tech-
nology in 1973. He enjoyed a career as a 
seismic interpreter at Texaco (1973–1977), 
Gulf (1977–1984), and most recently Sohio/
BP (1984–2008). Since retirement in 2008, 
he has worked as an independent geophysi-
cal consultant for Petroleum Geo-Services 
(PGS) as a geosciences advisor, and with 
several oil companies as a seismic interpre-
tation instructor. At Gulf and Sohio/BP he taught in-house courses in seis-
mic interpretation and was co-instructor for the SEG Continuing Education 
course “Seismic Interpretation in the Exploration Domain” (1995–2007). 
He was a member of the Editorial Board of The Leading Edge (2002–2007, 
chairman in 2006–2007) and is author of the bi-monthly “Interpreter Sam” 
column in The Leading Edge. He is an active member of SEG, AAPG, and 
Sigma Xi.
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vii
Preface
This book begins with an introduction that is more philosophical than 
technical, followed by five chapters on fundamentals of reflection seis-
mic (titled Seismic Response, Seismic Attributes, Velocity, Migration, and 
Resolution). The gist of what I really have to say about the correlation of 
seismic records is in Chapters 7 (Correlation Concepts) and 8 (Correlation 
Procedures). Chapter 9 (Data Quality and Management) certainly should 
not be glossed over, and Chapter 10 (Other Considerations) contains my 
thoughts on several worthy topics that do not fit neatly into any of the pre-
ceding chapters.
In large part, this book is a compilation of notes from seismic inter-
pretation courses that I’ve had the good fortune to teach over the past three 
decades. Because I’ve assumed that readers are familiar with basic concepts 
and principles of geology and reflection seismology, the book is best viewed 
as a synthesis rather than a fundamental treatment of those concepts and 
principles. When I use the expression “geologically reasonable” to qualify 
interpretation results, which I do throughout the book, I mean “reasonable” 
in the sense of “analogous to known geology” or “consistent with known 
geology or sound geologic models” or “within the context of expectation or 
realization of some geologic concept or model.”
I certainly don’t intend this book to be the definitive primer on inter-
preting reflection seismic data or a comprehensive treatise on the latest in 
correlation tools and techniques; rather, I’m seeking to give voice to a con-
cern about “this particular art” that I’ve had ever since my first foray into 
interpretation in the early 1970s. My concern is founded on a statement by 
a man from whom I had the privilege to learn about exploration geophysics 
in the classroom and in the field. In his own book he wrote that “the cor-
relation procedure itself is of such a nature that it can hardly be adequately 
described in a book.”
Well, with the utmost respect for that man, here goes.
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ix
Acknowledgments
I thank Rebecca Latimer, Bill Barkhouse, Bruce Hart, and John O’Brien 
for their constructive reviews of my manuscript and also BP (Amal Ray and 
Tim Summers), PGS (Nathan Oliver), TGS (Tom Neugebauer), and West-
ernGeco (Lee Hooper) for permission to include data and images from their 
companies in this book. I thank Mike Schoenberger for sharing his charac-
terization of seismic data quality with me; it’s the most concise and practi-
cal description of data quality I’ve ever known, so I’ve used it to set context 
throughout the book. I extend my thanks also to members of the SEG publi-
cations and graphics groups in Tulsa, in particular Jennifer Cobb and Kathy 
Gamble, without whose skill and patience this book could not have come 
into being. I’m especially grateful to Kathy Pile and Gary Stewart, whose 
editing gave my text the clarity and consistency it needed. In creating this 
book, I’m indebted to countless geoscientists, old and young alike, from 
whom I’ve learned so much over the years. Among all those talented men 
and women, I owe the most to Tim Smith, perhaps the most insightful inter-
preter I’ve ever known and an excellent teacher as well, with whom I’ve had 
the distinct privilege numerous times to share the front of a classroom. 
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Interpretation is telling the geologic story contained in seismic data. It is 
correlating the features we see in seismic data with elements of geology as 
we know them. The story is read from a book having many chapters, some 
of which are either illegible or unintelligible, and others are lost or yet to be 
written. And although the story doesn’t always have a happy ending, only in 
its telling do we expand our knowledge.
—Interpreter Sam
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1
Chapter 1
Introduction 
Accurate interpretation of geophysical data — in particular, reflection 
seismic data — is one of the most important elements of a successful oil and 
gas exploration program. Despite technological advances in data acquisition 
and processing and the regular use of powerful computers and sophisticated 
software applications, you still face a tremendous challenge each time you 
begin to reconstruct the geologic story contained in a grid or volume of seis-
mic data — that is, to interpret the data. On occasion, this interpretive tale 
can be clearly told; but most of the time, each page of each chapter is slowly 
turned, and rarely is the full meaning of the story completely understood.
Where the correlation of one reflection record with another is very 
easy, little needs to be said. Almost anyone can understand such a 
correlation. On the other hand, this is a rare occurrence. The usual 
thing is for the correlation to be so difficult as to be impossible. It 
is for this reason that correlation procedure can hardly be described 
in words (Dix, 1952).
Although Dix is speaking about the correlation of individual reflec-
tion records, which were used routinely before the advent of continuous 
common-depth-point (CDP) profiling, he clearly recognized the essence 
of interpretation as the considered extraction of geologic information from 
indirect geophysical measurements. His words are no less relevant and 
applicable now than they were 60 years ago, even in view of the high stan-
dards of data quality made possible by advances in seismic acquisition and 
processing, to say nothing of accompanying developments in interpretation 
technology. In the modern interpretation environment, you still face correla-
tions that are “so difficult as to be impossible” because these correlations 
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2 First Steps in Seismic Interpretation
define the frontiers of opportunity, the ones posing the sternest challenges 
and ultimately leading to the greatest rewards.
The primary aim of this book is to describe Dix’s correlation procedure in 
terms of the science, data, tools, and techniques now used in seismic interpre-
tation in the oil and gas industry. As an individual geoscientist, you develop 
and apply your own approach and style when interpreting seismic data. You 
continually revise and refine correlation procedures during the course of your 
career and expand them as you complete different interpretation projects. 
With experience, you learn to check and recheck the validity of your proce-
dures to fully understand the rules of evidence that govern their use:
•฀ What arethe physical laws that control the phenomena you observe 
and consider as evidence?
•฀ What are the uncertainties in your evidence?
You must have a good understanding of seismic acquisition and pro-
cessing principles as well as fundamentals of geology before beginning to 
collect interpretive evidence and solve interpretation problems correctly. 
Continuing from Dix, then, you must also know when enough interpreting 
is enough:
The threshold of impossibility is reached by different interpreters at 
different levels. The important thing is for each interpreter to under-
stand his limitations. Obviously it is foolish to go ahead and corre-
late when no correlation is possible. This involves giving a definite 
interpretation that is almost sure to be misleading and therefore very 
expensive (Dix, 1952). 
