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A Bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone

05 Mar 2007-Journal of the Acoustical Society of America (Acoustical Society of America)-Vol. 121, Iss: 3, pp 1519-1528

TL;DR: The author describes a Bayesian method to combine the information contained in those acoustic data plus visual observations that could be used to study the behavior of sperm whales using a single hydrophone in any location no matter what the depth, the relief, or the constitution of the seafloor might be.

AbstractSome bioacousticians have used a single hydrophone to calculate the depth/range of phonating diving animals. The standard one-hydrophone localization method uses multipath transmissions (direct path, sea surface, and seafloor reflections) of the animal phonations as a substitute for a vertical hydrophone array. The standard method requires three multipath transmissions per phonation. Bioacousticians who study foraging sperm whales usually do not have the required amount of multipath transmissions. However, they usually detect accurately (using shallow hydrophones towed by research vessels) direct path transmissions and sea surface reflections of sperm whale phonations (clicks). Sperm whales emit a few thousand clicks per foraging dive, therefore researchers have this number of direct path transmissions and this number of sea surface reflections per dive. The author describes a Bayesian method to combine the information contained in those acoustic data plus visual observations. The author’s tests using synthetic data show that the accurate estimation of the depth/range of sperm whales is possible using a single hydrophone and without using any seafloor reflections. This method could be used to study the behavior of sperm whales using a single hydrophone in any location no matter what the depth, the relief, or the constitution of the seafloor might be.

Topics: Sperm whale (55%), Whale (52%), Hydrophone (52%)

Summary (3 min read)

Introduction

  • Submitted on 7 Mar 2013 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.
  • A Bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone.
  • Journal of the Acoustical Society of America, Acoustical Society of America, 2007, Vol. 121, pp. 1519-1528. pp. 1519-1528. ISSN 0001-4966 Open Archive Toulouse Archive Ouverte is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible.

A Bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone

  • Christophe Laplanchea Laboratoire Images, Signaux et Systèmes Intelligents, Groupe Ingénierie des Signaux Neuro-Sensoriels, Université Paris 12, Créteil, France Some bioacousticians have used a single hydrophone to calculate the depth/range of phonating diving animals.
  • The author’s tests using synthetic data show that the accurate estimation of the depth/range of sperm whales is possible using a single hydrophone and without using any seafloor reflections.
  • Therefore, if the unidimensional array is vertical, then the circle is horizontal, and its depth and radius give the depth and the horizontal range of the sound source.
  • One can seldom clearly detect the seafloor echoes of clicks emitted by sperm whale using a hydrophone close to the sea surface towed by a research vessel.
  • The Bayesian approach has already proven to be efficient to locate sound sources using TOADs Spiesberger, 2005 .

A. Trajectory model

  • First, one would need a representation of the underwater trajectory of the whale using a mathematical model.
  • Let t A be the time when the sperm whale flukes-up and starts diving, t B the time when the whale starts clicking, t C the time when the whale stops clicking, and t D the time when the whale resurfaces.
  • One can then define the trajectory T using the depth, range, and heading of the whale.

B. Prior information

  • This latter definition of T uses 3n+5 parameters, that is to say redundant information.
  • Indeed, the coordinates of the last summit E n+1 can be calculated given the coordinates of the first summit E 1 and using the values of speeds and change of heading in the n segments.
  • Sperm whales usually keep a constant vertical speed while descending to reach their prey and while ascending to reach the sea surface Miller et al., 2004a .
  • Relative to the research vessel, between the fluking and the resurfacing points.
  • Such uncertainties are modeled in the following section using random variables.

E. Probability distribution of the prior

  • The author will give a more accurate description of the prior distributions appearing in Eq. 13 .
  • By choosing such a prior, the author indicates that the underwater movement of the whale is not erratic, and that it is likely that the whale tends to swim in a given direction.
  • The probability density functions have a maximum at the most likely prior value of the parameters, and its width illustrates the confidence the authors grant to this most likely prior value.
  • One could choose for instance gamma probability distributions.
  • For practical reasons, the author has chosen truncated normal distributions.

G. MCMC algorithm

  • One can use a Markov Chain Monte Carlo MCMC algorithm to draw samples of the posterior.
  • One can use the Metropolis-Hastings algorithm Robert and Casella, 2004 to draw such samples.
  • Calculate the acceptance ratio of this sample to determine if the new sample Instead of drawing a whole new trajectory at each iteration i, draw the summits one by one.
  • In practice, the second method requires a lower total amount of samples, as it requires less than I /C samples per subchain to lead to a correct estimate T̂. The author has used the subchain method.

H. Data set

  • The author has run simulations using the free software SBPLASH implemented in MATLAB.
  • The efficiency of the algorithm is illustrated using synthetic data.
  • The depth, range, vertical speed, and horizontal speed of the whale when following this trajectory are given in Figs. 3–6, respectively.
  • The initial value of the trajectory is in the author’s simulations the rectilinear, constant speed trajectory linking the prior locations of the points E 1 and E n+1 Figs.
  • These parameters here are constants which are empirically chosen.

A. Convergence

  • The algorithm generates trajectories with values close to the data after I0=100 iterations.
  • The acceptance orithm left, circles of the depth z of the sperm whale throughout the dive.
  • Draws of the first s=1 and last s=n+1 summits are more often accepted than the others 2 s n , given the different constraints and priors they are bound to.

B. Depth, range, and speeds

  • The vertical and horizontal speeds of the whale are estimated using Eq. 19 .
  • The estimate of the depth and the range of the whale are calculated using these values.
  • Results of vz, vr, z, and r are plotted plus/minus twice their standard deviation.

IV. DISCUSSION AND CONCLUSION

  • The algorithm correctly estimates the depth and the range of the whale throughout the dive Figs. 3 and 4 .
  • The author is, however, confident regarding the choice of I1 for the given data set.
  • The parameters used in the probability distributions of the priors have been fixed and empirically chosen.
  • As stated regarding the standard deviation of the prior speed, an optimal value of could be estimated while sampling and estimating the trajectory parameters.
  • Not requiring seafloor echoes, the method could be used to estimate the depth and the range of foraging sperm whales using a single hydrophone in any location no matter what the depth, the relief, or the constitution of the seafloor might be.

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A Bayesian method to estimate the depth and the range
of phonating sperm whales using a single hydrophone
Christophe Laplanche
To cite this version:
Christophe Laplanche. A Bayesian method to estimate the depth and the range of phonating sperm
whales using a single hydrophone. Journal of the Acoustical Society of America, Acoustical Society
of America, 2007, Vol. 121, pp. 1519-1528. �10.1121/1.2436644�. �hal-00797709�

To link to this article: DOI: 10.1121/1.2436644
http://dx.doi.org/10.1121/1.2436644
This is an author-deposited version published in: http://oatao.univ-toulouse.fr/
Eprints ID: 5607
To cite this version: Laplanche, Christophe A Bayesian method to estimate the
depth and the range of phonating sperm whales using a single hydrophone.
(2007) The Journal of the Acoustical Society of America (JASA), 9ol. 121 (n°3).
pp. 1519-1528. ISSN 0001-4966
Open Archive Toulouse Archive Ouverte (OATAO)
OATAO is an open access repository that collects the work of Toulouse researchers and
makes it freely available over the web where possible.
Any correspondence concerning this service should be sent to the repository
administrator: staff-oatao@inp-toulouse.fr