The primary goal of seismic interpretation is always to describe geology, 
and all aspects of interpretation facilitate and support this goal. The prod-
ucts of seismic interpretation are an important subset of the indispensable 
elements used by geoscientists to define and develop oil and gas prospects. 
Although seismic interpretation is a very important part of the exploration-
development-production stream, it is only one of the elements used when 
integrating all available data to build a geophysically consistent and geo-
logically reasonable picture of subsurface structure and stratigraphy. Draw-
ing this picture accurately is a critical factor in successful identification of 
drillable prospects and exploitation of known hydrocarbon accumulations.
Interpretation, the description of geology, depends critically on seis-
mic data quality: The better the quality, the more accurate and reliable the 
interpretation. In the most general terms, quality is the degree to which 
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Chapter 1: Introduction 3
something fulfills its intended purpose; because you use seismic data for 
different purposes, depending on where you are in the value stream (e.g., 
exploration versus production), you know that data quality appropriate and 
acceptable for one project may not be for another. For example, the quality 
of a high-resolution seismic survey used to detail the shallow subsurface 
and identify potential drilling hazards would be completely inadequate and 
essentially useless for deep exploration. In the same way, a 3D survey pur-
posely acquired and processed to image deep subsalt targets would have 
little or no value for shallow hazards assessment. At the same time, qual-
ity may be less than optimal owing to problems in data acquisition or pro-
cessing, and you need to be able to recognize these shortcomings, seeking 
advice from acquisition and processing specialists as needed, accounting 
for the shortcomings during interpretation, and making appropriate recom-
mendations for improvements. 
There are three primary elements of seismic data quality: detection (sig-
nal-to-noise), resolution (temporal and spatial), and image fidelity (focusing 
and positioning). All efforts in seismic data acquisition and processing are 
designed to optimize data quality and “interpretability.” You are responsible 
for assessing data quality for each of your interpretation projects and for 
communicating this assessment as part of any presentation of project results.
Seismic interpretation is, by the nature of seismic data and the earth 
itself, nonunique and highly subjective. You bring your perspective and 
powers of observation to bear on the interpretation problem at hand, the 
effects of which cannot be clearly identified in or separated from your maps 
and calculations — and yet are a controlling factor in your results. Stephen 
Jay Gould recognizes and appreciates the importance of talent for observa-
tion in naturalists, which can easily apply to interpreters:
All field naturalists know and respect the phenomenon of “search 
image” — the best proof that observation is an interaction of mind 
and nature, not a fully objective and reproducible mapping of out-
side upon inside, done in the same way by all careful and compe-
tent people. In short, you see what you are trained to view — and 
observation of different sorts of objects often requires a conscious 
shift of focus, not a total and indiscriminate expansion in the hopes 
of seeing everything. The world is too crowded with wonders for 
simultaneous perception of all; we learn our fruitful selectivities 
(Gould, 1993).
Although acquiring, processing, and analyzing seismic data are math-
ematically intensive and now almost exclusively digital, interpretation 
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4 First Steps in Seismic Interpretation
activity per se is still primarily a visual (human and therefore fallible) pro-
cess. Correlation of seismic records involves pattern recognition, depending 
heavily on the display of data and your knowledge and understanding of 
patterns in geology. Interpretation of any element of geology from seismic 
data involves answering the questions “What is it?” and “Where is it?” — 
answers that are rarely independent of each other. In other words, you often 
interpret what something is by where it is in relation to other features, or 
where and how large a feature should be because of what it is. Hence, we 
confirm the importance of migration of seismic data and, ultimately, the 
ability to visualize and reconstruct in depth what is only indirectly measured 
in time. Of course, it goes without saying that you will not be too terribly 
successful in the oil and gas business if you can’t accurately specify what, 
where, and how big your exploration targets are.
Seismic acquisition, processing, and interpretation are related, as shown 
in Figure 1. Acquisition and processing can be thought of as forward pro-
cesses in which acoustic-impedance contrasts in the subsurface produce 
measurable seismic responses (acoustic impedance [AI] and reflection coef-
ficient [RC] are defined in Chapter 2). The interpretation of this response, 
which in Figure 1 is called “ideal” but often is very far from being so, is an 
inverse process that describes the original AI contrasts and ultimately the 
subsurface geology. Notice that the forward processes of acquisition and 
processing can give rise to different, nonunique responses, depending on 
the particular acquisition and processing techniques used. This is another 
way of saying that acquisition and processing determine data quality. The 
inverse process of interpretation can result in many different descriptions of 
geology, again because of varying data quality and also because the funda-
mental relationships among subsurface geometry, acoustic impedance, and 
geology are nonunique. In your better humors, you thank your good fortune 
for this nonuniqueness because it is an important factor contributing to your 
job security.
Your domain of information in interpretation consists of facts (there 
may not be as many of these as you would like to believe), observations, 
inferences drawn from observations and their resultant models, and, of 
course, experience gained from having established facts, made observa-
tions, drawn inferences, and revised models over time. Taken together, these 
still represent a relatively small volume of your domain, the largest por-
tion of which is the unknown. Accurate, well-integrated interpretations can 
reduce the volume of the unknown, but only if you maintain awareness of 
the distinctions among facts, observations, and models, all of which can be 
considered interpretive “evidence.” This awareness is a critical element in 
your assessmentof technical risk in exploration projects, which, contrary to 
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Chapter 1: Introduction 5
the way you would like it to be, is at best as subjective as the interpretation 
on which it is based.
Perhaps the most common intellectual difficulty encountered in corre-
lating seismic data is maintaining a clear distinction between observation 
and interpretation (see Figure 2). Observation is the essential foundation 
for meaningful interpretation; think of observation as “What do I see?” and 
interpretation as “What does it mean?” These questions can be easily and 
often unwittingly confused, allowing bias to enter an interpretation and 
resulting in premature or unwarranted interpretive conclusions. Experience 
does not guarantee that you will be able to keep observation and interpre-
tation separate because there is a sense of urgency in the desire to explain 
observations and “get on with business” that can prevent you from devoting 
sufficient time to making an appropriate number of careful observations. 
Similarly, the lack of patience that often accompanies inexperience can lead 
to the same unfortunate result.
Figure 1. The interrelationship of seismic data acquisition and processing with 
seismic interpretation. The former are forward processes, and the latter is an 
inverse process. AI = acoustic impedance; RC = reflection coefficient.
Interpretation 
Data acquisition and processing 
– + 
Lithology 
Acoustic 
impedance 
Reflection 
coefficient 
Ideal 
seismic 
response 
No 
depth 
scale 
implied 
No 
time 
scale 
implied 
+ 
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6 First Steps in Seismic Interpretation
As said before, you can think of the objectives of seismic interpretation 
as seeking to answer two questions about subsurface geology: 
•฀ “What is it?” — What elements of geology can you recognize (observe 
and explain)?