A Bayesian method to estimate the depth and the range of
phonating sperm whales using a single hydrophone
Christophe Laplanche
a
Laboratoire Images, Signaux et Systèmes Intelligents, Groupe Ingénierie des Signaux Neuro-Sensoriels,
Université Paris 12, Créteil, France
Some bioacousticians have used a single hydrophone to calculate the depth/range of phonating
diving animals. The standard one-hydrophone localization method uses multipath transmissions
direct path, sea surface, and seafloor reflections of the animal phonations as a substitute for a
vertical hydrophone array. The standard method requires three multipath transmissions per
phonation. Bioacousticians who study foraging sperm whales usually do not have the required
amount of multipath transmissions. However, they usually detect accurately using shallow
hydrophones towed by research vessels direct path transmissions and sea surface reflections of
sperm whale phonations clicks. Sperm whales emit a few thousand clicks per foraging dive,
therefore researchers have this number of direct path transmissions and this number of sea surface
reflections per dive. The author describes a Bayesian method to combine the information contained
in those acoustic data plus visual observations. The author’s tests using synthetic data show that the
accurate estimation of the depth/range of sperm whales is possible using a single hydrophone and
without using any seafloor reflections. This method could be used to study the behavior of sperm
whales using a single hydrophone in any location no matter what the depth, the relief, or the
constitution of the seafloor might be.
DOI: 10.1121/1.2436644
I. INTRODUCTION
Sperm whales undertake long foraging dives to catch
their prey. They breathe at the sea surface, fluke-up and swim
downwards to reach their prey, hunt at depth, and reascend
back to the sea surface Miller et al., 2004a. During forag-
ing dives, sperm whales emit echolocation clicks Backus
and Schevill, 1966. They emit echolocation clicks at a tre-
mendous source level Møhl et al., 2003, 2000 and in series
Whitehead and Weilgart, 1990.
Since sperm whales emit long series of clicks of high
source level, passive acoustics is an effective tool to study
the foraging behavior of these animals. Researchers have de-
veloped and used different passive acoustic localization tech-
niques. These techniques require synchronous recordings
made on tridimensional Watkins and Schevill, 1972, bidi-
mensional Thode, 2004, or unidimensional Villadsgaard et
al., 2007 arrays of hydrophones.
To locate the sound source using an array of n hydro-
phones, one would need to isolate one signal emitted by the
source and to measure the n times of arrival TOA of this
signal on the n hydrophones of the array. The differences
between TOAs TOAD are calculated. A TOAD provides
information on the location of the source: The source is on a
sheet of a two-sheeted hyperboloid of geometry given by the
TOAD itself and by the location of the hydrophones used to
calculate the TOAD. By repeating this localization process
using different TOADs, one can, using the required amount
of hydrophones Spiesberger, 2001, geometrically or ana-
lytically compute the intersection of the hyperboloid sheets
and therefore be able to more accurately identify the location
of the source. A unidimensional array requires at least three
hydrophones Villadsgaard et al., 2007. In this case the hy-
perboloid sheets defined by the TOADs intersect into a
circle. The plane containing this circle is perpendicular to the
line of the array and the center of the circle sits on this line.
Therefore, if the unidimensional array is vertical, then the
circle is horizontal, and its depth and radius give the depth
and the horizontal range of the sound source.
One noteworthy passive acoustic localization technique
requires a single hydrophone see for instance Thode et al.
2002 or Laplanche et al. 2005兲兴. Signals emitted by sound
sources may reflect on the sea surface and the seafloor while
propagating to the hydrophones. The detection on a hydro-
phone of the echoes from the surface/seafloor serves as a
substitute of an unidimensional vertical hydrophone array
Urick, 1983. By measuring the TOADs of the echoes later
referred to as echo delays relative to the nonreflected trans-
mitted signal, one can calculate the depth and the range of
the sound source. If the source is a phonating sperm whale,
one can theoretically, by repeating the localization process
on every click emitted by the whale during a whole dive, plot
the values of the depth and the range of the whale during this
dive.
Unfortunately, sperm whales usually forage above con-
tinental slopes or abyssal plains, i.e., areas of either deep or
high relief seafloor. One can seldom clearly detect the seaf-
loor
echoes
of clicks emitted by sperm whale using a hydro-
phone close to the sea surface towed by a research vessel.
Usually one can only detect seafloor echoes at the beginning
a
Electronic mail: laplanche@gmail.com