•฀ “Where is it?” — How accurately can you delineate elements of geol-
ogy in three-dimensional space?
To be of any use in a successful exploration program, your answers to these 
questions require that you understand how to accurately transform measure-
ments and observations made in the reflection time domain into the depth 
domain. Except in the simplest cases, the inherent nonuniqueness of inter-
pretation often allows your answers to “What is it?” to promote erroneous 
inferences about “Where is it?” or “How big is it?” — and vice versa. Which 
of these questions can or should be answered first, and the confidence with 
which either can be answered at all, clearly depends on the quality of available 
data, the tools at hand for analyzing those data, and your skill and experience 
as an interpreter. Often, prior knowledge of and experience in an area enable 
you to answer one of these questions with greater certainty than the other, and 
you effectively conduct a model-based interpretation, in which the course of 
the interpretation is guided by more than just observations and correlation 
of the data. There is nothing implicitly wrong with such an interpretation 
because you should incorporate all available information and experience into 
your interpretations. The peril lies in the possibility that prior knowledge can 
subconsciously (or otherwise) drive your interpretation, and so contradictory 
observations or correlations are downplayed or ignored because they don’t 
fit the model. In such cases, the objectivity essential to all interpretations is 
seriously at risk, and you may see only what you want to see.
Figure 2. The observe– 
interpret–test cycle when 
working with seismic data. 
We make observations on 
uninterpreted data, explain those 
observations in an interpretation 
(telling the geologic story 
contained in seismic data), and 
test conclusions with wells 
or additional data, leading to 
more observations and revised 
interpretation.
Interpret 
Correlate/explain/synthesize 
build model 
Test 
Observe 
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Chapter 1: Introduction 7
Your fundamental concern in seismic interpretation is recognizing that 
reflection seismic data do not (yet) provide one-to-one images of true sub-
surface geology. You must decide which features in the data are “real” and 
correlative and which are not, and you must always try to understand the 
differences between the two. At the same time, you need to determine how 
well resolved are the real features you see and how accurate are their spatial 
positions; hence, the importance of data quality and the ability to properly 
couch interpretation results within the context of that quality. In a philo-
sophical sense, you should maintain healthy skepticism throughout your 
interpretations, using methodologies based on assumptions of doubt with 
the aim of gaining approximate or relative — but never absolute — certainty 
in your results.
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9
Chapter 2
Seismic Response
Seismic response is measured by the reflection generated at an acoustic 
impedance boundary according to the properties of the layers above and 
below the boundary and the nature of the seismic pulse impinging on that 
boundary.
Referring to Figure 1, the equation below defines acoustic impedance 
(AI) as the product of compressional-wave velocity V and bulk density ρ:
AI = Vρ
The following equation defines the reflection coefficient (RC) in terms 
of AI for normal incidence of a seismic pulse at an AI boundary: 
RC
AI AI
AI AI
2 2 1 1
2 2 1 1
2 1
2 1
=
−( )
+( )
=
−( )
+(
V V
V V
ρ ρ
ρ ρ ))
.
The Zoeppritz equations define the reflection coefficient for nonnormal 
angles of incidence of a seismic pulse at an AI boundary; these equations 
generally are applied in a simplified form (e.g., Shuey, 1985). For the pur-
poses of this text and defining seismic as “having to do with elastic waves” 
(Sheriff, 2002), here we describe seismic response in terms of compres-
sional-wave (P-wave) reflections but do not discuss shear waves (S-waves) 
or mode conversions in detail. 
You can initially and most easily describe seismic response with refer-
ence to an isolated impedance boundary and can further develop understand-
ing of the composite response from multiple, closely spaced boundaries by 
way of the convolutional model (discussed later in this chapter). You need 
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10 First Steps in Seismic Interpretation
to be familiar with a mathematical description for a waveform in terms of its 
frequency, amplitude, and phase characteristics, being especially careful to 
define phase and polarity as used in describing the shape or “character” of 
a reflection. The confidence with which you identify and correlate a reflec-
tion from an acoustic impedance boundary, which interpreters call a seismic 
event or horizon, based on its appearance or character dependson seismic 
data quality, on simple and well-known impedance relationships, and, per-
haps most importantly, on correlation of seismic data to available well data 
via well ties. 
The importance of horizon identification increases as you move along 
the value stream from wildcat exploration through appraisal and devel-
opment to production because this movement is toward greater detail of 
description in telling your geologic story. When interpreting and mapping in 
a frontier area, it may not be important to know whether a particular reflec-
tion corresponds to the top of a sand or a shale. But for a production project 
in the same area many years and millions of dollars later, it could be crucial 
to understand the seismic response for the top of a reservoir sand when 
choosing well locations and calculating reserves — hence, the importance 
of understanding seismic response in identifying horizons for interpretation.
Understanding the seismic response to an AI boundary requires knowl-
edge of the seismic pulse incident to that boundary and the behavior of the 
Figure 1. Definitions of acoustic impedance (AI) as a rock property, defined as the 
product of compressional-wave velocity V and bulk density ρ. The contrast in AI 
between two layers of rock gives rise to a seismic reflection when a seismic pulse 
impinges on the boundary between the layers. 
V = compressional-wave velocity, r = bulk density
V1, r1
V2, r2
Upper layer 
Lower layer 
Incident
pulse
Reflected
pulse
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Chapter 2: Seismic Response 11
pulse as it propagates through the earth. The seismic pulse causes particle 
motion in the subsurface through a medium treated as elastic in response to 
stress applied in the form of an impulse (e.g., detonating a charge of dynamite 
or firing an air gun). Dix (1952, his Figures 11.4 and 11.5) presents schematic 
diagrams illustrating these particle motions for positive and negative reflec-
tion processes. A seismic waveform is a description of this particle motion as 
a function of time, which can be treated as a composite of many individual 
functions of time for the different frequency components present in the wave-
form; the analytical representation of a seismic waveform as the sum of indi-
vidual sinusoidal functions is called Fourier analysis (Sheriff, 2002). 
For the sake of clarity and proper use of terminology, you should always 
be careful to distinguish between a reflector and a reflection: the former is a 
surface or boundary across which there is an acoustic impedance contrast, 
and the latter is a measurement of the particle motion caused by impinge-
ment of a seismic pulse upon the former. Keep in mind that you observe 
reflections and interpret reflectors (that is, elements of geology) from your 
observations — in that order. Maintaining a clear distinction between reflec-
tions and reflectors will help you remember that no seismic line or volume, 
no matter how carefully acquired and processed, is a completely accurate 
representation of true subsurface geology.