of the whale’s dive, while the whale both swims and clicks
downwards Thode et al., 2002. In low sea state conditions,
using a hydrophone close to the sea surface, one can how-
ever clearly detect surface echoes during the whole dive see
for instance Thode 2004 or Laplanche et al. 2005兲兴.
Nevertheless, the measurement of a single echo delay
e.g., a surface echo delay is not enough to calculate the
depth and the range of a phonating sperm whale, since, as
aforementioned, by using a single TOAD one can only know
that the whale is on a hyperboloid sheet. Is it not possible to
more accurately identify the location of the whale using a
single hydrophone but not using seafloor echoes?
Actually one can still estimate the depth and the range of
the whale under such constraints, and the aim of this work is
to demonstrate the feasibility of this process. Every single
surface echo delay contains information regarding the loca-
tion of the whale. By combining the pieces of information
contained in the set of the surface echo delays of the clicks
emitted by the whale during a dive, one should be able to
give a more accurate description of the location of the whale
during this dive. The efficient combining of these pieces of
information can be achieved in a Bayesian frame. The Baye-
sian approach has already proven to be efficient to locate
sound sources using TOADs Spiesberger, 2005.
The author proposes a Bayesian passive acoustic tech-
nique to estimate the depth and the range of foraging sperm
whales using a single hydrophone and without using any
seafloor echoes. To be applied, this technique requires a set
of values of the sea surface echo delay of clicks emitted by
the whale during the whole dive. It also requires the mea-
surement, by a visual observer, of the approximate location
i.e., range and azimuth relative to the research vessel of the
whale when beginning and ending the foraging dive.
II. MATERIAL AND METHODS
A. Trajectory model
First, one would need a representation of the underwater
trajectory of the whale using a mathematical model. Let t
A
be the time when the sperm whale flukes-up and starts div-
ing, t
B
the time when the whale starts clicking, t
C
the time
when the whale stops clicking, and t
D
the time when the
whale resurfaces. The author will decompose the trajectory
of the whale for t t
B
,t
C
into nN
*
pieces of equal
duration
0
=t
C
t
B
/ n Fig. 1. Let t
s
=t
B
+s−1
0
be
the time when the whale is at the beginning s 1,... ,n其兲
or at the end s 2,...,n+1其兲 of such trajectory pieces.
Let E
s
be the location in the terrestrial reference frame,
let z
s
be the depth, let r
s
be the horizontal range, and let
s
be the azimuth of the whale at time t
s
Fig. 2. Let E
p
s
and H
p
be the projections of E
s
and H H is the location of
the hydrophone on a horizontal plane. Let
b
s
and
e
s
be
the angles E
p
s
E
p
s+1
,H
p
E
p
s
and E
p
s
E
p
s+1
,H
p
E
p
s+1
.
The value of n is chosen high enough to be able to
assume that the whale moves at constant speed and
constant heading for t t
s
,t
s+1
兴共s 1, ... ,n其兲. Let S
s
=E
s
E
s+1
be the segment defining the location of the
whale for t t
s
,t
s+1
. Let
v
z
s
R and
v
r
s
R
+
be the
vertical and horizontal speeds of the whale along the seg-
ment S
s
. Each segment S
s
is entirely defined by the coor-
dinates of the points E
s
and E
s+1
. The trajectory of the
whale for t t
B
,t
C
is labeled T. It itself is entirely de-
fined by the location of the summits E
s
s 1, ...,n +1其兲,
that is to say T ⬅共E
1
, ...,E
n+1
. This definition of T re-
quires, by using the coordinates in the terrestrial reference
frame of the n+ 1 summits E
s
, a set of 3n +3 parameters.
One can then define the trajectory T using the depth,
range, and heading of the whale. Each segment S
s
can be
recursively defined by writing
S
1
⬅共z
1
,r
1
,
1
,z
2
,r
2
,
b
1
,
S
s
⬅共z
s+1
,r
s+1
,
b
s
S
s−1
for s 2, ... ,n其共1
and the trajectory T is entirely defined by the set of 3n +3
parameters
T ⬅共z
1
,r
1
,
1
,z
2
,r
2
,
b
1
, ... ,z
n+1
,r
n+1
,
b
n
. 2
This leads to the following definition of T, which is
required by the algorithm described later. Let
s
=
b
s
e
s−1
be the change of heading of the whale at time t
s
.
Each segment S
s
is recursively defined by writing
FIG. 1. The whale dives at t = t
A
, starts clicking at t= t
B
, stops clicking at
t=t
C
, and resurfaces at t=t
D
. The whale is at the depth z =z
s
at t=t
s
=t
B
+s −1
0
s 1, ... ,n+1, in this example n =14. The vertical speed
of the whale is constant and equal to
v
z
s
for t t
s
,t
s+1
.
FIG. 2. The whale is at E
s
at t= t
s
. The hydrophone is at H. E
p
s
and H
p
are
the projections of E
s
and H on a horizontal plane. The whale moves in a
constant heading and at a constant horizontal speed
v
r
s
for t t
s
,t
s+1
along the segment S
s
to reach E
s+1
at t = t
s+1
. The angles
b
s
and
e
s
are
defined as the angles between E
p
s
E
p
s+1
and H
p
E
p
s
, and E
p
s
E
p
s+1
and
H
p
E
p
s+1
, respectively. The change of heading from S
s−1
to S
s
is
s
=
e
s−1
b
s
. The horizontal range and the azimuth of the whale at t=t
s
are
r
s
=H
p
E
p
s
and
s
, respectively.