A seismic pulse propagates through a subsurface that is not really elas-
tic, so you can’t expect the pulse to retain its exact shape as it travels from 
the seismic source to a receiver. The change in shape of a wavelet, which is 
to say in the amplitude and phase characteristics of its different frequency 
components, because of propagation through a nonelastic earth is called 
attenuation. The physical properties of the subsurface of the earth cause 
the higher-frequency components of a wavelet to be preferentially reduced 
in strength, primarily because of converting the energy of particle motion 
to the heat of friction. In general, the farther or longer a signal travels, the 
more it is attenuated. Attenuation correction of seismic data, which can be 
done probabilistically (based on measurements of the data themselves) or 
deterministically (based on correlation with other physical measurements) 
is an important step in a seismic data-processing sequence.
The change in shape of a wavelet as a result of attenuation suggests 
that, all other things being equal, you should not expect to see the same seis-
mic response to the same impedance boundary that occurs at two different 
depths. A modeled product such as a synthetic seismogram, which usually 
is generated with an invariant wavelet, will therefore be better for making 
an accurate well tie in that portion of the seismic section where the wavelet 
used for the synthetic seismogram is a good approximation for the actual 
wavelet in the data. This is why wavelets are extracted from seismic data 
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12 First Steps in Seismic Interpretation
over windows or intervals of specific interest and then are used to generate 
synthetic seismograms for correlation only in that interval. Where possible, 
these extractions are done at or near points of well control so that log data 
can be used in the extraction process. 
In the time domain, a periodic function for a single frequency can be 
described as a sinusoidal wave, as with the cosine wave illustrated in Fig-
ure 2. The general form of the equation for this cosine wave as a function 
of time is 
y t A ft( ) cos ( ),= +2π φ
where A is the amplitude, f the frequency, t the traveltime, and φ the phase 
of the waveform. The value φ is the angle, measured in degrees (where 
360° = 1 cycle), that represents the lead (the amount of time the waveform is 
advanced) or lag (the amount of time the waveform is delayed) with respect 
to a reference starting time. Phase φ is defined as the negative of phase lag 
(Yilmaz, 2001), which is to say that a negative time shift (time delay) cor-
responds to a positive phase value and a positive time shift (time advance) 
corresponds to a negative phase value. For example, Figure 3 shows that a 
cosine wave lags a sine wave by π/2 or 90°: 
sin cos cos( ) , sin cos
π π π
2 2 2
0 1 0




= −



= = ( ) = 00
2 2
0−



= −



=
π π
cos ,. . .
or
cos sin sin( ) , cos sin
π π π
π
2 2 2
0 0




= +



= = ( ) = 00
2 2
1+



= 



=
π π
sin ,. . . .
Figure 2. A simple sinusoid defined as a cosine wave. The shape of this waveform 
is determined by its amplitude A, frequency f, and phase φ. T is the period of the 
waveform.
t
T 
A 
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Chapter 2: Seismic Response 13
The waveforms shown in Figures 2 and 3 are infinite, single-frequency 
sinusoids; however, all of the wavelets with which you work in practical 
seismic interpretation are finite and have limited bandwidth. They are the 
summation of discrete sinusoids, each with its own amplitude, frequency, 
and phase characteristics. This is the basis of Fourier analysis. An exam-
ple of a finite, band-limited wavelet and its component sinusoids is shown 
in Figure 4; in this example, the amplitude and phase of the components 
are constant (phase = 0) and only the frequency of the individual sinusoids 
varies.
Knowledge of the phase of a waveform is important in Fourier analysis 
because this angle sets a reference for the starting time (zero time, effec-
tively) for each component waveform defined by its own frequency and 
amplitude. An illustration of phase rotation of a simple band-limited wave-
let symmetric about t = 0 through one full cycle from 0° to 360° for 90° 
incrementsis shown in Figure 5. As expected, phase rotations of 180° and 
–180° are identical.
The wavelet in the center trace in Figure 5 is symmetric about t = 0, 
meaning that it literally describes particle motion that occurs before t = 0, 
which is physically nonrealizable. For this reason, the wavelet is called a non-
causal wavelet (see Figure 6). Because of its symmetry, it is also referred to 
as a zero-phase wavelet; each of its component sinusoids is zero phase, and 
each is uniquely defined by its own amplitude and frequency according to 
Figure 2. In terms of signal processing, a zero-phase wavelet has the shortest 
time duration (pulse width) for a given bandwidth (frequency range). The 
Figure 3. Phase relationship between a sine wave (red) and a cosine wave (blue). 
The sine wave leads the cosine wave by 90°, and the cosine wave lags the sine 
wave by 90°. 
cos(0) = sin(0 + p /2) = sin(p /2) = 1
sin(0) = cos(0 – p /2) = cos(–p /2) = 0
t
–p /2 p /2 3p /2 2pp0
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14 First Steps in Seismic Interpretation
Figure 4. Illustration of a finite, band-limited wavelet as the summation of five 
component sinusoids. All of the components have the same amplitude and phase 
(phase = 0).
Finite
band-limited
wavelet
40 Hz 
30 Hz 
20 Hz 
10 Hz 
5 Hz 
t
Figure 5. Phase rotation of a zero-phase wavelet (center trace) through a full 360° 
in increments of 90°. The display convention used in this figure is described in 
Figure 7.
0°–180° –90° +90° +180°
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Chapter 2: Seismic Response 15
seismic response for a zero-phase wavelet also is easier and more intuitive to 
visualize because its maximum amplitude corresponds exactly to the posi-
tion of the reflecting interface (see Figures 5 and 6). Displays that show the 
amplitude and phase characteristics of the sinusoids for every frequency 
component of a wavelet are called the amplitude (amplitude as a function 
of frequency) and phase (phase as a function of frequency) spectra. Given 
these amplitude and phase spectra, a resultant wavelet can be uniquely con-
structed by summing individual frequency components having the charac-
teristics defined by these spectra.
Figures 5 and 6 use the same display convention, i.e., they represent 
seismic response in the same way with reference to a standard impedance 
configuration. The display convention most commonly used by SEG is the 
positive standard polarity convention (Figure 7), in which polarity means 
positive or negative trace deflection. When discussing or presenting your 
work, you should state the phase of your data, to the degree it is known, and 
the display convention you are observing. Similarly, you should ask about 
wavelet phase and the display convention being used in any discussion or 
presentation involving seismic data if that information is not communicated 
or clearly annotated on seismic displays.
Figure 8 illustrates the four different display formats for reflection seis-
mic data. Of these, the most common used on workstation displays is vari-
able density, often with user-defined or customized color schemes. Wiggle 
traces superimposed on a variable density background is also a popular dis-
play format.
Figure 6. Noncausal and causal wavelets. The causal wavelet involves particle 
motion only after time = 0, whereas the noncausal wavelet involves particle 
motion before time = 0, which is not physically realizable. The display convention 
used in this figure is described in Figure 7.
0 
Causal
wavelet
Noncausal
wavelet
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16 First Steps in Seismic Interpretation
In virtually all cases, reflection seismic data represent a composite 
response to many closely spaced impedance boundaries, some of which 
are sharp and distinct and others of which are gradational. This composite 
response actually is the result of constructive and destructive interference 
of the discrete responses to individual impedance boundaries, described 
by the so-called convolutional model. Convolution is a mathematical 
operation that, in simplest terms, involves multiplication, shifting, and 
summation of two functions of the same variable (for seismic data the 
variable is traveltime t). You can think of convolution as simulating the 
propagation of a seismic pulse through a layered earth. The output of a 
1D convolution, such as the convolution of an RC series calculated from 
an AI log (which has been converted to the time domain) with a seismic 
wavelet to produce a synthetic seismogram is probably much easier to 
visualize than to describe in words or to understand from exacting math-
ematical language. 
In Figure 9, the RC series consists of four coefficients, each correspond-
ing to an AI boundary; the coefficients are not evenly spaced, and they do 
not all have the same magnitude and sign. This RC series will be convolved 
with the zero-phase wavelet shown to the left of the series, and both must 
have the same sample rate. Note that this wavelet is a wiggle trace that 
uses the SEG positive standard polarity convention. In the convolutional 
model, the seismic response to a given RC is created by reproducing the 
seismic wavelet scaled to the magnitude and sign of that RC. As shown in 
Figure 9, the scaled wavelet is reproduced as the seismic response for each 
of the four RCs, and the final convolution output or composite response is 
Figure 7. The SEG positive standard display convention for reflection seismic 
data. “For a zero-phase wavelet, a positive reflection coefficient is represented by a 
central peak, normally plotted black on a variable area or variable density display” 
(Sheriff, 2002). 
Acoustic 
impedance
Reflection 
coefficient
+ – Low 
High 
Wavelet 
0 
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Chapter 2: Seismic Response 17
Figure 8. Four display formats for reflection seismic data. Display formats are 
independent of the polarity convention used for a given data set.
Variable density Variable area Variable-area wiggleWiggle
the sum of the individual scaled responses. There is both constructive and 
destructive interference between individual seismic responses in the com-
posite response. This interference is substantial when the effective width 
of the seismic pulse is greater than the interval between adjacent RCs. For 
purposes of this discussion, consider the pulse width to be the breadth of the 
central peak or peak/trough. Notice also that there is no individual seismic 
response for any points in the RC series where RC = 0, that is, where there 
is no impedance contrast. The differences between the composite responses 
in Figure 9a and 9b indicate that your interpretation of geology from seismic 
data depends critically on the wavelet in your data. 
Knowledge of wavelet phase is important because it relates seismic 
response to geology in terms of the characteristics of the source wavelet 
(pulse) as defined in Figure 2, that is, the reflection seismic response to a 
given geologic boundary or feature changes for different source wavelets. 
The phase of the wavelet contained in any seismic data set can vary laterally 
and vertically (temporally) andis estimated most accurately by determin-
istic methods using well control. In the absence of well control, you can 
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18 First Steps in Seismic Interpretation
Figure 9. (a) The convolutional model. The individual responses of each 
reflection coefficient to the input seismic wavelet, scaled to the magnitude 
and sign of the reflection coefficient, are summed to generate the composite 
seismic response. There are destructive and constructive interference of the 
individual responses in producing the composite response. (b) Convolution of 
the reflection coefficient series shown in (a) with a different source wavelet. 
The differences between the composite responses for the two wavelets show 
that accurate interpretation of these responses depends on knowledge of the 
source wavelets.
Input 
wavelet 
Reflection a)
coefficients 
Individual 
responses 
Overlay 
responses 
Composite 
response 
– + 
Input 
wavelet 
Reflection b)
coefficients 
Individual 
responses 
Overlay 
responses 
Composite 
response 
– + 
visually estimate wavelet phase by observing certain reflections that may be 
present in your data (see Table 1). 
Using reflections from any of the boundaries listed in Table 1 assumes 
that the boundary can be identified conclusively, that there is a well-known 
and consistent acoustic impedance contrast across it (the algebraic sign of the 
reflection coefficient across the boundary is known), and that it is isolated 
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Chapter 2: Seismic Response 19
from other nearby boundaries so that its character is not a composite reflec-
tion response. In marine settings, the seafloor reflection is commonly used to 
check wavelet phase because the impedance contrast between seawater and 
sediment is almost always positive. Similarly, a hydrocarbon/water contact, 
which appears as a seismic flat spot in a reservoir that is thick enough to be 
resolved seismically, can be used confidently to estimate wavelet phase (see 
the discussion of seismic resolution and tuning in Chapter 6). A seismic flat 
spot occurs because the presence of hydrocarbons as the pore-filling fluid 
lowers the AI of the hydrocarbon-bearing portion of a reservoir below that of 
the nonhydrocarbon-bearing or brine-filled portion of that reservoir. Not all 
flat spots are perfectly flat because velocity effects in time imaging can tilt 
or distort them and because some hydrocarbon/water contacts are not truly 
horizontal. A flat spot can occur only for reservoirs in which the hydrocarbon-
bearing portion of the reservoir is seismically resolved because the seismic 
response from a hydrocarbon-bearing interval whose thickness is below a cer-
tain value called the tuning thickness will be a composite of responses from 
the top and base of the interval that will not directly represent wavelet phase. 
The flat spot indicated by the arrow in Figure 10 shows a well-defined, 
symmetric peak (black). According to the accepted polarity standard and 
display convention for this image, within the visual acuity of the observer to 
see asymmetry in the waveform, the phase of the data is zero. Note that near 
the right-hand edge of this flat spot is a high-amplitude trough-over-peak 
amplitude response; this point marks the tuning thickness of the low-imped-
ance, hydrocarbon-bearing portion of the reservoir. Continuing to the right, 
the decrease in the amplitude of the trough-over-peak signature reflects the 
decrease in thickness of the hydrocarbon-bearing portion of the reservoir. 
Note also that the top of the reservoir is not marked by a single, sharply 
defined reflection (a trough or a peak) along its full extent, suggesting that 
the top of the reservoir interval might be gradational in some places.
Table 1. Subsurface boundaries that can be used for visual estimation of 
wavelet phase. No single boundary is absolute or foolproof. 
Best:
 Seafloor
 Hydrocarbon/water contact (seismic flat spot)
Use with care:
 Top of salt/volcanics
 Base of salt
 Basement
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20 First Steps in Seismic Interpretation
The problem with using boundaries such as top and/or base of salt, top 
of volcanics, and basement (which can take on a variety of geologic and eco-
nomic meanings) for estimating wavelet phase is that these boundaries often 
are gradational and poorly defined, so their seismic responses are effec-
tively composite responses to multiple, closely spaced impedance contrasts 
rather than to a single, well-known impedance contrast. At the same time, 
the impedance properties of the materials above and below these boundar-
ies, especially for basement, are not necessarily well known or regionally 
consistent; so neither the magnitude nor the sign of the impedance contrast 
across such boundaries can be inferred confidently without well control.