S
1
⬅共z
1
,r
1
,
1
,
v
z
1
,
v
r
1
,
b
1
,
S
s
⬅共
v
z
s
,
v
r
s
,
s
S
s−1
for s 2, ... ,n其共3
given the coordinates of the first summit E
1
, defined by the
triplet z
1
,r
1
,
1
兲兴 and the whale vertical speed, horizon-
tal speed, and change of heading in the n segments. Alterna-
tively, given the coordinates of the last summit E
n+1
, de-
fined by the triplet z
n+1
,r
n+1
,
n+1
兲兴 and the whale
vertical speed, horizontal speed, and change of heading in
the n segments, each segment S
s
is recursively defined by
writing
S
s
⬅共
v
z
s
,
v
r
s
,
s+1
S
s+1
for s 1, ... ,n −1
S
n
⬅共
v
z
n
,
v
r
n
,
e
n
,z
n+1
,r
n+1
,
n+1
. 4
Using Eq. 3, the trajectory T is also entirely defined by the
set of 3n +3 parameters
T ⬅共z
1
,r
1
,
1
,
v
z
1
,
v
r
1
,
b
1
,
v
z
2
,
v
r
2
,
2
, ... ,
v
z
n
,
v
r
n
,
r
n
, 5
or alternatively, using Eq. 4, by the set of 3n+3 parameters
T ⬅共
v
z
1
,
v
r
1
,
2
, ... ,
v
z
n−1
,
v
r
n−1
,
n
,
v
z
n
,
v
r
n
,
e
n
,z
n+1
,r
n+1
,
n+1
. 6
B. Prior information
By choosing
1
=0,
n+1
represents the change in azi-
muth of the whale, relative to the research vessel, between
the points E
1
and E
n+1
. One can combine the definitions of
T given in Eqs. 5 and 6 by writing
T ⬅共z
1
,r
1
,
v
z
1
,
v
r
1
,
b
1
,
v
z
2
,
v
r
2
,
2
, ... ,
v
z
n
,
v
r
n
,
n
,z
n+1
,r
n+1
,
n+1
. 7
This latter definition of T uses 3n+5 parameters, that is to
say redundant information. Indeed, the coordinates of the last
summit E
n+1
can be calculated given the coordinates of the
first summit E
1
and using the values of speeds and change
of heading in the n segments. The consequences of such
redundancy during the estimation process will be discussed
later. The aim of combining Eqs. 5 and 6 into Eq. 7 is to
gather in a single definition of T information on the fluking
and resurfacing points of the whale.
Let z
A
=0 m and r
A
be the depth and the horizontal
range of the whale at the time t=t
A
. Let
v
z
A
and
v
r
A
be the
average vertical and horizontal speeds of the whale for t
t
A
,t
B
. The depth and the range of the whale at t =t
1
are then z
1
=
v
z
A
t
B
t
A
and r
1
. Let z
D
=0 m and r
D
be
the depth and the horizontal range of the whale at the time
t= t
D
. Let
v
z
D
and
v
r
D
be the average vertical and horizon-
tal speeds of the whale for t t
C
,t
D
. The depth and the
range of the whale at t =t
n+1
are then z
n+1
=
v
z
D
t
C
t
D
and r
n+1
.
Sperm whales initiate their foraging dives by fluking-up
and diving vertically as observed from the sea surface at the
very beginning of the dive . Sperm whales usually keep a
constant vertical speed while descending to reach their prey
and while ascending to reach the sea surface Miller et al.,
2004a. There are exceptions however: Sperm whales may,
for instance, horizontally translate during the ascent likely
due to the presence of conspecifics Miller et al., 2004b.
Assuming that the main objective of the whale while de-
scending is to reach bathypelagic prey and that the main
objective of the whale while ascending is to reach oxygen at
the sea surface, the whale would swim vertically for t
t
A
,t
B
and t t
C
,t
D
. This leads to
v
r
A
0ms
−1
,
v
r
D
0ms
−1
, r
1
r
A
, and r
n+1
r
D
. In this case
n+1
represents the change in azimuth of the whale, relative to the
research vessel, between the fluking and the resurfacing
points. A visual observer can measure the parameters z
1
,
r
1
, z
n+1
, r
n+1
, and
n+1
from the research vessel. The
visual measurement process is inaccurate and thus results in
uncertainties on z
1
, r
1
, z
n+1
, r
n+1
, and
n+1
. The as-
sumption of verticalness may also be inaccurate resulting in
additional uncertainties on these variables. Such uncertain-
ties are modeled in the following section using random vari-
ables.
C. Likelihood
The algorithm which will be described later is used to
estimate the depth and the range of the whale during a dive,
and requires the set of values of the surface echo delay of the
clicks that the whale has emitted during this dive. Let K
e
be
the number of echolocation clicks that the whale has emitted
for t t
B
,t
C
. The author assumes that KK
e
clicks are
correctly detected both the direct path and the surface echo.
The observer then makes K consistent measurements of the
surface echo delay at the time t t
1
, ...,t
K
其共t
1
=t
B
and t
K
=t
C
, labeled
M =
t
1
, ... ,
t
K
兲兲. 8
The trajectory model T is close to the true trajectory that
the whale follows. Let fT , t
k
be the value at the time t
k
k
1,...,K其兲 of the surface echo delay if the whale were on
the trajectory T. Such value, however, due to the inaccuracy
of the measurement and modeling processes, is not exactly
equal to
t
k
. The difference
T , t
k
between the data
t
k
and the model fT,t
k
is defined as
t
k
= fT,t
k
+
T,t
k
for k 1, ... ,K. 9
The author assumes that the errors due to such inaccu-
racies are centered the mean of the error is equal to zero,
independent the error made on
t
i
is independent with the
error made on
t
j
, i j, and of equal variance. In that case,
one can model the above-described inaccuracies by interpret-
ing them as an additive white Gaussian noise. Let
be a
centered, white Gaussian noise of standard deviation
,
N0,
.
The noise on the data is modeled using the random vari-
able
. One can model the fluctuations in the values of the
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Citations
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Journal ArticleDOI
TL;DR: This work combines the SSMC algorithm and a fast search algorithm in order to efficiently determine decay parameters, their uncertainties, and inter-relationships with a minimum amount of required user tuning and interaction.
Abstract: Room-acoustic energy decay analysis of acoustically coupled-spaces within the Bayesian framework has proven valuable for architectural acoustics applications. This paper describes an efficient algorithm termed slice sampling Monte Carlo (SSMC) for room-acoustic decay parameter estimation within the Bayesian framework. This work combines the SSMC algorithm and a fast search algorithm in order to efficiently determine decay parameters, their uncertainties, and inter-relationships with a minimum amount of required user tuning and interaction. The large variations in the posterior probability density functions over multidimensional parameter spaces imply that an adaptive exploration algorithm such as SSMC can have advantages over the exiting importance sampling Monte Carlo and Metropolis–Hastings Markov Chain Monte Carlo algorithms. This paper discusses implementation of the SSMC algorithm, its initialization, and convergence using experimental data measured from acoustically coupled-spaces.

23 citations


Journal ArticleDOI
TL;DR: Three dimensional measurement of tracking revealed several different "foraging" strategies, including active chasing of prey, lining up slow-moving or unsuspecting prey, and foraging on demersal or benthic prey.
Abstract: Observations are presented of the vocal behavior and three dimensional (3D) underwater movements of sperm whales measured with a passive acoustic array off the coast of Kaikoura, New Zealand. Visual observations and vocal behaviors of whales were used to divide dive tracks into different phases, and depths and movements of whales are reported for each of these phases. Diving depths and movement information from 75 3D tracks of whales in Kaikoura are compared to one and two dimensional tracks of whales studied in other oceans. While diving, whales in Kaikoura had a mean swimming speed of 1.57 m/s, and, on average, dived to a depth of 427 m (SD = 117 m), spending most of their time at depths between 300 and 600 m. Creak vocalizations, assumed to be the prey capture phase of echolocation, occurred throughout the water column from sea surface to sea floor, but most occurred at depths of 400–550 m. Three dimensional measurement of tracking revealed several different “foraging” strategies, including active chasing of prey, lining up slow-moving or unsuspecting prey, and foraging on demersal or benthic prey. These movements provide the first 3D descriptions underwater behavior of whales at Kaikoura.

16 citations


Cites methods from "A Bayesian method to estimate the d..."

  • ...Markov-chain Monte-Carlo localization algorithms, such as that described by Laplanche (2007), or state-space algorithms, such as those employed by Jonsen et al. (2003, 2005) or Tremblay et al. (2009) may be able to take advantage of these additional data, thus such algorithms should be considered…...

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ReportDOI
01 Jan 2014
Abstract: New underwater passive sonar techniques are developed for enhancing target localization capabilities in shallow ocean environments. The ocean surface and the seabed act as acoustic mirrors that reflect sound created by boats or subsurface vehicles, which gives rise to echoes that can be heard by hydrophone receivers (underwater microphones). The goal of this work is to leverage this “multipath” phenomenon in new ways to determine the origin of the sound, and thus the location of the target. However, this is difficult for propeller driven vehicles because the noise they produce is both random and continuous in time, which complicates its measurement and analysis. Further, autonomous underwater vehicles (AUVs) pose additional challenges because very little is known about the sound they generate, and its similarity to that of boats. Existing methods for localizing propeller noise using multiple hydrophones have approached the problem either purely theoretically, or empirically such as by analyzing the interference patterns between multipath arrivals at different frequencies, however little has been published on building localization techniques that directly measure and utilize the time delays between multipath arrivals while simultaneously accounting for relevant environmental parameters. This research develops such techniques through a combination of array beamforming and advanced ray-based modeling that account for variations in bathymetry (seabed topography) as well as variations of the sound speed of the water. The basis for these advances come from several at-sea experiments in which different configurations of passive sonar systems recorded sounds emitted by different types of targets, including small boats and an autonomous underwater vehicle. Ultimately, these contributions may reduce the complexity and cost of passive systems that need to be deployed close to shore, such as for harbor security applications. Further, they also create new possibilities i for applying passive sonar in remote ocean regions for tasks such as detecting illegal fishing activity. This dissertation makes three key contributions: 1. Analysis of the aspect-dependent acoustic radiation patterns of an underway autonomous underwater vehicle (AUV) through full-field wave modeling. 2. A two-hydrophone cross-correlation technique that leverages multipath as well as bathymetric variations to estimate the range and bearing of a small boat, supported by a mathematically rigorous performance analysis. 3. A multi-target localization technique based on directly measuring multipath from multiple small surface vessels using a small hydrophone array mounted to the nose of an AUV, which operates by cross-correlating two elevation beams on a single bearing.

11 citations


Journal ArticleDOI
Abstract: (2009). RECENT BIOACOUSTIC PUBLICATIONS (2008 and earlier) Bioacoustics: Vol. 18, No. 3, pp. 291-318.