Most interpreters prefer to work with zero-phase data, for which a seis-
mic event or horizon is symmetrically disposed about its correlative imped-
ance boundary and thus is most easily and intuitively visualized. Knowledge 
of wavelet phase and the display convention of your data should enable you 
to draw geologically reasonable conclusions when correlating a given seis-
mic response to a particular AI boundary. At the same time, you should rec-
ognize that a given impedance boundary can give rise to different seismic 
responses, depending on the phase of your data. This knowledge is critical for 
accurate interpretation of seismic attributes, as discussed in the next chapter.
Figure 10. Example of a well-imaged seismic flat spot, denoted by the yellow 
arrow, on time-migrated data. This image suggests that the seismic data are zero 
phase (courtesy PGS). 
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21
Chapter 3
Seismic Attributes
By definition, a seismic attribute is a measurement based on seismic 
data (Sheriff, 2002). In the strictest sense, then, two-way traveltime, also 
known as horizon time, is perhaps the most important and frequently used 
seismic attribute, although it isn’t usually considered an attribute. 
Brown (1996) includes horizon time in his list of 66 different attributes 
and indicates that an attribute is “necessarily a derivative of a basic seismic 
measurement.” He presents a generalized classification scheme that breaks 
attributes into four categories: time, amplitude, frequency, and attenuation. 
Brown also poses two questions that all interpreters must address when ana-
lyzing seismic attributes:
1) What do they all mean?
2) When do we use one and when another?
In the same vein, a paper with the delightful title “Redundant and Useless 
Seismic Attributes” by Barnes (2007) offers several common-sense sugges-
tions for distinguishing “useful attributes from those of doubtful utility,” 
including, among other characteristics, their clear and useful meanings in 
a geologic and/or geophysical context as opposed to mathematical terms.
With Barnes’ distinctions in mind, you can visualize a “utility spectrum” 
for seismic attributes (Figure 1), in which using an attribute or combination 
of attributes (e.g., by way of principal component analysis) to identify and 
correlate featuresof interest proceeds with varying degrees of attention to 
the true physical meaning(s) of the attribute(s). At one end of this spectrum 
is the mentality that “I’ll use this attribute for correlation when it helps me 
find what I’m looking for, regardless of what it means physically.” At the 
other end is “I’ll only use attributes whose physical meaning I fully under-
stand.” In practice, there is no right or wrong mindset or approach — only 
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22 First Steps in Seismic Interpretation
personal preference. Your position on this spectrum for any project in which 
attributes are important will depend on preference (that is, experience), seis-
mic data quality, and availability of calibration information, such as well 
control.
Seismic attributes are used to assist interpretation at all scales, rang-
ing from analyzing regional depositional systems to mapping fine details of 
structure, stratigraphy, and rock properties.* They are also used to illustrate 
data quality (see Chapter 9). Two of the most commonly used seismic attri-
butes are amplitude and coherence.
Amplitude
The reference or baseline value for the amplitude of reflection seismic 
data is zero; so amplitudes are positive or negative in accordance with agreed 
polarity and display conventions, as discussed in Chapter 2. In terms of the 
simple two-layer model shown in Figure 1 of Chapter 2, the magnitude and 
algebraic sign (positive or negative) of the amplitude of a reflection from a 
single, isolated acoustic-impedance (AI) boundary is directly proportional 
to the magnitude and algebraic sign of the reflection coefficient (RC) at that 
boundary; the convolutional model (Figure 9 of Chapter 2) extends this rela-
tionship to the general case of the composite seismic response to a reflection 
coefficient series. In Figure 2, amplitude A is the departure of the waveform 
from the baseline value, as shown by the red arrows. The time separation 
between the apex of a peak (or trough) and the apex of its adjacent trough 
(or peak) is often called delta time or delta T (∆T), and the absolute value 
of the difference in amplitudes measured at the same two points is called 
delta amplitude or delta A (∆A). Notice that Figure 2 refers to data that are 
*Excellent references for seismic attribute analysis are Seismic Attributes for Pros-
pect Identification and Reservoir Characterization by Chopra and Marfurt (2007) 
and Interpretation of 3D Seismic Data by Brown (2011). 
Figure 1. Utility spectrum for a seismic attribute. Depending on experience and 
the project at hand, you analyze attributes with varying degrees of attention to their 
physical meaning.
“What does it
physically mean?”
“Does it help me
correlate?”
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Chapter 3: Seismic Attributes 23
processed in the time domain, and ∆T is equal to one-half of the period of 
the displayed waveform.
Before you proceed with any quantitative analysis of seismic amplitudes, 
you must ensure that amplitudes were handled carefully and consistently 
through all stages of data acquisition and processing. In many cases, this 
involves some detective work on your part, but your effort is well worth the 
trouble if you are to gain any meaningful and reliable information or value 
from amplitude analysis. You need to review all processing steps involving 
gain recovery and preservation of relative amplitudes as well as the qual-
ity of stacking (to be discussed in Chapter 4), giving particular attention to 
processes such as automatic gain control (AGC) that affect prestack or post-
stack amplitude balancing. Many interpreters prefer to apply AGC to their 
data when the primary objective is structural interpretation, but they would 
not use the same data for amplitude or attribute studies. At the same time, 
the data used for those types of studies must have sufficient dynamic range 
to include the maximum processed amplitude values and not have restricted 
this range by excluding or clipping values (a workstation-related issue).
Figure 2. Seismic amplitude (red 
arrows) and the quantities ∆T and ∆A 
(blue arrows) that are based on picking 
adjacent peak and trough reflections. 
This display is for time-processed data.
0
– +
∆A
∆T
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24 First Steps in Seismic Interpretation
Any quantitative use of seismic amplitudes must be based on careful 
picking of reflections so that amplitudes are consistently and accurately 
measured or extracted from the seismic data. You should automatically track 
these reflections whenever possible (data quality permitting) so that they are 
consistently picked as peaks (maxima) or troughs (minima) if they are to be 
used for amplitude analysis. You can autotrack — track or pick reflections 
using computer-based processes (see Chapter 7) — or you can use an event 
that has not been autotracked as a reference horizon to construct a gate or 
window of sufficient size to contain the individual reflection or reflection 
package whose amplitude you want to measure. These gates do not need to 
be symmetrically arranged around a reference horizon and can be defined by 
two separately tracked horizons.
Seismic amplitudes are manifestations of geology because they are 
the response to AI contrasts that are themselves measures of rock and fluid 
properties. Changes in amplitudes therefore reflect changes in geology, and 
every seismic line or volume exhibits a range of seismic amplitudes that can 
be correlated to trends in rock and fluid properties and ultimately to lithol-
ogy and pore-fluid type. Amplitudes at the extremes of this range are anom-
alous in the sense that they are out of the ordinary or are departures from an 
established trend and, as such, can be of particular exploration interest. For 
example, in Cenozoic basins with predominantly clastic fill, anomalously 
high amplitudes known as bright spots have proven to be attractive explora-
tion targets, although not guaranteed or completely risk-free. Bright spots 
reflect the reduction of the acoustic impedance of a reservoir sand caused 
by the presence of hydrocarbons in the pore-filling fluid in comparison to 
the acoustic impedance of the same reservoir filled with brine. Validation 
of seismic amplitude anomalies as direct hydrocarbon indicators (DHIs) is 
a very important element of successful exploration and development pro-
grams in many areas of the world. 
In general, you observe reflections as having anomalous amplitude 
when you visually inspect data (see Figure 3). This is a very qualitative 
measure, and amplitudes identified in this way are said to be above back-
ground, meaning that there is an ambient or background level of reflec-
tivity associated with the overall impedance trends (i.e., the geology) of 
the area under investigation. Figure 4 shows the results of one quick and 
easy technique for highlighting amplitude anomalies by simply clipping 
or blanking all amplitudes below a certain threshold or background level. 
You should reference amplitude values to a statistically based background 
level and calibrate them to well control if you intend to use them for quan-
titative purposes such as calculating reservoir thickness and estimating 
reserves.
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Chapter 3: Seismic Attributes 25
Figure 3. A 2D time-migrated line with an anomalous high-amplitude reflection, 
indicated by the black arrow. Red events are negative amplitudes (troughs) and 
blue events are positive amplitudes (peaks). The trough-over-peak signature of the 
anomaly suggests that it can be interpreted as a thin (below tuning) hydrocarbon-
bearing reservoir. Note similar trough-over-peak anomalies to the right of the 
highlighted event (courtesy PGS).
t
Most if not all modern workstation systems and interpretation pack-
ages have standard routines for extracting amplitudes of individual reflec-
tions as well as of user-specified intervals of interest. In the latter case, the 
attribute is usually a statistical measure of the data within an interval (such 
as average absolute amplitude, maximum positive amplitude, or root mean 
square [rms] amplitude). As indicated by Barnes (2007), amplitude-related 
attributes such as these often provide duplicate measurements. You should 
test different attributes before choosing the one that works best — gives 
the most stable and clearly defined results, obviously involving interpretive 
judgment and experience. As Barnes goes on to say, “If you can’t tell which 
one works best, then it doesn’t matter which one you choose.” Not only does 
Barnes imply that you should take the time to test different attributes on 
your data, but he also requires that you have some idea of what the different 
attributes mean physically.
The extraction of amplitude-related attributes over user-specified inter-
vals can be particularly helpful when integrated with the results of sequence 
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26 First Steps in Seismic Interpretation
stratigraphic or seismic facies analysis to aid in identifying and interpreting 
depositional systems, especially valuable in regional studies. You should 
carefully select and then pick the horizons that bound intervals of interest 
so that these intervals are neither too broad (effectively mixing or masking 
several different amplitude signatures) nor too narrow (excluding data that 
are needed for accurate characterization of an interval). Again, appropri-
ate definition of intervals involves interpretive judgment, and any integrated 
analysis using seismic facies and attributes should include well control 
when calibrating results.
The quantities ∆T and ∆A shown in Figure 2 are used in tuning or time-
amplitude (time-amp) analysis (see Chapter 6, especially the wedge model 
shown in Figure 2 of that chapter) to study thin beds. In general terms, you 
should recognize that meaningful results from tuning analysis depend on 
critical factors in data processing and interpretation. For processing, these 
factors include true relative amplitude recovery and preservation, knowl-
edge of the seismic wavelet and the signal-to-noise ratio (S/N) of the data, 
and the availability of carefully edited well data for calibrating the seismic 
Figure 4. Same line as in Figure 3 but displayed with amplitudes less than 
approximately half of the maximum absolute amplitude value for the entire line clipped 
to highlight anomalously high and low amplitudes. Clipping was done visually by 
dynamic modification of the display color table. The clipping value can be thought of as 
a threshold between background and anomalous amplitude values (courtesy PGS).
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Chapter 3: Seismic Attributes 27
response. For interpretation, the seismic data being analyzed must have the 
objective peak-and-trough reflections consistently and accurately picked, 
which requires autotracking and thorough quality control before proceeding 
with tuning calculations.
Measurement of ∆T also provides a quick means for estimating the 
dominant frequency within a window of seismic data. By definition, domi-
nant frequency is the predominant frequency determined by measuring the 
time between successive peaks or troughs (the period T of a waveform, as 
shown in Figure 2 of Chapter 2) and taking the reciprocal (Sheriff, 2002). 
In practical terms, the dominant frequency within a window of data can be 
thought of as the frequency of the waveform that dominates your view of 
the data within that window. Remember that the dominant frequency tends 
to decrease with increasing reflection time owing to attenuation, so any esti-
mate of dominant frequency is applicable only to a window of data and not 
to the full reflection record.
Using a visual estimate of ∆T in milliseconds for well-defined, coherent 
reflections within a window of interest and recalling that ∆T as defined in 
Figure 2 is equal to one-half the period of the waveform, you can calculate 
the dominant frequency in hertz (Hz, cycles per second) as 1000/(2 × ∆T). 
In the example shown in Figure 5 for an estimated ∆T of 30 ms for the 
Figure 5. Example of estimation of dominant frequency based on observed peak-to-
trough time separation ∆T (courtesy PGS).
Dominant frequency = 1000/(2 × 30 ms) = 17 Hz 
~30 ms 
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28 First Steps in Seismic Interpretation
high-amplitude peak/trough reflections within the window outlined in red, 
the dominant frequency is 1000/(2 × 30 ms) = 17 Hz. Note that this tech-
nique is implicitly meaningful only for time-domain data because depth-
processed data cannot be characterized by a measurement (∆T) that can be 
made only in the time domain unless the depth data have been converted 
back or stretched to the time domain using the velocity model with which 
they were processed. 
For additional information on physical principles and techniques for 
analysis and interpretation of seismic amplitudes, refer to Seismic Ampli-
tude Interpretation by Hilterman (2001). 
Coherence
Coherence is a seismic volume attribute; it is run only on 3D seismic data 
and measures the trace-to-trace similarity of the seismic waveform within 
a small analysis window. Coherence technology was originally developed 
by Amoco (Bahorich and Farmer, 1995) to enable more complete use of the 
abundance of information contained in a 3D seismic volume to complement 
standard interpretation techniques. Because important elements of geology 
such as faults and stratigraphic features (e.g., channel margins) are evident 
as discontinuities in seismic data, an attribute such as coherence can be very 
useful in identifying and visualizing these features (see Figures 6 and 7). 
Generating a coherence volume is an automated process that requires select-
ing values for several input parameters, most important of which is the size 
of the data-analysis window (in three dimensions). A very large window 
will include too much data and produce output with a pronounced structural 
overprint, whereas a window that is too small will include too little data and 
produce output that is more a manifestation of noise in the data rather than 
geologic content. Input-parameter values usually are chosen following a 
series of tests to determine which combination of values produces the most 
interpretable output. As with the results of many other analytical processes, 
the quality or interpretability of the output depends heavily on the noise 
content of the input; a very noisy data set will probably contain very little 
useful or reliable coherence information. Your experiencecomes into play 
in selecting input parameters for coherence processing and evaluating/inter-
preting coherence output; that experience also helps you properly gauge the 
value added to your interpretation through the use of coherence data.
Many interpreters generate a coherence volume for their 3D data as one 
of the first steps in a new interpretation project. A cursory review of these 
coherence data can be very helpful in gaining initial impressions of the geol-
ogy of the project area and building work flows and schedules for ensuing 
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Chapter 3: Seismic Attributes 29
interpretation tasks. Coherence displays such as that shown in Figure 7b 
also can be very useful for quality control of fault picks to ensure that faults 
are not miscorrelated in structurally complicated areas. Some interpreters 
prefer to pick faults primarily on coherence slices rather than on vertical 
sections when data quality permits.
Coherence data usually are viewed on a horizontal slice through the 
coherence volume or along a suitably tracked horizon (the latter producing 
a coherence horizon slice). Alternatively, the original 3D volume can be 
flattened along a suitably picked horizon, and coherence can be generated 
from that volume to produce a coherence horizon slice. These slices can 
be particularly helpful for interpreting details of stratigraphy, but you must 
pick the input horizon very carefully — entirely by automatic tracking if 
possible — so that tracking artifacts are not passed through the coherence 
process and subsequently interpreted as real geology.
Figure 6. (a) Traditional 3D time slice; faults parallel to strike are difficult to see. 
(b) Coherence time slice; faults are clearly visible. From Bahorich and Farmer (1995).
a) b)
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30 First Steps in Seismic Interpretation
Inversion
Although you can consider seismic inversion to be more a process 
than an attribute in the sense that amplitude and coherence are attri-
butes, some discussion of inversion is warranted because interpretation 
of inversions most often involves analysis of attributes extracted from the 
inverted data. 
The place of seismic inversion within the sequence of acquisition- 
processing-interpretation of reflection seismic data is best visualized using 
Figure 8, a slightly modified version of Figure 1 from Chapter 1. In simplest 
terms, the inversion process involves calculating AI data from reflectivity 
data; as such, seismic inversion can be considered the first step in inter-
preting ideally processed seismic data. As shown in Figure 8, conventional 
reflectivity data, the ideal seismic response, provide information about the 
boundaries between subsurface layers, whereas seismic inversions (AI) 
measure the properties of the layers themselves. 
If you approximate an RC series as the derivative of an AI function, then 
the inversion of “optimally processed” reflection seismic data effectively is 
integrating those data to produce AI data. The following equations show that 
this approximation is based on the assumption that the difference between 
Figure 7. Comparison of a conventional 3D horizontal slice through (a) a reflectivity 
volume and (b) a coherence volume generated from the parent reflectivity volume. In 
this example from a depth-processed 3D volume, faults that appear as dark lineations 
on the coherence slice are more clearly seen and accurately interpreted on the 
coherence data than on the reflectivity data (courtesy BP).
a) b)
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Chapter 3: Seismic Attributes 31
adjacent samples in an AI series is incrementally small, that is, AI varies 
slowly and smoothly with depth. From the equation in Chapter 2, 
RC
AI AI
AI AI
( ) .
( ) ( )
( ) ( )
j
j j
j j
=
−( )
+( )
+
+
1
1
The first equation below contains the key assumption that allows the RC 
series to be approximated as the derivative of an AI function:
RC
RC
RC
( )
( )
( )
,
( ) ln
( )
,
(
t
t
t
t
t
C
e
t
~
AI
2AI
~
AI
∆
∫ +2 1
))
( ),∫ ~ AIC t2
where C is a constant.
Figure 8. Seismic inversion, creating AI data from an “ideal seismic response,” is an 
inverse process that can be considered an interpretive process (refer to Figure 1 of 
Chapter 1).
– + 
Lithology AI RC
Ideal 
seismic 
response 
No 
depth 
scale 
implied 
No 
time 
scale 
implied 
+ 
Inversion
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32 First Steps in Seismic Interpretation
Inversion is done in the time domain, so depth-processed data must be 
converted to time before inversion processing and then converted back to 
depth after inversion if desired. Note that scaling of the inverted data is 
implicit in the integration; this scaling is determined by calibration to well 
control or to regional impedance trend curves. Of course, any reflection 
seismic data can be inverted, but the quality of the inversion depends on 
calibration and the quality of the input reflectivity data. 
Lindseth (1979), who refers to seismic inversion as generating a syn-
thetic sonic log from a processed seismic trace, captures the uncertainty 
inherent in seismic inversion:
The inversion of seismic reflection data is much more demand-
ing (and revealing) of data acquisition and processing quality than 
are conventional displays. While inferior data, as in many seismic 
operations, does not totally impede the execution of any process, 
the quality of output will be degraded, eventually reaching a point 
where any benefits from the procedure are doubtful.
In the context of Lindseth’s comment on the dependence of seismic inver-
sion on the quality of data acquisition and processing, following is a list of 
conditions you should check about the processing of input reflectivity data 
before proceeding with inversion:
•฀ Amplitudes are true relative amplitudes.
•฀ Amplitude variation with offset (AVO) effects are accounted for.
•฀ Data are zero phase.
•฀ Seismic wavelet is invariant (at least over the window of interest).
•฀ Bandwidth is maximized.
•฀ All multiple reflections have been removed (see Chapter 7).
All of these conditions are rarely if ever met, so the quality of a seismic 
inversion will always need to be assessed carefully in terms of its correlation 
to well data and the accuracy of its representation of real geology.
Seismic inversions are correlated in much the same way as are con-
ventional reflectivity data (see Chapters 7 and 8). Although a primary 
objective of correlating both types of data is to define the boundaries of 
the intervals of interest, you pick zero crossings on inverted data to iden-
tify these boundaries and then examine attributes of the defined layers to 
study their internal properties. Contrast this with picking troughs, peaks, 
and, occasionally, zero crossings on zero-phase reflectivity data, where 
the picked horizons also define layers of interest but extracted attributes 
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Chapter 3: Seismic Attributes 33
Figure