7 citations


Journal ArticleDOI
TL;DR: The author describes and evaluates a Bayesian method to reconstruct three-dimensional toothed whale trajectories from a series of echolocation signals which renews passive acoustics as a valuable tool to study the underwater behavior of toother whales.
Abstract: The author describes and evaluates a Bayesian method to reconstruct three-dimensional toothed whale trajectories from a series of echolocation signals. Localization by using passive acoustic data (time of arrival of source signals at receptors) is assisted by using visual data (coordinates of the whale when diving and resurfacing) and tag information (movement statistics). The efficiency of the Bayesian method is compared to the standard minimum mean squared error statistical approach by comparing the reconstruction results of 48 simulated sperm whale (Physeter macrocephalus) trajectories. The use of the advanced Bayesian method reduces bias (standard deviation) with respect to the standard method up to a factor of 8.9 (13.6). The author provides open-source software which is functional with acoustic data which would be collected in the field from any three-dimensional receptor array design. This approach renews passive acoustics as a valuable tool to study the underwater behavior of toothed whales.

7 citations


Cites background or methods from "A Bayesian method to estimate the d..."

  • ...(2012) The Journal of the Acoustical Society of America, vol. 132 (n°5). pp. 3225-3233....

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  • ...Visual methods use photo-identification to differentiate individuals, map their surface movements, and catalogue their clustering preferences (Whitehead, 2003, pp. 206–285)....

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  • ...Electronic mail: laplanche@gmail.com procedure (Davis and Pitre, 1995; Laplanche, 2007; Tiemann et al., 2006)....

    [...]


References
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Journal ArticleDOI
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Abstract: The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.

12,022 citations


"A Bayesian method to estimate the d..." refers methods in this paper

  • ...It could be interesting to use a convergence diagnosis, like the criterion proposed by Gelman and Rubin 1992 , to make a more rigorous choice of I1 and I2 ....

    [...]


Journal ArticleDOI
TL;DR: On-axis click properties support previous work proposing the nose of sperm whales to operate as a generator of sound.
Abstract: Traditionally, sperm whale clicks have been described as multipulsed, long duration, nondirectional signals of moderate intensity and with a spectrum peaking below 10 kHz. Such properties are counterindicative of a sonar function, and quite different from the properties of dolphin sonar clicks. Here, data are presented suggesting that the traditional view of sperm whale clicks is incomplete and derived from off-axis recordings of a highly directional source. A limited number of assumed on-axis clicks were recorded and found to be essentially monopulsed clicks, with durations of 100 micros, with a composite directionality index of 27 dB, with source levels up to 236 dB re: 1 microPa (rms), and with centroid frequencies of 15 kHz. Such clicks meet the requirements for long-range biosonar purposes. Data were obtained with a large-aperture, GPS-synchronized array in July 2000 in the Bleik Canyon off Vesteralen, Norway (69 degrees 28' N, 15 degrees 40' E). A total of 14 h of sound recordings was collected from five to ten independent, simultaneously operating recording units. The sound levels measured make sperm whale clicks by far the loudest of sounds recorded from any biological source. On-axis click properties support previous work proposing the nose of sperm whales to operate as a generator of sound.

271 citations


"A Bayesian method to estimate the d..." refers background in this paper

  • ...They emit echolocation clicks at a tremendous source level Møhl et al., 2003, 2000 and in series Whitehead and Weilgart, 1990 ....

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Journal ArticleDOI
TL;DR: The hypothesis that creaks are an echolocation signal adapted for foraging, analogous to terminal buzzes in taxonomically diverse echlocating species, strongly support the hypothesis thatcreaks are produced during prey capture.
Abstract: During foraging dives, sperm whales (Physeter macrocephalus) produce long series of regular clicks at 0.5-2 s intervals interspersed with rapid-click buzzes called "creaks". Sound, depth and orientation recording Dtags were attached to 23 whales in the Ligurian Sea and Gulf of Mexico to test whether the behaviour of diving sperm whales supports the hypothesis that creaks are produced during prey capture. Sperm whales spent most of their bottom time within one or two depth bands, apparently feeding in vertically stratified prey layers. Creak rates were highest during the bottom phase: 99.8% of creaks were produced in the deepest 50% of dives, 57% in the deepest 15% of dives. Whales swam actively during the bottom phase, producing a mean of 12.5 depth inflections per dive. A mean of 32% of creaks produced during the bottom phase occurred within 10 s of an inflection (13x more than chance). Sperm whales actively altered their body orientation throughout the bottom phase with significantly increased rates of change during creaks, reflecting increased manoeuvring. Sperm whales increased their bottom foraging time when creak rates were higher. These results all strongly support the hypothesis that creaks are an echolocation signal adapted for foraging, analogous to terminal buzzes in taxonomically diverse echolocating species.

249 citations


"A Bayesian method to estimate the d..." refers background in this paper

  • ...There are exceptions however: Sperm whales may, for instance, horizontally translate during the ascent likely due to the presence of conspecifics Miller et al., 2004b ....

    [...]

  • ...Sperm whales usually keep a constant vertical speed while descending to reach their prey and while ascending to reach the sea surface Miller et al., 2004a ....

    [...]

  • ...They breathe at the sea surface, fluke-up and swim downwards to reach their prey, hunt at depth, and reascend back to the sea surface Miller et al., 2004a ....

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Journal ArticleDOI
TL;DR: Previously published properties of sperm whale clicks underestimate the capabilities of the sound generator and therefore cannot falsify the Norris and Harvey theory.
Abstract: In sperm whales (Physeter catodon L. 1758) the nose is vastly hypertrophied, accounting for about one-third of the length or weight of an adult male. Norris and Harvey [in Animal Orientation and Navigation, NASA SP-262 (1972), pp. 397–417] ascribed a sound-generating function to this organ complex. A sound generator weighing upward of 10 tons and with a cross-section of 1 m is expected to generate high-intensity, directional sounds. This prediction from the Norris and Harvey theory is not supported by published data for sperm whale clicks (source levels of 180 dB re 1 μPa and little, if any, directionality). Either the theory is not borne out or the data is not representative for the capabilities of the sound-generating mechanism. To increase the amount of relevant data, a five-hydrophone array, suspended from three platforms separated by 1 km and linked by radio, was deployed at the slope of the continental shelf off Andenes, Norway, in the summers of 1997 and 1998. With this system, source levels up to 223 dB re 1 μPa peRMS were recorded. Also, source level differences of 35 dB for the same click at different directions were seen, which are interpreted as evidence for high directionality. This implicates sonar as a possible function of the clicks. Thus, previously published properties of sperm whale clicks underestimate the capabilities of the sound generator and therefore cannot falsify the Norris and Harvey theory.

213 citations


"A Bayesian method to estimate the d..." refers background in this paper

  • ...They emit echolocation clicks at a tremendous source level Møhl et al., 2003, 2000 and in series Whitehead and Weilgart, 1990 ....

    [...]


Journal ArticleDOI
TL;DR: Whales glides more during portions of dives when buoyancy aided their movement, and whales that glided more during ascent glided less during descent (and vice versa), supporting the hypothesis that buoyancy influences behavioural swimming decisions.
Abstract: SUMMARY Drag and buoyancy are two primary external forces acting on diving marine mammals. The strength of these forces modulates the energetic cost of movement and may influence swimming style (gait). Here we use a high-resolution digital tag to record depth, 3-D orientation, and sounds heard and produced by 23 deep-diving sperm whales in the Ligurian Sea and Gulf of Mexico. Periods of active thrusting versus gliding were identified through analysis of oscillations measured by a 3-axis accelerometer. Accelerations during 382 ascent glides of five whales (which made two or more steep ascents and for which we obtained a measurement of length) were strongly affected by depth and speed at Reynold9s numbers of 1.4–2.8×107. The accelerations fit a model of drag, air buoyancy and tissue buoyancy forces with an r2 of 99.1–99.8% for each whale. The model provided estimates (mean ± s.d.) of the drag coefficient (0.00306±0.00015), air carried from the surface (26.4±3.9 l kg-3 mass), and tissue density (1030±0.8 kg m-3) of these five animals. The model predicts strong positive buoyancy forces in the top 100 m of the water column, decreasing to near neutral buoyancy at 250–850 m. Mean descent speeds (1.45±0.19 m s-1) were slower than ascent speeds (1.63±0.22 m s-1), even though sperm whales stroked steadily (glides 5.3±6.3%) throughout descents and employed predominantly stroke-and-glide swimming (glides 37.7±16.4%) during ascents. Whales glided more during portions of dives when buoyancy aided their movement, and whales that glided more during ascent glided less during descent (and vice versa), supporting the hypothesis that buoyancy influences behavioural swimming decisions. One whale rested at∼ 10 m depth for more than 10 min without fluking, regulating its buoyancy by releasing air bubbles.

206 citations


"A Bayesian method to estimate the d..." refers background in this paper

  • ...There are exceptions however: Sperm whales may, for instance, horizontally translate during the ascent likely due to the presence of conspecifics Miller et al., 2004b ....

    [...]

  • ...Sperm whales usually keep a constant vertical speed while descending to reach their prey and while ascending to reach the sea surface Miller et al., 2004a ....

    [...]

  • ...They breathe at the sea surface, fluke-up and swim downwards to reach their prey, hunt at depth, and reascend back to the sea surface Miller et al., 2004a ....

    [...]


Frequently Asked Questions (1)
Q1. What are the contributions in "A bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone" ?

Laplanche et al. this paper proposed a Bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone.