OCS Study

BOEM 2020-011

US Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

A Parametric Analysis and 

Sensitivity Study of the Acoustic 

Propagation

for Renewable Energy 
OCS Study

BOEM 2020-011

US Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

A Parametric Analysis and 

Sensitivity Study of the Acoustic 

Propagation

for Renewable Energy Sources

10 February 2020

Authors:

Kevin D. Heaney, Michael A. Ainslie, Michele B. Halvorsen, Kerri D. Seger, Roel A.J. Müller, 

Marten J.J. Nijhof, Tristan Lippert

Prepared under BOEM Award M17PD00003

Submitted by:

CSA Ocean Sciences Inc.

8502 SW Kansas Avenue

Stuart, Florida 34997

Telephone: 772-219-3000
DISCLAIMER

Study concept, oversight, and funding were provided by the U.S. Department of the Interior, Bureau of

Ocean Energy Management (BOEM), Environmental Studies Program, Washington, D.C., under Contract

Number M15PC00011. This report has been technically reviewed by BOEM, and it has been approved

for publication. The views and conclusions contained in this document are those of the authors and should

not be interpreted as representing the opinions or policies of the U.S. Government, nor does mention of

trade names or commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the U.S. Department of the Interior, Bureau of Ocean Energy

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and

search on 2020-011. The report is also available at the National Technical Reports Library at

https://ntrl.ntis.gov/NTRL/.

CITATION

Heaney KD, MA Ainslie, MB Halvorsen, KD Seger, RAJ Müller, MJJ Nijhof, T Lippert. 2020. 

A Parametric Analysis and Sensitivity Study of the Acoustic Propagation for Renewable Energy 

Sources. Sterling (VA): U.S. Department of the Interior, Bureau of Ocean Energy Management. 

Prepared by CSA Ocean Sciences Inc. OCS Study BOEM 2020-011. 165 p.

ABOUT THE COVER

Cover graphic by Kerri Seger and Kevin Heaney, based on Figure 6-22. Propagation modeling performed 

by Peregrine software of Applied Ocean Sciences, LLC. Plotting performed through Jupyter notebooks 

with Python environment in Miniconda 4.7.10.

ACKNOWLEDGMENTS

We thank Dr. Stephan Lippert (Hamburg University of Technology) for providing data from the 2014 

COMPILE workshop (Lippert et al., 2016). We thank Dr. Martine Graafland (Directorate-General for 

Public Works and Water Management) for permission to use the TNO report TNO 2016 R11338 

(Binnerts et al., 2016) describing a validation study for the Gemini and Luchterduinen wind farms. We 

thank Dr. Luuk Folkerts (Ecofys) and Dr. Sytske van den Akker (ENECO) for permission to use data 

from the Gemini and Luchterduinen wind farms. We thank the CSA document production team for their 

attention to fine details.
i

Contents

List of Figures............................................................................................................................................... iv

List of Tables................................................................................................................................................ ix

List of Acronyms, Symbols, and Abbreviations............................................................................................ xi

Extended Abstract......................................................................................................................................... 1

1 Introduction.......................................................................................................................................... 4

1.1 Background................................................................................................................................ 4

1.1.1 Project Team.................................................................................................................. 4

1.1.2 Project Scope, Objectives, and Framework................................................................... 5

1.1.3 Technical Foundation..................................................................................................... 6

1.2 Overview of Acoustic Propagation Models................................................................................ 8

1.2.1 State-of-the-art Underwater Acoustic Models ................................................................8

1.2.2 Recommended Project Model: Peregrine PE-RAM model ..........................................11

1.3 Environmental and Source Parameters ..................................................................................11

1.3.1 Water Depth and Bathymetry.......................................................................................12

1.3.2 Sediment Type .............................................................................................................13

1.3.3 Sound Speed Profile in Water......................................................................................15

1.3.4 Wind Speed..................................................................................................................15

1.3.5 Source Parameters ......................................................................................................16

1.3.6 Summary of Environmental Parameters ......................................................................18

1.4 Propagation Physics................................................................................................................18

1.4.1 Short-range Propagation..............................................................................................18

1.4.2 Long-range Propagation ..............................................................................................20

1.5 Acoustic Threshold Criteria .....................................................................................................21

1.5.1 Marine Mammals..........................................................................................................21

1.5.2 Fishes and Sea Turtles ................................................................................................23

2 Metrics ...............................................................................................................................................25

2.1 Accumulation of Sound Exposure ...........................................................................................26

2.2 Applicability of Frequency Weighting ......................................................................................26

3 Damped Cylindrical Spreading in Excel ............................................................................................28

3.1 Practical Spreading Loss Model..............................................................................................28

3.2 Transmission Loss and Propagation Loss ..............................................................................28

3.2.1 Practical Spreading Loss Model ..................................................................................29

3.2.2 Damped Cylindrical Spreading Model for LLEE................................................................33

3.3 Empirical Regressions for LLpp,pppp and LLpp,rrrrrr ..............................................................................37

3.3.1 Regressions Based on Lippert et al. (2015).................................................................37

3.3.2 Regressions Based on Luchterduinen Measurements ................................................38

3.3.3 Regressions Based on Combining all Four Data Sets.................................................47

3.3.4 Extrapolating to Long Range........................................................................................47

3.4 Recommendations for Predicting LLEE, LLpp,pppp, and LLpp,rrrr ............................................................47

3.5 Validation.................................................................................................................................51

3.5.1 Borkum Riffgrund 1 ......................................................................................................51
ii

3.5.2 Gemini..........................................................................................................................53

3.5.3 DCS-based Spreadsheet (DCSiE)...............................................................................54

3.6 Conclusions .............................................................................................................................57

4 Model Verification and Validation Methods .......................................................................................58

4.1 Finite Element Method to Estimate the Sound Field Around the Source................................60

4.2 Parabolic Equation Method .....................................................................................................62

4.2.1 Propagation..................................................................................................................62

4.2.2 Integration with Finite Element Starter Field................................................................62

4.3 Model Verification – COMPILE I..............................................................................................62

4.3.1 Pile Characterization and Forcing Function.................................................................63

4.3.2 Propagation Medium ....................................................................................................65

5 Verification and Validation Results....................................................................................................66

5.1 Model Verification Results for COMPILE I ..............................................................................66

5.1.1 Comparisons ................................................................................................................67

5.1.2 Weighted SEL Comparisons........................................................................................72

5.1.3 Frequency Accumulation of Weighted Sound Exposure Level....................................74

5.2 Model Validation for Gemini Measurements: Piles U8 and Z2................................................75

5.2.1 Inputs – Gemini ............................................................................................................75

5.2.2 Data Analysis ...............................................................................................................79

5.2.3 Z2 Pile Comparison......................................................................................................93

5.3 Summary of Model Verification and Validation........................................................................98

5.3.1 Verification....................................................................................................................98

5.3.2 Validation......................................................................................................................98

6 Sensitivity Study ................................................................................................................................98

6.1 Sensitivity Study Methods .......................................................................................................98

6.1.1 Influence and Rationale of Selected Environmental Parameters ................................99

6.2 Acoustic Propagation Modeling Methods ..............................................................................100

6.2.1 Environment and Variables Selection ........................................................................100

6.2.2 Methodology for 17.5 to 2,238.5 Hz...........................................................................105

6.2.3 Methodology for 2,238.5 Hz to 20,000 Hz .................................................................105

6.2.4 Modeling Results for Acoustic Propagation from 17.5 to 2,238.5 Hz ........................109

6.2.5 Statistical Characteristics ...........................................................................................115

6.2.6 Sound Exposure Level Results as Plan View Plots...................................................123

6.3 Impact Volumes.....................................................................................................................125

6.3.1 Applying Dual Impact Criteria.....................................................................................125

6.3.2 Impact Volumes for Marine Mammals .......................................................................126

6.3.3 Impact Volumes for Fishes and Sea Turtles ..............................................................136

6.3.4 Impact Versus Vibratory Piles....................................................................................149

6.4 Sensitivity Study Conclusions................................................................................................153

7 Overall Conclusions ........................................................................................................................155

7.1 Damped Cylindrical Spreading in Excel ................................................................................155

7.2 Model Verification and Validation Methods ...........................................................................155

7.2.1 Verification..................................................................................................................155

7.2.2 Validation....................................................................................................................156

7.3 Sensitivity Study ....................................................................................................................156

8 Recommendations for Practical Application of Sensitivity Results .................................................158
iii

9 Bibliography.....................................................................................................................................160

Appendix A: Definitions ............................................................................................................................. A-1

Appendix B: Extracting Aggregated Quantities for the Hammer Strikes .................................................. B-1

Appendix C: Borkum Riffgrund 1 Measurements ..................................................................................... C-1
iv

List of Figures

Figure 1-1. Dependence of one-third octave band SEL on sediment properties..................................14

Figure 1-2. Total signal attenuation after multiple surface reflections in the Bristol Channel 

between May and June 1969. ............................................................................................16

Figure 1-3. Dependence of one-third octave band single strike SEL on pile penetration 

depth...................................................................................................................................17

Figure 1-4. Dependence of one-third octave band single strike SEL on pile diameter, based 

on U8 (7 m) and B3 (2.4 m) data........................................................................................18

Figure 1-5. Illustration of a Mach cone (From: Dahl et al., 2015). ........................................................19

Figure 1-6. Auditory frequency weighting functions..............................................................................23

Figure 3-1. Equation for transmission loss used by the Practical Spreading Loss Model 

(From: Caltrans, 2015). ......................................................................................................30

Figure 3-2. SEL versus range using the PSLM.....................................................................................31

Figure 3-3. Geographical location of the Borkum Riffgrund 1 wind farm in the German North 

Sea. ....................................................................................................................................33

Figure 3-4. SEL versus range using the DCS model............................................................................35

Figure 3-5. COMPILE: SEL versus range using the DCS model..........................................................36

Figure 3-6. Scattergram of LLpp,pppp−pppp versus SEL ...................................................................................37

Figure 3-7. SEL at Luchterduinen wind farm. .......................................................................................39

Figure 3-8. Location of the relevant wind farms....................................................................................40

Figure 3-9. Linear regression analysis comparing SEL and LLpp,pppp.........................................................41

Figure 3-10. Correlations at Luchterduinen EL39...................................................................................43

Figure 3-11. Correlations at Luchterduinen EL42...................................................................................43

Figure 3-12. Correlations at Luchterduinen EL39 and EL42. .................................................................44

Figure 3-13. Correlations at Luchterduinen EL42..................................................................................45

Figure 3-14. Correlations at Luchterduinen EL42 and EL39. .................................................................46

Figure 3-15. Correlations at Luchterduinen EL42 and EL39. .................................................................46

Figure 3-16. Correlations at Luchterduinen EL39 and EL42. .................................................................47

Figure 3-17. Worked example of extrapolation from LLEE..........................................................................51
v

Figure 3-18. Single strike LLEE and LLpp,pppp versus range at Borkum Riffgrund 1..........................................52

Figure 3-19. Measured LLpp,pppp versus LLEE at Gemini wind farm..................................................................54

Figure 3-20. Input parameters for the DCSiE model. .............................................................................55

Figure 3-21. DCSiE model example outputs for MF cetacean criteria. ..................................................57

Figure 4-1. Location of the Gemini wind farm.......................................................................................60

Figure 4-2. Schematic overview of a Finite Element model..................................................................61

Figure 4-3. COMPILE I forcing function ................................................................................................64

Figure 5-1. COMPILE I output grid for distant receivers.......................................................................67

Figure 5-2. Verification of FE/PE with the FE/NM models based on the COMPILE I 

workshop. ...........................................................................................................................67

Figure 5-3. COMPILE I participants and models. .................................................................................68

Figure 5-4. Verification of FE/PE with the COMPILE I workshop results for SEL versus 

range...................................................................................................................................69

Figure 5-5. Verification of FE/PE with the COMPILE I workshop results for LLpp,pppp................................71

Figure 5-6. Comparison of the decidecade band SEL computation for LF cetaceans. 

Decidecade band nominal center frequencies are 8 Hz to 2 kHz. .....................................72

Figure 5-7. Comparison of the decidecade band SEL computation for MF cetaceans. .......................73

Figure 5-8. Comparison of the decidecade band SEL computation for HF cetaceans.........................73

Figure 5-9. Comparison of the frequency-accumulated SEL (faSEL) computation for LF 

cetaceans. ..........................................................................................................................74

Figure 5-10. Comparison of the frequency-accumulated SEL (faSEL) computation for MF 

cetaceans. ..........................................................................................................................75

Figure 5-11. Definition of the pile geometries. ........................................................................................76

Figure 5-12. Sketch and detail of the anvil..............................................................................................78

Figure 5-13. Axisymmetric representation of the meshed pile, anvil, and environments. ......................79

Figure 5-14. Sound pressure time series, p(t), recorded at 732 m range, for the U8 pile. .....................80

Figure 5-15. Measured acoustic metrics for the U8 pile at 732 m range................................................81

Figure 5-16. Normalized likelihood for LLpp,pppp, for the U8 pile at 732 m range..........................................81

Figure 5-17. Acoustic metrics for the U8 pile at 7 km range...................................................................82

Figure 5-18. Normalized likelihood statistics for LLpp,pppp, for the U8 pile at 7 km range. ............................82
vi

Figure 5-19. Acoustic metrics for the U8 pile at 32 km range.................................................................83

Figure 5-20. Acoustic metrics for the U8 pile at 66 km range.................................................................84

Figure 5-21. Wentworth grain size (φφ) distribution table. .......................................................................85

Figure 5-22. Hamilton-Bachman sediment profile of compressional speed for hard sand (0 φφ)............86

Figure 5-23. Modeled broadband SELs versus range. ...........................................................................87

Figure 5-24. Comparison of measured (dots) and modeled (lines) SEL for hard sand (0 φφ).................88

Figure 5-25. Comparison of measured (dots) and modeled (lines) SELs for soft sand (2 φφ). ...............89

Figure 5-26. Comparison of measured (dots) and modeled (lines) SELs for pebble sediment 

(-2 φφ). .................................................................................................................................89

Figure 5-27. Comparison of the modeled broadband LLpp,pppp (line) for hard sand (0 φφ) with 

Gemini U8 measurements (dots)........................................................................................90

Figure 5-28. Comparison of the broadband LLpp,rrrrrr (line) for hard sand (0 φφ) with Gemini U8 

measurements (dots)..........................................................................................................91

Figure 5-29. Comparison of measured (dots) and modeled (lines) decidecade SELs for hard 

sand (0 φφ)...........................................................................................................................92

Figure 5-30. Comparison of measured (left) and modeled (right) time series p(t) of the U8 pile 

at 732 m range....................................................................................................................92

Figure 5-31. Comparison of measured (dots) and modeled (lines) received SELs for the Z2 

pile for hard sand (0 φφ).......................................................................................................93

Figure 5-32. Comparison of measured (dots) and modeled (lines) SELs for the Z2 pile for 

hard sand (0 φφ)...................................................................................................................94

Figure 5-33. U8 measured unweighted decidecade band single strike SEL. .........................................95

Figure 5-34. U8 measured low-frequency weighted decidecade band single strike SEL. .....................95

Figure 5-35. U8 measured mid-frequency weighted decidecade band single strike SEL. .....................96

Figure 5-36. U8 measured high-frequency weighted decidecade band single strike SEL. ....................97

Figure 5-37. U8 measured phocid-underwater weighted decidecade band single strike SEL. ..............97

Figure 6-1. Map of RIsland and VAbeach sites. .................................................................................101

Figure 6-2. Bathymetry at RIsland. .....................................................................................................102

Figure 6-3. Bathymetry at VAbeach....................................................................................................103

Figure 6-4. Summer SSPs. .................................................................................................................104

Figure 6-5. Winter SSPs. ....................................................................................................................104
vii

Figure 6-6. Low frequency (17.5 to 2,238.5 Hz) versus full band (17.5 to 20,000 Hz) 

weighted single strike SEL for marine mammal groups. ..................................................107

Figure 6-7. Unweighted single strike decidecade band SEL versus frequency and range. ...............108

Figure 6-8. Low-frequency weighted single strike SEL decidecade band versus full band................108

Figure 6-9. High-frequency weighted SEL decidecade band versus full band...................................109

Figure 6-10. Single strike SEL as influenced by RIsland environmental variables...............................110

Figure 6-11. LLpp,pppp as influenced by RIsland environmental variables...................................................111

Figure 6-12. LLpp,rrrrrr as influenced by RIsland environmental variables .................................................112

Figure 6-13. Single strike SEL as influenced by VAbeach environmental variables. ...........................113

Figure 6-14. LLpp,pppp as influenced by VAbeach environmental variables. ...............................................114

Figure 6-15. LLpp,rrrrrr as influenced by VAbeach environmental variables...............................................115

Figure 6-16. Mean acoustic metric values at RIsland...........................................................................117

Figure 6-17. Median acoustic metric values at RIsland. .......................................................................118

Figure 6-18. Percentiles of unweighted single strike SEL at RIsland. ..................................................119

Figure 6-19. Mean acoustic metric values at VAbeach. .......................................................................121

Figure 6-20. Median acoustic metric values at VAbeach......................................................................122

Figure 6-21. Percentiles of unweighted single strike SEL at VAbeach.................................................123

Figure 6-22. Plan view of the depth-averaged, low-frequency SELw,24h at RIsland..............................124

Figure 6-23. Plan view of the depth-averaged, low-frequency weighted, SELw,24h at VAbeach...........125

Figure 6-24. Comparison of unweighted single strike SEL in decidecade bands for Gemini 

U8-pile measurements and RIsland model predictions....................................................126

Figure 6-25. Environmental influence on impact volumes for low-frequency cetacean PTS, 

weighted, SELw,24h. ...........................................................................................................129

Figure 6-26. Environmental influence on impact volumes for high-frequency cetacean PTS, 

LLpp,pppp...................................................................................................................................131

Figure 6-27. Environmental influence on impact volumes for high-frequency cetacean TTS 

weighted SELw,24h. ............................................................................................................133

Figure 6-28. Environmental influence on impact volumes for cetacean behavioral, unweighted 

LLpp,rrrrrr.................................................................................................................................136

Figure 6-29. Environmental influence on impact volumes for fish with swim bladder, 

recoverable injury, unweighted 960-strike SEL (SEL#960). ...............................................138
viii

Figure 6-30. Environmental influence on impact volumes for fishes with swim bladder, 

unweighted 5-strike SEL (SEL#5) TTS..............................................................................141

Figure 6-31. Environmental influence on impact volumes for small fishes unweighted 12-hour 

SEL (SEL12h) injury. ..........................................................................................................143

Figure 6-32. Environmental influence on impact volumes for small fish LLpp,pppp injury.............................145

Figure 6-33. Environmental influence on impact volumes for small fish unweighted LLpp,rrrrrr

behavioral. ........................................................................................................................147

Figure 6-34. Environmental influence on impact volumes for sea turtles unweighted LLpp,rrrrrr

behavioral. ........................................................................................................................149

Figure 6-35. Modeled impact (top) and vibratory (bottom) pile comparison. ........................................150

Figure 6-36. Modeled impact (black) and vibratory (blue) pile comparison of unweighted, 

single strike SEL, bearing north........................................................................................151

Figure 6-37. Modeled impact (black) and vibratory (blue) pile comparison of unweighted, 

single strike SEL bearing east..........................................................................................151

Figure 6-38. Modeled impact (black) and vibratory (blue) pile comparison of LLpp,pppp, bearing 

north..................................................................................................................................152

Figure 6-39. Modeled impact (black) and vibratory (blue) pile comparison of LLpp,pppp, bearing 

east. ..................................................................................................................................152

Figure A-1. Definition of effective signal duration in terms of complex envelope (From: ISO 

18405:2017)...................................................................................................................... A-1

Figure A-2. Definition of complex envelope in terms of analytic signal (From: ISO 

18405:2017)...................................................................................................................... A-2

Figure A-3. Definition of analytic signal in terms of sound pressure (From: ISO 18405:2017)........... A-2
ix

List of Tables

Table 1-1. Acoustic metrics.................................................................................................................... 6

Table 1-2. List of common propagation models and general characteristics. .....................................11

Table 1-3. Project-specific acoustic criteria and metrics on impulsive sounds (From: NMFS, 

2018a; Federal Register, 2005)..........................................................................................22

Table 1-4. Project-specific acoustic criteria and metrics for non-impulsive sounds (From: 

NMFS, 2018a; Federal Register, 2005). ............................................................................22

Table 1-5. Project-specific acoustic criteria and metrics for fishes (From: Popper et al., 

2014)...................................................................................................................................24

Table 1-6. Project-specific acoustic criteria and metrics for fishes (From: Stadler and 

Woodbury, 2009; GARFO, 2016). ......................................................................................24

Table 1-7. Acoustic criteria and metrics for sea turtles (From: Popper et al., 2014; GARFO, 

2016)...................................................................................................................................24

Table 2-1. Summary of frequency bands and sub-bands for COMPILE, Gemini, and 

Luchterduinen data sets. ....................................................................................................26

Table 2-2. Auditory weighting parameters for weighting functions, and estimated lower and 

upper functional hearing limits (From: NMFS, 2018a)........................................................27

Table 3-1. Water depth attenuation coefficient for four scenarios.......................................................36

Table 3-2. Coefficients, AA, BBpppp−pppp, and the derived values of BBpppp.......................................................38

Table 3-3. Ranges of measurement positions for Luchterduinen monopiles EL39 and EL42. ...........39

Table 3-4. Linear regression coefficients relating LLpp,pppp, LLpp,9999%, and LLpp,eeeeee to LLEE at 

Luchterduinen EL39 and EL42...........................................................................................41

Table 3-5. Comparison of depths and ranges between the current investigation and Lippert 

et al. (2015).........................................................................................................................42

Table 4-1. COMPILE I steel pile parameters (From: Lippert et al., 2016). ..........................................63

Table 4-2. COMPILE I steel pile parameters, derived from Table 4-1. ...............................................63

Table 4-3. Calculated (horizontal) energy flux density and total (horizontal) energy flux at 

eight ranges........................................................................................................................64

Table 4-4. COMPILE I water and sediment parameters......................................................................65

Table 5-1. Dimensions of the U8 and Z2 pile geometries. ..................................................................77

Table 5-2. Water depth, pile penetration, and sample interval for the U8 and Z2 piles. .....................77

Table 5-3. Material properties used for the U8 and Z2 cases. ............................................................78
x

Table 6-1. Marine mammal PTS LLEE,ww,2222hh impact volumes. ................................................................128

Table 6-2. Marine mammal PTS onset LLpp,pppp impact volumes ...........................................................130

Table 6-3. Marine mammal TTS LLEE,ww,2222hh impact volumes..................................................................132

Table 6-4. Marine mammal TTS onset LLpp,pppp impact volumes. ...........................................................134

Table 6-5. Marine mammal behavioral LLpp,rrrrrr impact volumes. .........................................................135

Table 6-6. Fish recoverable Injury LLEE,#999999 impact volumes................................................................137

Table 6-7. Fish recoverable injury LLpp,pppp impact volumes. ..................................................................139

Table 6-8. Fish TTS LLEE,#55 impact volumes.........................................................................................140

Table 6-9. Fish injury LLEE,11 hh impact volumes. ....................................................................................142

Table 6-10. Fish injury LLpp,pppp impact volumes.......................................................................................144

Table 6-11. Fish behavioral LLpp,rrrrrr impact volumes. ............................................................................146

Table 6-12. Sea turtle mortal injury LLEE,11 hh, physiological LLpp,pppp, and behavioral LLpp,rrrrrr impact 

volumes. ...........................................................................................................................148

Table C-1. Borkum Riffgrund 1 measurements from ITAP (2015; Section 12.1.2, translated 

into English)...................................................................................................................... C-1
xi

List of Acronyms, Symbols, and Abbreviations

10logR cylindrical spreading

20logR spherical spreading

2D two dimensional

μPa micropascal

ττ90 90% energy signal duration

ττeff effective signal duration

φφ phi (grain size)

AOS Applied Ocean Sciences, LLC

BO1 BARD Offshore 1 wind farm (German)

BOEM Bureau of Ocean Energy Management

BR1 Borkum Riffgrund 1 wind farm (German)

Caltrans California Department of Transportation

CASS/GRAB comprehensive acoustic simulation system/gaussian ray bundle

CDF cumulative density function

CMST Curtin University (Australia), Centre for Marine Science and Technology

COMPILE a portmanteau combining computation, comparison, and pile

CSA CSA Ocean Sciences Inc.

dB decibel

DCS damped cylindrical spreading

ECMWF European Commission Medium Weather Forecast

EMODnet European Marine Observation and Data Network

EUSeaMap EMODnet broadband seabed habitat map (for Europe)

f frequency

faSEL frequency-accumulated sound exposure level

FD finite difference

FE finite element

FHWG Fisheries Hydroacoustic Working Group

gg(tt) forcing function

GARFO Greater Atlantic Regional Fisheries Office

GEBCO general bathymetric chart of the oceans

GFS global forecasting system

GT1 Global Tech I wind farm (German)

HF high frequency

Hz hertz

IEC International Electrotechnical Commission

IFFT inverse fast Fourier transform

ISO International Organization for Standardization

ITAP Institut für technische und angewandte Physik

JASCO Jasco Applied Sciences

kHz kilohertz

km kilometer

LF low-frequency
xii

LLEE sound exposure level

LLEE,12h cumulative 12-hour sound exposure level

LLEE,ss single-strike sound exposure level

LLEE,w weighted sound exposure level

LLEE,w,24h broadband weighted sound exposure level cumulated for 24 hours

LLpp sound pressure level

LLpp,90% 90% root-mean-square sound pressure level

LLpp,eff effective sound pressure level

LLpp,pk zero to peak sound pressure level

LLpp,pk−pk peak to peak sound pressure level

LLpp,rms root-mean-square sound pressure level

Lpk peak sound pressure level

m meter

MF mid frequency

MMPA Marine Mammal Protection Act

MN meganewton

MP measurement position

MSP mean-square sound pressure

NCEI NOAA’s National Centers for Environmental Information

NCOM US Navy Coastal Ocean Model

NN24h Number of pile strikes in a 24-hour period

NM normal mode

NMFS National Marine Fisheries Service

NOAA National Oceanic and Atmospheric Administration

Np neper

NPL National Physical Laboratory (United Kingdom)

pp(tt) sound pressure time series

PDF probability density function

PE parabolic equation

PL propagation loss

PML perfectly matched layer

PSLM practical spreading loss model

PTS permanent (hearing) threshold shift

RAM range-dependent acoustic model

re with reference to

rms root-mean-square

s second

SEL sound exposure level

SEL#n cumulative sound exposure level caused by n pile strikes

SEL12h 12-hour sound exposure level

SELss single-strike sound exposure level

SELw weighted sound exposure level

SELw,24h broadband weighted sound exposure level cumulated for 24 hours

SL source level
xiii

SNU Seoul National University (South Korea)

SOFAR sound fixing and ranging

SPL sound pressure level

SSP sound speed profile

TL transmission loss

TNO Netherlands Organisation for Applied Scientific Research (Nederlandse Organisatie 

voor Toegepast Natuurwetenschappelijk Onderzoek)

TTS temporary (hearing) threshold shift

TUHH Hamburg University of Technology (Germany)

U.S. United States

UoS University of Southampton (United Kingdom)

USGS US Geological Survey

WOA World Ocean Atlas

WTD71 Bundeswehr Technical Centre for Ships and Naval Weapons (Germany)
1

Extended Abstract

As countries explore renewable energy opportunities, offshore wind farms are being developed along 

coastlines. To construct these wind farms, piles are driven into the seafloor as foundational structures for 

the turbines. Pile driving involves risk of noise pollution that can affect marine fauna. To mitigate these 

risks, risk assessments and permits are required and are based on the best oceanographic understanding 

and acoustic modeling available at the time of the permitting process. This project aims to facilitate 

improved risk prediction by recommending the most efficient acoustic models and ranking the most 

influential environmental parameters at two representative sites. 

Many underwater sound sources, like sonar transmitters, appear point-like from a distance when they 

have a maximum dimension that is small enough. That is, they radiate sound in all directions into their far 

field, where sound propagates spherically away from the source, as if it originated from a point. Even 

large sources (e.g., ships, airgun arrays) have this far-field property because the ocean is deeper than the 

wavelengths these sources emit. If the water is deep enough, it is possible to get far enough away from 

these sources for them to behave as if they were point sources—the basic requirement for a far field. 

A sound source with this salient property of having a far field can be characterized in terms of its source 

level (SL). The SL typically is obtained from the far-field sound pressure level (SPL, i.e., LLpp) and is 

scaled depending on the propagation pathway and distance the sound travels from the source to the 

receiving location. When the SL value is derived based on spherical spreading, only the distance in which 

the sound travels to the far field is needed (not the direction).

Pile driving involves a source (the monopile) that extends from the surface to the seafloor and into the

sediment. Being a large, cylindrical source in shallow water, the requirement for attaining a far field will 

never be met. There is no position anywhere in the water column to which the sound arrives after some 

propagation loss (PL) as if from a point source. This means that a pile driver does not have a SL, which 

also means that traditional methods of calculating the SPL using the sonar equation (SPL = SL - PL) 

cannot be applied. 

Methods

To address the issue of a pile not having an SL, the quantitative characterization of the sound field 

radiated by the pile needs to be estimated in some other way. The method applied in this project is to 

simulate the sound field around the source (up to a 5 m distance from the pile axis of symmetry) using a 

specialized finite element (FE) model of the hammer, pile, and surrounding media (air, water, and 

sediment). For this project, such an FE model was created, and its field computationally propagated to 

assumed receiver positions at least 60 km from the pile using a parabolic equation (PE) model. The sea 

surface was assumed to be perfectly smooth. This procedure provided the sound pressure time series as a 

function of receiver position. Each time series was then processed for the metrics root-mean-square sound 

pressure level (LLpp,rms ), peak sound pressure level (LLpp,pk), and single strike sound exposure level (LLEE,ss).

The complexity of the FE and PE resulted in a practical upper frequency limit for application to the 

pile-driving problem of approximately 2 kHz. Although most of the acoustic energy radiated by pile 

driving is in the range of 100 to 200 Hz, the relatively weak contribution in the 2 to 20 kHz band creates a 

potential risk to some marine fauna, especially mid-frequency (MF) and high-frequency (HF) cetaceans. 
2

Therefore, frequencies above 2 kHz were accounted for by weighting the strength of sounds according to 

their frequency and the available weighting functions for animal groups. This was achieved by 

extrapolating the field at 2 kHz to higher frequencies using a frequency-dependent scaling of the volume 

attenuation within the PE. This procedure was followed to calculate the weighted sound exposure level 

(LLEE,w), using weighting functions published by the National Marine Fisheries Service. Cumulative sound 

exposure level (LLEE,cum) (weighted or unweighted) was calculated from LLEE,w by assuming 3,500 identical 

hammer strikes in 24 hours for a stationary receiver (animal). 

Model sensitivity to environmental parameters was evaluated across season, site, bearing, and grain size. 

These parameters were proxies for sound speed profile (SSP), water column depth, range-dependent 

bathymetry, and sediment type variation, respectively. The validated model was used to compute the 

sound pressure time series and subsequent LLEE, LLpp,pk, and LLpp,rms metrics, and to investigate the overall 

impact on modeling predictions from varying environmental parameters. Impact volumes were then 

computed based on acoustic thresholds for various marine faunal groups and compared across

environmental variables.

Results

As proxies for water depth and oceanography, two sites off the United States Atlantic Coast, Rhode Island 

(RIsland)and Virginia Beach (VAbeach), were chosen for modeling. The environmental variable with the 

greatest impact on acoustic propagation, and thereby on impact volume, was site. Although site was 

intended as a proxy for water column depth, the sites also differed by distance to shore and had unique 

bathymetries. RIsland was closer to land and deeper than VAbeach, leading to differences of 40 dB at 

ranges beyond 20 km. Changes in sediment type exerted the next largest influence on impact volume. 

Hard sediments reflect sound more than soft sediments, leading to larger impact volumes over harder 

sediments. The environmental driver with comparatively less impact was season (the proxy for SSP), with 

better propagation in winter than in summer, which led to higher received levels at all ranges and, 

therefore, larger impact volumes in winter. Summer impact volumes were smaller because warm surface 

waters lead to increased refraction towards the seafloor and thus more absorption of sound into the 

seafloor. 

There was substantial variability in the impact volumes. Marine mammal impact volumes were as small 

as 0.0001 km3 (permanent threshold shift [PTS] in MF cetaceans; Table 6-1) and as large as 218 km3

(temporary threshold shift [TTS] for 24-hour exposure in low-frequency [LF] cetaceans; Table 6-3). In 

fishes, with application of the Greater Atlantic Regional Fisheries Office (2016) criteria, impact volumes 

varied between 17 km3 (injury, large fish; Table 6-9) and 221 km3 (behavior, any fish; Table 6-11). For 

comparison, application of the Popper et al. (2014) guidelines resulted in volumes between 0.04 km3

(recoverable injury, fishes with no swim bladder; Table 6-6) and 127 km3 (TTS, fishes with a swim 

bladder involved in hearing; Table 6-8). On average for fishes with and without a swim bladder, the 

calculated impact volumes for injury based on the criteria of Stadler and Woodbury (2009) (Table 6-9) 

were 30x and 1,600x larger than for recoverable injury based on the criteria of Popper et al. (2014) 

(Table 6-6), respectively. In sea turtles, the impact volume spread was between 0.5 km3 (physiological; 

Table 6-12) and 25 km3 (behavior; Table 6-12).
3

Recommendations

The following suggestions about the important features of the project results provide guidance for future 

projects modeling pile driving in the coastal ocean. A very specific starter field based on a pile size and 

forcing function, among other parameters, was used for this project and this starter field may not apply to 

future modeling efforts. However, the results from this project provide the basis for recommendations on 

the degree of difference between two sites that is needed for additional modeling efforts to be desirable. 

The environmental aspects that were considered include water depth, season, latitude along the 

U. S. Atlantic Coast, sediment type, bathymetry, and distance from shore. Each of these aspects must be 

considered when deciding how much one pile-driving site’s model can represent other sites. 

Most pile-driving sites will not occur in water deeper than 50 m, but variation in sound propagation exists 

across water columns from 0 to 50 m deep. For identical propagation conditions a change of 50% in water 

depth should change the impact volume by 50%. Therefore, it is recommended that pile-driving sites that 

differ by 50% deeper or shallower (e.g., 5, 7, 11, 16, 23, 34, or 50 m deep) should be modeled separately 

and those results can be extrapolated to the depths between.

The extremes of SSPs that exist between Virginia and Rhode Island during opposite seasons were 

represented in the models, but seasonality may be more pronounced farther north and less pronounced 

farther south. Therefore, models should be run for the month of a proposed pile driving to adequately 

capture the most likely SSP for that site.

Pile driving likely will occur in areas where the seafloor is mud or soft sand instead of rocky, and the

models in this project incorporated the span of these grain sizes. Uniform sediment type over the extent of 

the modeled environment was assumed. To address this model assumption in the future, three potential 

recommendations are made. If no new high-resolution sediment data are available and a uniform sediment 

type needs to be assumed, then (1) new models should be run when grain size (in “phi” φφ units) differs 

between piles by 1 φφ, or (2) incorporate Monte Carlo simulations of grain size into the model(s). If new 

or higher-resolution data on sediment type are available for a site, then run new models using the known 

φφ values at each range step.

Distance from shore can drastically alter impact volumes. Sites that shallow quickly at one or more 

bearings will have smaller impact volumes than sites where bearings either follow the same isobath or get 

deeper. When water depth increases, it enables sound to propagate farther. The 30 km difference in 

distance to shore between the two sites (VAbeach’s closest distance to shore was 56 km while RIsland’s 

was 26 km) could also have contributed to the impact volumes of VAbeach being 2 to 3 times larger than 

those at RIsland. This would suggest that two sites whose closest distances to shore are 15 km different, 

could have impact volumes different by 1 to 1.5 times each other. Therefore, sites that have distances at 

least 15 km farther or closer from shore than this study’s sites (i.e., closest distances of 11, 41, 71, 86 km 

etc. to shore) should be re-evaluated to understand the specific bathymetrical effect on propagation. 

In general, if a pile site differs by more than 50% in depth, more than 1 φφ in grain size, may be installed 

during different seasons, or at intervals of 15 km farther away or closer to shore, then new models should 

be generated during the month of proposed activity to understand how sound may travel through the

environment.
4

1 Introduction

1.1 Background

Environmental compliance documents are prepared for offshore renewable energy projects located 

throughout federal waters of the United States (U.S.) Outer Continental Shelf. Preparation and analysis of 

acoustic fields for these documents are conducted by numerous contractors who often use different 

models, databases, and approaches, resulting in varying estimations of predicted acoustic fields produced 

by renewable energy activities. This variability makes it difficult to determine if the calculations were 

overly conservative, and if so, to what degree. Overly conservative calculations could unnecessarily 

restrict a proposed activity. 

Activities performed during renewable energy projects have common noise sources and occur in similar 

environmental conditions. Some commonalities include limited: (1) numbers of sources (e.g., pile driving 

for a meteorological tower, wind turbine, or servicing platform); (2) range of pile diameters and hammer 

energies; (3) locations (typically on the continental shelf, in waters <50 m deep with similar acoustic 

propagation modes); (4) substrate types (e.g., sand, gravel, rock areas) with relatively strong acoustic 

reflection characteristics; and (5) oceanographic conditions (e.g., low sea states or wind speeds) within 

which the activity can be completed.

Modern acoustic propagation models make generalized assumptions that are partially inapplicable to pile 

driving. The main assumption that does not hold is that a far field exists for pile driving. For a far field to 

exist, there must be a region far enough from a source that the sound spreads spherically, therefore 

appearing to radiate from a single point. The non-existence of a far field for pile driving means that the 

concept of source level (SL) is undefined, which in turn means the concept of propagation loss (PL) is 

undefined, making widely used models such as the range-dependent acoustic model (RAM) or 

SCOOTER inapplicable unless the model is coupled to a known starting field close to the pile. The 

approach using a known starter field was applied first by Reinhall and Dahl (2011), who constructed a 

field close to the source by treating it as a compound source with a vertical array of phase-delayed 

monopoles. It was applied later by Zampolli et al. (2013), who used a finite element (FE) model of the 

hammer and pile to generate the starter field. The main point is that the concepts of SL and PL only exist 

for a source if that source has a far field. A pile driver has no far field, and therefore no SL or PL (Ainslie 

et al., 2012). 

1.1.1 Project Team

The project team comprised Dr. Michele Halvorsen (Project Manager, CSA Ocean Sciences Inc. [CSA]), 

Dr. Kevin Heaney (Lead Principle Investigator, formerly OASIS, currently Applied Ocean Sciences, LLC

[AOS]), Dr. Kerri Seger (AOS), Dr. Michael Ainslie (formerly Netherlands Organisation for Applied 

Scientific Research [TNO], currently Jasco Applied Sciences [JASCO]), Dr. Marten Nijhof (TNO), and 

Dr. Roel Müller (TNO). Dr. Tristan Lippert (AqustiX) contributed by providing advice and regression 

analyses. The core expertise is provided by Drs. Halvorsen, Heaney, and Ainslie, all three of whom have 

previously worked together successfully on projects and proposals. Dr. Halvorsen is an experienced 

Project Manager who understands the U.S. regulatory requirements for environmental impact statement 

(EIS)‐related projects, is familiar with the peer-reviewed scientific literature leading to the regulatory 
5

requirements (e.g., National Oceanic and Atmospheric Administration [NOAA], California Department of 

Transportation [Caltrans]), and has contributed extensively to that literature. Dr. Heaney regularly 

provides acoustic propagation expertise to customers such as the U.S. Navy, the Preparatory Commission 

for the Comprehensive Nuclear-Test-Ban Treaty Organization, and the Bureau of Ocean Energy 

Management. Dr. Seger is a biological acoustician trained in soundscape mapping and cetacean response 

to anthropogenic noise with experience in recording and analyzing baseline acoustic conditions along 

coastlines of multiple continents to compare to subsequent environmental states. Dr. Ainslie is a founding

member of the expert group that advises European Union (EU) member states on the implementation of 

the EU’s Marine Strategy Framework Directive and works with international colleagues on solving 

propagation issues associated with sound radiated by percussively driven piles. Notably, he is the 

originator of the damped cylindrical spreading rule described by Zampolli et al. (2013) and validated by 

Lippert et al. (2018). Dr. Nijhof developed a state-of-the-art FE source model for a hammer-driven pile, 

coupled this FE model to a normal mode (NM) program to enable computation of the sound field at long 

range from the pile, and demonstrated the model’s accuracy by verification at an international pile-driving 

modeling workshop (co-organized by Dr. Nijhof himself) and by validation against pile-driving 

measurements in the North Sea. Drs. Ainslie, Müller, and Nijhof have worked together on a series of 

projects for the Netherlands Government involving the development, testing, and validation of a modeling 

capability to predict the sound radiated by pile driving in the context of North Sea wind farm 

development.

1.1.2 Project Scope, Objectives, and Framework

The objective of this project was to improve understanding of shallow-water acoustic propagation 

characteristics and the sound field created by activities associated with the construction of renewable 

energy facilities as well as understanding of the potential risk to marine fauna. This involved specifically 

addressing pile-driving sources and predicting effects on marine life from the sound radiated by these 

activities. 

A technical analysis of acoustic propagation modeling of pile driving for wind turbine installations on the 

U.S. Atlantic continental shelf was performed to (1) provide recommendations for a consistent 

underwater acoustic modeling approach across renewable energy projects, including recommendations for 

models, databases, and methodologies that address pile driving; (2) provide a baseline range of expected 

modeling results, including impact volumes around typical sources; (3) identify a baseline of variability 

for acoustic modeling results based on ranges of critical environmental input parameters; (4) perform a 

sensitivity study of critical environmental parameter influences; and (5) validate and apply the damped 

cylindrical spreading method, including its implementation in the form of an Excel spreadsheet. Data 

from wind farm construction and other measured-at-sea sources were used to validate the recommended 

modeling approach.

Confusion arises from inconsistent use of underwater acoustical terminology (see Ainslie, 2015; Hawkins 

et al., 2015; National Marine Fisheries Services [NMFS], 2018a,b). To minimize the risk of such 

confusion in this report, and more generally throughout this project, the project will follow the 

International Organization for Standardization (ISO) 18405:2017 “Underwater Acoustics – Terminology” 

standard (ISO, 2017).
6

This report has many mathematical formulas and complex analyses; therefore, to avoid confusion of

acoustic terms, the mathematical symbols are used instead of the abbreviations usually seen in text 

(Table 1-1; Appendix A).

Table 1-1. Acoustic metrics

Symbol Abbreviation Metric Name Units

LLEE SEL Sound exposure level dB re 1 μPa2 s

LLEE,12h SEL12h Cumulative 12-hour sound exposure level dB re 1 μPa2 s

LLEE,ss SELss Single-strike sound exposure level dB re 1 μPa2 s

LLEE,w SELw Weighted sound exposure level dB re 1 μPa2 s

LLEE,w,24h SELw,24h Broadband weighted, cumulative 24-hour sound exposure level dB re 1 μPa2 s

LLpp,pk SPLpk Peak sound pressure level dB re 1 μPa

LLpp,pk−pk SPLpk-pk Peak to peak sound pressure level dB re 1 μPa

LLpp,rms SPLrms Root-mean-square sound pressure level dB re 1 μPa

LLpp,eff Leff Effective sound pressure level dB re 1 μPa

LLpp,90% L90 90% energy sound pressure level dB re 1 μPa

μPa = micropascal; dB = decibel; LL = level; LLpp = sound pressure level; re = with reference to.

1.1.3 Technical Foundation

The sound field radiated by complicated sources (e.g., a driven pile), in principle, can be computed by 

summing the contributions of multiple point sources distributed around the source’s acoustically active 

surface. However, doing so requires knowledge of the SL of each individual point source. The challenge 

lies in how to obtain values for these individual point source SLs, which remains an open research 

question, especially for impact pile driving. An alternative approach (e.g., Zampolli et al., 2013) is to 

sidestep the uncertainty of SLs and instead calculate the acoustic field radiated from the pile by 

constructing an FE model of the source combined with a given forcing function (i.e., the time-dependent 

force imparted by the hammer). Research on this alternative approach is ongoing, and the best method for 

computing a nearby sound field has yet to be determined. This uncertainty led to an international 

workshop, called COMPILE, in June 2014, organized by the Hamburg University of Technology 

(TUHH) and the TNO. One of the key findings of the COMPILE workshop was a comparison that led to 

increased confidence in modeling techniques for impact-driven piles (Lippert et al., 2016), including the 

FE source model used in this project (Zampolli et al., 2013).

The output of an FE source model is a representation of the acoustic field at a small distance (i.e., a few 

centimeters or meters) from the pile, within the near field of the source. This acoustic near field can be 

coupled to an acoustic propagation model without knowledge of the SL, but it requires an interface be 

developed between the FE source model and the acoustic propagation model. The construction of a pile

model is labor intensive, and consequently, FE model predictions were produced and processed for a 

single pile only, with the sensitivity assessed by considering variants around that single pile.
7

The Mach cone, a conical wave radiated by a compressional wave traveling down the pile wall 

(as explained further in Section 1.4), propagates out to approximately 10 kilometers (km), depending on 

water depth, after which it is replaced by some other contribution, the nature of which is an open research 

question. Measurements of the sound exposure level (SEL, i.e., LLEE) at close range (approximately 10 m) 

can be used to estimate the total acoustic energy put into the water. However, an extrapolation of range 

from this measurement, via the 15logR rule described below (NMFS, 2018b), or some other propagation 

model, is difficult because most models assume a point source. The concept of a point source is 

incompatible with the presence of a Mach cone. One of the goals of this project is to develop a clear 

understanding of the range-evolution of the Mach cone within the first few kilometers of the source. In

the region dominated by the Mach cone, propagation follows a modified (exponentially damped) version 

of the cylindrical spreading rule, referred to as damped cylindrical spreading (DCS) (Section 3). In the 

DCS region, the acoustic field varies as defined by Equation 1.1 (Zampolli et al., 2013; Lippert et al., 

2018):

LL(rr) = LL(rr1) − 10 log10

rr

rr1

dB − αα(rr − rr1) Equation 1.1

where:

LL(rr) = sound exposure level at distance rr

LL(rr1)= sound exposure level at distance rr1

αα = attenuation coefficient

dB = decibel.

The following is a brief discussion regarding two equations currently used by regulatory agencies to 

assess disturbance to marine mammals and fish. A NMFS memo issued in January 2012 (NMFS, 2012a) 

provided guidance for evaluating the effects of pile driving on marine mammals under the Marine 

Mammal Protection Act (MMPA). The memo provided risk thresholds of the root-mean-square sound 

pressure level (LLpp,rms) for disturbance and injury and advised how to extrapolate a measurement made at 

distance rr1 of an acoustic received level LL(rr1) to that at some other distance rr. Specifically, NMFS 

(2012a) advised the use of Equation 1.2:

LL(rr) = LL(rr1) − 15 log10

rr

rr1

dB, Equation 1.2

to estimate the received level LL(rr) at distance r. This formula (Equation 1.2), henceforth referred to as 

the 15logR rule, was made available in the form of an Excel spreadsheet by NMFS (2012b). Since 

publication of NMFS (2012a), NMFS (2018a) prepared comprehensive guidelines for marine mammals in 

the form of dual risk thresholds of the peak sound pressure level (LLpp,pk) and LLEE.

For fish, potential adverse effects are addressed via protective measures (for listed species and Essential 

Fish Habitat) included in the Endangered Species Act and Magnuson-Stevens Fishery Conservation and 

Management Act. Acoustic impacts initially were covered by an “Agreement in Principle” published by 

the Fisheries Hydroacoustic Working Group (FHWG, 2008), which suggested risk thresholds for LLEE and 

LLpp,pk. Little or no scientific research had been published at that time on the effects of pile driving on fish, 

and the “Agreement in Principle” was precautionary. 
8

A comprehensive set of guidelines for fishes and sea turtles subsequently was published by Popper et al. 

(2014). Based on the pioneering research of Halvorsen and her colleagues (Halvorsen et al., 2011, 

2012a,b; Casper et al., 2012, 2013), the Popper et al. (2014) guidelines recommended LLEE risk thresholds 

for fish that were between 16 and 33 dB higher than those of the FHWG (2008). Subsequently, a report 

published by Caltrans (2015) acknowledged the Popper et al. (2014) guidelines, but did not adopt the 

recommendations, advising instead to continue using the precautionary risk thresholds from the FHWG 

(2008). 

The Caltrans (2015) report further recommended use of Equation 1.3:

LL(rr) = LL(rr1) − FF log10

rr

rr1

dB, Equation 1.3

where FF is a “site-specific (sound) attenuation factor based on several conditions, including water depth, 

pile type, pile length, substrate type, and other factors” (Caltrans, 2015). Equation 1.2 commonly is used 

with rr1 = 10 m to calculate LL(rr) at distances rr >10 m, but the absence of an attenuation term (αα ) limits 

its applicability to interpolation between measured data points. Predictions based on extrapolation 

(outside the region between measurement points on which interpolation is based) are prone to error.

To summarize, use of the 15logR rule (Equation 1.2), or the site-specific version (Equation 1.3), for 

transmission loss (TL) leads to errors in acoustic predictions for sound radiation from impulsive 

pile-driving activities because these equations do not account for the exponential decay of the Mach cone 

arising from multiple seafloor reflections. For impulsive pile driving, to avoid error in propagation 

predictions caused by the influence of the Mach cone, it is recommended to first compute the immediate 

near field using an FE source model for the pile, and then propagate the predicted near field (i.e., starter 

field) to long range using a suitable propagation model. 

1.2 Overview of Acoustic Propagation Models

1.2.1 State-of-the-art Underwater Acoustic Models 

Most modeling is used to predict the propagation of sound energy, sound intensity, or sound pressure. The 

appropriateness of different propagation modeling solutions is determined largely by three general 

characteristics of the source and propagation medium: (1) frequency characteristics of the source output; 

(2) environmental dependence (e.g., range-dependent bathymetry, seafloor type, sound speed) of the 

propagation region; and (3) water depth. Propagation environments generally are categorized as either 

range-independent, for which the input parameters are constant with propagation range, or 

range-dependent, for which any of the environmental parameters (e.g., water depth, sound speed) vary 

with distance from the source. The most suitable propagation model can be selected given a particular 

frequency band and environment. 

A variety of models are available; some are free to download and others are publicly available. 

State-of-the-art models are archived and distributed by the Ocean Acoustics Library 

(http://hlsresearch.com/oalib). These state-of-the-art models are complex and require an experienced user 

in order to generate reasonable results. 
9

Available propagation models were categorized according to their underlying computational solution 

approach. The following model groups capture the most common methods:

• Ray tracing;

• Normal mode (NM); 

• Parabolic equation (PE);

• Wavenumber integration;

• Energy flux; and

• Finite difference (FD)/FE (least common).

Ray Tracing Models

Ray tracing models are efficient and address range-dependent environments but are high-frequency (HF) 

approximations to the wave equation, requiring special attention to properly address sediment 

propagation, focusing (of the acoustic signal), and wave diffraction (Tindle, 1983; Westwood and Tindle, 

1987). The most popular ray tracing model is BELLHOP, developed by Porter (2011). The U.S. Navy 

standard model is the Comprehensive Acoustic Simulation System/Gaussian Ray Bundle (CASS/GRAB).

Normal Mode Models

NM models compute a solution to the range-independent wave equation, providing local NMs in the 

vertical. This solution naturally includes effects of refraction and diffraction as well as sound penetration 

into the seafloor. The number of propagating modes increases with water depth and frequency, so the NM

solution approach is most efficient at low frequencies or in shallow water. NM models include KRAKEN 

and ORCA. Range-dependent environments can be addressed by coupled mode models; however, there 

are high computational costs associated with this approach, making them unpopular.

Parabolic Equation Models

The PE model solves the wave equation using a paraxial approximation. This modeling approach is 

computationally efficient and can address range-dependent environments. The computational time of the 

PE model increases linearly with water depth and frequency squared; therefore, the PE often is avoided 

for HF modeling in deep water. The PE model is the industry standard for full-wave propagation of 

lower-frequency sound (usually up to 1 kilohertz [kHz]). The U.S. Navy uses the PE model for 

frequencies below 600 hertz (Hz). Within the acoustics community, the primary PE model used is RAM, 

developed by Collins (1993, 1996). PE models are limited in vertical angle support, making them 

problematic where steep angles tend to prevail (i.e., at ranges less than the water depth). In this project,

results generated using the PE model were extrapolated from 2,380 Hz to 20 kHz using an effective 

attenuation scaling.
10

Wavenumber Integration Models

Wavenumber integration models are analytic solutions to the wave equation and cover very high-angle 

energy. This mathematical solution is only accurate for a range-independent environment, and therefore is 

used primarily for short-range propagation or for benchmarking other models.

Energy Flux Models

Energy flux models have become popular among acoustic modelers, permitting rapid computation of the 

incoherent energy in range-dependent environments. These models are a hybrid of the ray tracing model 

and NM model methodologies, numerically solving the wave equation for a bundle of modes that follow a 

ray-like trajectory.

Finite Difference/Finite Element Models 

FD/FE models offer a complete full-wave solution on a very high-resolution grid. FE modeling was used 

in this project for the source model (hammer and pile). FE models also can be used for scattering from 

submerged objects. FD/FE models do not handle long-range propagation (>100 m) because of the

demands of the dense computational grid.

Image and Multipath Models

Other methods such as the image method model (Brekhovskikh, 1980) and the multipath expansion 

model (Weinberg, 1975) are not considered here because they only work for iso-velocity sound speed 

profiles (SSPs) and point sources. Neither of these features exist in this project and thus are not applicable 

to the modeling of pile-driving sources out to distance in shallow water. 
11

The common propagation models and their general characteristics are provided in Table 1-2.

Table 1-2. List of common propagation models and general characteristics.

Model Name Method Class

Frequency

Class

Water Depth

Range-dependent

Peregrine Parabolic equation 10 Hz – 3.5 kHz* All depths

Range-dependent Acoustic 

Model (RAM) Parabolic equation 5 Hz – 3.5 kHz All depths

KRAKEN Normal mode 5 Hz – 1 kHz All depths

ORCA Normal mode 5 Hz – 1 kHz All depths

BELLHOP Ray 200 Hz – 200 kHz All depths

CASS/GRAB Ray 600 Hz – 200 kHz All depths

Aquarius Energy flux 100 Hz – 200 kHz All depths

-- Finite difference/finite element 5 Hz – 200 kHz Shallow water (≤100 m)

Range-independent

-- Image/multipath 200 Hz – 200 kHz Shallow water

OASES Wavenumber integration 5 Hz – 200 kHz All depths

CASS/GRAB = Comprehensive Acoustic Simulation System/Gaussian Ray Bundle; Hz = hertz; kHz = kilohertz; m = meter.

* Peregrine results were extrapolated to high frequencies up to 20 kHz using ray methods.

1.2.2 Recommended Project Model: Peregrine PE-RAM model

To address the project goals and objectives, a full-wave RAM was needed that could efficiently handle 

10 to 2,000 Hz in shallow water (<60 m) at high-resolution range stepping (meters). For this project, the 

PE method was recommended because of its ability to address propagation within the sediment, 

range-dependent bathymetry, high-angle support, and frequency range. 

OASIS Inc. developed a new version of RAM, named Peregrine, that has increased efficiency. The first 

major improvement in the writing of Peregrine was to the interfaces between the central processing unit 

and memory access that increased speed of processing; no changes were made to the physics algorithms. 

The second major improvement in Peregrine was an increase in efficiency in the environmental databases, 

enabling rapid computation of the sound field as a function of three-dimensional position around a single 

point source. A third benefit, developed for this project, was the accommodation of HFs up to 20 kHz 

using the PE model using a scaling of the frequency-dependent volume attenuation. Peregrine was 

recommended for and used in this project.

1.3 Environmental and Source Parameters

Underwater sound propagation is influenced by the local propagation medium (the “environment”), which 

may vary spatially and temporally. As such, the accuracy of a propagation model’s output relies on 

representative environmental inputs for the time and place of interest. Changes in environmental variables 

such as bathymetry, seafloor properties, and SSP influence the propagating sound. Other more specific 

model input parameters, such as wind speed, can be used as a proxy input if surface scattering requires 

consideration. 
12

Sensitivity to key environmental and source parameters that influence sound propagation depends on 

distance from the source (i.e., the pile). At “short range”, the most influential parameters are those 

associated with the propagation of the Mach cone, typically up to 5 to 10 km. At “long range”, defined for 

this project as the region after the Mach cone has died away (less than approximately 10 km), the 

propagation is dominated by sound paths traveling horizontally. Environmental parameters that influence 

the short and long ranges are discussed in this section.

During this project, the degree of influence on sound propagation by environmental parameters was 

investigated with a sensitivity study. The study evaluated changes in PL as environmental parameters 

were changed. In order to highlight some of the most common sources of uncertainty, attention was given 

to environmental model inputs for bathymetry, sound speed, and geoacoustical seafloor properties. 

In addition, publicly accessible environmental data were reviewed and provided bounds for water depths, 

sediment types, and sound speeds.

A sensitivity study of the influence on sound propagation by environmental parameters was carried out by 

TNO in 2016 (Binnerts et al., 2016) for the Netherlands Government. Where appropriate, conclusions of 

that work are included and summarized herein.

1.3.1 Water Depth and Bathymetry

1.3.1.1 Sensitivity to Water Depth and Bathymetry

Bathymetry is particularly important for shallow-water propagation. Seafloor shape and local water depth 

influence acoustic propagation. Near continental shelves, a sloping seafloor can substantially influence 

sound propagation. High-resolution bathymetry data may capture small-scale seafloor features such as 

sand waves and ripples; however, these features are dynamic and expected to change over time. 

In addition to the resolution and accuracy of bathymetric data, the accuracy of modeled outputs also may 

be influenced by water depth. Water depth typically is based on mean sea level or the lowest astronomical 

tide. While tidal changes usually are less important in deep water, they can have a notable effect on the 

assumed water depth in shallow coastal regions. At the two sites considered (Rhode Island [RIsland] and

Virginia Beach [VAbeach]), the tidal variation is only 1.5 m at both sites. Therefore, tidal variation is not 

a factor that required consideration in this project. However, where tidal variation is a substantial ratio of 

the water depth (e.g., the Bay of Fundy), it should be included in environmental sensitivity 

considerations. 

Very high-resolution bathymetric survey data may be available in some cases, generally in relation to a 

local site with specific stakeholder interest. Global bathymetric data, or bathymetric data from another 

source, may be used for adjacent areas, outside the spatial extent of the site-specific survey data. Such 

global bathymetric data may be obtained from the General Bathymetric Chart of the Oceans (GEBCO, 

http://www.gebco.net/); these data are available at an angular resolution of “30 seconds of arc” (an 

angular, not temporal, unit equivalent to 1/120 of one degree). High-resolution bathymetric databases will 

be used in coastal regions of the U.S. In particular, the NOAA Coastal Relief Map database can be used 

off the U.S. East Coast.
13

1.3.2 Sediment Type

1.3.2.1 Sensitivity to Sediment Properties

Knowledge of the geoacoustic properties of the seafloor is essential for shallow-water acoustic 

propagation modeling. In general, these properties determine how much sound is reflected from the 

seafloor, and of the sound that was transmitted into the seafloor, how much sound re-enters the water 

column. For example, a stratified seafloor bends sound waves, while a hard seafloor layer, such as rock, 

reflects sound waves. Both mechanisms result in sound energy being retransmitted into the water column. 

Acoustic properties of the sediment, and in some cases the stratification of the seafloor, often can be 

incorporated into an acoustic propagation model. However, the accuracy of this process is limited by the 

availability of data at a sufficient spatial scale and with the necessary resolution, such that the data are 

representative of the actual environment. It can be challenging, and in some cases impossible, to 

incorporate the necessary spatial variations of the seafloor into a RAM. Core measurements of the 

seafloor can provide information of the underlying geology, although the specific sediment types might 

be localized, and it might not be correct to extrapolate those characteristics across a broad region. 

Binnerts et al. (2016) described measurements of pile driving from two operations: during the 

construction of an offshore wind farm in the Dutch North Sea (Luchterduinen), and at a nearby Dutch site 

(Gemini), in one-third octave bands. (A one-third octave band is a unit of logarithmic frequency interval 

equal to one third of an octave.) A comparison between measured and modeled LLEE values was made for 

the Gemini wind farm (Binnerts et al., 2016). The analysis examined the sensitivity of LLEE to sediment 

type, as shown in Figure 1-1 (left). Sediment type varied from very fine to coarse sand. The chosen 

sediment parameters represented a uniform fluid sediment with an attenuation coefficient (αα) proportional 

to frequency (f) (i.e., αα ∝ ff). These sediment parameters are valid in the frequency range of 1 to 10 kHz 

(i.e., 1 kHz < f < 10 kHz). Below 1 kHz, frequency dependence of the attenuation coefficient is thought to 

depart from simple proportionality, leading to an attenuation coefficient that varies with frequency 

according to αα ∝ ffxx, with xx > 1. Binnerts et al. (2016) applied a f 1.8 power law (xx = 1.8 instead of 

xx = 1.0) at frequencies below 1 kHz, the result of which is shown in Figure 1-1 (right). The dashed lines 

in this graph were calculated using non-linear frequency dependence, making for less attenuation at low 

frequencies, showing an increasingly larger effect with frequency below approximately 500 Hz. From 

these results, it was concluded that the value of the exponent xx would require consideration in the 

sensitivity study (Section 6). However, it was determined during the validation study (Section 4.2) that 

the associated frequency dependence could be modeled instead by using a layered sediment model 

(i.e., one with a sound speed gradient). 
14

Figure 1-1. Dependence of one-third octave band SEL on sediment properties.

Left: SEL sensitivity to sediment type. Sediments range from very fine sand (pink; lowermost curve in each shaded 

group) to coarse sand (red; uppermost curve). Right: The effect of non-linear sediment attenuation on frequencies 

<1 kHz (From: Binnerts et al., 2016). μPa = micropascal; dB = decibel; re = with reference to; SEL = LLEE = sound 

exposure level.

1.3.2.2 Sediment Properties Database

Seafloor survey data may provide information about the upper sediment layer, such as that provided by 

the European Marine Observation and Data Network (EMODnet) and the EMODnet broadband seabed

habitat map for Europe (EUSeaMap) (http://jncc.defra.gov.uk/page-5040). However, this sediment 

information needs to be converted into acoustic properties for the seafloor before it can be used in 

modeling. Unfortunately, sufficient information is not always available in order to correlate with 

published acoustics properties of various sediment types (e.g., Hamilton, 1980; Hamilton and Bachman, 

1982; Bachman, 1985; Lurton, 2002; Ainslie, 2010). 

For regions of proposed wind farms on the U.S. Outer Continental Shelf, a comprehensive understanding 

of the seafloor type (particularly geotechnical information) is expected to be available. Often surficial 

sediment types (e.g., sand, mud, rock, clay) can be extracted from navigational charts. Other potential 

sources may include the NOAA Coast Survey; the National Centers for Coastal Ocean Science; the 

NOAA National Environmental Satellite, Data, and Information Service; and region- or site-specific 

studies.
15

1.3.3 Sound Speed Profile in Water

1.3.3.1 Sensitivity to Sound Speed Profile

The underwater SSP can substantially influence how sound propagates, especially in deep water. The SSP 

is determined by changes in water temperature, pressure, and salinity, with depth. Variation in sound 

speed with depth can bend and trap sound, which in some cases can lead to sound traveling substantially 

farther due to reduced spreading and less interaction with the seafloor and sea surface. A well-known 

example in deep water (>2,000 m) is that of sound trapped in the sound fixing and ranging (SOFAR) 

channel. In shallow water (<100 m), while the SSP may influence acoustic propagation through bending 

of sound, bathymetry and sediment acoustic properties generally are more influential propagation 

parameters.

In shallow water (<100 m) during the winter months, wind mixing results in an approximately isothermal 

water column. In these circumstances, sound speed increases gradually with increasing depth (small but 

positive sound speed gradient) due to increasing pressure, resulting in a slightly upward refracting SSP 

and more interaction with the sea surface. Solar heating during the summer results in a negative 

temperature gradient, and hence a strongly downward refracting (large negative sound speed gradient) 

SSP that results in increased interaction with the seafloor and more absorption.

1.3.3.2 Sound Speed Profile Database

In principle, the SSP may be measured in situ or obtained from global data sets such as those available 

from the World Ocean Atlas (WOA) database (http://www.nodc.noaa.gov/OC5/indprod.html). The WOA 

provides information about SSP geographic and seasonal variability. Within select U.S. coastal waters, 

the U.S. Navy Coastal Ocean Model (NCOM) is available for some periods of time and can show 

variability on a daily scale (www.ftp.ncep.noa.gov/data/nccf/com/ncom/proc/).

1.3.4 Wind Speed

1.3.4.1 Sensitivity to Wind Speed

Measurements of surface reflection loss are negligible for frequencies around 1 to 4 kHz and wind speeds 

less than 5 meters per second (m s-1

) (Figure 1-2; Weston and Ching, 1989). The losses increase with 

increasing frequency, suggesting that losses at lower frequencies should be lower still, and as predicted by 

theoretical considerations (Ainslie, 2005). Nevertheless, Binnerts et al. (2016) predicted some sensitivity 

to wind speed, possibly caused by extrapolation of empirical surface loss models outside their region of 

validity. 
16

Figure 1-2. Total signal attenuation after multiple surface reflections in the Bristol Channel 

between May and June 1969.

Measured surface loss was negligible for wind speeds less than 5 m s-1 (From: Weston and Ching, 1989).

1.3.4.2 Wind Speed Database

Wind speed data sets can be obtained from NOAA/National Centers for Environmental Information 

(NCEI), which provides the Global Forecasting System (GFS) model (https://www.ncdc.noaa.gov/dataaccess/model-data/model-datasets/global-forcast-system-gfs), a global wind weather forecasting model. 

The European Commission Medium Weather Forecast (ECMWF) 

(https://www.ecmwf.int/en/forecasts/datasets) also includes wind data sets.

1.3.5 Source Parameters

1.3.5.1 Pile Penetration Depth

Binnerts et al. (2016) predicted the effects of varying the pile penetration depth between 11 and 26 m 

(Figure 1-3). At short range (<10 km), the LLEE was predicted to decrease with increasing penetration 

depth, with the largest reductions occurring at a few tens of hertz. Similar reductions in low-frequency 

(LF) LLEE were predicted at distances of a few tens of kilometers. At frequencies above 200 Hz, the effect 

reversed and LLEE increased with increasing penetrating depth.
17

Figure 1-3. Dependence of one-third octave band single strike SEL on pile penetration depth.

μPa = micropascal; dB = decibel; f = frequency; Hz = hertz; re = with reference to; SEL = LLEE= sound exposure level 

(From: Binnerts et al., 2016).

1.3.5.2 Pile Diameter

Binnerts et al. (2016) considered piles of two similar lengths with different diameters – U8 (7.0 m) and 

B3 (2.4 m) – from the Gemini wind farm, with the anvil scaled accordingly. The predicted difference in 

LLEE between these piles is shown in Figure 1-3. The broadband LLEE for the 7.0 m pile was about 7 to 8 dB 

higher than for the 2.4 m pile. The largest effects in any one-third octave band occurred at LF (f < 30 Hz). 

For Figure 1-4 and subsequent figures showing results from Binnerts et al. (2016), the penetration depth 

of the pile was 21 m.
18

Figure 1-4. Dependence of one-third octave band single strike SEL on pile diameter, based on 

U8 (7 m) and B3 (2.4 m) data.

μPa = micropascal; dB = decibel; ff = frequency; Hz = hertz; re = with reference to; SEL = LLEE = sound exposure level 

(From: Binnerts et al., 2016).

1.3.6 Summary of Environmental Parameters

In summary, the most important short-range environmental parameters that influence acoustic 

propagation were expected to be water depth and sediment type (e.g., grain size) at the pile. At long 

distances, bathymetry, sediment distribution, and SSP were expected to be influential also.

1.4 Propagation Physics

1.4.1 Short-range Propagation

1.4.1.1 Mach Cone and Damped Cylindrical Spreading

When a pile is struck with a hammer, a compressional wave travels down the pile’s steel wall, radiating 

sound into the water (Figure 1-5). There is a delay in the compressional wave from top to bottom of the 

pile, which results in the sound radiating from the pile at an angle of approximately 15° to 17° off the 

horizontal, depending on the properties of the pile wall. The resulting conical wavefront is known as a 

“Mach cone.”
19

Figure 1-5. Illustration of a Mach cone (From: Dahl et al., 2015).

For a highly directional source (such as a driven pile) that radiates sound close to the horizontal angle, the 

energy in the radiated sound is attenuated (damped) due to multiple interactions with the seafloor. 

Because sound energy decays exponentially, this type of propagation is referred to as damped cylindrical 

spreading (DCS). The steeper the ray path, the heavier the expected dampening. The concept of DCS was 

introduced by Zampolli et al. (2013) and was developed further by Ainslie et al. (2014). 

Undamped cylindrical spreading a few tens of meters from the pile was demonstrated by Dahl et al. 

(2012). Farther from the pile, after several seafloor interactions, an exponential decay is superimposed on 

the cylindrical spreading behavior, as demonstrated to distances of several kilometers by Binnerts et al. 

(2016) and Lippert et al. (2018). DCS applies to LLEE, but not to LLpp,pk or LLpp,rms. However, statistical 

techniques were used to bridge this gap through identification of empirical correlations between LLEE and 

other parameters of interest (e.g., LLpp,pk, LLpp,rms).

Initially, most of the radiated energy is contained in the Mach cone, which therefore dominates the 

short-range propagation. The field associated with the Mach cone depends on two key parameters: the ray 

cycle distance (the horizontal distance between successive interactions of a ray with the seafloor), and the 

seafloor reflection loss per cycle. The cycle distance, rrc, depends on the water depth HH and Mach cone 

angle θθ, and is given by:

rrc = 2HH cot θθ . Equation 1.4

The reflection loss per cycle, LLc, is given by: 

LLc = −20 log10|RR(θθ)| dB. Equation 1.5

The decay rate = LLc/rrc , expressed in units of decibels per meter (dB m-1) (see Zampolli et al., 2013; 

Lippert et al., 2018), is given by:

αα

dB/m = −10 log10|RR(θθ)|

tan θθ

HH/m Equation 1.6
20

where three key parameters control the decay rate: the water depth HH; reflection coefficient RR, which 

depends on the sediment type (especially grain size); and the Mach cone angle θθ, which depends on the 

properties of the pile wall.

1.4.1.2 Water Depth

Water depth influences propagation due to its influence on the cycle distance, resulting in a decay rate 

that is inversely proportional to water depth (Equation 1.6). Thus, the greater the water depth, the higher 

the acoustic signal decay rate.

1.4.1.3 Sediment Type

Sediment type influences propagation due to its influence on the reflection loss, and is given by:

LLc ≡ −loge RR = −20 log10|RR(θθ)| dB Equation 1.7

which results in a decay rate directly proportional to reflection loss. 

The sediment type was characterized by grain size. Coarse sediments (e.g., gravel, coarse sand) are 

acoustically hard, meaning they have a high sound speed (large impedance contrast) relative to water, and 

therefore a low reflection loss. This low reflection loss means the sound signal will travel farther (with a 

lower decay rate) than would be the case for an acoustically soft sediment such as silt or fine sand, for

which (though less relevant for this project) sound would travel less far.

1.4.1.4 Mach Cone Angle

The Mach cone angle influences propagation due to its influence on the cycle distance and reflection

coefficient, resulting in an increased rate of decay rate with increasing Mach cone angle θθ. In practice, 

however, this angle does not vary much from its nominal value of 15° to 17° because the Mach cone 

angle is determined by the properties of water and steel, which are mostly fixed. This angle is steep 

enough to be unaffected by refraction in shallow water.

1.4.2 Long-range Propagation

The Mach cone propagation angle of approximately 17° causes multiple lossy interactions with the 

seafloor, resulting in exponential decay. By contrast ray paths traveling close to the horizontal direction, 

though initially weaker in amplitude, decay more slowly and can be expected to dominate the sound field 

at long range.

The precise distance at which the Mach cone decays depends on water depth and sediment type. Based on 

a sand-sediment and 30 m water depth scenario, the Mach cone dies away after approximately 10 km 

(Lippert et al., 2018) and is replaced by horizontally traveling sound energy. At greater distances, the SSP 

in the water column and long-range bathymetry become important. While surface scattering might 

become an issue at long range, it is considered less important than the effects of SSP or bathymetry. 

1.4.2.1 Bathymetry

At long ranges, the effects of changing water depth, however gradual, become important. For example, a 

decrease in water depth for a fixed amount of sound power means that power is compressed into a smaller 
21

area, resulting in a higher intensity. On the other hand, a decrease in water depth also leads to an increase 

in ray grazing angle (Weston, 1976) that results in an increased rate of decay and lower intensity 

(see Equation 1.6). In summary, this means the signal travels less distance. This increase in decay rate is 

further strengthened by an increase in duct cut-off frequency with decreasing water depth, which filters 

out LF sound. Thus, the travel range of the signal is even shorter. In extreme cases, where water depth 

decreases significantly in the first few kilometers, the angle of the Mach cone would increase with a 

corresponding increase in the decay rate. 

1.4.2.2 Sound Speed Profile

The effect of the SSP on acoustic signals is to create acoustical waveguides, which means horizontally 

traveling sound can be trapped as if in a tunnel. Steeper paths are not trapped and decay more quickly due 

to multiple lossy interactions with the seafloor and sea surface. To function effectively, these waveguides 

require the acoustic frequency to exceed a minimum value, known as the “cut-off frequency” of the 

waveguide (Ainslie, 2010). The SSP is most likely to have an effect on HF sound and at long range, 

where the sound is traveling horizontally. The most likely consequence of the SSP is refraction of the 

sound in one of two directions, either (1) towards the sea surface (in upward refracting conditions, most 

likely in winter because of wind mixing and reduced solar heating), resulting in reduced decay rate in 

calm conditions (sound travels farther); or (2) towards the seafloor (in downward refracting conditions, 

most likely in summer), resulting in increased decay rate (sound travels less far).

1.4.2.3 Wind Speed 

Wind roughens the sea surface and generates near-surface air bubbles. The roughness causes surface 

scattering (Weston and Ching, 1989), which increases attenuation. Scattering is enhanced by the presence 

of near-surface bubbles, which further increases attenuation (Ainslie, 2005). As a result, the presence of 

wind is most likely to influence (i.e., decrease) the propagation of HFs, in upward refracting conditions 

and at long ranges.

1.5 Acoustic Threshold Criteria

1.5.1 Marine Mammals

The acoustic criteria and metrics used for marine mammals (i.e., cetaceans and pinnipeds) were based on 

the NMFS (2018a) criteria for impulsive and non-impulsive sounds pertinent to auditory impacts, and the 

older behavioral impacts by NOAA (Federal Register, 2005). The acoustic criteria are shown in 

Table 1-3 for impulsive sounds and Table 1-4 for non-impulsive sounds. For marine mammals, the 

relevant metrics for auditory impacts of permanent threshold shift (PTS) and temporary threshold shift 

(TTS), are both weighted sound exposure level (SELw, i.e., LLEE,w) and LLpp,pk. The relevant metric for the 

behavioral criteria is LLpp,rms. 
22

Table 1-3. Project-specific acoustic criteria and metrics on impulsive sounds (From: NMFS, 

2018a; Federal Register, 2005).

Hearing Group

Impulsive Signals

PTS onset TTS onset Behavior

LLEE,ww,2222

dB re 1 μPa2 s

LLpp,pppp

dB re 1 μPa 

(unweighted)

LLEE,ww,2222

dB re 1 μPa2 s

LLpp,pppp

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr

dB re 1 μPa 

(unweighted)

Low-frequency 

cetaceans 183 219 168 213 160

Mid-frequency 

cetaceans 185 230 170 224 160

High-frequency 

cetaceans 155 202 140 196 160

Phocid pinnipeds 

underwater 185 218 170 212 160

Otariid pinnipeds 

underwater 203 232 188 226 160

μPa = micropascal; dB = decibel; LLpp,pk = peak sound pressure level; LLpp,rms= 90% energy root-mean-square sound pressure level; 

PTS = permanent threshold shift; re = with reference to; SELw,24h = LLEE,w,24h = weighted sound exposure level over 24 hours; 

TTS = temporary threshold shift.

Table 1-4. Project-specific acoustic criteria and metrics for non-impulsive sounds (From: NMFS, 

2018a; Federal Register, 2005).

Hearing Group

Non-Impulsive Signals

PTS onset TTS onset Behavior

LLEE,ww,2222hh

dB re 1 μPa2 s

LLEE,ww,2222hh

dB re 1 μPa2 s

LLpp,rrrrrr

dB re 1 μPa

(unweighted)

Low-frequency cetaceans 199 179 120

Mid-frequency cetaceans 198 178 120

High-frequency cetaceans 173 153 120

Phocid pinnipeds 201 181 120

Otariid pinnipeds 219 199 120

μPa = micropascal; dB = decibel; LLpp,rms = root-mean-square sound pressure level; PTS = permanent threshold shift; re = with 

reference to; SELw,24h = LLEE,w,24h = weighted sound exposure level over 24 hours; TTS = temporary threshold shift.

The weighting functions for cetaceans and pinnipeds are shown in Figure 1-6 (otariids currently do not

reside along the U.S. Atlantic Coast so were not included in this project).
23

Figure 1-6. Auditory frequency weighting functions.

From the NMFS (2018a) parametrization. dB = decibel; Hz = hertz; HF = high-frequency; LF = low-frequency; 

MF = mid-frequency; WWaud(ff) = logarithmic auditory weighting function.

1.5.2 Fishes and Sea Turtles

The acoustic criteria and metrics for fishes and sea turtles were based on the sound exposure guidelines 

developed by Popper et al. (2014) for impulsive and non-impulsive sounds (Table 1-5) with an inclusion 

to address the Stadler and Woodbury (2009) criteria as well. The criteria recommendations made by 

Popper et al. (2014) are based on the most recent science available at the time of their publication and are 

widely cited (Sabet et al., 2015; Borger et al., 2016; Farcas et al., 2016; Hawkins and Popper, 2016; 

Lucke et al., 2016; Roberts et al., 2016a,b; de Jong et al., 2018; Taormina et al., 2018; Davidsen et al., 

2019; Higgs and Humphrey, 2019). Most of the criteria recommendations for impulsive sounds from pile 

driving and airgun use are based on a series of publications by Halvorsen et al. (2011, 2012a,b) and 

Casper et al. (2012, 2013). 
24

Table 1-5. Project-specific acoustic criteria and metrics for fishes (From: Popper et al., 2014).

Fish Group

Impulsive Signals Non-impulsive Signals

Recoverable injury TTS Recoverable 

injury TTS

LLEE,#999999

dB re 1 μPa2 s 

(unweighted)

LLpp,pppp

dB re 1 μPa 

(unweighted)

LLEE,#55

dB re 1 μPa2 s 

(unweighted)

LLpp,rrrrrr, 44hh

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr,11 hh

dB re 1 μPa 

(unweighted)

Fish without swim bladder >216 >213 >>186 -- --

Fish with swim bladder not 

involved in hearing 203 >207 >186 -- --

Fish with swim bladder 

involved in hearing 203 >207 186 170 158

μPa = micropascal; dB = decibel; LLpp,pk = peak sound pressure level; LLpp,rms,12h = root-mean-square sound pressure level for 12 hours 

continuous exposure; LLpp,rms,48h = root-mean-square sound pressure level for 48 hours continuous exposure; SEL#5 = LLEE,#5 = sound 

exposure level from 5 airgun shots (Popper et al., 2005); SEL#960 = LLEE,#960 = sound exposure level from 960 pile strikes; 

PTS = permanent threshold shift; re = with reference to; TTS = temporary threshold shift.

The current criteria used by NOAA for fishes are presented in Table 1-6 (Stadler and Woodbury, 2009; 

Greater Atlantic Regional Fisheries Office [GARFO], 2016), and the proposed criteria for sea turtles from 

Popper et al. (2014) and GARFO (2016) are presented in Table 1-7.

Table 1-6. Project-specific acoustic criteria and metrics for fishes (From: Stadler and Woodbury, 

2009; GARFO, 2016).

Fish Group

Injury Physiological Behavior

LLEE,11 hh 

dB re 1 μPa2 s

(unweighted)

LLpp,pppp

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr

dB re 1 μPa 

(unweighted)

Small fish (mass <2 g) 183 a 206 a -- 150 b

Large fish (mass ≥2 g) 187 a 206 a -- 150 b

-- = not applicable; μPa = micropascal; dB = decibel; SEL12h = LLEE,12h = cumulative sound exposure level, over 12 hours; LLpp,pk = peak 

sound pressure level; LLpp,rms = root-mean-square sound pressure level; PTS = permanent threshold shift; re = with reference to;

TTS = temporary threshold shift. a Stadler and Woodbury (2009). b GARFO (2016).

Table 1-7. Acoustic criteria and metrics for sea turtles (From: Popper et al., 2014; GARFO, 2016).

Animal Group

Mortal Injury Physiological Behavior

LLEE,11 hh 

dB re 1 μPa2 s

(unweighted)

LLpp,pppp

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr

dB re 1 μPa 

(unweighted)

LLpp,rrrrrr

dB re 1 μPa 

(unweighted)

Sea turtles 210 a >207 a 180 b 166 b

μPa = micropascal; dB = decibel SEL12h = LLEE,12h = sound exposure level for 12 hours continuous exposure; LLpp,pk = peak sound 

pressure level; LLpp,rms = root-mean-square sound pressure level; re = with reference to.

a Popper et al. (2014). b GARFO (2016). *The GARFO criteria were updated in 2019. However, the 2016 criteria were applied for this project because the 

2019 update could not be accommodated by the project schedule.
25

2 Metrics

The metrics LLEE, LLpp,pk, and LLpp,rms are broadband quantities for which the frequency band needs to be

specified (Appendix B). To ensure comparability between predictions comparing like with like, the same 

frequency bands should be used for both/all measurements. Furthermore, it is desirable to consider a 

sufficiently wide range of frequencies to avoid omitting bands that contribute significantly to the total 

acoustic energy, particularly the main spectral peak, after weighting. 

For Gemini and Luchterduinen, measurements were available in the approximate frequency band of 

20 Hz to 20 kHz, with the unweighted one-third octave spectrum peaking in the bands with a center 

frequency between 100 and 200 Hz. Weighting for mid-frequency (MF) and (especially) HF cetaceans 

has the effect of emphasizing frequencies above 200 Hz relative to those below 100 Hz. Therefore, in the 

MF and HF cetacean hearing groups, the weighted main spectral peaks occurred at a higher frequency 

than the unweighted main spectral peaks. The goal was to model up to this main weighted spectral peak to 

include the frequency contributing the most to the acoustic energy as perceived by the HF cetacean 

hearing group. This approach avoided omission of any frequency band that is biologically important. For 

computational reasons, the maximum acoustic frequency used for FE and PE modeling was 

approximately 2.2 kHz. The higher frequencies required for MF and HF cetacean hearing group impact 

volumes were extrapolated by adjusting the 2.2 kHz sound field according to the known frequency 

dependence of the absorption coefficient in seawater.

Sound pressure level and related quantities are sometimes presented with logarithmic frequency spacing, 

such as in octave bands (i.e., in the band ffmin to 2ffmin, where ffmin is the LF limit of the band) or in decade 

bands (i.e., in the band ffmin to 10ffmin). Such bands are typically subdivided into smaller bands such as 

one-third of an octave (i.e., one-third octave or 1/3 octave) or one-tenth of a decade (i.e., one-tenth 

decade, decidecade, or 1/10 decade). The frequency band corresponding to a decidecade is approximately 

equal to one-third of an octave, and for historical reasons, the difference between these two bands is 

sometimes neglected, with both occasionally referred to as a “one-third octave”. This project used 

base 10 bands (decidecades), as required for compliance with the international standard IEC 61260-1, also 

followed by the COMPILE (2014) workshop. 

While the standard IEC 61260-1 requires use of base 10 (decidecade) bands, the measurements by Institut 

für technische und angewandte Physik (ITAP, 2015a,b) used base 2 (one-third octave) bands, which 

results in a small discrepancy. However, the difference in bandwidth between base 2 and 

base 10 “one-third octave” bands is about 0.3% (amounting to less than 0.1 dB difference in band level 

for white broadband noise). This means the discrepancy was negligible for this project and should be 

determined for each project independently to make such determinations. Lower and upper frequency 

bands and the size of sub-bands, whether base 10 (decidecades) or base 2 (one-third octaves) are 

summarized in Table 2-1. The value of ffmaxshown in parentheses is the upper frequency limit used for FE

and PE modeling work. Extrapolation to 20 kHz was carried out using the HF model.
26 

Table 2-1. Summary of frequency bands and sub-bands for COMPILE, Gemini, and 

Luchterduinen data sets. 

Test Case fmin fmax

Base 2 or 

Base 10 

(measurement) 

Base 2 or 

Base 10 

(model) 

COMPILE 0 2.5 kHz (2.5 kHz) 10 10 

Gemini U8, Z2 20 Hz 20 kHz (2 kHz) 2 10 

Luchterduinen EL39, EL42 20 Hz 20 kHz (2 kHz) 2 10 

base 2 = one-third octave; base 10 = one-tenth decade (decidecade); fmax = maximum frequency; fmin = minimum frequency; 

Hz = hertz; kHz = kilohertz. 

From the available data set options (Gemini and Luchterduinen), two piles were chosen from the 

Luchterduinen data set. The measured pile data from Luchterduinen were combined with data from three 

German sites (introduced in Section 1.3.2). These data were used to carry out a regression analysis 

between Lா and other parameters of interest (L௣,୮୩ and L௣,୰୫ୱ). Measurements from Gemini were used for 

validation of the regression formulas obtained. 

2.1 Accumulation of Sound Exposure 

The cumulative sound exposure level (SELcum, i.e., Lா,ୡ୳୫) is calculated from the single strike sound 

exposure level (SELss, i.e., Lா,ୱୱ) using 

Lா,ୡ୳୫ ൌ Lா,ୱୱ ൅ 10 logଵ଴ Nଶସ୦ dB Equation 2.1 

where Nଶସ୦ is the number of pile strikes in a 24-hour period. The choice of Nଶସ୦ is determined by 

estimating the maximum number of pile strikes that could occur in the 12-hour period of daylight 

available for work to be carried out. It was assumed that no construction work would take place during 

the night. The total number of strikes during a 24-hour period (day and night) for the construction of 

Gemini pile U8 was 6,921 strikes (ITAP, 2015b). Dividing this number by two and rounding up results in 

3,500 strikes in 12 hours, which can also represent the total daily 24 hours of working and non-working 

time. Therefore, for this project, 3,500 strikes was the value used throughout for Nଶସ୦. Substituting Nଶସ୦

with 3,500 into the (Lா,ୡ୳୫) equation above leads to a value of 35.4 dB, which is then added to the 

Lா,ୱୱvalue. 

2.2 Applicability of Frequency Weighting 

The GARFO (2016) spreadsheet is not applicable for Lா,୵; it addresses cetaceans for behavior with an 

unweighted L௣,୰୫ୱ threshold and for physiology by advising its user to consult NMFS (2018a) for the 

weighted thresholds. This project developed a DCS spreadsheet calculator (named Damped Cylindrical 

Spreading in Excel [DCSiE], pronounced “Dixie”), which evaluates the marine mammal frequency 

weighting functions at a frequency of 2 kHz, as specified by NMFS (2018a), Appendix D. 

One way to compute Lா,୵ is from the spectrum of the unweighted sound exposure, which can be attained 

with the sound pressure time series [p(t)]. 
27

The applied weighting functions were from the NMFS (2018a) technical guidance report and have the 

following functional form:

wwaud(ff) = vv(ff)

max vv(ff) Equation 2.2

vv(ff) = ff2

2bbff2aa

(ff1

2 + ff2)aa(ff22 + ff2)bb Equation 2.3

Here, the f1 and f2 cutoffs are defined specifically for each hearing group (Figure 1-6). 

The logarithmic auditory weighting function WWaud(ff) defined by NMFS (2018a; Table 2-2), is related to 

the auditory frequency weighting function wwaud(ff) (with a lower-case w) according to 

WWaud(ff) = 10 log10wwaud(ff) dB, Equation 2.4

which corresponds to NMFS (2018a) Eq. (1):

WWaud(ff) = CC + 10 log10vv(ff) dB Equation 2.5

with C defined as:

CC = −10 log10 max vv(ff) dB. Equation 2.6

Where: 

wwaud(ff) = auditory frequency weighting function

WWaud(ff) = logarithmic auditory frequency weighting function

vv(ff) = function of frequency used in NMFS auditory frequency weighting function

CC = weighting function gain, a constant offset to ensure the logarithmic auditory frequency weighting 

function has a maximum value of 0 dB

Table 2-2. Auditory weighting parameters for weighting functions, and estimated lower and 

upper functional hearing limits (From: NMFS, 2018a).

Hearing Group aa bb ff11

(kHz)

ff22

(kHz)

CC 

(dB)

Low-frequency cetaceans 1.0 2 0.2 19 0.13

Mid-frequency cetaceans 1.6 2 8.8 110 1.20

High-frequency cetaceans 1.8 2 12 140 1.36

Phocid pinnipeds (underwater) 1.0 2 1.9 30 0.75

Otariid pinnipeds (underwater) 2.0 2 0.94 25 0.64

aa = low-frequency exponent in equation for vv(ff); bb = high-frequency exponent in equation for vv(ff); CC = weighting function gain, a 

constant offset in equation for WWaud(ff) ; dB = decibel; ff1 = low-frequency cutoff; ff2 = high-frequency cutoff; kHz = kilohertz; 

NMFS = National Marine Fisheries Service; vv(ff) = quantity proportional to the auditory frequency weighting function.
28

3 Damped Cylindrical Spreading in Excel 

The purpose of this section is to describe the development of a simple tool to model propagation for 

impact pile driving. The tool provides an alternative to the Practical Spreading Loss Model (PSLM), as 

found in the GARFO (2016) and NMFS (2018b) spreadsheet. The PSLM uses an empirical extrapolation 

to predict LLEE and LLpp,pk. Methods used to replace the empirical extrapolations for LLEE and LLpp,pk are 

described in this section. LLpp,rms was also considered. The tool is applicable to marine mammals, fishes 

and sea turtles. For fishes the criteria of Stadler and Woodbury (2009) are supplemented by the more 

recent guidelines of Popper et al. (2014).

The updated DCS calculator spreadsheet model, DCSiE, is based on the DCS model (Lippert et al., 2018), 

which is most accurate near the pile, where injury is the primary concern and the metrics LLEE and LLpp,pk are 

most relevant. By contrast, LLpp,rms is mainly of interest at long range from the pile, where the DCS model 

is not applicable. LE is calculated directly using DCS. LLpp,pk and LLpp,rms are then estimated from LLEE using 

empirical correlations with LLEE (Lippert et al., 2015).

The PSLM method used in the NMFS (2018b) spreadsheet is described in Section 3.2.1. This is followed 

by a description of the DCS (Section 3.2.2), the correlations used to obtain LLpp,pk and LLpp,rms

(Section 3.3), and recommendations for calculating all three quantities (Section 3.4). Conclusions are 

drawn in Section 3.6.

3.1 Practical Spreading Loss Model

The latest NMFS technical guidance for acoustic assessment on marine mammals was published in 

June 2018 (NMFS, 2018a). The NMFS (2018b) acoustic spreadsheet prompts the user to enter 

source-level information for an impact pile driver (Figure 3-22). The user is prompted for “Source Level 

(RMS SPL)” and “Source Level (PK SPL)”. As described in Section 1.1, the SL is not a well-defined 

concept for pile driving (Ainslie et al., 2012), making the NMFS (2018b) spreadsheet difficult to 

interpret. There is no need for, and no benefit derived from, the use of SL in this context. One possible 

interpretation is that the authors of the NMFS (2018b) spreadsheet intend the user to calculate this entry 

by taking the measured level at range rr1 and subtracting from it the quantity FF log10 �

rr1

rr0

� dB, where

rr0 = 1 m. It is suggested that the calculation method be made explicit and to avoid using the term 

“source level” with regard to pile driving.

The PSLM method used in the NMFS spreadsheet (NMFS, 2012b, 2018b; Caltrans, 2015; GARFO, 

2016) involves extrapolation of measurements for LLEE, LLpp,pk, or LLpp,rms at a known range from the pile (rr1) 

to some other range (rr) at which the value of the level is required. The PSLM extrapolation is carried out 

using the TL estimation. However, to understand the limitations of the PSLM approach, an understanding 

of TL and PL is required. 

3.2 Transmission Loss and Propagation Loss

TL often is used interchangeably with PL; however, in underwater acoustics, it is important to distinguish 

between the two concepts. Some of the confusion stems from conflict in definitions between ANSI and 
29

ISO standards. Following the most recent ISO 18405:2017 standard, the following definitions are 

important.

TL is a “reduction in ... level between two specified points ...”

Specifically, TL usually is a difference in LLpp or LLEE between two positions at different ranges from the 

source, which can be measured or calculated in given conditions (e.g., for a specified site and season). 

This difference is used to interpolate between the two positions, thus providing intermediate values of TL 

for the specified conditions.

PL is the “difference between source level ... and ... sound pressure level ...”

PL at range r from a source is the difference between a measure of source power (its SL) and the sound 

pressure level at that range. 

“note 7 to entry: the term propagation loss is sometimes used as a synonym of transmission loss 

(3.4.1.3). This use is deprecated.”

In other words, PL is not synonymous with TL, and it is not calculated the same way as TL.

The distinction between TL and PL is important because sometimes the acoustic field at range r is 

estimated by extrapolating from a sound pressure level measurement at some distance close to the source 

(in pile driving, this distance often is 10 m). The extrapolation is most appropriately performed by 

estimating the TL between the two locations. It is appropriate to use TL, not PL, in these circumstances 

because TL is the difference in level between two positions, whereas PL requires knowledge of the SL, 

which is unknown for pile driving (Ainslie et al., 2012; Section 1.1). The concept of SL is fundamental to 

the sonar equation and is a measure of the sound power radiated to a source’s far field (Ainslie, 2010). No 

method has been developed to measure or define the SL of a pile driver (Ainslie et al., 2012), making the 

concepts of PL and SL inapplicable to pile driving. 

3.2.1 Practical Spreading Loss Model

The NMFS (2018b) spreadsheet’s PSLM extrapolation is carried out using the TL estimation described in 

Figure 3-1. 
30

Figure 3-1. Equation for transmission loss used by the Practical Spreading Loss Model 

(From: Caltrans, 2015).

Incorporating the TL estimation results in the following expressions for LLEE, LLpp,pk, and LLpp,rms respectively:

LLEE(rr) = LLEE(rr1) − FF log10

rr

rr1

dB Equation 3.1

LLpp,pk(rr) = LLpp,pk(rr1) − FF log10

rr

rr1

dB Equation 3.2

LLpp,rms(rr) = LLpp,rms(rr1) − FF log10

rr

rr1

dB Equation 3.3

Where:

LLEE = sound exposure level

LLpp,pk = peak sound pressure level

LLpp,rms = root-mean-square sound pressure level

rr1 = range at which LL(rr1) is known, usually near the source (typically 10 m for pile driving)

rr = range at which LL(rr) is to be estimated (for determining impact volume)

FF = attenuation factor, site specific; commonly seen as 10logR (FF = 10), 15logR (FF = 15),

20logR (FF = 20)

NMFS recommends FF = 15, i.e., 15logR (NMFS, 2012b; GARFO, 2016). A constant value of

FF (independent of range) implies a power law variation of energy flux density, ~rr− FF

10. For example,

10logR corresponds to cylindrical spreading and 20logR corresponds to spherical spreading. Figure 3-2 

shows a comparison of results from the PSLM as FF = 5, 10, 15 for the COMPILE workshop and a North 

Sea wind farm construction event. 
31

Figure 3-2. SEL versus range using the PSLM

Top: Comparison with simulated measurements for the COMPILE workshop (Lippert et al., 2016), using rr1 = 11 m as 

the intercept point. Bottom: Comparison with measurements from Borkum Riffgrund 1 (Lippert et al., 2018), using 

rr1 = 234 m as the intercept point. Straight lines are the Practical Spreading Loss Model based on FF = 5, 10, 15. 

Circles represent real or simulated measurements. μPa = micropascal; dB = decibel; DCS = damped cylindrical 

spreading; PSLM = Practical Spreading Loss Model; re = with reference to; SEL = LLEE = sound exposure level.
32

The top graph in Figure 3-2 plots a simulation from the COMPILE workshop (Lippert et al., 2016). The 

advantage of this situation is that it is a well-specified synthetic scenario. The environment, hammer, and 

pile parameters were known precisely (because they were specified by the workshop organizers) and there 

was high confidence in the theoretical prediction (Lippert et al., 2016). A (simulated) measurement at 

rr1 = 11 m was used, which resulted in LLEE(rr1) = 187 dB; this was the point where all three PSLM lines 

(F = 5, 10, and 15) were forced to intersect. The PSLM line that worked best out to 1 km was FF = 10. 

The PSLM line based on FF = 15 overestimated LLEE at short ranges (<11 m; specifically, rr < rr1) and 

underestimated LLEE at longer ranges (>11 m; specifically, rr > rr1). The opposite was true for the PSLM 

line based on FF = 5.

The bottom graph in Figure 3-2 plots the measurements made during construction of the Borkum

Riffgrund 1 (BR1) wind farm in the German North Sea (Figure 3-3) (Bellmann et al., 2016; ITAP, 

2015c). The first measurement position from Lippert et al. (2018), is rr1 at 234 m; this was the point where 

all three PSLM lines (F = 5, 10, and 15) were forced to intersect. The PSLM line that worked best out to 

1 km was FF = 10. At ranges greater than 234 m (specifically, rr > rr1), the PSLM lines based on

FF = 15 underestimated the LLEE while those based on FF = 5 overestimated the LLEE.

In both graphs, the symbols (representing synthesized or actual measurements) fall below the PSLM lines

based on FF = 10 (i.e., cylindrical spreading) at ranges exceeding 1 km. The deviation increases with 

increasing range, indicating the received levels were exponentially damped beyond 1 km and did not 

follow a power law, as is implicitly assumed when using the PSLM.

The PSLM lines based on FF = 10 (i.e., cylindrical spreading), corresponding to the energy flux density

(JJ) being proportional to the inverse of range (JJ~rr−1) are expected to work best near the pile. This 

behavior was predicted theoretically by Zampolli et al. (2013) and demonstrated by Dahl et al. (2012). 

The cylindrical spreading model proposed by Zampolli et al. (2013) included an exponential damping 

term (Ainslie et al., 2014; Lippert et al., 2018), which is the DCS model.
33

Figure 3-3. Geographical location of the Borkum Riffgrund 1 wind farm in the German North 

Sea.

3.2.2 Damped Cylindrical Spreading Model for LLEE

Close to the pile, the radiated sound field is dominated by the Mach cone (Reinhall and Dahl, 2011; 

Zampolli et al., 2013; Section 1.4.1.1). The Mach cone carries a certain amount of energy, GG0, from the

pile. At range rr, this energy is spread into area AA = 2πrr , where H = water depth. Ignoring boundary 

losses, the energy flux density is JJ = GG0

2πrrrr, which corresponds to cylindrical spreading. The attenuation 

due to multiple boundary reflections needs to be accounted for, where the energy reaching range rr can be 

written as:

GG = GG0 exp (−2αα ) Equation 3.4
34

Where:

α = attenuation coefficient

rr = range from the pile axis of symmetry

GG0 = Mach cone energy

Cylindrical spreading corresponds to using JJ = GG

AA for the energy flux density, but it is modified in 

Equation 3.4 by exponential decay. The resulting DCS model by Lippert et al. (2018) is: 

LLEE(rr) = LLEE(rr1) − 10 log10

rr

rr1

dB − αα(rr − rr1) Equation 3.5

Where:

LLEE = sound exposure level

rr1 = range at which LL(rr1) is known, usually near the source

rr = range at which L(r) is to be estimated (for determining impact volume)

αα = attenuation coefficient, related to the plane wave reflection coefficient (RR) and cycle distance 

(rrcc) according to:

αα = − loge|RR|

rrc

= − log10|RR|

2 dB

rrc

Equation 3.6

Equation 3.5 has a similar form to Equation 3.1, but with three important differences:

● The F coefficient of the log10

rr

rr1

term in Equation 3.1 is no longer a variable because the 

geometry leads naturally to FF = 10, corresponding to cylindrical spreading.

● The extra αα term provides exponential damping at long ranges.

● The derivation of Equation 3.5 relies on the energy conservation principle and thus applies only 

to LLEE [calculation of LLpp,pk or LLpp,rms is addressed in Section 3.3]).

The DCS is plotted in Figure 3-4 for the COMPILE workshop and a BR1 event. The DCS prediction 

neatly explains the 10logR behavior at short range and the exponential damping beyond 1 km. The DCS 

follows the shape of the COMPILE simulated values (top graph) and the BR1 field measurements 

(bottom graph). By contrast, there is no constant value of FF for the PSLM that aligns with the observed 

acoustic behavior. In addition to BR1, the suitability of DCS has been demonstrated by Lippert et al. 

(2018) for two other German sites: BARD Offshore 1 (BO1) and Global Tech I (GT1). The water depths 

and attenuation coefficients calculated by Lippert et al. (2018) for COMPILE and the three German sites 

are listed in Table 3-1.
35

Figure 3-4. SEL versus range using the DCS model

Top: Comparison with simulated measurements from the COMPILE workshop (Lippert et al., 2016), using rr1 = 11 m 

as the intercept point. Bottom: Comparison with measurements from Borkum Riffgrund 1 (Lippert et al., 2018), using 

rr1 = 234 m as the intercept point. Straight blue lines are the PSLM based on F = 5, 10, and 15. Circles represent 

measurements. Red dotted lines illustrate the uncertainty in the DCS curve resulting from a spread of ±50 percent in 

the attenuation coefficient (αα). μPa = micropascal; BR1 = Borkum Riffgrund 1; dB = decibel; DCS = damped 

cylindrical spreading; PSLM = Practical Spreading Loss Model; re = with reference to; SEL =LLEE= sound exposure 

level.
36

Table 3-1. Water depth attenuation coefficient for four scenarios.

Scenarios Water depth

(m)

αα

(dB km-1)

COMPILE (simulated site) 10 3.5

BARD Offshore 1 (BO1)1 40 1.41

Global Tech I (GT1)1 39.5 0.94

Borkum Riffgrund 1 (BR1)1 27 1.38

α = attenuation coefficient; dB = decibel; km = kilometer; m = meter. 

1 Lippert et al. (2018).

For the αα term, αα is a constant and r is range (variable); the product αα is proportional to range and 

predicts the total sound attenuation. Nearest to the pile (closer than say 100 m), αα is essentially zero 

because r is small. As range increases (between 1 and 5 km), this attenuation term becomes increasingly 

important (Figure 3-4). However, at greater ranges (beyond 5 km), the DCS model (which incorporates 

ααrr) begins to overestimate the attenuation and deviates from the trend apparent in the COMPILE data 

(Figure 3-5). When DCS is subtracted from the PSLM based on FF = 10, the DCS is accurate in the 

region where the difference is less than 20 dB (i.e., αα < 20 dB; Figure 3-5). Beyond 20 dB, the TL is not

accurately predicted because it appears to follow a power law; therefore, the sound exposure ceases to 

decay exponentially with range.

Figure 3-5. COMPILE: SEL versus range using the DCS model.

When the attenuation term (αα ) exceeds 20 dB, the damped cylindrical spreading model (red line) overestimates 

transmission loss, which then underestimates sound exposure level, after which a power law using F = 25, 35, 45 

(blue lines) provides a better match to the simulated COMPILE data. μPa = micropascal; dB = decibel; 

DCS = damped cylindrical spreading; re = with reference to; SEL = LLEE= sound exposure level.
37

3.3 Empirical Regressions for LLpp,pp and LLpp,rrrrrr

Using the DCS model, LLEE can be predicted up to a few kilometers (the precise maximum range depends 

on water depth and decay rate; for Figure 3-5, it is accurate up to 5 or 6 km). LLEE is a natural product of 

the DCS model because the derivation uses an energy conservation approach. It is also desirable to 

predict LLpp,pk and LLpp,rms, but these do not follow naturally from DCS. However, LLpp,pk is known to be 

correlated with LLEE (Lippert et al., 2015), and a similar correlation is expected between LLpp,rms and LLEE.

3.3.1 Regressions Based on Lippert et al. (2015)

Lippert et al. (2015) demonstrated an empirical linear correlation (Figure 3-6) between LLEE and the peak 

to peak sound pressure level (LLpp,pk−pk).

LLpp,pk−pk = AALLEE + BBpk−pk Equation 3.7

Where: 

LLpp,pk−pk = peak to peak sound pressure level

A = constant gradient (regression coefficient)

LE = sound exposure level

BBpk−pk = constant intercept (regression coefficient)

Figure 3-6. Scattergram of LLpp,pppp−pppp versus SEL 

Illustrates the correlation between these two quantities for the wind farm construction site Global Tech I (Lippert et al., 

2015). μPa = micropascal; dB = decibel; LLpp,pk−pk = peak to peak sound pressure level; re = with reference to; 

SEL = LLEE = sound exposure level.
38

For the present application, focus was on LLpp,pk, which is related to LLpp,pk−pk, via the equation:

LLpp,pk = LLpp,pk−pk − μμ Equation 3.8

Where: 

μμ = the difference between LLpp,pk−pk and LLpp,pk (typically between 5 and 6 dB). 

It follows that:

LLpp,pk = AALLEE + BBpk Equation 3.9

Where: 

LLpp,pk = peak sound pressure level

AA = constant gradient (regression coefficient)

LLEE = sound exposure level

BBpk = constant intercept (regression coefficient)

Therefore (Table 3-2):

BBpk = BBpk−pk − μμ Equation 3.10

Table 3-2. Coefficients AA, BBpppp−pppp, and the derived values of BBpppp.

Site1 AA BBpppp−pppp μμ BBpppp = BBpppp−pppp − μμ 

BARD Offshore I (BO1) 1.40 -43.4 dB 5.7 dB -49.1 dB

Global Tech I (GT1) 1.39 -39.4 dB 5.3 dB -44.7 dB

Borkum Riffgrund I (BR1) 1.43 -44.0 dB 5.7 dB -49.7 dB

μ = difference between peak-to-peak and zero-to-peak sound pressure; A = constant gradient; B = constant intercept; dB = decibel. 1 Lippert et al. (2015).

3.3.2 Regressions Based on Luchterduinen Measurements

The DCS model provides a reliable means of predicting LLEE up to a few kilometers from an impact-driven 

pile. For the acoustic injury criteria, LLpp,pk is also needed, which cannot be calculated directly using DCS 

because the output of DCS is LLEE, not LLpp,pk. Nevertheless, a strong correlation is known to exist between 

LLEE,ss and LLpp,pk (Lippert et al., 2015), and this correlation can be exploited to estimate LLpp,pk if LLEE is 

known.

In principle, a correlation obtained for one of the three sites analyzed by Lippert et al. (2015) could be 

applied, unmodified, to another site. Alternatively, data could be analyzed from multiple sites to obtain a 

more generally applicable correlation, and that is the method used for this project. Measurements made of 

the installation of two monopiles (EL39 and EL42) during construction of the Luchterduinen wind farm 

were analyzed (Figure 3-7). The measurement ranges from the piles are provided in Table 3-3. The 

locations of the Luchterduinen and other wind farms are shown in Figure 3-8.
39

Figure 3-7. SEL at Luchterduinen wind farm.

For two monopiles (left: EL39; right: EL42), the sound exposure level per hammer blow at three measurement 

ranges, 2 m above the seafloor (From: ITAP, 2015a). μPa = micropascal; dB = decibel; re = with reference to; 

SEL = LLEE = sound exposure level.

Table 3-3. Ranges of measurement positions for Luchterduinen monopiles EL39 and EL42.

Monopile ID Position

(km)

Position 2 

(km)

Position 3 

(km)

EL391 0.75 4.72 13.23

EL421 0.75 5.24 13.75

km = kilometer. 

1 ITAP (2015a).
40

Figure 3-8. Location of the relevant wind farms.

Left: Wind farms off the western Dutch coast. Right: Wind farms off the northern Dutch-German coast (Images from: 

thematicmapping.org, map.openseamap.org, www.openstreetmap.org).

Correlation Between LLEE and LLpp,pppp

In the following text, only BBpk (not BBpk−pk) will be discussed; thus, the subscript will be dropped, making 

no further distinction between BB and BBpppp. Lippert et al. (2015) demonstrated a strong correlation that 

LLpp,pk is approximately equal to AALLEE + BB. For this project, a similar analysis was performed on the EL39 

and EL42 data, and the results show that although such a relation can be found (Figure 3-9), the 

coefficients (Table 3-4) are different from those determined by Lippert et al. (2015; Table 3-2). The 

comparable data from Table 3-2 are included at the bottom of Table 3-4. The last column of Table 3-4

lists the spread of the LLpp,pk to LLEE distribution (uncertainty in LLpp,pk for fixed LLEE), which defines the 

vertical deviation from the correlation line where 95% of the data points are found (i.e., BB95%). In other 

words, 95% of the LLpp,pk values are within BB95% of the regression line.
41

Figure 3-9. Linear regression analysis comparing SEL and LLpp,pppp.

The inset is the original figure by Lippert et al. (2015) for Global Tech I, shown as grayscale, and scaled to match the 

main graph’s x-axis, while the y-axis does not coincide because of the offset between LLpp,pk−pk and LLpp,pk. Colored dots 

represent measured data, and each straight line is an individual correlation as specified in the legend. 

μPa = micropascal; dB = decibel; LLpp,pk = peak sound pressure level; LLpp,pk−pk = peak to peak sound pressure level;

re = with reference to; SEL = LLEE = sound exposure level.

Table 3-4. Linear regression coefficients relating LLpp,pppp, LLpp,9999%, and LLpp,eeeeee to LLEE at Luchterduinen 

EL39 and EL42

Acoustic 

metric Luchterduinen monopile ID AA BB 

(dddd) 

95% confidence 

bound1 BB99 % (dddd) 

LLpp,pk

EL39 1.172 -9.1 2.1

EL42 1.157 -6.4 1.5

EL39 and EL42 1.162 -7.3 1.8

LLpp,90%

EL39 1.172 -18.7 1.6

EL42 1.132 -12.0 1.5

EL39 and EL42 1.150 -15.0 1.7

LLpp,eff

EL39 1.185 -17.9 1.5

EL42 1.175 -15.3 1.1

EL39 and EL42 1.176 -15.8 1.5

Lippert et al. (2015)

-- Wind farm site AA BB

(dB) --

LLpp,pk

BARD Offshore I (BO1) 1.40 -49.1 --

Global Tech I (GT1) 1.39 -44.7 --

Borkum Riffgrund I (BR1) 1.43 -49.7 --

-- = not applicable; A = constant gradient; B = constant intercept; dB = decibel; LLEE = sound exposure level; LLpp,eff = effective sound 

pressure level; LLpp,pk = peak sound pressure level; LLpp,90% = 90% energy sound pressure level.

1 The distance within which 95% of the measurements lie (difference between the 2.5% and 97.5% percentiles).
42

The difference between the three sets of coefficients (for EL39, EL42, and the average of EL39 combined 

with EL42) is small. Therefore, only the coefficients from the averaged combined data set are considered 

further.

Lippert et al. (2015) wrote about the piling situations in the North Sea:

These sites represent different environmental conditions and source types, with water depths 

ranging from 27 to 40 m, strongly varying pile dimensions, and different foundation types, 

i.e., tri-piles, tripods and mono piles. The acoustic far-field measurements for these sites were taken 

at ranges varying from 250 to 5,000 m from the pile, and at a depth of approximately 2 m above 

the seafloor. In all three cases, no sound mitigation system was employed during the measurements.

The description of the study sites was compared to the conditions for the Luchterduinen and Gemini wind 

farms (Table 3-5). Furthermore, the sediment type between the Lippert et al. (2018) study sites and the 

two Dutch sites, Luchterduinen and Gemini (ITAP, 2015a,b), was medium sand. 

Table 3-5. Comparison of depths and ranges between the current investigation and Lippert et al. 

(2015).

Study site and 

monopile ID

Site water depth

(m)

Minimum 

measurement 

range (km)

Maximum 

measurement 

range (km)

Sediment 

type

Lippert et al. (2015) Minimum: 27; Maximum: 40 0.25 5.0 Medium sand

Current study

Luchterduinen EL39 21.5 0.75 31.0 Medium sand

Luchterduinen EL42 20.6 0.75 13.8 Medium sand

Gemini U8 34.1 0.73 66.0 Medium sand

Gemini Z2 30.0 0.73 66.0 Medium sand

km = kilometer; m = meter. 

Correlation Between LLEE and LLpp,rms

In addition to LLEE and LLpp,pk, the LLpp,rms was used for acoustic criteria. To incorporate LLpp,rms, two different 

calculations were introduced: (1) 90% energy sound pressure level (LLpp,90%) averaged over the 90% 

energy signal duration (90% is assumed to be used in typical calculations of this metric), and (2) effective 

sound pressure level (LLpp,eff) derived from the effective signal duration; a new approach employed during 

this project for addressing the LLpp,rms metric and its inherent variability.

Correlation Between LLEE and LLpp,90%

A correlation analysis was performed by replacing LLpp,pk with LLpp,90%, defined by:

LLpp,90% = 10 log10 �

0.9 EE/ττ90

pp0

2 � dB , Equation 3.11
43

Where: 

pp0 = 1 μPa

LLpp,90% = 90% energy sound pressure level

ττ90 = 90% energy signal duration

E = single strike sound exposure (100% energy)

The results of replacing LLpp,pk with LLpp,90% are shown with the results of LLpp,pk in Figures 3-10 to 3-12. 

The resulting coefficients for LLpp,90% are listed in Table 3-4. 

Figure 3-10. Correlations at Luchterduinen EL39.

Left: Correlations between peak sound pressure level, ‘Lpk’ (LLpp,pk), and sound exposure level (LLEE). Right: 

Correlations between the 90% energy sound pressure level, ‘L90’ (LLpp,90%), and LLEE. Colored dots are measured data; 

straight lines are correlations. μPa = micropascal; dB = decibel; LLpp,pk = peak sound pressure level; re = with reference 

to; SEL = LLEE = sound exposure level. 

Figure 3-11. Correlations at Luchterduinen EL42.

Left: Correlations between peak sound pressure level (LLpp,pk) and sound exposure level (LLEE). Right: Correlations 

between the 90% energy sound pressure level (LLpp,90%) and LLEE . μPa = micropascal; dB = decibel; LLpp,pk= peak sound 

pressure level; re = with reference to; SEL = LLEE = sound exposure level.
44

Figure 3-12. Correlations at Luchterduinen EL39 and EL42.

Left: Correlations between peak sound pressure level (LLpp,pk) and sound exposure level (LLEE). Right: Correlations 

between the 90% energy sound pressure level (LLpp,90%) and LLEE. μPa = micropascal; dB = decibel; LLpp,pk = peak sound 

pressure level; re = with reference to; SEL = LLEE = sound exposure level.

Correlation Between LLEE and LLpp,eff

In Figure 3-12, the spread in LLpp,90% (right graph) for a fixed LLEE is noticeably larger than the 

corresponding spread in LLpp,pk (left graph). This spread is attributed to a spread in the 90% energy signal 

duration, ττ90. A signal consisting of two bursts, caused by the bounce of a hammer, will show variation in 

LLpp,90% , depending on the interval between the bursts, and the proportion of the total energy contained in 

the weaker burst. Therefore, in this section, an alternative to LLpp,90% was considered for the LLpp,rms. 

The alternative is motivated by the arbitrary choice of 90% energy for the averaging window (e.g., why 

should 90% be preferred over 50% or 99%?) and suggests the effective duration of the envelope function 

(Burdic, 1991) for the entire pulse, thus precluding the need for an arbitrary choice for the duration. This 

duration is the effective signal duration, ττeff (defined in Appendix A), which is strongly correlated with 

but shows less variability than ττ90 (Figure 3-13, left graph). 
45

Figure 3-13. Correlations at Luchterduinen EL42.

Left: EL42, correlations between effective signal duration ( ττeff) and the 90% signal duration (ττ90), both in seconds. 

Right: EL42 and EL39 combined, correlations between 90% energy sound pressure level (LLpp,90%) and effective 

sound pressure level (LLpp,eff). Colored dots are measured data; straight lines are correlations. μPa = micropascal;

dB = decibel; re = with reference to; SEL = LLEE = sound exposure level. 

Given a value of ττeff of a pulse, it seems natural to introduce an effective value of the mean-square sound 

pressure (MSP), defined as the value of MSP that, if held constant for a signal of duration ττeff, would 

result in the observed sound exposure of that pulse, EE. In other words, EE = �pp

��2��

effττeff, or (equivalently) 

�pp

��2��

eff = EE

ττeff

. Thus, the effective sound pressure level (LLpp,eff) is the sound pressure level corresponding 

to a pulse whose sound exposure, EE, is compressed into a time interval of duration ττeff that captures the 

main energy of the signal:

LLpp,eff = 10 log10 �

EE/ττeff

pp0

2 � dB , Equation 3.12

where: 

pp0 = 1 μPa

LLpp,eff = effective sound pressure level

ττeff = effective signal duration

E = single strike sound exposure.

The right graph of Figure 3-13 illustrates the spread in LLpp,90% (for fixed LLpp,eff), which is a result of the 

scatter in ττ90 (for fixed ττeff). The scatter is visible in the left graph. This reduced scatter in LLpp,eff results 

in better correlation between LLpp,eff and LLEE (Figures 3-14 and 3-15). Therefore, it would be reasonable to 

use LLpp,eff instead of LLpp,90% to lead to a more robust estimate of LLpp,rms. 
46

Figure 3-14. Correlations at Luchterduinen EL42 and EL39.

Left: EL39, correlations between effective sound pressure level, Leff (LLpp,eff) and sound exposure level (LLEE). 

Right: EL42, correlations between LLpp,eff and LLEE. Colored dots are measured data; straight lines are correlations. 

μPa = micropascal; dB = decibel; re = with reference to; SEL =LLEE= sound exposure level.

Figure 3-15. Correlations at Luchterduinen EL42 and EL39.

Left: EL39 and EL42, correlations between 90% energy sound pressure level, L90 (LLpp,90%) and sound exposure level 

(LLEE). Right: EL39 and EL42, correlations between effective sound pressure level, Leff �LLpp,eff � and LLEE. Colored dots 

are measured data; straight lines are correlations. μPa = micropascal; dB = decibel; re = with reference to; 

SEL = LLEE= sound exposure level.

To emphasize the benefit of using LLpp,eff, one final comparison is provided, plotting LLpp,eff alongside LLpp,pk

(Figure 3-16). The small spread on both graphs is immediately apparent, indicating strong correlations 

with LLEE in both cases. There also is a similarity in shape that is not apparent from Figure 3-13, suggesting 

further correlation between LLpp,eff and LLpp,pk.
47

Figure 3-16. Correlations at Luchterduinen EL39 and EL42.

Left: EL39 and EL42, correlations between peak sound pressure level,LLpk (LLpp,pk) and SEL (LLEE). Right: EL39 and 

EL42, correlations between effective sound pressure level, LLeff (LLpp,eff) and SEL (LLEE). Blue dots are measured data; 

straight lines are correlations. μPa = micropascal dB = decibel; re = with reference to; SEL = LLEE = sound exposure 

level.

3.3.3 Regressions Based on Combining all Four Data Sets

Thus far, the data sets considered in the regression analyses were the Dutch site (Luchterduinen) and three 

German sites (BO1, GT1, and BR1), separately. One final regression analysis on the measurements from 

all four sites combined yielded the coefficients AA = 1.201 and BB = −12.8 dB.

3.3.4 Extrapolating to Long Range 

As seen in Figure 3-5, the DCS is limited to αα < 20 dB, at least for the theoretical example from the 

COMPILE workshop. However, at longer ranges, the mechanism is unclear, and the acoustics follow a 

different propagation behavior. Based on Figure 3-5, preliminary indications are that a power law is 

followed at ranges beyond αα = 20 dB. This power law seems to correspond to application of the PSLM 

with a value of F between 25 and 45. Measurements are needed to support any modeling for this region to 

be certain. Until such measurements are available, extrapolating DCS predictions to distances beyond 

αα = 20 dB could be done using the PSLM with FF = 25, as a precautionary interim approach.

3.4 Recommendations for Predicting LLEE, LLpp,pppp, and LLpp,rrrrrr

Step 1 (LLEE):

Calculate LLEE as a function of a measurement (from the center of the monopile) at range rr1 using 

Equation 3.5 and determine αα using Equation 3.6. For best results, range rr1 should be chosen in the 

region where the cumulative reflection loss is small, such that ααrr1 < NN1 dB, where N1 is a constant to be

decided. For example, choosing NN1 = 3 gives:

rr1 <

3 dB

αα

Equation 3.13
48

where:

αα = attenuation coefficient

rr1= range for LLEE measurement.

The DCS formula is assumed valid up to a maximum range determined by ααrr2 = NN2 dB. Assuming 

NN2 = 20 (Figure 3-5), the condition becomes:

rr < rr2 Equation 3.14

where:

rr2 = range limit of maximum range validity (= (20 dB)/ αα).

Continuing with the example, if αα = 2 dB/km (corresponding to a cycle distance of 200 m and a 

reflection loss of 0.4 dB per cycle; Equation 3.6), then Equations 3.13 and 3.15 yield the constraints 

rr1 < 1.5 km for the measurement position and rr < 10 km for the limit of maximum range validity. While 

these values seem plausible, they have not been tested.

At ranges exceeding rr2, where rr2 = (20 dB)⁄αα, the suggested (and untested) PSLM extrapolation with 

FF = 25 results in:

LLEE(rr) = LLEE(rr2) − 25 log10

rr

rr2

dB Equation 3.15

Step 2 (LLpp,pk):

From Step 1, given a value of LLEE at a range of interest, the LLpp,pk can be estimated at the same range using 

the coefficients (A = 1.201 and B = 12.8) derived by combining the Luchterduinen data set with the three 

German sites:

LLpp,pk = 1.201LLEE − 12.8 dB Equation 3.16

Step 3 (LLpp,rms):

LLpp,90% or LLpp,eff can be estimated from LLEE in the same way as LLpp,pk, using the linear regression 

coefficients in Table 3-4:

LLpp,90% = 1.150LLEE − 15.0 dB Equation 3.17

and

LLpp,eff = 1.176LLEE − 15.8 dB. Equation 3.18
49

Worked Example

Step 1: Consider a measurement of LLEE = 160 dB made at rr1 = 0.2 km, with a decay rate (calculated 

using Equation 3.6) of αα = 2.3 dB/km (corresponding to a water depth of 20 m, Mach cone angle of

17°, and reflection loss of 0.3 dB per cycle). Substituting these values into Equation 3.5 gives 

(for RR < RR2 = 8.7):

LLEE(RR)

dB = 160.0 − 10 log10

RR

0.2 − 2.3(RR − 0.2)

where L/dB is the level in decibels and R is the range in kilometers. It follows that:

LLEE

dB = 153.5 − 10.0 log10 RR − 2.30RR RR < 8.70.

At ranges exceeding RR2 = 8.7, the PSLM with FF = 25 gives:

LLEE

dB = 133.5 − 25 log10

RR

RR2

RR > 8.70,

i.e.,

LLEE

dB = 147.6 − 25 log10 RR RR > 8.70.

Step 2: Substituting the result from Step 1 into Equation 3.16 gives (for RR < 8.7):

LLpp,pk

dB = 1.201(153.5 − 10 log10RR − 2.3RR ) − 12.8,

therefore:

LLpp,pk

dB = 171.5 − 12.0 log10RR − 2.76RR.

Application of Equation 3.16 to the LLEE formula for ranges exceeding 8.7 km gives:

LLpp,pk

dB = 1.201(147.6 − 25 log10RR ) − 12.8

LLpp,pk

dB = 164.5 − 30.0 log10RR.
50

Step 3: Substituting the result from Step 1 into Equations 3.17 and 3.18 results in the following equations 

for LLpp,90% and LLpp,eff, respectively (for RR < 8.7):

LLpp,90%

dB = 1.150(153.5 − 10 log10RR − 2.3RR) − 15.0

LLpp,90%

dB = 161.5 − 11.5 log10RR − 2.64RR

and

LLpp,eff

dB = 1.176(153.5 − 10 log10RR − 2.3RR) − 15.8

LLpp,eff

dB = 164.7 − 11.8 log10RR − 2.70RR.

Application of Equations 3.17 and 3.18 to the LLEE formula for ranges exceeding 8.7 km gives:

LLpp,90%

dB = 1.150(147.6 − 25 log10RR ) − 15.0

LLpp,90%

dB = 154.7 − 28.8 log10RR

and

LLpp,eff

dB = 1.176(147.6 − 25 log10RR ) − 15.8

LLpp,eff

dB = 158.6 − 29.4 log10RR.
51

The resulting levels, extrapolated from a single measurement of LLEE at 200 m, are plotted versus range in

Figure 3-17.

Figure 3-17. Worked example of extrapolation from LLEE.

Shows the measurement of sound exposure level at range [LLEE(RR1)] = 160 dB re 1 μPa2 s at rr1 = 200 m (blue 

circle), extrapolation using damped cylindrical spreading (LLEE) (solid blue curve), regression for peak sound pressure 

level LLpp,pk (dB re 1 μPa) using Equation 3.15 (solid black curve), and regressions for 90% energy sound pressure 

level (LLpp,90%, dB re 1 μPa) and effective sound pressure level (LLpp,eff, dB re 1 μPa) using Equations 3.17 and 3.18

(solid red and green curves). The dashed curves show the suggested practical spreading loss model extrapolation. 

μPa = micropascal; dB = decibel; km = kilometer; re = with reference to; SEL = LLEE = sound exposure level.

3.5 Validation 

3.5.1 Borkum Riffgrund 1

BR1 measurements from ITAP (2015c) (Appendix C), were power averaged over the two measurement 

depths and are shown as circles (blue: median LLEE; black: LLpp,pk) in the upper graph of Figure 3-18. 

Asterisks indicate the result of applying the Luchterduinen correlations for LLpp,pk, LLpp,90% , and LLpp,eff to the 

median LLEE values. The upper graph of Figure 3-18 includes additional measurement positions from ITAP 

(2015c) and is for the same case as that shown in the lower graph of Figure 3-4, where the DCS 

prediction is compared with three different power laws (F = 5, 10, 15). Similar results were reported for 

BO1 in ITAP (2014) and GT1 in ITAP (2015d).
52

Figure 3-18. Single strike LLEE and LLpp,pppp versus range at Borkum Riffgrund 1.

Top: Extrapolation results of peak sound pressure level (LLpp,pk, in dB re 1 μPa) and root-mean-square sound pressure 

level (LLpp,rms, in dB re 1 μPa 2 s) based on sound exposure level (LLEE, in dB re 1 μPa 2 s) measurements, using DCS 

correlations identified in the legend. Bottom: Results of modeling LLEE, LLpp,pk, LLpp,rms using each spreading model 

method. Blue circles = single strike LLEE; black circles = LLpp,pk. μPa = micropascal; dB = decibel; DCS = damped 

cylindrical spreading; m = meter; PSLM = practical spreading loss model; re = with reference to; s = second; 

SEL = LLEE = sound exposure level.
53

The results from a test of the recommended algorithm are shown in the lower graph of Figure 3-18. 

Specifically:

• LLEE curve is based on Equation 3.5, derived from the measurement at rr1 = 28 mm (blue);

• LLpp,pk curve is based on Equation 3.15 (black);

• LLpp,90% curve is based on Equation 3.16 (red);

• LLpp,eff curve is based on Equation 3.17 (green).

DCS enabled accurate estimations of LLEE at all ranges considered. While a systematic validation of the 

empirical correlations for LLpp,pk and LLpp,rms is needed, their use permits extrapolation from LLEE to obtain 

estimates of LLpp,pk and LLpp,rms.

For comparison, the equivalent PSLM results with FF = 15 were plotted (dashed lines) in the lower graph 

of Figure 3-18. Using FF = 15, the PSLM overestimates the initial slope of the TL curve, resulting in the 

following consequences:

• PSLM overestimates LLEE for rr < rr1

• PSLM underestimates LLEE for rr > rr1 (up to 8 km)

Furthermore, using FF = 10 leads to the correct TL slope; however, LLEE is overestimated for rr > 1 km in 

the absence of an attenuation (αα ) term.

3.5.2 Gemini

The original DCS enabled calculation of LLEE as a function of range from the pile (Lippert et al., 2018). The 

correlation observed is between LLEE and LLpp,pk (Lippert et al., 2015) and was extended in Section 3.3.2 by 

analyzing the correlation between LLEE and LLpp,rms, which permitted estimation of LLpp,rms and LLpp,pk, and 

extended the correlation to a wider range of LLEE values (roughly 138 to 178 dB).

The purpose of this section is to investigate the regression formula for LLpp,pk (Equation 3.15) by applying 

it to an additional data set. The additional data set was from the Gemini wind farm, which was not used in 

the development of the regression formula. The results were a comparison of the predicted values of LLpp,pk

using Equation 3.15 with the measured values of that quantity.

Three different regression lines are shown in Figure 3-19. The first is based on measurements at the 

Luchterduinen (orange line; 1.162LLEE), the second is based on site GT1 (green line; 1.4LLEE), and the third is 

based on the four-site regression (red; 1.201LLEE) (Equation 3.15). The three regression lines were 

compared with measurements made during construction of the Gemini wind farm (dots). The four-site 

regression (red) and the Luchterduinen regression (orange) are similar, and both have reasonable 

agreement with the Gemini measurements (dots) over a range of 50 dB for LLEE values (130 to 180 dB).

The four-site regression is referred to henceforth as the DCSiE regression because it is used in the 

spreadsheet tool of that name, described in Section 3.5.3.
54

Figure 3-19. Measured LLpp,pppp versus LLEE at Gemini wind farm.

Dots = measurements from Gemini wind farm. LLpk (LLpp,pk) = peak sound pressure level, which is compared with three 

trend lines: (1) green = applied correlations from Lippert et al. (2015); (2) orange = applied Luchterduinen regression; 

(3) red = applied DCSiE regression representing a combination of the four sites. dB = decibel; DCSiE = damped 

cylindrical spreading in Excel; re = with reference to; SEL = LLEE = sound exposure level. 

3.5.3 DCS-based Spreadsheet (DCSiE)

The DCS model was implemented in an Excel spreadsheet (DCSiE). The user can enter values for the 

parameters HH (water depth, H), LLEE,ss (measured value of single strike, SELss_1), rr1 (range from pile axis 

of symmetry at which LLEE,ss is measured, R_1), NN (number of hammer strikes # Pile Strikes), and DD (pile 

diameter, D). Optionally, the user may also enter values for ccw (water sound speed, cw), ρρw (water 

density, rhow), Φ (sediment grain size, Mz), ββsed(αα⁄ff, betased), and θθ (Mach cone angle, thetaMach). 

For values not entered by the user, the calculator will apply the default values shown in Figure 3-20, 

which are based on BR1 (Lippert et al., 2018).
55

Figure 3-20. Input parameters for the DCSiE model.

Default values of the DCSiE inputs based on Borkum Riffgrund 1: H = water depth; SELss_1 = single strike sound 

exposure level at distance R_1; R_1 = distance at which measured single strike sound exposure level (SELss_1); 

# Pile Strikes = number of pile strikes; cw = sound speed in water; rhow = density of water; Mz = grain size; 

betased = αα⁄ff; and thetaMach = Mach cone angle. μPa = micropascal; dB = decibel; DCSiE = damped cylindrical 

spreading in Excel; kg = kilogram; kHz = kilohertz; m = meter; re = with reference to; s = second; SEL = LLEE = sound 

exposure level.

The parameter betased (ββsed) is related to the sediment attenuation coefficient αα according to:

ββsed = αα

ff Equation 3.19

Often the value of αα is specified in decibels per wavelength (abbreviated dB⁄λλ or dB λλ−1), in which case 

it can be converted to ββsed (in dB (m kHz)-1

) by dividing by the sediment sound speed (in km s-1). Thus:

ββsed

dB (m kHz)−1 = αα (dB λλ−1 ⁄ )cc (km s−1 ⁄ )

. Equation 3.20

In this example, αα = 0.5 dB⁄λλ and c = 1.7967 km s-1

. Therefore (to four significant figures):

ββsed = αα

ff = 0.2783 dB (m kHz)−1 Equation 3.21
56

The main DCSiE outputs predict effect ranges to PTS, TTS, injury, and behavior using the criteria from 

Section 1.5. The tool is applicable to marine mammals (LF cetaceans, MF cetaceans, HF cetaceans, and 

phocid pinnipeds), fishes and sea turtles. For fishes, both Stadler and Woodbury (2009) and Popper et al. 

(2014) criteria were considered, enabling their implications to be compared for identical conditions. An 

example is shown for marine mammals in Figure 3-21.

Figure 3-21 shows the ranges to TTS for MF cetaceans based on the BR1 example given in Figure 3-18. 

Predicted effect ranges are plotted from the pile axis of symmetry (between half the pile diameter [or 1 m 

if larger]) to 5,000 m. Results are not given at ranges greater than 5,000 m because the measurements 

used for validation (Figure 3-18) only extend to 5,000 m.

However, Figure 3-5 suggests the DCS validity holds for ranges where rr < 8.7 km; based on ranges that 

satisfy αα < 20 dB and based on the example of αα = 2.3 dB km-1

. Until new measurements become 

available, the interim suggestion is to use DCS up to this distance (20 dB/αα); beyond that distance, switch 

to applying a power law using FF = 25. (FF = 25 corresponds to dipole propagation and is in reasonable 

agreement with measurements for the Gemini wind farm.).
57

Figure 3-21. DCSiE model example outputs for MF cetacean criteria.

Upper panel: graph of SELcum (dB re 1 μPa2 s) and LLpppp, LLpp,pk (dB re 1 μPa) versus range (m), calculated using the 

input parameters shown in Figure 3-20. Lower panel: table of effect ranges in meters (green cells), and the criteria 

(white cells). μPa = micropascal; dB = decibel; DCSiE = damped cylindrical spreading in Excel; LLpppp = LLpp,pk = peak

sound pressure level; m = meter; MF = mid-frequency; NMFS = National Marine Fisheries Service (2018a); 

PTS = permanent threshold shift; re = with reference to; s = second; SELcum = LLEE,cum = cumulative sound exposure 

level; SELw,24h = LLEE,w24h = cumulative sound exposure level; TTS = temporary threshold shift.

3.6 Conclusions

A recently developed DCS model permits extrapolation of acoustic metrics from LLEE based on physical 

principles, leading to an improved understanding and prediction of the propagation mechanism in the first 

few kilometers from the pile. The form of TL in DCS is 10 log10

rr

rr1

dB + αα(rr − rr1). A comparison 

between the DCS and PSLM revealed the DCS model performs better out to 5 or 6 km, after which it 

reaches its limitations and underestimates LLEEbeyond this range for the COMPILE test case. For the 

Behavior

SELw,24h Lpk SELw,24h Lpk Lrms 

dB re 1 μPa2∙s dB re 1 μPa 

(unweighted) dB re 1μPa2

∙s dB re 1 μPa 

(unweighted)

dB re 1 μPa 

(unweighted)

Mid-frequency 

cetaceans

185 230 170 224 160

Mid-frequency 

cetaceans

30 0 753 3 > 5000

RESULTS (m)

Hearing Group

Impulsive Signals (NMFS 2018)

PTS Onset TTS Onset
58

German wind farm site BR1, there are measurements showing good agreement with DCS predictions up 

to 5 km, and no measurements beyond that range.

DCS enables estimation of LLEE at ranges that satisfy αα < 20 dB. For ranges that do not satisfy this 

inequality, the DCS model is expected to overestimate TL and underestimate LLEE. Therefore, it remains 

necessary to supplement the DCS model with an alternative method at long ranges, where the field 

appears to follow a power law instead of an exponential decay. Figure 3-5 suggests the possibility of 

extrapolating for αα < 20 dB using the PSLM with FF~35 (based empirically), although caution is 

warranted and validation is needed for this untested approach. In Section 3.5.3, extrapolating using 

F = 25 was recommended as a precautionary approach.

While systematic validation of the empirical correlations for LLpp,pk and LLpp,rms to LLEE is needed, 

extrapolation of LLEE to estimates of LLpp,pk and LLpp,rms may be permitted. In this way, impact assessments 

involving the metrics LLEE, LLpp,pk, and LLpp,rrrrrr can be carried out based on a single measurement of LLEE near 

the pile, without requiring measurements of LLpp,pk or LLpp,rms. 

Conversion from LLEE to LLpp,pk (and LLpp,rms) remains an empirical correlation, and thus is limited to the

range of parameters (water depth, sediment type, hammer energy, and pile diameter) considered during

the regression analysis. Therefore, the approach described in this chapter is applicable, but the values are 

site specific. A validation of the recommended method at a suitable U.S. site, though outside this project’s 

scope, is needed to test the accuracy of the predictions of LLEE, LLpp,pk, and LLpp,rms. A validation study likely 

would lead to improved understanding of the observed correlations and the possibility of improving these 

further.

Finally, the concept of LLpp,eff was introduced alongside LLpp,90% as an alternative metric to represent LLpp,rms. 

Both LLpp,eff and LLpp,90% are measures of the LLpp,rms, but they are calculated in different ways. Using LLpp,eff

leads to stronger (more robust) correlations with LLEE than LLpp,90%. Therefore, the LLpp,eff metric is suggested 

as a supplement to LLpp,90%.

4 Model Verification and Validation Methods

This section describes the verification and validation of the acoustic modeling approach composed of an 

FE model of the pile (driven by a hammer forcing function) and a PE of sound propagation from the pile 

to a distant receiver. The FE output generated the sound field around the pile, which was then used as the 

PE starter field. The acoustic model (comprising the FE model and PE propagation model) was validated 

by comparison to available field measurements. The primary data set was the field observations that 

included received acoustic pressure versus range in shallow water from the Gemini wind farm in the 

North Sea (http://geminiwindpark.nl/e_home.html; Binnerts et al., 2016). For each site, a full numerical 

acoustic computation was performed and compared with the measured data, then a parametric fit to the 

data was generated. In general, a comparison of measurements with an acoustic model is only a validation 

of the model to the extent that the environment is well measured (e.g., sediment, bathymetry, water 

properties) and the source forcing function (COMPILE, 2014) is understood. A comparable location of a 

pile driven by an impact hammer was selected, appropriate databases were used for environmental 

variables, and the full acoustic-field computation was performed to calculate the acoustic metrics. The 

site-specific parameters from Gemini permitted the definition of a subgroup of parameters used for 
59

selecting the acoustic geometric fits. A statistical analysis (goodness of fit, root-mean-square-error) was 

performed to test the ability of the modeled data and the parametric fit to match the measured acoustic 

metrics. 

Data from the 2014 COMPILE workshop (Lippert et al., 2016) were used for model verification, data

from Gemini for model validation, and data from Gemini or Luchterduinen for the DCSiE spreadsheet 

calculator (Section 3).

Model verification was intended to check that model outputs were consistent with expectation (i.e., did 

the models solve the specified equations correctly?). By comparison, the purpose of model validation was 

to investigate the extent to which these models predicted the measured sound field in the vicinity of a 

pile-driving operation (i.e., how well did the specified equations represent reality?).

For model verification, the results of the 2014 COMPILE workshop (henceforth referred to as 

COMPILE I; Lippert et al., 2016, to distinguish it from the second COMPILE workshop [COMPILE II]) 

were used. The COMPILE I workshop aimed to gain insight into the advantages and disadvantages of 

different methods for modeling the sound field radiated by underwater pile driving. To achieve this goal, 

workshop participants were supplied with a fully specified test scenario and requested to make predictions 

based on the specifications. The workshop itself provided a forum for model-to-model comparisons 

(Lippert et al., 2016). The test scenario involved a pile that was 25 m long and 2 m in diameter driven by 

a particular hammer forcing function. The propagation medium was a Pekeris waveguide with a water 

depth of 10 m and a sandy sediment. 

The model validation used measurements made during construction of the Gemini wind farm in the North 

Sea on the Dutch continental shelf at a water depth of approximately 30 m (Figure 4-1; ITAP, 2015b). 

These measurements had been used for the validation of the TNO’s Aquarius model (Binnerts et al., 

2016).
60

Figure 4-1. Location of the Gemini wind farm.

In the North Sea (54.036 N, 5.963 E), about 60 km north of the Netherlands’ coast. Source of background map: 

Thematicmapping.org, OpenStreetMaps, and OpenSeaMap.

Three different versions of Aquarius were considered in Binnerts et al. (2016). The Aquarius version of 

interest for this project used an FE source model coupled with an NM acoustic propagation model. For 

model verification and validation, the same FE source model was used and coupled with Peregrine, a PE 

propagation model. The FE model predicts the sound field at a specified range, which was used as a 

starting field for Peregrine.

4.1 Finite Element Method to Estimate the Sound Field Around the Source 

The source model consisted of a linear, axially symmetric frequency domain FE model in COMSOL, a 

Multiphysics® simulation software. A schematic overview of the FE setup is provided in Figure 4-2. The 

depth and range dimensions of the sediment and water waveguide had infinite extent and were modeled 

by lining the bottom and outer range of the domain with perfectly matched layers (PMLs), which quickly 

attenuate all outgoing waves (Berenger, 1994; Zampolli et al., 2008). This heavy attenuation prevented 

substantial reflections at the FE domain boundaries. The FE model was excited by applying a unit force to 

the area of the hammer impact (Figure 4-2). When a pile is in sediment and receives a hammer impact, 

acoustic waves enter the sediment and generate seismic body waves called P- and S-waves that move 

through the sediment. Damping due to friction between the pile and sediment was represented by 

applying an equivalent damping to the P- and S-waves along the section of the pile embedded in the 

sediment. The equivalent damping was empirically determined using the FE source model for a 

pile-driving scenario in combination with acoustic measurements at close range (a few meters) to the pile.
61

Boundary conditions occur between materials with different states (i.e., air, solid, and fluid). In pile 

driving, three interfaces were addressed: solid/air, air/water, and solid/fluid-sediment. For the interface 

between the solid (hammer) and air, stress release boundary conditions were applied. For the interface 

between air and water, pressure release boundary conditions were applied. The boundary conditions 

between solid (pile surface) and fluid (water) consisted of enforced continuity of normal force and normal 

velocity. A slip condition was applied in the direction tangential to this interface. 

Figure 4-2. Schematic overview of a Finite Element model. 

A generic pile driving case (left) and the COMPILE I benchmark case (right). H = water depth; PML = perfectly 

matched layers.

The sediment can be modeled as either (1) an elastic solid (i.e., a solid that is deformed by application of 

a force that returns to its original shape once the force is removed, thus supporting shear waves), or (2) an 

equivalent fluid (i.e., a medium that supports compressional waves but not shear waves). The FE model 

allows the sediment material properties to vary with depth for an accurate representation of the effects of 

sub-bottom layering. In addition, the FE model allows the sediment material properties to be defined as a 

function of frequency for an accurate representation of the effects of material dispersion caused by the 

pile and sound waves. 

In the absence of relevant information about the dependence of sediment properties on depth and 

frequency, constant values (i.e., no sub-bottom layering) were assumed for the validation cases. This 

means the sound speed and density were constants, while a constant damping term means the absorption 

coefficient increased linearly with frequency. The absence of sub-bottom layering implies 

downward-traveling waves in the sediment will not re-enter the water column, as they would if there were 

reflective sediment layers. The downward-only waves condition allows for the use of an equivalent fluid. 

The effects of shear waves in the sediment, on the reflection coefficient of the water-sediment interface, 

were represented by changing the density and damping of the fluid. For the validation case, the sediment 

was modeled as an equivalent fluid. Therefore, the source model involved a range-independent 

bathymetry with a fluid sediment (i.e., the water waveguide in the source model was a Pekeris 

waveguide) (Jensen et al., 1994). Use of a Pekeris waveguide implies a perfectly reflecting sea surface, an 

isovelocity water column (i.e., uniform sound speed throughout the water column), and an infinite 

uniform fluid sediment. 
62

4.2 Parabolic Equation Method

The PE model interfaced with the FE model and was used to propagate the sound field.

4.2.1 Propagation

The PE model was the method used for solving the wave equation by marching the outgoing field (and 

ignoring the incoming field) one range step at a time using a one-way propagator. This method was 

efficient and handled high-angle energy (up to 88° from the horizontal) as well as range dependence in 

the environment. Range dependence was not an issue for the model verification and validation because 

the validation environments had uniform bathymetries, SSPs, and waveguides. This is not always the 

case, however, and use of a RAM is appropriate for impact studies in shallow water where bathymetric 

changes are substantial. The types of bathymetric changes that require special modeling were addressed in 

the sensitivity analysis (Section 6). 

4.2.2 Integration with Finite Element Starter Field

The PE model used in this study was a fluid-fluid model, meaning it did not handle shear waves in the 

sediment or in the pile. Also, the water column did not support shear wave propagation; it is expected that 

this is a reliable approximation for modeling the propagation from a few meters beyond the pile out to 

substantial ranges. The model used an FD approach, meaning differential operators were approximated by 

taking small finite steps, with the size of the range and depth steps being inversely proportional to 

frequency. Therefore, the size of the two-dimensional (2D) computational grid (and hence the 

computational cost) increased with the frequency squared. The PE typically is run using a point-source 

starter function generated by allowing the sound waves to spread spherically from the source location, and 

the PE algorithm marches the complex pressure field forward one range step at a time. However, the FE 

model provided the field generated by the whole pile, which did not approximate to a point source; thus, 

the sound field did not propagate spherically. In order to accommodate the FE starter field at a specified 

range, the PE algorithm was modified and then allowed to march the complex pressure field forward one 

range step at a time. 

The complex pressure field generated by the FE model at 3 m was used as the starter field for the PE 

model. Both the PE and FE models are frequency-domain models, meaning each frequency is run 

independently. The band from 0 to 2,000 Hz was computed with 0.5 Hz steps. When the output pressure 

field was mapped to the time domain by taking the inverse fast Fourier transform (IFFT), the 0.5 Hz 

frequency spacing produced a time window of 2 seconds. The pressure field predicted by the FE model 

was sampled 3 m from the pile center, from the water surface down to a depth well below the pile toe 

(95 m and 65 m for Gemini and COMPILE I, respectively) with a resolution of 0.05 m. This sampled 

field was used as the starter field for the PE. Thus, the acoustic field was computed out to long ranges 

efficiently with high-angle support and range dependence (when necessary). 

4.3 Model Verification – COMPILE I

The COMPILE I scenario (2014) is fully defined in Lippert et al. (2016). The scenario involves a straight 

homogenous steel pile without an anvil; the hammer force was applied directly to the top surface of the 

pile. The pile length, outer diameter, and thickness were 25, 2, and 0.05 m, respectively. The height of the 
63

water column was 10 m, and the pile penetrated 15 m into the sediment, implying the pile top aligned 

with the water surface.

4.3.1 Pile Characterization and Forcing Function

The parameters defining the steel pile are listed in Table 4-1 and were converted to density, wave speed, 

and absorption coefficients in Table 4-2. For more information, see Lippert et al. (2016).

Table 4-1. COMPILE I steel pile parameters (From: Lippert et al., 2016).

Material property Unit Value

Young’s modulus GPa 210

Poisson ratio 1 0.30

Density kg m-3 7,850

Absorption P-wave αα/ff

(section in the sediment) Np m-1 Hz-1 3∙10-5

Absorption S-wave αα/ff

(section in the sediment) Np m-1 Hz-1 11∙10-

5

αα = absorption coefficient; ff = frequency; GPa = gigapascal; Hz = hertz; m = meter; Np = neper.

Table 4-2. COMPILE I steel pile parameters, derived from Table 4-1.

density 

(kg m-3)

P-wave speed 

ccpp

(m s-1)

P-wave 

absorption αα

(dB λλ-1)

S-wave speed 

cc

(m s-1)

S-wave 

absorption αα

(dB λλ-1)

Pile in water 7,850 6,001 0 3,208 0

Pile in sediment 7,850 6,001 1.564 3,208 3.065

αα = absorption coefficient; λλ = wavelength; dB = decibel; kg = kilogram; m = meter; s = second. P-wave speed ccp = speed of sound 

(compressional wave) in steel; S-wave speed ccs= speed of shear wave in steel.

For COMPILE I, the forcing function was specified by the organizers (Lippert et al., 2016). The specified 

forcing function is plotted in Figure 4-3 and given by the following formula:

FF(tt) = FFp �

tt/ttr tt < ttr

exp

ttr − tt

ttd

tt > ttr

Equation 4.1

where: 

F = force

t = time

FFp = peak force (equal to 20 meganewtons [MN])

ttr = time of maximum force (equal to 0.2 milliseconds [ms])

ttd = pulse duration (equal to 1.6 ms)
64

Figure 4-3. COMPILE I forcing function 

(from Lippert et al., 2016). MN = meganewton; s = second.

The resulting impulse is:

II = FFp �

ttr

2 + ttd� = 34 kN s, Equation 4.2

where: 

I = impulse

FFp = peak force (20 MN)

ttr = time of maximum force

ttd = pulse duration

kN s = kilonewton second

The acoustic energy flux density in the water column is calculated as the integral over depth (through the 

height of the water column) and time of the horizontal component of sound intensity. The total 

(horizontal) energy flux, in joules (J), at any distance (rr) is obtained by multiplying this quantity with the 

circumference of a circle of radius rr. The energy flux values are shown in Table 4-3 at the eight ranges

defined in COMPILE I.

Table 4-3. Calculated (horizontal) energy flux density and total (horizontal) energy flux at eight 

ranges. 

Parameter

Range

Unit Range (m)

1 11 31 750 1,500 11 ⋅ 11 33 22 ⋅ 11 33 55 ⋅ 11 33

Energy flux 

density J m-1 930 80.6 26.5 0.503 0.152 0.00109 6.74⋅10-5 2.21⋅10-6

Total energy 

flux kJ 5.84 5.57 5.16 2.37 1.43 0.0685 0.0085 0.0007

J = joule; kJ = kilojoule; m = meter.
65

4.3.2 Propagation Medium

Density, Sound Speed, and Attenuation

Density, sound speed, and attenuation profiles in the water and sediment layers are specified in 

Table 4-4. 

Table 4-4. COMPILE I water and sediment parameters.

Density

ρρ

(kg m-3)

P-wave speed

cc

(m s-1)

P-wave absorption

αα

(dB λλ-1)

water 1,025 1,500 See explanation below and Lippert et al. (2016)

sediment 2,000 1,800 0.4690

αα = absorption coefficient; cc = water or sediment sound speed; λλ = wavelength; ρρ = water or sediment density; dB = decibel; 

kg = kilogram; m = meter; s = second.

Absorption Coefficient in Water and Sediment

In the water, the absorption coefficient (αα) in nepers per meter (Np m-1) is (Lippert et al., 2016):

αα

Np/m = 1.4 ∙ 10−5 ff2

ff2 + ffB

2 + 5.58 ∙ 10−3 ff2

ff2 + ffMg

2 . Equation 4.3

Converting to decibels per kilometer gives:

αα

dB/km = 20loge 10 �0.014 ff2

ff2 + ffB

2 + 5.58 ff2

ff2 + ffMg

2 � Equation 4.4

The absorption coefficient in the sediment is (Lippert et al., 2016):

αα/ff = 3 ∙ 10−5Np/(m Hz)

or (to 5 significant figures):

αα/ff = 0.26058 dB/(m kHz).

The attenuation per cycle is obtained by multiplying left and right sides by the speed of sound in the 

propagation medium (in this case the sediment):

cc = 1,800 m s−1

αα λλ = 0.4690 dB.
66

Dividing through by λλ gives the value shown in Table 4-4:

αα = 0.4690 dB/λλ, 

where: 

dB = decibel

Hz = hertz

Np = neper

αα = absorption coefficient

ff = acoustic frequency

λλ = acoustic wavelength in the sediment

5 Verification and Validation Results 

5.1 Model Verification Results for COMPILE I 

In order to validate the hybrid approach combining FE/PE models, the same output fields as the 

COMPILE I workshop were computed. For COMPILE I, the FE model was combined with an NM 

propagation model (FE/NM). The same FE model was used in the present study. Given there is no “true” 

solution for this scenario, in order to be considered verified, the hybrid FE/PE model approach’s results 

would need to agree with the hybrid p(t) predictions at depths of 3, 11, and 31 m, and with the FE/NM LLEE

predictions at ranges 1 and 3 km. These distances are from the pile axis of symmetry. (The same distance 

convention is adopted throughout this report). At greater distances, there are larger spreads between 

model predictions caused by different assumptions made by the various propagation models. FE model 

outputs were requested at depths of 1, 5, and 9 m at nearby receivers at 1, 11, and 31 m range. The distant 

receiver positions are illustrated in Figure 5-1. The FE field was matched with the PE at a range of 3 m, 

meaning there would be no meaningful PE results less than 3 m (from the pile axis of symmetry). 

Therefore, the only relevant nearby results were at ranges of 11 and 31 m. Other acoustic metrics such as 

LLpp,pk and LLpp,rms were derived from p(t).
67

Figure 5-1. COMPILE I output grid for distant receivers.

From: Lippert et al., 2016. m = meter.

5.1.1 Comparisons 

The first comparison of p(t) was between the PE field and the FE/NM field. Figure 5-2 shows p(t) for a 

5 m deep receiver at range of 31 m. The results provided verification of the FE/PE model predictions for 

p(t).

Figure 5-2. Verification of FE/PE with the FE/NM models based on the COMPILE I workshop.

Sound pressure versus time at 5 m depth and 31 m range. The TNO FE/NM model (blue), FE/PE model (black); 

UoS/NPL FE model (orange). FE = finite element; NM = normal mode; Pa = pascal; PE = parabolic equation; 

s = second; TNO = Netherlands Organisation for Applied Scientific Research; UoS/NPL = University of 

Southampton/National Physical Laboratory.
68

The following research institutions participated in and contributed results to the COMPILE I workshop 

(Figure 5-3): Curtin University, Centre for Marine Science and Technology (Australia, CMST), Hamburg 

University of Technology (Germany, TUHH), Bundeswehr Technical Centre for Ships and Naval 

Weapons (Germany, WTD 71), JASCO Applied Sciences (Canada, JASCO), Seoul National University 

(South Korea, SNU), Netherlands Organisation for Applied Scientific Research (Netherlands, TNO), and 

University of Southampton (UK, UoS) in collaboration with National Physical Laboratory (UK, NPL).

Figure 5-3. COMPILE I participants and models.

(From: Lippert et al., 2016)

Acoustic metrics were computed for the ranges and depths of the COMPILE I workshop. LLEE and LLpp,pk

were compared. For LLEE, the time-integrated squared sound pressure was plotted as a function of range 

(Figure 5-4). This figure is an overlay of six results from the COMPILE I workshop participants, 

including the FE/NM and FE/PE model results. The top panel of Figure 5-4 is LLEE at a depth of 9 m for 

receivers at ranges of 1, 11, and 31 m. The PE result was valid from a distance of 3 m because that is the 

range at which the field was mapped from the FE code to the PE code. The FE/PE results for LLEE at 11 and 

31 m aligned with the other five predictions. The bottom panel is the received LLEE at 1 m depth at ranges 

of 0.75, 1.5, 10, 20, and 50 km. The FE/PE model results aligned with the other COMPILE I results. 

These results provided verification of the FE/PE model predictions for LLEE.
69

Figure 5-4. Verification of FE/PE with the COMPILE I workshop results for SEL versus range.

Top: SEL in the nearby receivers at 9 m depth; Bottom: SEL in the distant receivers at 1 m depth. The FE/PE 

(cyan +) are overlaid on the COMPILE I data (From: Lippert et al., 2016). μPa = micropascal; dB = decibel; FE = finite 

element; m = meter; PE = parabolic equation; re = with reference to; s = second; SEL = LLEE = sound exposure level.

SEL units = dB re 1 μPa2 s.

LLpp,pk was plotted, and Figure 5-5 is an overlay of six results from the COMPILE I workshop and the 

FE/PE results (plotted as the cyan +). The top panel of Figure 5-5 is LLpp,pk at a depth of 1 m for the nearby 
70

receivers at 1, 11, and 31 m ranges. The FE field is matched to the PE at a range of 3 m; thus, the PE 

results were not plotted for 1 m. The FE/PE model results for LLEE at 11 and 31 m ranges aligned with the 

other six predictions. The lower panel of Figure 5-5 is LLpp,pk at a depth of 5 m at the distant receivers at 

ranges of 0.750, 1.5, 10, 20, and 50 km. The FE/PE result aligned with the other COMPILE I results. 

These results provided verification of the FE/PE model predictions for LLpp,pk.
71

Figure 5-5. Verification of FE/PE with the COMPILE I workshop results for LLpp,pppp.

Top: nearby receivers at 1 m depth (From: Lippert et al., 2016). Bottom: the distant receivers at 5 m depth 

(From: Lippert et al., 2014). The FE/PE model solutions (cyan +) are within the spread of the other models. 

μPa = micropascal; dB = decibel; FE = finite element; m = meter; PE = parabolic equation; re = with reference to; 

SEL = LLEE = sound exposure level; SPLpeak = LLpp,pk = peak sound pressure level. LLpp,pk units = dB re 1 μPa.
72

5.1.2 Weighted SEL Comparisons

The TNO and Curtin University p(t) submissions to the COMPILE I workshop and the FE/PE modeled 

fields were made available. Each COMPILE I workshop participant’s p(t) was only available at 31 m; 

therefore, that was the range where LLEE,ww was computed. A comparison was made of the LLEE,ww

computations for these three modeled results. Figures 5-6, 5-7, and 5-8 show the comparison for the LF, 

MF, and HF cetacean frequency weightings, respectively. There is consistent agreement across all models 

for the three groups of weightings and most decidecade bands. The decidecade bands with the largest 

discrepancies between the models were the frequencies below 32 Hz, which were low level and difficult 

to model accurately in shallow water.

Figure 5-6. Comparison of the decidecade band SEL computation for LF cetaceans. 

Decidecade band nominal center frequencies are 8 Hz to 2 kHz. 

The total frequency range covered by these bands is 7.1 Hz to 2.2 kHz. TNO (black), Curtin University (red), 

FE/PE (blue). μPa = micropascal; dB = decibel; FE = finite element; Hz = hertz; LF = low-frequency; PE = parabolic 

equation; re = with reference to; s = second; SEL = LLEE = sound exposure level; TNO = Netherlands Organisation for 

Applied Scientific Research. SEL units = dB re 1 μPa2 s.
73

Figure 5-7. Comparison of the decidecade band SEL computation for MF cetaceans. 

TNO (black), Curtin University (red), FE/PE (blue). μPa = micropascal; dB = decibel; FE = finite element; Hz = hertz; 

MF = mid-frequency; PE = parabolic equation; re = with reference to; s = second; SEL =LLEE= sound exposure level; 

TNO = Netherlands Organisation for Applied Scientific Research. SEL units = dB re 1 μPa2 s.

Figure 5-8. Comparison of the decidecade band SEL computation for HF cetaceans.

TNO (black), Curtin University (red), FE/PE (blue). μPa = micropascal; dB = decibel; FE = finite element; 

HF = high-frequency; Hz = hertz; PE = parabolic equation; re = with reference to; s = second; SEL = LLEE = sound 

exposure level; TNO = Netherlands Organisation for Applied Scientific Research. SEL units = dB re 1 μPa2 s.
74

5.1.3 Frequency Accumulation of Weighted Sound Exposure Level 

The impact of the frequency weighting (Figure 1-6; NMFS, 2018) on the decidecade band LLEE received by 

selected marine mammal hearing groups was examined. Of particular interest was whether the assumption 

of truncating the propagation modeling at an upper frequency band of 1 kHz was justifiable (to address 

the concern: is there energy above 1 kHz that affects the broadband LLEE?). To determine this, the LLEE,w was 

added across all decidecade bands from the lowest frequency to an upper frequency limit, and the result 

was calculated as a function of the upper frequency. This resulting quantity is referred to as the 

frequency-accumulated SEL (faSEL). The faSEL result for LF cetaceans is plotted in Figure 5-9. 

Figure 5-9. Comparison of the frequency-accumulated SEL (faSEL) computation for LF 

cetaceans.

TNO (black), Curtin University (red), FE/PE (blue). μPa = micropascal; dB = decibel; FE = finite element; 

LF = low-frequency; PE = parabolic equation; re = with reference to; s = second; SEL = LLEE = sound exposure level; 

TNO = Netherlands Organisation for Applied Scientific Research. SEL units = dB re 1 μPa2 s. 

The majority of energy contributing to the faSEL was from frequencies below 400 Hz. There was 

approximately a 1 dB loss of the broadband LLEE (accumulated over all frequencies from 7.1 Hz to 2.2 kHz) 

for LF cetaceans, and this assumed there was no net contribution of energy from above 2.2 kHz. The MF 

cetacean faSEL is plotted in Figure 5-10. Frequencies above 1 kHz will impact the weighted LLEE,cum for 

MF and HF cetaceans. 
75

Figure 5-10. Comparison of the frequency-accumulated SEL (faSEL) computation for MF 

cetaceans.

TNO (black), Curtin University (red), FE/PE (blue). μPa = micropascal; dB = decibel; FE = finite element; Hz = hertz;

MF = mid-frequency; PE = parabolic equation; re = with reference to; s = second; SEL = LLEE = sound exposure level; 

TNO = Netherlands Organisation for Applied Scientific Research; SEL units = dB re 1 μPa2 s.

To determine the contribution to the LLEE,w from frequencies above 2.2 kHz on the broadband LLEE, the full 

spectrum was needed up to 20 kHz. This was not possible with the COMPILE I data set because the 

upper frequency bound was 2.2 kHz. Therefore, to address energy at frequencies above 2.2 kHz, 

measured data from the Gemini wind farm were used.

5.2 Model Validation for Gemini Measurements: Piles U8 and Z2

5.2.1 Inputs – Gemini

5.2.1.1 Pile Geometry and Forcing Function

The forcing function [g(t)] was determined by matching the measured and modeled sound fields at 732 m 

from the U8 pile. This forcing function was used to predict the sound field for U8 at other distances and 

for Z2 (Figure 5-11). (U8 and Z2 are the names of two piles from the Gemini wind farm.) The length, 

thickness, and diameter (at the top and bottom of each pile section) are provided in Table 5-1. The water 

depth, pile penetration, sample interval, and range for the U8 and Z2 piles are provided in Table 5-2. The 

material properties used for the U8 and Z2 pile scenarios are provided in Table 5-3.
76

Figure 5-11. Definition of the pile geometries. 

Top: U8; Bottom: Z2. The length, thickness, and diameter (at top and bottom of each section) are provided in 

Table 5-6.
77

Table 5-1. Dimensions of the U8 and Z2 pile geometries.

Section

U8 Z2

Length 

(m)

Diameter

(m) Thickness

(mm)

Length 

(m)

Diameter

(m) Thickness

(mm) Upper Lower Upper Lower

1 0.230 5.500 5.500 80 0.230 5.500 5.500 80

2 2.000 5.500 5.500 80 2.000 5.500 5.500 80

3 3.000 5.500 5.500 80 3.000 5.500 5.500 80

4 2.770 5.500 5.500 80 2.770 5.500 5.500 80

5 3.180 5.500 5.778 80 2.800 5.500 5.744 80

6 3.500 5.778 6.083 73 3.500 5.744 6.050 73

7 3.500 6.083 6.388 68 3.500 6.050 6.600 68

8 3.500 6.388 6.694 68 2.800 6.600 6.600 68

9 3.500 6.694 7.000 70 3.500 6.600 6.600 68

10 2.800 7.000 7.000 70 2.540 6.600 6.600 71

11 2.800 7.000 7.000 73 2.540 6.600 6.600 74

12 2.490 7.000 7.000 73 4.000 6.600 6.600 82

13 4.000 7.000 7.000 83 3.500 6.600 6.600 82

14 2.625 7.000 7.000 80 3.500 6.600 6.600 82

15 2.625 7.000 7.000 80 3.500 6.600 6.600 79

16 2.625 7.000 7.000 80 3.500 6.600 6.600 79

17 2.625 7.000 7.000 80 3.500 6.600 6.600 74

18 3.500 7.000 7.000 76 3.500 6.600 6.600 64

19 3.500 7.000 7.000 70 3.500 6.600 6.600 60

20 3.500 7.000 7.000 65 3.420 6.600 6.600 63

21 3.500 7.000 7.000 60 2.300 6.600 6.600 73

22 2.720 7.000 7.000 67 -- -- -- --

23 2.000 7.000 7.000 77 -- -- -- --

m = meter; mm = millimeter.

Table 5-2. Water depth, pile penetration, and sample interval for the U8 and Z2 piles.

Parameter

U8 Z2

Value Unit Value Unit

Water depth, at pile 34 m 30 m

Sediment penetration, final 27.41 m 28.4 m

Sediment penetration, model 20.8 m 20.8 m

Sample interval 0 – 95 m 0 – 95 m

Sample range 5 m 5 m

m = meter.
78

Table 5-3. Material properties used for the U8 and Z2 cases.

Parameter Value Unit

Sound speed water 1,510 m s-1

Density water 1,026 kg m-3

Sound speed sediment 1,810 m s-1

Density sediment 2,140 kg m-3

Young’s modulus steel 210 GPa

Poisson’s ratio steel 0.3 --

Density steel 7,850 kg m-3

Sediment damping* 0.88 dB λλ-1

P-wave steel (in sediment**), αα 1.5 dB λλ-1

S-wave steel (in sediment**), αα 3.0 dB λλ-1

αα = absorption coefficient; λλ = wavelength in the sediment or steel, as appropriate; dB = decibel; GPa = gigapascal; kg = kilogram; 

m = meter; s = second.

* The sediment damping was not used in the source model.

** This equivalent damping value accounts for losses due to pile/sediment interaction and was only applied to the pile section that 

penetrated the sediment.

The hammer used to drive the pile was an IHC Hydrohammer S-2000. It was assumed the hammer was 

mounted on the pile via an anvil, as sketched in the IHC Merwede (2015), “Large pile diameters”

brochure (Figure 5-12). The mass of the anvil is listed as 120 metric tons. A detailed view of the anvil 

geometry in the FE model is shown in the right panel of Figure 5-12. The excitation force is distributed 

over the flat top surface. Based on this geometry and the density of steel in the model, the mass of the 

anvil in the model is approximately 135 metric tons. The axisymmetric representation of the meshed pile, 

anvil, and environments as seen in COMSOL-software is shown in Figure 5-13. A close-up comparison 

of the mesh at the water surface and between the grids is shown as well.

Figure 5-12. Sketch and detail of the anvil.

Sketch of the anvil used for piles U8 and Z2 from brochure IHC Hydrohammer (left; IHC Merwede [2015]). Detailed 

view of the anvil geometry in the finite element model (right). The excitation force is distributed over the flat top 

surface.
79

Figure 5-13. Axisymmetric representation of the meshed pile, anvil, and environments.

Representation in COMSOL (left). Comparison of a close-up of the mesh at the water surface, between the grid for 

lowest frequencies (middle) and highest frequencies (right).

5.2.1.2 Gemini Wind Farm Model Validation

The combined FE/PE methodology was validated by comparing modeled results to measurements 

obtained during construction of the Gemini wind farm. 

5.2.2 Data Analysis

In order to compare acoustic model predictions with observations, it was necessary to compute 

quantitative acoustic metrics from the measured and simulated time series. The three acoustic metrics 

chosen were LLpp,pk, LLEE, and LLpp,90%. LLpp,pk is the maximum received pressure across the signal reception. 

LLEE is the level of the time-integrated sound pressure squared. LLpp,90% is the root-mean-square pressure 

evaluated in the time window between the 5th and 95th signal energy percentiles.

After processing, the measurements generated a set of files with time series from single hydrophone sites 

at ranges of 0.732, 7.017, 31.816, and 65.764 km. Each site had two receivers, positioned 2 and 10 m 

above the seafloor. The water depth varied by less than 2 m for the entire region. Data were sampled at a 

rate of 48 kHz and were time-aligned for each location. A 3-minute piling sequence was selected with a 

hammer energy of 750 kilojoules (kJ), approximately constant across the sequence. The start time of this 

sequence was 12:34 UTC on July 21, 2015. The pile strike rate was 0.76 Hz (one strike every 
80

1.32 seconds). The data were further processed to isolate the received signal for each strike. This was 

done by reshaping the single time-series vector into a slow-time/fast-time matrix corresponding to 

1.3 seconds of fast-time data. A matrix was generated for each pile strike. This approach worked well, but 

occasionally a strike arrival was on the boundary between two windows and resulted in a mislabel or 

mis-association of strike arrivals between two instruments. This is problematic because, if an arrival spans 

the boundary, then the LLEE computation only includes a portion of the signal. This issue was addressed by 

manually discarding outliers. An example of a single received pile signal for U8 for measurement 

position 1 (MP1) at 732 m range, recorded from a 2-m depth hydrophone, is shown in Figure 5-14.

Figure 5-14. Sound pressure time series, p(t), recorded at 732 m range, for the U8 pile.

kPa = kilopascal; s = second.

Acoustic metrics were computed for each pile strike (i.e., across each row of fast-time in the 

fast-time/slow-time matrix); those computed for the MP1 location of the U8 pile are shown in 

Figure 5-15. The acoustic metrics shown in Figure 5-15 for the 10- and 2-m height hydrophones were 

grouped by series, meaning pile strikes 1 to 287 are from the 10-m hydrophone. The two notable drops in 

level correspond to the 1-minute periods when there were no pile strikes. There are a few pile strikes for 

each metric that fall as much as 10 dB. These represent pile strikes where the received signal spanned two 

different slow-time rows, so the LLEE computation only included a portion of the signals. Values more than 

5 dB below the median were dropped manually from the statistics so they would not be used in the 

computation of the received level distributions in subsequent analyses. A notable observation from the 

MP1 location is the uniform 2 to 3 dB difference between the 2- and 10-m hydrophones.
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Figure 5-15. Measured acoustic metrics for the U8 pile at 732 m range. 

Peak sound pressure level (LLpp,pk) (Red); SEL (Black); Root-mean-square sound pressure level (LLpp,rms) (Blue). 

Measurements are from the 2-m and 10-m hydrophones. μPa = micropascal; dB = decibel; re = with reference to; 

s = second; SEL = LLEE = sound exposure level. LLpp,pk and LLpp,rms units = dB re 1 μPa; SEL units = dB re 1 μPa2 s.

In order to compare the model results and measured observations, a distribution of each data set was

computed. After manual thresholding (low values being discarded due to the signal straddling a 

boundary), the probability density function (PDF) and cumulative density function (CDF) were 

computed. The PDF and CDF for the U8 pile at 732 m range are shown in Figure 5-16. 

Figure 5-16. Normalized likelihood for LLpp,pppp, for the U8 pile at 732 m range. 

Probability density function (PDF) (Black) and cumulative density function (CDF) (Blue). μPa = micropascal;

dB = decibel; m = meter; re = with reference to; SPLpeak = LLpp,pk = peak sound pressure level.

LLpp,pk units = dB re 1 μPa.
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The statistics at the MP2 location (7 km) show a higher right-hand tail, which is driven by the rise in 

received values at the end of the sequence of strikes (Figure 5-17). The PDF and CDF for the U8 pile at 

7 km range are shown in Figure 5-18. 

Figure 5-17. Acoustic metrics for the U8 pile at 7 km range. 

Red: peak sound pressure level (LLpp,pk); Black: SEL; Blue: root-mean-square sound pressure level (LLpp,rms). 

Measurements are from 2-m and 10-m hydrophones. μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk = peak 

sound pressure level; m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. LLpp,pk and 

LLpp,rms units = dB re 1 μPa; SEL units = dB re 1 μPa2 s. 

Figure 5-18. Normalized likelihood statistics for LLpp,pppp, for the U8 pile at 7 km range.

Probability density function (PDF) (Black) and cumulative density function (CDF) (Blue). μPa = micropascal;

dB = decibel; km = kilometer; LLpp,pppp = peak sound pressure level; re = with reference to. LLpp,pk and 

LLpp,rms units = dB re 1 μPa.
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The MP3 location’s (32 km) acoustic metric values are shown in Figure 5-19. At this range, the 

signal-to-noise ratio dropped, which complicated the computation of LLpp,rms because the noise was 

included in the computation of the root-mean-square sound pressure. The effect of this noise is observed 

in the blue dots, showing a repetitive structure of high values and then lower values. The effect by the 

noise was even more pronounced at the farthest range in the MP4 (66 km range) reception. LLpp,pk was not 

affected by this signal-to-noise issue.

Figure 5-19. Acoustic metrics for the U8 pile at 32 km range. 

Red: peak sound pressure level (LLpp,pk); Black: SEL; Blue: root-mean-square sound pressure level (LLpp,rms). 

Measurements are from 2-m and 10-m hydrophones. μPa = micropascal; dB = decibel; km = kilometer; m = meter; 

re = with reference to; s = second; SEL = LLEE = sound exposure level. LLpp,pk and LLpp,rms units = dB re 1 μPa;

SEL units = dB re 1 μPa2 s.
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At MP4 (66 km range, Figure 5-20), there was a 4-dB difference between the 10- and 2-meter receivers. 

The LLpp,rms value was affected by the position of the signal within the window, making it difficult to 

determine the best values. It was also likely that the LLEE value had acoustic noise increasing its value.

Figure 5-20. Acoustic metrics for the U8 pile at 66 km range. 

Red: peak sound pressure level (LLpp,pppp); Black: SEL; Blue: root-mean-square sound pressure level (LLpp,rms). 

Measurements are from 2-m and 10-m hydrophones. μPa = micropascal; dB = decibel; km = kilometer; m = meter; 

re = with reference to; s = second; SEL = LLEE = sound exposure level. LLpp,pk and LLpp,rms units = dB re 1 μPa;

SEL units = dB re 1 μPa2 s.

5.2.2.1 Sediment Modeling

One of the purposes of this project is to evaluate the influence of critical environmental parameters on the 

propagation of acoustic energy radiating from pile-driving activity. Sediment characteristics are a key 

environmental parameter. Particularly, sediment structure versus depth, which can be a driver in the 

frequency dependence of the propagation. For the analysis performed by TNO on Gemini wind farm data, 

a single half-space seafloor (meaning a seafloor of infinite thickness and uniform properties throughout) 

was used to approximate hard sand, which was the expected sediment type in the North Sea. The seafloor

was modeled as a semi-infinite uniform sediment of sound speed 1,810 m s-1

, density 2,140 kilograms per 

cubic meter (kg m-3

), and HF attenuation 0.88 dB λ-1, implying an attenuation coefficient proportional to 

frequency f. At LFs, it was recognized that the sediment attenuation decreased with decreasing frequency 

more quickly due to dispersion, an effect that was modeled by a frequency-dependent attenuation with 

alpha proportional to f

1.8 at LF (Binnerts et al., 2016).

In addition to the rapid decrease of alpha with decreasing frequency modeled in the TNO report, 

dispersion is known to result in a sound speed that also decreases with decreasing frequency (Zhou et al., 

2009). For this project, rather than model dispersion, the effect of a sediment sound speed gradient was 

applied. In order to model the received energy across the frequency band of 10 to 2,500 Hz, a sediment 

model that had some depth dependence to it was necessary. A deep (infinite), unconsolidated single layer

sediment model with a uniform sediment grain size was chosen, implying that sound speed increases with 

depth due to increasing pressure. This model was completely defined by the sediment grain size (which, 

in “phi” φφ units, is the negative log2 of the mean sediment grain diameter in millimeters). A valuable way 
85

of seeing how grain size relates to sediment type is through the Wentworth grain size chart, originally 

created in 1922, and presented in Figure 5-21 from the U.S. Geological Survey (USGS) Open File Report 

2006-1195 (Williams et al., 2007).

Figure 5-21. Wentworth grain size (φφ) distribution table.

(From: Williams et al., 2007). 
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The mapping from sediment grain size to compressional speed, density, and attenuation was performed 

based on the work of Bachman et al. (1996).

For a sediment grain size 0 φφ, which corresponds to the boundary between coarse sand and very coarse 

sand (referred to henceforth as “hard sand”), the compressional speed and attenuation as a function of 

depth is shown in Figure 5-22.

Figure 5-22. Hamilton-Bachman sediment profile of compressional speed for hard sand (0 φφ).

φφ = grain size.

The broadband acoustic pressure field was computed as a function of range (at a receiver depth of 2 m 

above the sediment) for each integer grain size between -3 φφ (pebbles) and +3 φφ (soft sand). The 

broadband LLEE as a function of range for these seven sediment types is shown in Figure 5-23. A flat 

spectral source provided a received LLEE at 731 m of 196 dB re 1 μPa2 s (for 0 φφ). At close ranges (<1 km), 

the sediment influence on received LLEE was negligible. At farther ranges (1 to 10 km), the sediment 

influence on received LLEE increased and became even more substantial at far ranges (10 to 70 km). The 

received LLEE at 65 km varied by 30 dB between pebbles (-3 φφ) and soft sand (+3 φφ) sediments.
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Figure 5-23. Modeled broadband SELs versus range.

For sediment grain size from -3 φφ (pebbles) to +3 φφ (soft sand). Orange = -3 φφ, dark blue = -2 φφ, black = -1 φφ, yellow 

= 0 φφ, purple = 1 φφ, green = 2 φφ, light blue = 3 φφ. μPa = micropascal; φφ = grain size; dB = decibel; re = with 

reference to; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s. 

5.2.2.2 Model-Data Comparison

The measured LLEE at each range from MP1 through MP4 (732 m to 65 km) for the broadband LLEE were 

compared, as were the decidecade bands centered at 125, 250, and 1,000 Hz. For each comparison, the 

measured LLEE at 732 m range was used as the calibration point. Figure 5-24 shows the 

model-measurement comparison as a function of range for each band of hard sand (0 φφ). The full band

comparison is excellent at all ranges. The model under-predicts the 125 Hz band at 7 km range and the 

250 Hz band at 32 and 65 km, each by approximately 5 dB. 
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Figure 5-24. Comparison of measured (dots) and modeled (lines) SEL for hard sand (0 φφ).

For full band (black) and decidecade bands at 125 Hz (blue), 250 Hz (red), and 1,000 Hz (yellow). Modeled data were 

normalized to be correct at 732 m range. μPa = micropascal; φφ = grain size; dB = decibel; Hz = hertz; km = kilometer;

m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.

To demonstrate that the 0 φφ sediment was the best match, the modeled and measured LLEE were compared 

using the same frequency bands for softer sediment (+2 φφ) (Figure 5-25) and for harder sediment (-2 φφ) 

(Figure 5-26). In both figures, the values at -2 φφ and +2 φφ were plotted instead of -1 φφ and +1 φφ simply 

to highlight the differences of the LLEE sensitivity to this environmental parameter more clearly. Modeled 

LLEE were normalized to be correct at 732 m range.

For the softer sediment (+2 φφ; Figure 5-25), all frequency band modeled LLEE were approximately 5 dB 

lower than measured LLEE when considering the 7 km range. At large ranges, the modeled LLEE were lower by 

approximately 20 dB (particularly for the 125 Hz decidecade band and the broadband) near the 65 km 

range. Modeled LLEE being consistently lower than measured LLEE across all ranges indicated the modeled 

sediment was, as expected, softer than the true sediment. This is because the model lost more sound 

energy (attenuated it) than was attenuated by the actual water column sediments.

For the harder “fine pebble” sediment (-2 φφ; Figure 5-26), all frequency band modeled LLEE were 2 to 3 dB 

too high at 732 m range and 5 to 10 dB too high for the 125 Hz and full band at 65 km range. The 

modeled higher-frequency band results (particularly at 1,000 Hz) were better matches to the 

measurements. But, the mismatches between modeled and actual LLEE at lower frequencies, particularly at

larger ranges, indicated the modeled sediment was harder than the true sediment. Given that HF sound 

does not penetrate the seafloor to a great extent (due to the shorter wavelength), this “better match” at 

125 Hz could be explained by a harder sediment near the seafloor interface with a soft sediment below. 
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However, a full geoacoustic inversion was beyond the scope of this work and this proof of concept to 

illustrate that 0 φφ was the most appropriate grain size to use held true.

Figure 5-25. Comparison of measured (dots) and modeled (lines) SELs for soft sand (2 φφ).

For full band (black) and decidecade bands centered at 125 Hz (blue), 250 Hz (red), and 1,000 Hz (yellow). 

μPa = micropascal; φφ = grain size; dB = decibel; re = with reference to; s = second; SEL = LLEE = sound exposure level.

SEL units = dB re 1 μPa2 s.

Figure 5-26. Comparison of measured (dots) and modeled (lines) SELs for pebble sediment 

(-2 φφ).

For full band (black) and decidecade bands centered at 125 Hz (blue), 250 Hz (red), and 1,000 Hz (yellow). Modeled 

data were normalized to be correct at 732 m range. μPa = micropascal; φφ = grain size; dB = decibel; m = meter; 

re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.
90

The measured and modeled LLpp,pk were compared and plotted (Figure 5-27). The band source integer 

source strength was set so the measured and modeled LLEE matched at the 732 m range. The source 

spectrum was assumed to be flat across frequency for the calculation of the peak sound pressure. This had 

the advantage that the frequency dependence (and thus pulse shape and dispersion) of the propagation

was consistently handled by the numerical models. However, this was likely inaccurate because the flat 

spectral hammer model is equivalent to a delta function in time (an impulse of infinitesimal duration),

which is not the case in practice. The agreement of the modeled peak and the measured peak is very good, 

particularly at distances beyond 10 km.

Figure 5-27. Comparison of the modeled broadband LLpp,pppp (line) for hard sand (0 φφ) with Gemini 

U8 measurements (dots). 

The model used a source hammer function with a flat spectrum with a power spectral density of 

188 dB re 1 μPa2 Hz -1. μPa = micropascal; φφ = grain size; dB = decibel; Hz = hertz; km = kilometer; LLpp,pk = peak 

sound pressure level; re = with reference to. LLpp,pk units = dB re 1 μPa.

The measured and modeled LLpp,rms metrics were compared (Figure 5-28). The comparisons were not as 

good as for LLpp,pk or LLEE. The model appeared to under-predict these levels beyond 732 m range. This 

could be due to the flat spectral approximation or the addition of background noise in the measured rms

computation. The model had no noise in it. The match between the measurements and model for LLpp,rms

was not as good as for LLpp,pk, likely because of the approximation of the impulse spectra being flat as a 

function of frequency, which leads to higher-frequency oscillations than are present in the true impact 

pile-forcing function. 
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Figure 5-28. Comparison of the broadband LLpp,rrrrrr (line) for hard sand (0 φφ) with Gemini U8 

measurements (dots). 

The model used a source hammer function that has a flat spectrum with a power spectral density of 

188 dB re 1 μPa2 Hz-1. μPa = micropascal; φφ = grain size; dB = decibel; Hz = hertz; km = kilometer;

re = with reference to; LLpp,rms = root-mean-square sound pressure level. LLpp,rms units = dB re 1 μPa. 

LLEE as a function of frequency, and range for all decidecade bands below 2.5 kHz were compared. The 

decidecade band source strength was estimated to match the measured LLEE at 732 m range. The frequency 

and range-dependent comparison is shown in Figure 5-29. The agreement in both range and frequency 

was excellent. The model over-predicted the loss for frequencies 7 to 18 Hz (decidecade bands centered 

on 8 through 16 Hz). The very LF sound was sensitive to the seafloor at great depths (the wavelength is 

100 m) and therefore was possibly being affected by a basement layer that is not in the model. An 

alternative explanation for the discrepancy is the effect of dispersion below 1 kHz (Zhou et al., 2009). 

This was not of considerable importance, however, because the measured energy levels of the pile 

hammer were small (below 18 Hz).
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Figure 5-29. Comparison of measured (dots) and modeled (lines) decidecade SELs for hard 

sand (0 φφ).

As a function of decidecade band center frequencies (8 Hz to 2 kHz) at receivers 732 m (black), 7,017 m (blue), 

32 km (red), and 65 km (yellow). The source strengths per decidecade band were set to provide consistent results at 

732 m range. μPa = micropascal; φφ = grain size; dB = decibel; Hz = hertz; m = meter; re = with reference to; 

s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.

The modeled and measured p(t) at 732 m range from the U8 pile were compared. The phase coherence of 

the FE and PE solutions needed to be maintained, thus there was hesitation in estimating the complex 

source function for each frequency by matching the LLEE at the 732 m range. Therefore, the single source 

strength (flat across the band) estimated in the comparison of Figure 5-24 was used. This meant the 

calibration was not across frequency, but rather considered the source to be a flat spectrum one equivalent 

to a delta function in time. The comparison of the two p(t) is shown in Figure 5-30. The peak correction 

was well matched (by design), and the time spread of the pulse, which was on the order of 0.2 seconds, 

also was well matched. The modeled signal had a substantial amount of energy at very low frequencies, 

which did not appear in the measured data. 

Figure 5-30. Comparison of measured (left) and modeled (right) time series p(t) of the U8 pile at 

732 m range.

Using the peak sound pressure matching as the broadband calibration level. There is no phase correction. 

M = meter; Pa = pascal; s = second.
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5.2.3 Z2 Pile Comparison

In addition to the recordings of the U8 pile hammer strikes, recordings were made available from the 

Z2 pile hammer strikes in the same environment for three sequences. The environment was similar, 

though the ranges and azimuthal propagation angles were slightly different. A sample of the results are 

presented, comparing the modeled and measured LLEE. Figure 5-31 presents the broadband and 125, 250, 

and 1,000 Hz decidecade bands as a function of range (similar to the U8 result in Figure 5-24). These 

results validated the model, demonstrating the calibration from one pile could be applied to a similar pile 

and the propagation versus range and frequency band was reproduced well.

Figure 5-31. Comparison of measured (dots) and modeled (lines) received SELs for the Z2 pile

for hard sand (0 φφ). 

Full band (black) and decidecade bands centered at 125 Hz (blue), 250 Hz (red), and 1,000 Hz (yellow). 

μPa = micropascal; φφ = grain size; dB = decibel; Hz = hertz; m = meter; re = with reference to; s = second; 

SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.

The decidecade band LLEE was compared for the Z2 pile at each range (0.732, 7.017, 31.816, and 

65.764 km) where the levels at 732 m were used for the calibrated decidecade band level. The results in 

Figure 5-32 are similar to those in Figure 5-29. In both cases, there was a tendency to underestimate the 

LLEE at low frequencies, but these values contributed little to the broadband calculations. In the case of the 

Z2 pile, there was a tendency to underestimate LLEE at frequencies around 300 Hz and above 600 Hz; this 

discrepancy was more important because these frequencies contribute substantially to the broadband LLEE,w.
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Figure 5-32. Comparison of measured (dots) and modeled (lines) SELs for the Z2 pile for hard 

sand (0 φφ).

As a function of decidecade band center frequency (from 8 Hz to 2 kHz) at receivers 732 m (black), 7,017 m (blue), 

32 km (red), and 65 km (yellow). The levels per decidecade band were set to match the U8 pile data at 732 m range. 

μPa = micropascal; φφ = grain size; dB = decibel; km = kilometer; m = meter; re = with reference to; s = second; 

SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.

5.2.3.1 Weighted Spectra for Gemini U8

The piles used in the COMPILE I study and those measured at the Gemini wind farm had peak energy in 

the 100 to 200 Hz frequency range. To evaluate whether the higher frequencies have an impact on the 

species-specific LLEE,w, it is important to have signals of interest beyond 2.5 kHz (the upper band for 

COMPILE I and the Gemini modeling). The Gemini data were reprocessed with an upper frequency 

bound of 20 kHz. The unweighted (uniform weighting) decidecade LLEE is plotted as a function of 

frequency and range in Figure 5-33. The peak is between 100 to 200 Hz for 0.732 and 7 km ranges.

Higher frequencies attenuate more quickly with range than lower frequencies, and the noise floor is

evident above 1, 2, and 8 kHz at 65, 32, and 7 km ranges, respectively.
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Figure 5-33. U8 measured unweighted decidecade band single strike SEL.

Using a uniform weighting (unweighted) with nominal center frequencies 8 Hz to 20 kHz, at receivers 732 m (black), 

7,017 m (blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; km = kilometer; 

m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s. 

The LF cetacean weighting was applied, and the decidecade LLEE is plotted for the four receiver ranges in 

Figure 5-34. The fall-off with frequency was severe for this species group, as seen by the approximately 

30 dB drop from 3.2 to 20 kHz at 732 m range. These results validated the decision to limit modeling to 

2.5 kHz for LF cetaceans.

Figure 5-34. U8 measured low-frequency weighted decidecade band single strike SEL.

Using the low-frequency cetacean weighting with nominal center frequencies 8 Hz to 20 kHz, at receivers 732 m 

(black), d(blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; km = kilometer; 

m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.
96

The results were different for MF cetacean weighting, shown in Figure 5-35. For this weighting, although 

the levels are relatively low, the peak frequency is on the order of 4 kHz. At ranges of 7 km and greater 

(up to 65 km), the noise floor is likely impacting the measurement. However, there is energy above 

2.5 kHz that must be accounted for when modeling LLEE,w for MF species. Therefore, LLEE,w predictions for 

the MF hearing group included frequencies between up to 20 kHz.

Figure 5-35. U8 measured mid-frequency weighted decidecade band single strike SEL.

Using the mid frequency cetacean weighting with nominal center frequencies 8 Hz to 20 kHz, at receivers 732 m

(black), d(blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; km = kilometer; 

m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.

The HF cetacean LLEE,w is plotted in Figure 5-36. The peak at 732 m range is 6 kHz. At ranges of 32 and 

65 km, the noise floor likely affected measurements at 3 kHz and above. Therefore, LLEE,w predictions for 

HF hearing groups included frequencies up to 20 kHz.
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Figure 5-36. U8 measured high-frequency weighted decidecade band single strike SEL. 

Using the high frequency cetacean weighting with nominal center frequencies 8 Hz to 20 kHz, at receivers 732 m 

(black), 7,017 m (blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; 

km = kilometer; m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level.

SEL units = dB re 1 μPa2 s.

The final hearing group-specific LLEE,w was for phocid pinnipeds (Figure 5-37). The peak energy is 

between 100 and 200 Hz at closer ranges and migrates up to 200 to 400 Hz at ranges beyond 32 km. The 

noise floor was encountered above 3 kHz at 32 and 65 km ranges so to accommodate for this, modeled 

LLEE,w predictions for phocid pinnipeds included frequencies up to 20 kHz.

Figure 5-37. U8 measured phocid-underwater weighted decidecade band single strike SEL. 

Using the phocid pinniped weighting with nominal center frequencies 8 Hz to 20 kHz, at receivers 732 m (black), 

7,017 m (blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; km = kilometer; 

m = meter; re = with reference to; s = second; SEL = LLEE = sound exposure level. SEL units = dB re 1 μPa2 s.
98

5.3 Summary of Model Verification and Validation 

5.3.1 Verification

To address the issue of sound radiation from pile driving, an FE model was used to generate the sound 

field close to the pile. This sound field then was matched to a PE propagation model, and the pile signals 

were propagated out to distances of interest. Using this combination of models (FE/PE), the results from 

the COMPILE I workshop were successfully reproduced, which verified the approach used for this 

project. 

5.3.2 Validation

Validation of the combined FE/PE model prediction showed differences when compared to the 

measurements from the construction of the Gemini wind farm (ITAP, 2015b). To address these 

differences, it was necessary to introduce a layered sediment. Validation was carried out against two 

Gemini piles, U8 and Z2.

For U8, good agreement was obtained with measurements at all frequencies above approximately 30 Hz.

Frequencies lower than 30 Hz did not contribute substantially to the broadband LLEE, even before frequency 

weighting. After weighting, they were negligible.

For Z2, at 3 m range from the pile axis of symmetry, the LLEE spectrum was assumed to be the same as for 

U8. Under this assumption, the same LF discrepancies were seen as for U8, but these were unimportant 

for the reasons already stated. For Z2, there were additional discrepancies at frequencies around 300 Hz 

and above 600 Hz, where the LLEE spectrum was underestimated. 

6 Sensitivity Study

Modeling pile-driving sound as it radiates through an environment is not a one-size-fits-all scenario. The 

variables of bathymetries, seasonal temperature cycles, water column depths, and sediment types affect 

how sound travels through the water column, and the extent to which each variable influences acoustic 

modeling predictions is unknown. The influence of these variables is particularly unknown for the 

U.S. Atlantic Coast that crosses many latitudinal lines, making it difficult to assume seasonal temperature 

or sediment type homogeneity. The sensitivity of acoustic models to each environmental parameter in a 

unique sound propagation location is best modeled site by site to rank the degree to which parameters 

influence the model results. 

The primary goals of the sensitivity study were to (1) improve predictive acoustic modeling by 

understanding how select environmental parameters influence propagation within a single site and

between sites, and (2) identify the most influential environmental parameters on acoustic propagation. 

6.1 Sensitivity Study Methods

To determine the sensitivity of the acoustic modeling predictions to environmental parameters, the 

propagation of pile-driving sounds up to 2,238.5 Hz was modeled (see Section 2.2 for the reasoning 

behind this delineation). Propagation modeling incorporated environmental parameters at different sites 
99

(a water depth proxy), across different seasons (an SSP proxy), for three sediment types, and along four 

bearings (a bathymetric shoaling proxy). Using the same FE starter field (a non-point source) at each site, 

the received level (via the computed metrics LLEE, LLpp,pk, and LLpp,rms) at each range and depth were plotted 

to show the relative sensitivity to each parameter: bathymetry, SSP, sediment type, and water depth. 

The influence on propagation of the selected environmental parameters were ranked in each environment. 

This was achieved by listing the acoustic metric means for each variable and ranking the differences 

between them from largest to smallest. Disturbances to animals were quantified in LLEE, LLpp,pk, and LLpp,rms; 

thus, the means, medians, and interquartile ranges for these metrics were used to investigate the influence 

of the environmental parameters on the sensitivity in each acoustic metric.

The sensitivity of the impact volumes (based on acoustic thresholds) from the influence of environmental 

parameters were determined. Ranges over which sound travels from a driven pile (as a non-point source), 

using the three metrics, were determined at two sites during two seasons (winter and summer) for three 

grain sizes (0.5, 1.5, and 2.5 φφ, corresponding to coarse, medium, and fine sand, respectively). Numerical 

values representative of impact volumes for each animal group’s thresholds were calculated, and the 

results are presented in Section 5.

Impact volumes were calculated at high frequencies, specifically between 2,238.5 and 20,000 Hz. This 

HF band is important for delphinid and pinniped hearing groups as the LLEE,w thresholds contain acoustic 

contributions well above 2,238.5 Hz.

The immediate environment’s impact on acoustic propagation also was investigated to explain how 

differences between environments may affect propagation, and ultimately underscored the importance of

site specificity. Traditionally, simple models such as the PSLM (often with 15logR) have been used. This 

sensitivity study illustrates the degree of validity of the PSLM when several environmental variables are 

considered. 

6.1.1 Influence and Rationale of Selected Environmental Parameters

When a pile is struck by a hammer, the sound wave travels through the pile at the speed of sound in metal 

(approximately 6,000 m s-1

). This sound pressure wave is coupled to the water column, which causes an 

acoustic wave to propagate from the pile out to different distances underwater in all directions. The ratio 

of the compressional speed in the pile and that in the water (approximately 1,500 m s-1

) leads to a focused 

beam at the Mach cone angle. This focused beam interacts with the seafloor and sea surface and is 

attenuated at substantial distance. The energy radiated at low angles is what dominates the field at long 

ranges. Source vertical directionality is handled by the FE modeling in this project. Acoustic energy 

propagation is driven by changing SSPs, refracting sound waves, and boundary conditions, which scatter 

sound waves off the sea surface and seafloor. Ocean sound speed primarily is a function of temperature, 

with warmer water having a higher sound speed than colder water. Sound waves refract downward, away 

from the higher surface sound speed (especially during the summer months), and toward the seafloor. The 

seafloor absorbs some of the sound at each reflection cycle, and after many cycles, the sound is heavily 

damped. The opposite happens during winter months when sound waves are refracted upwards, toward 

the colder sea surface. The sea surface is an almost perfect reflector of sound, so it continues to propagate 

(not absorb) sound waves even after multiple surface interactions; thus, a winter SSP makes the sea 
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appear more conducive to long distance sound propagation. For this modeling study, summer and winter 

profiles taken from the World Ocean Atlas were used. 

The acoustic properties (e.g., sound speed, density, attenuation) of the seafloor layers strongly influence 

the amount of sound reflection and thus the amount of sound that remains in the water column. Hard 

(coarse) sand is relatively reflective, which preserves the amount of acoustic energy in the water column. 

Mud and soft (fine) sand are absorptive, attenuating the acoustic energy out of the water column. 

Sediment grain size is categorized using the grain size variable (in φφ units) on the Wentworth chart. 

Site-appropriate grain size values were used in this modeling study. 

The final environmental driver addressed is bathymetric range dependence, which can lead to more 

(shallower) or fewer (deeper) seafloor interactions as sound moves away from the source. The ETOPO1 

bathymetric database was used to provide accessible global coverage for modeling. The primary reason

for placing the modeled source at a specified longitude and latitude position was to generate a realistic 

bathymetry versus range profile. The details are site specific, but the examples illustrate the impact that 

shallowing or deepening bathymetry has on acoustic propagation.

Two Test Sites

Two sites were chosen to evaluate the extent that environmental variability plays on the sensitivity of 

sound propagation from water depth in combination with bathymetry, sediment type, and ocean 

temperature structure. The two chosen sites were offshore Virginia Beach (VAbeach) and Rhode Island 

(RIsland) where considerable planning is under way for future wind farms. Each site was modeled for 

three uniform (range-independent) sediment types: coarse, medium, and fine sand (0.5, 1.5, and 2.5 φφ, 

respectively). These sediment types, while not evenly distributed, are ubiquitous on the shallow 

continental shelf and represent most regions where pile driving is likely to be conducted. The inclusion of 

range dependence in the seafloor, although a real phenomenon, is strongly site specific and beyond the

scope of this project. For each selected grain size, a parametrized single unconsolidated sediment was 

used, providing a depth-dependent compressional speed, density, and attenuation profile.

Wind speed was not included in the environmental sensitivity computations for two reasons: (1) the 

emphasis on LF sounds makes surface waves less important, and (2) pile-driving operations usually are 

limited to sea states below 3 (i.e., significant wave height less than 0.5 m) and when the wind speed is 

less than 7.5 m s-1 (15 knots). The inclusion of wind-driven waves and surface roughness can be done 

with the PE, as modeled earlier for this project, but was not included in the sensitivity analysis. On severe 

weather days, when acoustic propagation is dominated by a wind-induced rough surface, pile-driving 

activities are not expected to occur.

6.2 Acoustic Propagation Modeling Methods 

6.2.1 Environment and Variables Selection

The influence of two environments in the western Atlantic Ocean was modeled with a starter field 

simulated from an actual pile installed in the North Sea off the coast of the Netherlands. RIsland was 

located 37.29 km offshore Rhode Island (41.08776° N, 71.22270° W) in 35 m water depth (Figure 6-1). 

VAbeach was located 43.16 km offshore Virginia (36.89629° N, 75.49163° W) in 24 m water depth 
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(Figure 6-1). For both sites, sediment grain sizes of 0.5, 1.5, and 2.5 φφ were used in the propagation 

models to capture a pile installation in hard through soft sands. 

Figure 6-1. Map of RIsland and VAbeach sites.

Sites (red dots) were mapped to illustrate and compare isobaths, latitudes, distances, and orientations to shore and 

the edge of the continental shelf. RIsland = Rhode Island site; VAbeach = Virginia Beach site.

Two seasons (summer and winter) were used to capture the extremes of temperature and salinity, and 

three sediment types (coarse, medium, and fine sand) were used to represent general interactions between 

sound waves and the seafloor. The relative influences of these variables were explored to better 

understand which environmental factors most influence the size of calculated impact volumes to the 

various acoustic thresholds for different species groups. 

Propagation modeling was performed using two databases: (1) World Ocean Atlas temperature/salinity 

fields for summer (August 1) and winter (February 1), and (2) ETOPO1 bathymetry for the four cardinal 

directions. The bathymetry for the 60-km tracks is plotted in Figures 6-2 and 6-3 for RIsland and

VAbeach, respectively.
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The specific bathymetry for RIsland is shown in Figure 6-2. The water shoals to less than 10 m in the 

north and west radials. The east radial water depth increases to 60 m, and the southern radial is mostly flat

around 40 m.

Figure 6-2. Bathymetry at RIsland.

The plotted cardinal directions are north (black), east (blue), south (red), and west (yellow). Water depth data from 

ETOPO1 were used in the simulation and are plotted out to 60 km. Water depth at the pile site is 35 m. 

RIsland = Rhode Island site.

The specific bathymetry for VAbeach is shown in Figure 6-3. The water shoals to 0 m depth in the west 

radial, and the east radial deepens to 60 m beyond the 40-km range. The north and south radials are 

mostly flat around 24 m.
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Figure 6-3. Bathymetry at VAbeach.

The plotted cardinal directions are north (black), east (blue), south (red), and west (yellow). Water depth data from 

ETOPO1 were used in the simulation and are plotted out to 60 km. Water depth at the pie site is 24 m. 

VAbeach = Virginia Beach site.

The U.S. Atlantic Coast experiences seasonal temperature changes in the Gulf Stream from seasonal solar 

input and because the ocean current meanders closer to and farther from shore in the fall and spring 

(Frankignoul et al., 2000). Preliminary analysis demonstrated that propagation measurements on May 1 

(spring) and November 1 (fall) were very similar to February 1 (winter), while August 1 (summer) was 

the most different. Therefore, environmental data from February 1 and August 1 were chosen to represent 

extremes in temperature and salinity profiles. These temperature and salinity differences affect the SSP 

whereby the marine environment would be more or less acoustically transparent. Sound is refracted away 

from the warm surface in the summer and therefore hits the seafloor more times, leading to higher 

attenuation of acoustic energy, particularly if the seafloor is soft.

A water depth difference of only 11 m was not expected to differentially influence propagation. However, 

a remarkable difference between RIsland (35 m water depth) and VAbeach (24 m water depth) was 

observed. A downward refracting SSP resulted in greater loss because of the increased interaction with 

the seafloor. As shown in Figure 6-4, RIsland’s ability to extend an extra 11 m (133% the depth of 

VAbeach) into colder, deeper water impacted the propagation substantially. The very cold water at the 

seafloor increased the grazing angle at which sound hits the seafloor (Snell’s law and refraction) and led 

to more attenuation from seafloor interactions.
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Figure 6-4. Summer SSPs.

The summer SSPs for the deeper RIsland (black) and the shallower VAbeach (blue). RIsland had a stronger gradient 

as well as a larger (2x) difference between the surface sound speed (1,525 m s-1) and the seafloor sound speed 

(1,475 m s-1). RIsland = Rhode Island site; SSP = sound speed profile; VAbeach = Virginia Beach site.

When the surface is not heated, the SSP tends to be an isovelocity, as shown for the winter profile in 

Figure 6-5.

Figure 6-5. Winter SSPs.

The winter SSPs for the deeper RIsland (black) and shallower VAbeach (blue). VAbeach had a slightly higher sound 

speed across all depths than RIsland, but both were approximately an isovelocity compared to their respective 

summer SSPs. RIsland = Rhode Island site; SSP = sound speed profile; VAbeach = Virginia Beach site.
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6.2.2 Methodology for 17.5 to 2,238.5 Hz

Seawater volume attenuation increases rapidly with increasing frequency and it is well known that only 

LF sound can travel great distances. For this reason, much of the project’s effort was focused on modeling 

the sound in the 17.5 to 2,238.5 Hz band. Furthermore, the FE source model and the PE are well suited 

for this frequency band.

The FE starter field (Section 3.1), was generated from the sound radiated by a hammer strike on a 7-m 

diameter pile. The FE starter field assumed a uniform SSP and constant water depth and was computed 

for six different combinations of water depth and sediment grain size (two water depths for each of the 

three sediment φφ values used). In order to model realistic pile energy, the pile’s LLEE was adjusted to match 

the acoustic measurement results from the Gemini installation. In particular, at 732 m, the measured 

unweighted LLEE,ss was 179 dB re 1 μPa2 s, which, for the sensitivity analysis, translated to an LLEE,ss near the

pile of approximately 200 dB re 1 μPa2 s.

The PE is the industry standard modeling solution for handling LF acoustic propagation in 

range-dependent environments. The PE used here, Peregrine, was developed by Richard Campbell and is 

a modification of the RAM (Collins, 1993). The PE model was integrated with the FE starter field to 

model the sound pressure field every 20 m in range (2 m in depth) out to a distance of 60 km from the 

pile. For both sites, four radials at cardinal directions were used to sample various bathymetries, 

specifically, shoaling towards land to the west/northwest and sloping to deep water towards the 

east/southeast. The PE was run on a fixed grid for a set of decidecade bands, from 17.5 to 2,238.5 Hz. The 

frequency domain fields from each band were Fourier transformed, producing a pressure time series on a 

2-second window that captures the entire pulse. The time series of all the decidecade bands were summed,

and the acoustic metrics (LLEE, LLpp,pk, and LLpp,rms) were computed in the time domain.

6.2.3 Methodology for 2,238.5 Hz to 20,000 Hz

Although sound intensity falls off exponentially with range at higher frequencies, the hearing sensitivity 

(and auditory frequency) of some animal groups, such as the MF and HF cetaceans, increases rapidly with 

frequency, making it necessary to extend the computation to higher frequencies. The methodology is 

described here.

The highest band computed during the LF modeling in Section 6.2.2 for the combined FE/PE was 

between 1,778.5 and 2,238.5 Hz. The frequency domain fields of this band (for each of the sites, seasons, 

bearings, and sediment grain sizes) were mapped to higher frequency bands by accruing the additional 

volume attenuation as a function of range.

As an example, the volume attenuation for water at 2 kHz is 0.139 dB km-1

, leading to a total volume 

attenuation of 1.39 dB at 10 km. For 10 kHz sound, the volume attenuation is 1.189 dB km-1

, which is 

11.8 dB at 10 km. To map the acoustic field to 10 kHz, an additional 1.05 dB loss per kilometer was 

applied. For the LLEE and LLpp,pk metrics, these factors were applied to the computed decidecade band 

metrics. In order to compute the LLpp,rms, however, the full band field was reduced and the LLpp,rms was 

recomputed.
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The time domain pressure signals (via the Fourier transform) and the acoustic metrics (LLEE,w, LLpp,pk and 

LLpp,rms) were computed. This allowed for evaluation of the metric received level for each weighting, out 

to 60 km, for an effective frequency band of 17.5 Hz to 20 kHz. This approach made three 

approximations. The first is that the acoustic time series at 20 kHz and at 2 kHz is the same, which 

follows the HF approximation used in ray-tracing methods. The second is that the attenuation added as a 

function of linear range is equivalent to the volume attenuation that should be added along the acoustic 

ray path (path length). For long-range propagation, where propagation angles are less than 15°, this 

amounts to a very small factor (approximately 4%) in volume attenuation. The third approximation is the 

assumption of a perfectly reflecting sea surface. Modeling the effects of rough surface scattering was 

outside the project scope but would have decreased contributions from frequencies above 1 kHz, 

especially during winter.

To evaluate the impact of adding the 2,238.5 Hz to 20 kHz acoustic energy to the modeled pile-driving 

signals, the LLEE of four weighting categories as a function of range was plotted. The example case was 

RIsland during summer at 90° (east) with a sediment grain size of 0.5 φφ. The 17.5 to 2,238.5 Hz sum 

(black line) and the full 17.5 Hz to 20 kHz sum (blue line) are shown in Figure 6-6 for each weighting. 

The results indicate there is no visible difference between the received LF weighted or the full band 

(including up to 20 kHz) LLEE values at any range for LF cetaceans (Figure 6-6, upper left panel) or for the 

unweighted fish/sea turtle groups (not plotted). Other marine mammal groups had higher levels of 

broadband LLEE, particularly for ranges shorter than 18 km. Specifically, the LLEE close to the pile for MF and 

HF cetaceans differed between the LF (17.5 to 2,238.5 Hz) and the full band (17.5 Hz to 20 kHz) by as 

much as 5 and 10 dB, respectively (Figure 6-6, right panels). The maximum received levels were at lower 

frequencies (100 to 500 Hz), but the weighting functions focus on higher frequencies for the MF and HF 

cetaceans; therefore, those groups have notably lower LLEE,w values than the LF cetaceans. For the phocid 

pinnipeds, the difference between the LF and full band computation varied by 1 to 2 dB within the first 

5 km from the pile (Figure 6-6, bottom left panel).
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Figure 6-6. Low frequency (17.5 to 2,238.5 Hz) versus full band (17.5 to 20,000 Hz) weighted 

single strike SEL for marine mammal groups. 

Results for RIsland to the east (90°). The LLEE,w,ss for low band (black) and full band (blue) propagation for 

low-frequency cetaceans (top left), mid-frequency cetaceans (top right), phocid pinnipeds (bottom left), and 

high-frequency cetaceans (bottom right). μPa = micropascal; dB = decibel; km = kilometer; LLEE,w,ss = weighted single 

strike sound exposure level; re = with reference to; RIsland = Rhode Island site; s = second; SEL = LLEE = sound

exposure level.

The decidecade band with the largest contribution to the unweighted broadband levels at all ranges was 

the 100 Hz band (Figure 6-7, eighth vertical column of dots from the left), followed by the 160 Hz band 

(tenth vertical column from left) at short range and the 125 Hz band (ninth vertical column from left) at 

long range. The 100, 125, and 160 Hz decidecade bands dominate the broadband value at all ranges.
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Figure 6-7. Unweighted single strike decidecade band SEL versus frequency and range.

The decidecade band LLEE,ss as a function of band center frequency at the Gemini recording ranges of 0.7 km (black), 

7 km (blue), 32 km (orange), and 62 km (yellow). μPa = micropascal; dB = decibel; LLEE,ss = single strike sound 

exposure level; re = with reference to; s = second; SEL = LLEE = sound exposure level.

The LF-weighted SEL was dominated by the 160 Hz band at short range (<10 km) and by the 100 and 

125 Hz bands combined at long range (>30 km) (Figure 6-8). At all ranges, the fourth strongest band is 

the one centered at 200 Hz, competing with the 80 Hz band at short range (<1 km). 

Figure 6-8. Low-frequency weighted single strike SEL decidecade band versus full band.

LLEE,w,ss for full band (black) and decidecade bands from 80 to 200 Hz (colors) for low-frequency cetaceans; 80 Hz 

(blue), 100 Hz (red), 125 Hz (yellow), 160 Hz (purple), and 200 Hz (green); μPa = micropascal; dB = decibel; 

LLEE,w,ss = weighted single strike sound exposure level; re = with reference to; s = second; SEL = LLEE = sound exposure 

level.
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The combined effect of a spectrum that decreases with increasing frequency and a weighting function that 

increases with increasing frequency is that their product is an almost flat weighted frequency spectrum. 

This nearly flat spectrum leads to multiple frequency bands contributing almost equally to the total 

short-range (<10 km) energy above 4 kHz (Figure 6-9). The frequencies contributing to HF-weighted 

levels are therefore mostly above 2 kHz, demonstrating the importance of including high frequency 

energy (above 2 kHz) in the calculations of LLEE,w.

Figure 6-9. High-frequency weighted SEL decidecade band versus full band.

The LLEE,w,ss for full band (black) and selected decidecade bands from 2.5 to 16 kHz for high-frequency cetaceans. 

Colors represent the bands centered at 2.5 kHz (blue), 4 kHz (red), 6.4 kHz (yellow), 10 kHz (purple), and 16 kHz 

(green). μPa = micropascal; dB = decibel; km = kilometer; LLEE,w,ss = weighted single strike sound exposure level; 

re = with reference to; RIsland = Rhode Island site; s = second; SEL = LLEE = sound exposure level.

6.2.4 Modeling Results for Acoustic Propagation from 17.5 to 2,238.5 Hz

Propagation modeling of broadband sounds up to 2,238.5 Hz (in 0.5 Hz increments) showed the 

environment is more conducive to acoustic propagation with increasing water depth, during colder 

(upward refracting) seasons, and over moderately hard sand sediments. At both modeled sites, the results 

were influenced by seasonality and grain size. Figures 6-10 to 6-15 compare LLEE, LLpp,pk, and LLpp,rms versus 

range for both sites in the four cardinal directions during summer and winter for three sediment grain 

sizes. The differences in propagation as a result of these variables are noticeable approximately 20 m 

from the pile-driving location for all bearings at both sites and becomes more pronounced with range.

The LLEE values for RIsland as a function of range, season, and sediment grain size are plotted for each 

cardinal direction in Figure 6-10. Within 10 km of the pile, there were small differences of less than 5 dB 

between the grain sizes across seasons. The sensitivity to bathymetry is demonstrated by comparing the 

four cardinal directions, one in each panel. As water depth decreases, there is more acoustic energy loss. 

At longer ranges, the consistent trends are that the summer levels are as much as 10 dB lower due to the 

warm surface waters refracting sound towards the seafloor, leading to more seafloor reflections with 

range and thereby a higher loss. Differences due to seafloor type (sediment grain size) sometimes are 
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negligible in winter but always are more noticeable in summer, which is due to more seafloor interactions 

caused by the warm, refractive surface waters. Beyond 10 km, soft sediment (2.5 φφ, fine sand) sound 

levels are 10 to 20 dB lower than over hard sediment (0.5 φφ, coarse sand). The west (270°) direction at 

RIsland becomes very shallow (approximately 3 m) at 40 km, causing the numerical model to become 

unstable. The north (0°) and west (270°) plots show a sudden drop in sound levels beyond 40 km because

of the extremely shallow water. (See Figure 6-2 for bathymetrical evidence of sudden shallowing.)

Figure 6-10. Single strike SEL as influenced by RIsland environmental variables. 

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 35 m. 

μPa = micropascal; φφ = grain size; dB = decibel; km = kilometer; re = with reference to; RIsland = Rhode Island site; 

SEL = LLEE = sound exposure level.

The LLpp,pk values for RIsland as a function of range, season, and sediment grain size were plotted for each 

cardinal direction (Figure 6-11). The sensitivity of LLpp,pk to environmental parameters is consistent with 

the LLEE results, with the order of importance (from high to low) being bathymetry, season, and grain size.
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Figure 6-11. LLpp,pppp as influenced by RIsland environmental variables.

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 35 m.

μPa = micropascal; φφ = grain size; dB = decibel; km = kilometer; LLpp,pk = peak sound pressure level; re = with 

reference to; RIsland = Rhode Island site.
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The LLpp,rms values for RIsland as a function of range, season, and sediment grain size were plotted for 

each cardinal direction (Figure 6-12). The sensitivity of LLpp,pk to the environment is consistent with the LLEE

results, with the order of importance (high to low) being bathymetry, season, and grain size.

Figure 6-12. LLpp,rrrrrr as influenced by RIsland environmental variables

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 35 m. 

μPa = micropascal; φφ = grain size; dB = decibel; km = kilometer; LLpp,rms = root-mean-square sound pressure level; 

re = with reference to; RIsland = Rhode Island site.

The LLEE values for VAbeach as a function of range, season, and sediment grain size were plotted for each 

cardinal direction (Figure 6-13). The LLEE values for VAbeach are markedly higher than for RIsland, 

showing the combined effect of water depth and SSP. This improved propagation at VAbeach is 

demonstrated by the substantially larger impact volume results relative to RIsland. RIsland has a 

downward refracting profile in a deeper water column. In the winter, VAbeach has an isovelocity from a 

uniform SSP, but its slight downward refracting SSP during the summer is truncated due to its shallower 

depth, effectively damping the refraction effect. This isovelocity at VAbeach also could be due to the 

more southern location, the proximity of the Gulf Stream, or better mixing by surface winds in shallow 

water. More regionally, the coastal counter current from the north creating a rougher sea combined with 

river runoff from the Chesapeake may lead to higher mixing than the more sheltered RIsland. This would 

contribute to the VAbeach isovelocity state throughout all seasons. Within the first 20 km, there were no 

large differences between the resulting ranges for LLEE at RIsland and VAbeach. The acoustic sensitivity to 
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bathymetry is visible when comparing the four cardinal directions. As water depth decreases, there is 

more acoustic energy loss, particularly to the south (180°). There is much less seasonal variability in the 

VAbeach results than in the RIsland results, which may be due, in part, to less severe winters in Virginia 

and the influence of the Gulf Stream that reduce the impact of air temperature on surface water 

temperature. Differences due to sediment grain size are noticeable during summer due to more seafloor 

interactions caused by the warmer surface waters but are nearly negligible during winter. At both sites, 

the LLEE with soft sediment (2.5 φφ, fine sand) is approximately 5 dB lower than the corresponding hard 

sediment (0.5 φφ, coarse sand) values beyond 20 km. At VAbeach, the west (270°) direction runs aground 

at a range of 44 km (Figure 6-3).

Figure 6-13. Single strike SEL as influenced by VAbeach environmental variables.

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 24 m. Data at 270° 

were truncated at 45 km to avoid numerical instabilities of the model output in very shallow water. μPa = micropascal; 

φφ = grain size; dB = decibel; km = kilometer; re = with reference to; s = second; SEL = LLEE = sound exposure level; 

VAbeach = Virginia Beach site.
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The LLpp,pk values for VAbeach as a function of range, season, and sediment grain size were plotted for

each cardinal direction (Figure 6-14). The sensitivity of LLpp,pk to the environment is consistent with the LLEE

results, with the order of importance (high to low) being bathymetry, season, and grain size.

Figure 6-14. LLpp,pppp as influenced by VAbeach environmental variables.

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 24 m. Data at 270° 

were truncated at 45 km to avoid numerical instabilities of the model output in very shallow water. μPa = micropascal; 

φφ = grain size; dB = decibel; km = kilometer; LLpp,pk = peak sound pressure level; re = with reference to; 

VAbeach = Virginia Beach site.
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The LLpp,rms values for VAbeach as a function of range, season, and sediment grain size were plotted for 

each cardinal direction (Figure 6-15). The sensitivity of LLpp,pk to the environment is consistent with the LLEE

results, with the order of importance (high to low) being bathymetry, season, and grain size.

Figure 6-15. LLpp,rr as influenced by VAbeach environmental variables.

Top left: north (0°), top right: east (90°), bottom right: south (180°), bottom left: west (270°). Summer (red) and winter 

(blue) depth-averaged propagation distances over sediments of coarse (0.5 φφ, dotted lines), medium (1.5 φφ, dashed 

lines), and fine (2.5 φφ, solid lines) sand. Water depth at the source position was approximately 24 m. Data at 270° 

were truncated at 45 km to avoid numerical instabilities of the model output in very shallow water. μPa = micropascal; 

φφ = grain size; dB = decibel; km = kilometer; LLpp,rms = root-mean-square sound pressure level; re = with reference to; 

VAbeach = Virginia Beach site.

6.2.5 Statistical Characteristics

The environmental variables with the largest influence were determined by calculating the average vector 

values of the depth-averaged acoustic metrics (LLEE, LLpp,pk, and LLpp,rms) in Figures 6-10 to 6-15, for three 

grain sizes, four bearings, and two seasons at each site. The mean and median for each acoustic metric 

provided averages that indicated a normal distribution, but neither provided an indication of the spread in 

the distribution. A large spread exists in the acoustic metrics at long ranges because, in bearings toward 

the direction of land, the water becomes very shallow. The standard deviation and interquartile range 

were used as statistical measures of this spread. For statistical comparison, acoustic metric means are 

presented in Figures 6-16 and 6-19, medians in Figures 6-17 and 6-20, and interquartile ranges for LLEEin 
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Figures 6-18 and 6-21. General trends in how means and medians can be used to understand the influence 

of different environmental parameters on sound propagation between two sites are discussed below.

Site selection was a proxy for water column depth. RIsland (35 m) was 11 m deeper than VAbeach (24 m) 

and farther from the continental shelf edge. When comparing the means (Figures 6-13 and 6-16) and 

medians (Figures 6-17 and 6-20), it is notable that the shallower VAbeach retained more sound energy 

overall than the deeper RIsland. The one exception towards the south is discussed in the bearing 

parameter subsection. The more pronounced case of summer to the north is discussed in subsequent 

parameter subsections. In general, the depth of the pile-driving site on its own does not sufficiently 

indicate how well sound will propagate in all directions from it, as site specificity/bathymetry needs to be 

considered. 

Season was a proxy for SSP. When comparing the summer and winter means (Figures 6-16 and 6-19) 

and medians (Figures 6-17 and 6-20), it is notable that for all situations considered, the winter SSPs 

retained more sound energy than the summer SSPs, which was expected at LFs. (At HFs, effects of 

surface scattering were not modeled, as it might have increased TL in winter.) 

Grain size was a proxy for sediment type. When comparing coarse, medium, and fine sediment means 

(Figures 6-16 and 6-19) and medians (Figures 6-17 and 6-20), the situations involving coarse (0.5 φφ) 

sediments retained more sound energy than those involving fine (2.5 φφ) sediments. This trend was more 

pronounced in summer SSPs along bathymetries that rapidly became shallow. In summary, finer 

sediments absorbed more sound energy than coarse sediments, especially during the summer.

Bearing was a proxy for bathymetry. When comparing the four cardinal directions of the means 

(Figures 6-16 and 6-19) and medians (Figures 6-17 and 6-20), the shallowing water columns lost more 

sound energy. At RIsland, the bearings from shallowest to deepest are north, west, east, and south 

(Figure 6-2). Sound energy attenuated most towards the north bearing with rapid shallowing, followed by 

west, east, and south (Figures 6-16 and 6-19). At VAbeach, the order of bearings from shallowest to 

deepest are west, north, south, and east (Figure 6-3). However, neither the means nor the medians 

followed the same bathymetrical influence as RIsland. This is likely because VAbeach is farther from 

land and generally flatter, so bathymetry has less of an influence overall. 
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Figure 6-16. Mean acoustic metric values at RIsland.

Panels left to right: bearings of north (0°), east (90°), south (180°), and west (270°) during winter (blues) and summer 

(reds). Means of SEL (asterisks), LLpp,rms (triangles), and LLpp,pk (squares) for grain sizes 0.5 φφ (dark shade), 1.5 φφ

(moderate shade), and 2.5 φφ (light shade). For the west direction, vectors were truncated at 45 km to avoid numerical 

instabilities of the model output in very shallow water. μPa = micropascal; φφ = grain size; dB = decibel;

km = kilometer; LLpp,pk = peak sound pressure level; LLpp,rms = root-mean-square sound pressure level; re = with 

reference to; RIsland = Rhode Island site; s = second; SEL = LLEE = sound exposure level.
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Figure 6-17. Median acoustic metric values at RIsland.

Panels left to right: four bearings of north (0°), east (90°), south (180°), and west (270°) during winter (blues) and 

summer (reds). Medians of SEL (asterisks), LLpp,rms (triangles), and LLpp,pk (squares) for grain sizes 0.5 φφ (dark shade), 

1.5 φφ (moderate shade), and 2.5 φφ (light shade). For the west direction, vectors were truncated at 45 km to avoid 

numerical instabilities of the model output in very shallow water. μPa = micropascal; φφ = grain size; dB = decibel;

km = kilometer; LLpp,pk = peak sound pressure level; LLpp,rms = root-mean-square sound pressure level; re = with 

reference to; RIsland = Rhode Island site; s = second; SEL = LLEE = sound exposure level.
119

Figure 6-18. Percentiles of unweighted single strike SEL at RIsland.

During summer (red, top panels) and winter (blue, bottom panels). Quartile LLEE,ss distributions presented as box and

whisker plots of 25th (bottom of box), median (red middle bar inside each box), and 75th (top of box) and extreme 

values (triangles) for four bearings (left to right): north (0°), east (90°), south (180°), and west (270°), and three grain

sizes, smallest to largest. μPa = micropascal dB = decibel; re = with reference to; RIsland = Rhode Island site; 

s = second; SEL = LLEE = sound exposure level.

Identifying the largest differences between the means and medians of the metrics across parameters and 

sorting them from most to least influential suggested the site parameter (water column depth proxy) had 

the largest influence compared to the other three parameters. However, the site parameter also could be 

accounting for proximity to land. The environmental sensitivity models may be driven more by how close 

a site is to shore than by the water depth at the precise pile location. Had the two sites been farther from 

shore, that propagation likely would have been more strongly influenced by water depth. If there existed 

bathymetry similarly complex to RIsland farther from land, then bathymetry would drive the sound 

propagation more than the nearest coastline. Because of the depths where piles are safe to drive into the 

seafloor, it is more likely those sites will exist close to shore where bathymetry is more likely to be 

complicated and the coastline can act as a physical barrier to sound.

If using only the difference in acoustic means to rank parameter influence within a single site, 

environmental factors influence acoustic propagation differently. For RIsland, bearing (bathymetry) was 

the most influential parameter, then season (SSP), and finally grain size (sediment type). For VAbeach,

bearing (bathymetry) was the most influential parameter (slightly), while grain size (sediment type) and 
120

season (SSP) exhibited nearly the same influence on sound propagation. In summary, the shallower, 

flatter water column exhibited less predictable influence on sound propagation compared to the deeper 

water column.

If using only the difference in acoustic medians to rank parameter influence within a single site, bearing 

(bathymetry), season (SSP), and grain size (sediment) had roughly the same effect on propagation at 

RIsland. At VAbeach, bearing (bathymetry) was slightly more influential than the equally ranked season 

(SSP) and grain size (sediment), though season was slightly more influential at higher grain sizes. In 

summary, using medians of the acoustic metrics to represent the influence of environmental factors on 

propagation illustrates bathymetry, sediment type, and the SSP all are impactful factors but not to the 

same degree as when using the means of the acoustic metrics.

Despite season being a proxy for the SSP, the static state of the SSP was not the most influential in terms 

of sound propagating through a water column while interacting with boundary conditions. This is because 

it is the change in SSP, not in the absolute water temperature, that matters. All three metrics captured 

some of these variations, but the LLpp,pk metric was the most common indicator for comparing the means 

and the level of influence across and between parameters and their magnitudes.

Comparing means and medians gave different rankings of environmental parameter influence on sound

propagation, particularly at RIsland; therefore, the interquartile ranges and extreme values are presented 

(as box and whisker plots) in LLEE (Figures 6-18 and 6-21) for both sites. The LLEE metric is the most robust 

as it represents the majority of acoustic criteria. The interquartile ranges are presented to provide insight 

on differences across the entire distribution. The interquartile results illustrate how the medians are more 

robust (less affected by outliers) than the means. While the medians are similar to one another across the 

environmental parameters, the spreads are quite different. However, they are in the same order as the 

means in terms of most to least influential. The larger spreads for RIsland versus VAbeach correspond 

with site being the most influential parameter (Figures 6-18 and 6-21). The second most influential 

parameter was bearing, especially between north and west versus east and south, followed by season, then 

grain size (Figures 6-18 and 6-21). 

The influence of parameters on acoustic propagation follows consistent trends between RIsland and 

VAbeach (e.g., better propagation towards deeper water, with harder sediments, and at LF in winter). 

Even though RIsland (35 m) was 45% deeper than VAbeach (24 m), the impact volumes rarely were 45% 

the size of each other (the only close comparison was RIsland’s impact volume being 47% the size of 

VAbeach’s for LF cetaceans’ mean PTS) (Section 6.3). 

Most pile-driving sites will not occur in water deeper than 50 m, but variation in sound propagation exists 

across water columns from 0 to 50 m deep. For identical propagation conditions, a change of 50% in 

water depth should change the impact volume by 50%. Relative to the depths at RIsland and VAbeach, 

the following water depths between 0 and 50 m are 50% deeper (or 33% shallower) than each other: 5, 7, 

11, 16, 23, 34, and 50 m. (This is similar to the Fibonacci sequence of ...5, 8, 13, 21, 34, and 50 found 

throughout the natural world.) RIsland actually was 45% deeper than VAbeach but resulted in impact 

volume differences exceeding 50%. Thus, water depth is not the only explanatory variable for impact 

volume size, surrounding bathymetry and propagation conditions also play a role. 

Because the contributions of each environmental parameter could be site-specific, it is advisable to repeat 

the propagation modeling procedure at each pile-driving site. Acoustic propagation at different sites likely 
121

experiences a different ranking of environmental parameter influence when a site has deeper water

(approximately 55 m), different oceanographic conditions (e.g., not in a major coastal current system), 

greater seasonal contrast (i.e., at higher latitudes), or is farther from shore (approximately 50 km), to 

name a few examples. Numerical quantifications of what makes a site deeper or farther from shore is 

discussed in Section 8. Parameter influence is site- and season-specific. A tentative overall ranking, based 

on an unweighted LLEE and on the specific sites considered along the U.S. Atlantic Coast (RIsland and 

VAbeach), is bearing relative to shallowing water, grain size, and season (SSP). The influence of 

environmental parameters on the holistic impact volumes was calculated and analyzed and is presented in 

Sections 6.4.2.1 to 6.4.3.7. These impact volumes provide a more robust perspective of the environmental 

influences than can be deduced by investigating effects on each acoustic metric independently.

Figure 6-19. Mean acoustic metric values at VAbeach.

Panels left to right: four bearings of north (0°), east (90°), south (180°), and west (270°) during winter (blues) and 

summer (reds). Means of SEL (asterisks), LLpp,rms (triangles), and LLpp,pk (squares) for grain sizes 0.5 φφ (dark shade), 

1.5 φφ (moderate shade), and 2.5 φφ (light shade). For the west direction, vectors were truncated at 45 km to avoid 

numerical instabilities of the model output in very shallow water. μPa = micropascal; φφ = grain size; dB = decibel;

km = kilometer; LLpp,pk= peak sound pressure level; LLpp,rms = root-mean-square sound pressure level; re = with 

reference to; s = second; SEL = LLEE = sound exposure level; VAbeach = Virginia Beach site.
122

Figure 6-20. Median acoustic metric values at VAbeach.

Panels left to right: four bearings of north (0°), east (90°), south (180°), and west (270°) during winter (blues) and 

summer (reds). Medians of SEL (asterisks), LLpp,pk (squares), and LLpp,rms (triangles) for grain sizes 0.5 φφ (dark shade), 

1.5 φφ (moderate shade), and 2.5 φφ (light shade). For the west direction, vectors were truncated at 45 km to avoid 

numerical instabilities of the model output in very shallow water. μPa = micropascal; φφ = grain size; dB = decibel; km 

= kilometer; LLpp,pk= peak sound pressure level; LLpp,rms = root-mean-square sound pressure level; re = with reference 

to; s = second; SEL = LLEE = sound exposure level; VAbeach = Virginia Beach site;.
123

Figure 6-21. Percentiles of unweighted single strike SEL at VAbeach.

During summer (red, top panels) and winter (blue, bottom panels). Quartile LLEE,ss distributions presented as box and

whisker plots of 25th (bottom of box), median (red middle bar inside each box), and 75th (top of box) and extreme 

values (triangles) for four bearings (left to right): north (0°), east (90°), south (180°), and west (270°) and three grain 

sizes, smallest to largest. μPa = micropascal; dB = decibel; re = with reference to; SEL = LLEE = sound exposure level;

VAbeach = Virginia Beach site.

6.2.6 Sound Exposure Level Results as Plan View Plots

The metrics (means, medians, and percentiles) presented required high-resolution in frequency (0.5 Hz 

spacing) and therefore were computed only in the four cardinal directions. If only the LLEEwere computed, 

then the frequency spacing could be larger (10 Hz). The plan view (720 radials) unweighted LLEE, averaged 

across depth, was computed and is presented in Figures 6-22 and 6-23 for both sites. Color-coded 

contours align with values of PTS and TTS LLEE,w,24h risk thresholds for LF cetaceans. A surrounding 

green contour is plotted for all LLEE values above the LLEE,w,24h noise floor for LF cetaceans calculated from 

a quieter Arctic soundscape example quantified by Martin et al. (2019). The Chukchi Sea soundscape was 

chosen as a comparison because it was quieter than other alternatives available and was considered free of 

man-made sounds (Martin et al., 2019).
124

Figure 6-22. Plan view of the depth-averaged, low-frequency SELw,24h at RIsland.

Cyan: 60-km radius modeled range, region below Chukchi Sea measured noise floor per Martin et al. (2019 and pers. 

comm.); green: region below TTS risk threshold; yellow: region below PTS risk threshold; red: region above PTS risk 

threshold. Color scale units are in dB re 1 μPa2 s. μPa = micropascal; dB = decibel; km = kilometer;

LLEE,w,24h = weighted 24-hour sound exposure level; PTS = permanent threshold shift; re = with reference to; 

RIsland = Rhode Island site; s = second; SEL = LLEE = sound exposure level; TTS = temporary threshold shift.
125

Figure 6-23. Plan view of the depth-averaged, low-frequency weighted, SELw,24h at VAbeach.

Cyan: 60-km radius modeled range, region below Chukchi Sea measured noise floor per Martin et al. (2019 and pers. 

comm.); green: region below TTS threshold; yellow: region below PTS threshold; red: region above PTS threshold. 

Color scale units are in dB re 1 μPa2 s. μPa = micropascal; dB = decibel; LLEE,w,24h = weighted 24-hour sound 

exposure level; PTS = permanent threshold shift; re = with reference to; s = second; SEL = LLEE = sound exposure 

level; TTS = temporary threshold shift; VAbeach = Virginia Beach.

6.3 Impact Volumes

6.3.1 Applying Dual Impact Criteria

Using the modeled ranges to the LLEE, LLpp,rms, and LLpp,pk threshold values, impact volumes were calculated 

for all effect criteria listed in Section 1.5. Specifically, criteria for the following 10 species groups were 

considered:

• Four groups of marine mammals specified by NMFS (2018): LF cetaceans, MF cetaceans, HF 

cetaceans, and phocid pinnipeds underwater;

• Three groups of fishes specified by Popper et al. (2014): without swim bladder, with swim 

bladder involved in hearing, and with swim bladder not involved in hearing; 

• Two size classes of fishes specified by GARFO (2016): fish mass greater or less than 2 grams; 

and 

• Sea turtles.

Impact volumes were determined using the computations across the four cardinal directions. Each 

range/depth cell of the computation represents a volume equivalent to range spacing 

(20 m) × depth spacing (1 m) × range (in m) × bearing sector (90°). All pixels that exceeded the specific 
126

threshold value were counted to reach the total impact volume. Dual impact criteria were implemented by 

computing the impact volume for each of the criteria separately. 

6.3.2 Impact Volumes for Marine Mammals 

Prior to presenting the methodology and results of the impact volume computation variability across sites

and animal groups, the received levels were compared from the combined FE/PE model and the 

observations made at the Gemini wind farm. The modeled Gemini site was an isovelocity SSP on a flat 

silt-sand seafloor 24 m deep. The recordings were made at ranges of 732 m, 7.017 km, 31.8 km, and 

65.7 km from the pile. The unweighted decidecade band LLEE for each range from the Gemini observations 

and RIsland (24 m deep) during winter (nearly isovelocity) are shown in Figure 6-24. The model was 

calibrated to match the Gemini result at 732 m.

Figure 6-24. Comparison of unweighted single strike SEL in decidecade bands for Gemini 

U8-pile measurements and RIsland model predictions.

RIsland (lines) winter model and Gemini U8 pile measurements (dots) for four ranges from the pile: 0.7 km (black), 

7 km (blue), 32 km (red), and 65 km (yellow). μPa = micropascal; dB = decibel; Hz = hertz; km = kilometer; 

m = meter; RIsland = Rhode Island site; s = second; SEL = LLEE = sound exposure level.

The propagation model results are consistent with the Gemini observations, with a few differences. The 

most notable difference was that the Gemini measurements at long ranges showed considerable energy in 

the band above 3 kHz. Sound at these frequencies are not expected to propagate long distances; therefore, 

the LLEE numbers in these bands very likely were dominated by ambient noise. The observed differences at 

very low frequencies (20 and 25 Hz) likely are due to propagation differences in the deep sediment. 

RIsland does appear to be more lossy than the Gemini site, which likely is due to its slightly softer 

seafloor (0.5 φφ for RIsland compared with 0.0 φφ for Gemini).

In order to set expectations for the impact ranges (and subsequent volumes), the observed Gemini LLEE,ww

values were investigated for HF and LF cetaceans. For these two cetacean hearing groups, the expected 

ranges for TTS were approximately 3 km and 15 km, respectively, based on the Gemini measurements.
127

As described below, there are significant differences between the impact volumes for a single strike and 

those for a 24-hour period.

Computed impact volumes are presented in cubic kilometers (km3

). One cubic kilometer is 109 (one 

billion) cubic meters. For these sites (assuming a nominal water depth of 31 m as the height of a 

cylinder), impact volumes of 0.01, 1.0, and 100 km3 correspond to impact ranges from the pile (the

cylindrical radii) of approximately 320 m, 3.2 km, and 32 km, respectively. An impact volume less than 

1·10-4 km3 has a cylindrical area with a radius less than 32 m. This can be visualized as two blue whales 

fitting nose to tail in the impact volume. To facilitate visual interpretation in Tables 6-1 to 6-12, the cubic 

kilometer impact range results are color coded by radius length (and cylindrical volume) as green, <32 m 

(<1·10-4 km3

); yellow, 32 to 320 m (<1·10-4 to 0.01 km3); orange, 320 m to 3.2 km (0.01 to 1.0 km3); red, 

3.2 to 32 km (1.0 to 100 km3

); and purple, >32 km (>100 km3). Impact volumes involving LLEE were 

calculated using the full frequency range of 17 Hz to 20 kHz. Impact volumes involving LLpp,pk and LLpp,rms

were calculated using the limited frequency range of 17.5 to 2,238.5 Hz because these metrics are

unweighted and therefore unaffected by contributions from HF energy. 

6.3.2.1 Impulsive Signals, PTS Onset, LLEE,ww,2222hh for Marine Mammals

Impact volumes were calculated for PTS onset using LLEE,w,24h thresholds. Recall that up to 3,500 strikes 

could occur in any one 24-hour period and that all strikes would contribute equally to the total sound 

exposure. These assumptions result in the equation for the single-strike threshold as LLEE,ss,PTS = LLEE,24h,PTS

- 10log10(3,500) dB. A practical example for LF cetaceans, the corresponding PTS single-strike threshold 

becomes LLEE,ss,PTS = 183 dB – 35.4 dB = 147.6 dB. The LLEE,w,24h thresholds and their respective impact 

volumes are shown in Table 6-1, which is color-coded to aid in visualization of impact volumes. 
128

Table 6-1. Marine mammal PTS LLEE,ww,2222hh impact volumes.

Marine Mammal PTS LLEE,ww,2222

Thresholdsa (dB re 1 μPa2 s) 183 185 155 185

Site Season Grain 

size (φφ)

Low-frequency 

cetacean

(km3)

Mid-frequency 

cetacean

(km3)

High-frequency 

cetacean

(km3)

Phocid 

pinniped,

underwater

(km3)

VAbeach Summer 0.5 64.20 0.0019 0.63 0.0015

VAbeach Summer 1.5 48.36 0.0016 0.55 0.0014

VAbeach Summer 2.5 34.27 0.0015 0.48 0.0013

VAbeach Winter 0.5 88.95 0.0022 0.68 0.0016

VAbeach Winter 1.5 65.84 0.0019 0.60 0.0015

VAbeach Winter 2.5 51.01 0.0019 0.52 0.0015

RIsland Summer 0.5 31.38 0.0001 0.17 0.0004

RIsland Summer 1.5 20.14 0.0001 0.12 0.0004

RIsland Summer 2.5 12.53 0.0001 0.06 0.0004

RIsland Winter 0.5 47.20 0.0009 1.78 0.0008

RIsland Winter 1.5 32.75 0.0007 1.07 0.0006

RIsland Winter 2.5 21.25 0.0005 0.57 0.0005

VAbeach Mean -- 58.77 0.0018 0.58 0.0015

VAbeach Median -- 64.20 0.0019 0.60 0.0015

VAbeach Std. Dev. -- 17.12 0.0002 0.07 0.0001

RIsland Mean -- 27.54 0.0004 0.63 0.0005

RIsland Median -- 31.38 0.0005 0.57 0.0005

RIsland Std. Dev. -- 11.17 0.0003 0.62 0.0001

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; m = meter; PTS = permanent threshold shift; re = with 

reference to; RIsland = Rhode Island site; SELw,24h = LLEE,w,24h = weighted, 24-hour sound exposure level; s = second; 

Std. Dev. = standard deviation; VAbeach = Virginia Beach site.

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3

.

a - National Marine Fisheries Service (2018).
129

The extent to which these impact volumes were influenced by grain size, SSP, and water depth are 

illustrated in Figure 6-25, which displays the impact volumes at both sites during both seasons and 

assumes sound energy is radiated into the deepening water to the east. The predicted impact volumes are 

larger and vary more at VAbeach. At both sites, impact volumes are larger during winter than during

summer. Regardless of site and season, impact volumes always are smaller with finer grain sizes.

Figure 6-25. Environmental influence on impact volumes for low-frequency cetacean PTS, 

weighted, SELw,24h.

The LLEE,w,24h = SELw,24h PTS threshold = 183 dB re 1 μPa2 s, and provided basis for impact volumes, which are 

based on 3,500 strikes in 24 hours. μPa = micropascal; φφ = grain size dB = decibel; km3 = cubic kilometer;

LLEE,w,24h = weighted 24-hour sound exposure level; PTS = permanent threshold shift; re = with reference to; 

RIsland = Rhode Island site; SEL = LLEE = sound exposure level; VAbeach = Virginia Beach site.

6.3.2.2 Impulsive Signals, PTS Onset, LLpp,ppppfor Marine Mammals

The impact volume for PTS for all marine mammal groups was calculated using an LLpp,pk threshold 

(Table 6-2). In all impact volume tables, impact range radii less than 32 m were truncated and listed as 

<1·10-4 km3 because of a lack of range resolution in the acoustic model prediction. The LLpp,pk impact 

volumes are smaller than the LLEE impact volumes, so according to the dual criteria application, the impact 

volumes are determined by the larger LLEE volumes in Table 6-1. The LLpp,pk impact volumes (and more 

generally, the smaller volumes for all dual criteria) are presented without color-coding.
130

Table 6-2. Marine mammal PTS onset LLpp,pppp impact volumes

Marine Mammal PTS LLpp,pppp

Thresholdsa (dB re 1 μPa) 219 230 202 218

Site Season Grain 

size (φφ)

Lowfrequency 

cetacean

(km3)

Mid-frequency 

cetacean

(km3)

Highfrequency 

cetacean

(km3)

Phocid 

pinniped,

underwater

(km3)

VAbeach Summer 0.5 0.000153 <1·10-4 0.0811 0.000235

VAbeach Summer 1.5 0.000150 <1·10-4 0.0785 0.000230

VAbeach Summer 2.5 0.000151 <1·10-4 0.0727 0.000214

VAbeach Winter 0.5 0.000122 <1·10-4 0.0862 0.000166

VAbeach Winter 1.5 0.000122 <1·10-4 0.0823 0.000157

VAbeach Winter 2.5 0.000113 <1·10-4 0.0776 0.000168

RIsland Summer 0.5 0.000161 <1·10-4 0.0592 0.000268

RIsland Summer 1.5 0.000153 <1·10-4 0.0471 0.000272

RIsland Summer 2.5 0.000163 <1·10-4 0.0359 0.000245

RIsland Winter 0.5 0.000173 <1·10-4 0.0691 0.000232

RIsland Winter 1.5 0.000172 <1·10-4 0.0581 0.000231

RIsland Winter 2.5 0.000172 <1·10-4 0.0445 0.000222

VAbeach Mean -- 0.000135 <1·10-4 0.0797 0.000195

VAbeach Median -- 0.000150 <1·10-4 0.0811 0.000214

VAbeach Std. Dev. -- 0.000016 -- 0.0042 3.198·10-5

RIsland Mean -- 0.000165 <1·10-4 0.0523 0.000245

RIsland Median -- 0.000172 <1·10-4 0.0581 0.000245

RIsland Std. Dev. -- 0.000007 -- 0.0109 0.000019

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk = peak sound pressure level; PTS = permanent 

threshold shift; re = with reference to; RIsland = Rhode Island site; Std. Dev. = standard deviation; VAbeach = Virginia Beach site.

a - National Marine Fisheries Service (2018).

The LLpp,pk impact volumes followed the same trends as LLEE for size, depending on the environmental 

parameters of site, season, and water column depth (Figure 6-26), but the details are unimportant because 

the larger impact volumes correspond to LLEE (Figure 6-25), not LLpp,pk.
131

Figure 6-26. Environmental influence on impact volumes for high-frequency cetacean PTS, 

LLpp,pppp.

The LLpp,pk PTS threshold = 202 dB re 1 μPa, provided basis for impact volumes. μPa = micropascal; φφ = grain size; 

dB = decibel; km3 = cubic kilometer; LLpp,pk = peak sound pressure level; PTS = permanent threshold shift; re = with 

reference to; RIsland = Rhode Island site; VAbeach = Virginia Beach site.

6.3.2.3 Impulsive Signals, TTS Onset, LLEE,ww,2222hh for Marine Mammals

For all marine mammal groups, the TTS impact volumes were calculated for LLEE and LLpp,pk, as shown in 

Tables 6-3 and 6-4, respectively. As the TTS onset threshold is lower than PTS threshold, the TTS 

impact volumes were larger than PTS impact volumes. In many cases, the TTS impact volumes 

(Table 6-3) were at least three times larger than PTS impact volumes (Table 6-1). These assumptions 

result in the equation for the single-strike threshold as LLEE,ss,TTS = LLEE,24h,TTS – 10log(3,500) dB. 

A practical example for HF cetaceans, the corresponding TTS single-strike threshold becomes

LLEE,ss,TTS = 140 dB – 35.4 dB = 104.6 dB.
132

Table 6-3. Marine mammal TTS LLEE,ww,2222hh impact volumes

Marine Mammal TTS LLEE,ww,2222hh

Thresholdsa (dB re 1 μPa2 s) 168 170 140 170

Site Season Grain size 

(φφ)

Low-frequency 

cetacean

(km3)

Mid-frequency 

cetacean

(km3)

High-frequency 

cetacean

(km3)

Phocid 

pinniped,

underwater

(km3)

VAbeach Summer 0.5 267.8 0.028 32.63 0.239

VAbeach Summer 1.5 238.8 0.025 24.15 0.215

VAbeach Summer 2.5 188.2 0.021 21.73 0.189

VAbeach Winter 0.5 281.6 0.030 31.65 0.253

VAbeach Winter 1.5 269.6 0.027 23.42 0.229

VAbeach Winter 2.5 246.4 0.023 21.08 0.204

RIsland Summer 0.5 143.9 0.019 4.92 0.142

RIsland Summer 1.5 94.5 0.012 2.84 0.112

RIsland Summer 2.5 61.1 0.008 1.52 0.088

RIsland Winter 0.5 263.7 0.150 27.15 0.222

RIsland Winter 1.5 203.2 0.101 16.96 0.173

RIsland Winter 2.5 158.0 0.058 9.37 0.122

VAbeach Mean -- 248.8 0.026 25.78 0.222

VAbeach Median -- 257.1 0.026 23.78 0.222

VAbeach Std. Dev. -- 33.6 0.003 5.06 0.023

RIsland Mean -- 154.1 0.058 10.52 0.143

RIsland Median -- 151.0 0.038 7.33 0.132

RIsland Std. Dev. -- 73.1 0.057 9.89 0.048

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; SELw,24h = LLEE,w,24h = weighted, 24-hour sound exposure 

level; re = with reference to; RIsland = Rhode Island site; s = second; Std. Dev. = standard deviation; TTS = temporary threshold 

shift; VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - National Marine Fisheries Service (2018).

The environmental parameters (site, season, and grain size) had similar influence trends on impact 

volume for TTS compared to PTS. The shallower water columns, during winter, with hard sediments had 

the largest impact volumes (Figure 6-27).
133

Figure 6-27. Environmental influence on impact volumes for high-frequency cetacean TTS 

weighted SELw,24h.

The LLEE,w,24h = SELw,24h TTS threshold = 140 dB re 1 μPa2 s, and provided basis for impact volumes, which are based 

on 3,500 strikes in 24 hours. μPa = micropascal; φφ = grain size; dB = decibel; km3 = cubic kilometer; 

SELw,24h = LLEE,w,24h = weighted 24-hour sound exposure level; re = with reference to; RIsland = Rhode Island site; 

SEL = LLEE = sound exposure level; TTS = temporary threshold shift; VAbeach = Virginia Beach site.

6.3.2.4 Impulsive Signals, TTS Onset, LLpp,pppp for Marine Mammals

Compared with the PTS impact volumes, TTS impact volumes quantified by LLpp,pk had similar levels of 

influence from the environmental parameters (site, season, and water column depth) but were smaller 

(Table 6-4). For this situation, the standard deviation is not appropriate to report because of the lack of 

precision for impact ranges less than 32 m (i.e., volume <1·10-4 km3

). Because the volumes in Table 6-4

are smaller than those in Table 6-3, the LLpp,pk volumes did not contribute to impact volume for TTS onset

in marine mammals at either VAbeach or RIsland.
134

Table 6-4. Marine mammal TTS onset LLpp,pppp impact volumes.

Marine Mammal TTS Onset LLpp,pppp

Thresholdsa (dB re 1 μPa) 213 224 196 212

Site Season Grain size 

(φφ)

Lowfrequency 

cetacean

(km3)

Midfrequency 

cetacean

(km3)

Highfrequency 

cetacean

(km3)

Phocid 

pinniped,

underwater

(km3)

VAbeach Summer 0.5 0.00146 <1·10-4 0.500 0.00222

VAbeach Summer 1.5 0.00142 <1·10-4 0.468 0.00214

VAbeach Summer 2.5 0.00134 <1·10-4 0.433 0.00201

VAbeach Winter 0.5 0.00133 <1·10-4 0.563 0.00196

VAbeach Winter 1.5 0.00128 <1·10-4 0.529 0.00185

VAbeach Winter 2.5 0.00132 <1·10-4 0.492 0.00180

RIsland Summer 0.5 0.00155 <1·10-4 0.307 0.00199

RIsland Summer 1.5 0.00142 <1·10-4 0.240 0.00189

RIsland Summer 2.5 0.00125 <1·10-4 0.177 0.00175

RIsland Winter 0.5 0.00146 <1·10-4 0.389 0.00210

RIsland Winter 1.5 0.00135 <1·10-4 0.309 0.00186

RIsland Winter 2.5 0.00115 <1·10-4 0.229 0.00154

VAbeach Mean -- 0.00135 <1·10-4 0.497 0.00200

VAbeach Median -- 0.00133 <1·10-4 0.500 0.00201

VAbeach Std. Dev. -- 0.00006 -- 0.041 0.00015

RIsland Mean -- 0.00136 <1·10-4 0.275 0.00185

RIsland Median -- 0.00142 <1·10-4 0.307 0.00188

RIsland Std. Dev. -- 0.00013 -- 0.068 0.00018

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk= peak sound pressure level; re = with reference to; 

RIsland = Rhode Island site; Std. Dev. = standard deviation; TTS = temporary threshold shift; VAbeach = Virginia Beach site. 

a - National Marine Fisheries Service (2018).

Table 6-4 does not have a corresponding figure showing environmental influence on impact volume 

because most of the ranges were less than 32 m, and dual criteria application states LLEE would be followed 

for impact ranges instead of LLpp,pk.

6.3.2.5 Impulsive Signals, Behavior, LLpp,rrrrrr for Marine Mammals

Table 6-5 shows the behavioral LLpp,rms impact volumes to the 160 dB re 1 μPa threshold for the marine 

mammal groups. Of note are the differences between the sites; impact volumes were three times larger at 

VAbeach than at RIsland. Additionally, seasonality had a stronger influence at VAbeach compared to 

RIsland, which is visualized in Figure 6-25.
135

Table 6-5. Marine mammal behavioral LLpp,rrrrrr impact volumes.

Marine Mammal Behavior LLpp,rrrrrr

Thresholdsa,b (dB re 1 μPa) 160 160 160 160

Site Season

Grain 

size 

(φφ)

Low-frequency 

cetacean

(km3)

Mid-frequency 

cetacean

(km3)

High-frequency 

cetacean

(km3)

Phocid 

pinniped,

underwater

(km3)

VAbeach Summer 0.5 49.93 49.93 49.93 49.93

VAbeach Summer 1.5 40.26 40.26 40.26 40.27

VAbeach Summer 2.5 31.98 31.98 31.98 31.98

VAbeach Winter 0.5 60.80 60.80 60.80 60.81

VAbeach Winter 1.5 51.51 51.51 51.51 51.51

VAbeach Winter 2.5 42.77 42.77 42.77 42.77

RIsland Summer 0.5 23.60 23.60 23.60 23.61

RIsland Summer 1.5 15.68 15.68 15.68 15.68

RIsland Summer 2.5 10.35 10.35 10.35 10.35

RIsland Winter 0.5 36.42 36.42 36.42 36.42

RIsland Winter 1.5 25.69 25.69 25.69 25.69

RIsland Winter 2.5 17.79 17.79 17.79 17.79

VAbeach Mean -- 46.21 46.21 46.21 46.21

VAbeach Median -- 49.93 49.95 49.93 49.93

VAbeach Std. Dev. -- 9.17 9.17 9.17 9.17

RIsland Mean -- 21.59 21.59 21.59 21.59

RIsland Median -- 23.61 23.61 23.61 23.60

RIsland Std. Dev. -- 8.33 8.33 8.33 8.33

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,rms= root-mean-square sound pressure level; 

re = with reference to; RIsland = Rhode Island site; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - National Marine Fisheries Service (2018).

b - Federal Register (2005).

Comparing Tables 6-1 and 6-5, the predicted volumes for PTS in LF cetaceans at RIsland are larger than 

those for behavior at the same site. This is a combined consequence of the behavior and PTS criteria 

along with the exclusion of animal movement from the scope of this study. The behavior volume is 

determined by the rms sound pressure from a single hammer strike and does not change with the number 

of strikes. On the other hand, an animal is assumed stationary during the entire PTS volume, which is 

determined by the cumulative sound exposure from multiple strikes at a fixed position. The PTS volume 

therefore increases indefinitely with the number of strikes and ultimately exceeds the behavior volume 

once the number of strikes is large enough for LLEE,w,24h to exceed the PTS threshold at the behavior range.
136

For the behavioral thresholds, the largest to smallest impact volumes ranked from RIsland winter to 

RIsland summer, VAbeach winter, and VAbeach summer, as occurred for the PTS and TTS 

(Figure 6-28).

Figure 6-28. Environmental influence on impact volumes for cetacean behavioral, unweighted

LLpp,rrrrrr.

The LLpp,rrrrrr behavioral threshold = 160 dB re 1 μPa and provided basis for impact volumes. Panels left to right: 

VAbeach summer, VAbeach winter, RIsland summer, and RIsland winter by grain size, 0.5 (black), 1.5 (blue), and 

2.5 φφ (red). μPa = micropascal; φφ = grain size; dB = decibel; km3 = cubic kilometer; LLpp,rms = root-mean-square sound 

pressure level; re = with reference to; RIsland = Rhode Island site; VAbeach = Virginia Beach site.

6.3.3 Impact Volumes for Fishes and Sea Turtles 

6.3.3.1 Impulsive Signals, Recoverable Injury, LLEE,#999999 for Fishes

The LLEE recoverable injury impact volumes for fish groups are shown in Table 6-6 and Figure 6-29. The 

assumed number of strikes in a 24-hour period being 3,500 strikes also was applied for fishes. The 

threshold for 3,500 strikes is assumed to be the same as for 960 strikes, on which the criteria are based 

(LLEE,#960). This is a precautionary approach because the cumulative LLEE injury threshold is known to

increase when the energy is spread over a greater number of strikes (Halvorsen et al., 2011, 2012a,b; 

Casper et al., 2017). The median impact volume for recoverable injury calculated in this way varied from 

0.06 to 3.2 km3 for VAbeach, and from 0.04 to 1.8 km3 for RIsland. The largest was 3.7 km3

, for 

VAbeach in winter, with coarse sand.
137

Table 6-6. Fish recoverable Injury LLEE,#999999 impact volumes.

Fishes Recoverable Injury (LLEE,#999999)

Thresholdsa (dB re 1 μPa2 s) >216 203 203

Site Season Grain size 

(φφ)

Without swim 

bladder

(km3)

With swim bladder 

not involved in 

hearing

(km3)

With swim bladder 

involved in 

hearing

(km3)

VAbeach Summer 0.5 0.0579 3.28 3.28

VAbeach Summer 1.5 0.0535 2.81 2.81

VAbeach Summer 2.5 0.0458 2.33 2.33

VAbeach Winter 0.5 0.0600 3.73 3.73

VAbeach Winter 1.5 0.0569 3.22 3.22

VAbeach Winter 2.5 0.0521 2.74 2.74

RIsland Summer 0.5 0.0437 2.00 2.00

RIsland Summer 1.5 0.0394 1.59 1.59

RIsland Summer 2.5 0.0365 1.22 1.22

RIsland Winter 0.5 0.0438 2.25 2.25

RIsland Winter 1.5 0.0402 1.80 1.80

RIsland Winter 2.5 0.0379 1.41 1.41

VAbeach mean -- 0.0544 3.02 3.02

VAbeach Median -- 0.0569 3.22 3.22

VAbeach Std. Dev. -- 0.0047 0.45 0.45

RIsland Mean -- 0.0402 1.71 1.71

RIsland Median -- 0.0402 1.80 1.80

RIsland Std. Dev. -- 0.0027 0.35 0.35

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; SEL#960 = LLEE,#960 = 960-strike sound exposure level; 

re = with reference to; RIsland = Rhode Island site; s = second; Std. Dev. = standard deviation; VAbeach = Virginia Beach site.

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - Popper et al. (2014).
138

A comparison of the impact volumes over each environmental parameter of site, season, and sediment 

grain size are shown in Figure 6-29. There was a factor of 4 spread between the lowest and highest 

impact volume based on site, season, and sediment type.

Figure 6-29. Environmental influence on impact volumes for fish with swim bladder, 

recoverable injury, unweighted 960-strike SEL (SEL#960).

The LLEE,#960 = SEL#960 recoverable injury thresholds are 203 and 216 dB re 1 μPa2 s, and provided basis for impact 

volumes, which are based on 3,500 strikes in 24 hours. μPa = micropascal; φφ = grain size; dB = decibel; km3 = cubic 

kilometer; re = with reference to; RIsland = Rhode Island site; s = second; VAbeach = Virginia Beach site.

6.3.3.2 Impulsive Signals, Recoverable Injury, LLpp,pppp for Fishes

The LLpp,pk recoverable injury impact volumes for fish groups are shown in Table 6-7. The volumes in

Table 6-7 are smaller than those in Table 6-6 and therefore not color-coded.
139

Table 6-7. Fish recoverable injury LLpp,pppp impact volumes.

Fishes Recoverable Injury (LLpp,pppp)

Thresholdsa (dB re 1 μPa) >213 >207 >207

Site Season Grain size 

(φφ)

Without swim 

bladder

(km3)

With swim bladder 

not involved in 

hearing

(km3)

With swim bladder 

involved in hearing

(km3)

VAbeach Summer 0.5 0.00146 0.0133 0.0133

VAbeach Summer 1.5 0.00142 0.0128 0.0128

VAbeach Summer 2.5 0.00134 0.0122 0.0122

VAbeach Winter 0.5 0.00134 0.0118 0.0118

VAbeach Winter 1.5 0.00128 0.0119 0.0119

VAbeach Winter 2.5 0.00132 0.0113 0.0113

RIsland Summer 0.5 0.00155 0.0113 0.0113

RIsland Summer 1.5 0.00142 0.0096 0.0097

RIsland Summer 2.5 0.00125 0.0079 0.0079

RIsland Winter 0.5 0.00146 0.0123 0.0124

RIsland Winter 1.5 0.00136 0.0108 0.0108

RIsland Winter 2.5 0.00116 0.0087 0.0088

VAbeach Mean -- 0.00136 0.0122 0.0122

VAbeach Median -- 0.00134 0.0121 0.0121

VAbeach Std. Dev. -- 0.00006 0.0006 0.0007

RIsland Mean -- 0.00137 0.0101 0.0101

RIsland Median -- 0.00142 0.0108 0.0108

RIsland Std. Dev. -- 0.00013 0.0015 0.0015

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk= peak sound pressure level; re = with reference to; 

RIsland = Rhode Island site; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

a - Popper et al. (2014).

6.3.3.3 Impulsive Signals, TTS, LLEE,#55 for Fishes

The LLEE TTS impact volumes for fish groups are shown in Table 6-8. Here, the threshold for 3,500 strikes 

is assumed to be the same as for 5 strikes, on which the criteria are based. For fishes without a swim 

bladder, the TTS volumes would be much smaller because their threshold is “much greater” than 186 dB 

re 1 μPa2 s (>>186). Therefore, the color-coding should be carefully considered as it incorrectly suggests 

that the impact volumes are just as large as for the other two columns of fishes with swim bladders. 
140

Table 6-8. Fish TTS LLEE,#55 impact volumes.

Fishes TTS (LLEE,#55)

Thresholdsa (dB re 1 μPa2 s) >>186 >186 186

Site Season Grain 

size (φφ)

Without swim 

bladder

(km3)

With swim bladder 

not involved in 

hearing

(km3)

With swim bladder 

involved in hearing

(km3)

VAbeach Summer 0.5 88.5 88.5 88.5

VAbeach Summer 1.5 65.1 65.1 65.1

VAbeach Summer 2.5 47.4 47.4 47.4

VAbeach Winter 0.5 126.7 126.7 126.7

VAbeach Winter 1.5 96.7 96.7 96.7

VAbeach Winter 2.5 71.2 71.2 71.2

RIsland Summer 0.5 45.4 45.4 45.4

RIsland Summer 1.5 30.6 30.6 30.6

RIsland Summer 2.5 19.6 19.6 19.6

RIsland Winter 0.5 73.5 73.5 73.5

RIsland Winter 1.5 52.7 52.7 52.7

RIsland Winter 2.5 36.8 36.8 36.8

VAbeach Mean -- 82.6 82.6 82.6

VAbeach Median -- 88.5 88.5 88.5

VAbeach Std. Dev. -- 25.3 25.3 25.3

RIsland Mean -- 43.1 43.1 43.1

RIsland Median -- 45.4 45.4 45.4

RIsland Std. Dev. -- 17.2 17.2 17.2

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; SEL#5 = LLEE,#5 = 5-strike sound exposure level; re = with 

reference to; RIsland = Rhode Island site; s= second; Std. Dev. = standard deviation; TTS = temporary threshold shift; 

VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - Popper et al. (2014).
141

For fishes, the influence on the 5-strike LLEE (LLEE,#5) TTS impact volumes were compared over the

environmental parameters of site, season, and sediment grain size (Figure 6-30).

Figure 6-30. Environmental influence on impact volumes for fishes with swim bladder, 

unweighted 5-strike SEL (SEL#5) TTS.

The LLEE,#5 = SEL#5 TTS threshold is 186 dB re 1 μPa2 s based on 5 strikes and provided basis for impact volumes. 

μPa = micropascal; φφ = grain size; dB = decibel; km3 = cubic kilometer; re = with reference to; RIsland = Rhode 

Island site; s = second; TTS = temporary threshold shift; VAbeach = Virginia Beach site.

6.3.3.4 Impulsive Signals, Injury, SEL12h for Fishes

The 12-hour LLEE(LLEE,12h) injury impact volumes for small and large fish (Stadler and Woodbury, 2009; 

GARFO, 2016) are shown in Table 6-9 and Figure 6-31. The median impact volume for injury 

calculated in this way varied from 76 to 130 km3 for VAbeach, and from 40 to 63 km3 for RIsland. 

Compared with the corresponding medians calculated for the Popper et al. (2014) criteria (Table 6-9

versus Table 6-6 and Figure 6-31 versus Figure 6-29), on average the impact volumes of Table 6-9 were

30x and 1,600x larger than those of Table 6-6 for fishes with and without a swim bladder, respectively.
142

Table 6-9. Fish injury LLEE,11 hh impact volumes.

Fishes, Injury (LLEE,11 hh)

Thresholdsa,b (dB re 1 μPa2 s) 183 187

Site Season Grain size (φφ) Small fish; mass <2 grams

(km3)

Large fish; mass >2 grams

(km3)

VAbeach Summer 0.5 133.7 75.9

VAbeach Summer 1.5 97.2 57.6

VAbeach Summer 2.5 68.5 40.8

VAbeach Winter 0.5 189.4 109.9

VAbeach Winter 1.5 144.7 82.3

VAbeach Winter 2.5 111.3 60.8

RIsland Summer 0.5 63.1 40.1

RIsland Summer 1.5 43.3 26.8

RIsland Summer 2.5 28.4 17.2

RIsland Winter 0.5 113.8 63.2

RIsland Winter 1.5 80.2 45.8

RIsland Winter 2.5 55.1 31.6

VAbeach Mean -- 124.1 71.2

VAbeach Median -- 133.7 75.9

VAbeach Std. Dev. -- 38.2 21.8

RIsland Mean -- 64.0 37.5

RIsland Median -- 63.1 40.1

RIsland Std. Dev. -- 27.4 14.7

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; SEL12h= LLEE,12h = 12-hour sound exposure level; re = with 

reference to; RIsland = Rhode Island site; s = second; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - Stadler and Woodbury (2009).

b - Greater Atlantic Regional Fisheries Office (2016).
143

For fishes, the LLEE,12h impact volumes were compared over the environmental parameters of site, season,

and sediment grain size in Figure 6-31. The volume sizes followed the same trends from largest to 

smallest as those calculated for the marine mammal groups.

Figure 6-31. Environmental influence on impact volumes for small fishes unweighted 12-hour 

SEL (SEL12h) injury.

The LLEE,12h = SEL12h injury threshold is 183 dB re 1 μPa2 s for small fishes, and provided basis for impact volumes, 

which are calculated for 3,500 strikes in 24 hours. μPa = micropascal; φφ = grain size; dB = decibel; km3 = cubic 

kilometer; re = with reference to; RIsland = Rhode Island site; s = second; VAbeach = Virginia Beach site.

6.3.3.5 Impulsive Signals, Injury, LLpp,pppp for Fishes

The LLpp,pk injury impact volumes for small (mass less than 2 grams) and large (mass greater than 2 grams) 

fishes are shown in Table 6-10. A comparison of the impact volumes for LLpp,pk over site, season, and 

sediment grain size are shown in Figure 6-32. Because the volumes in Table 6-10 are smaller than those 

from Table 6-9, when applying the criteria proposed by Stadler and Woodbury (2009), the LLpp,pk volumes 

did not contribute to impact volume for injury in fishes at VAbeach or RIsland.
144

Table 6-10. Fish injury LLpp,pppp impact volumes.

Fishes, Injury (LLpp,pppp)

Thresholdsa (dB re 1 μPa) 206 206

Site Season

Grain size 

(φφ)

Small fish; mass <2 grams

(km3)

Large fish; mass >2 

grams

(km3)

VAbeach Summer 0.5 0.0192 0.0192

VAbeach Summer 1.5 0.0190 0.0190

VAbeach Summer 2.5 0.0180 0.0180

VAbeach Winter 0.5 0.0184 0.0184

VAbeach Winter 1.5 0.0179 0.0179

VAbeach Winter 2.5 0.0182 0.0182

RIsland Summer 0.5 0.0158 0.0158

RIsland Summer 1.5 0.0137 0.0137

RIsland Summer 2.5 0.0113 0.0113

RIsland Winter 0.5 0.0176 0.0176

RIsland Winter 1.5 0.0153 0.0153

RIsland Winter 2.5 0.0127 0.0127

VAbeach Mean -- 0.01845 0.01845

VAbeach Median -- 0.01836 0.01836

VAbeach Std. Dev. -- 0.00048 0.00048

RIsland Mean -- 0.01438 0.01438

RIsland Median -- 0.01528 0.01528

RIsland Std. Dev. -- 0.00206 0.00206

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk= peak sound pressure level; re = with reference to; 

RIsland = Rhode Island site; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

a - Stadler and Woodbury (2009). 
145

The order of impact volumes for LLpp,pk over site, season, and sediment grain size for small fish is the first 

to differ from the rest of the hearing groups, Figure 6-32. The impact volumes for these criteria show 

RIsland summer and winter sizes being very similar. This is because at the short ranges associated with 

crossing the LLpp,pk thresholds, the difference in the propagation conditions is inconsequential. Both 

RIsland seasons are noticeably larger than both VAbeach seasons.

Figure 6-32. Environmental influence on impact volumes for small fish LLpp,pppp injury

The LLpp,pk injury threshold is 206 dB re 1 μPa and provided basis for impact volumes. μPa = micropascal; φφ = grain 

size; dB = decibel; km3 = cubic kilometer; re = with reference to; RIsland = Rhode Island site; VAbeach = Virginia 

Beach site.

6.3.3.6 Impulsive signals, behavior, LLpp,rrrrrr for Fishes

The LLpp,rms behavioral impact volumes for small (mass less than 2 grams) and large (mass greater than 

2 grams) fishes are shown in Table 6-11. A comparison of the impact volumes for LLpp,rms over site, 

season, and sediment grain size are shown in Figure 6-33.
146

Table 6-11. Fish behavioral LLpp,rrrrrr impact volumes.

Fishes, Behavior (LLpp,rrrrrr)

Thresholdsa (dB re 1 μPa) 150 150

Site Season Grain size (φφ)

Small fish; mass <2 

grams

(km3)

Large fish; mass >2 

grams

(km3)

VAbeach Summer 0.5 164.5 164.5

VAbeach Summer 1.5 129.9 129.9

VAbeach Summer 2.5 100.2 100.2

VAbeach Winter 0.5 221.2 221.2

VAbeach Winter 1.5 184.1 184.1

VAbeach Winter 2.5 151.5 151.5

RIsland Summer 0.5 72.8 72.8

RIsland Summer 1.5 49.9 49.9

RIsland Summer 2.5 33.0 33.0

RIsland Winter 0.5 146.8 146.8

RIsland Winter 1.5 108.7 108.7

RIsland Winter 2.5 78.3 78.3

VAbeach Mean -- 158.6 158.6

VAbeach Median -- 164.5 164.5

VAbeach Std. Dev. -- 38.5 38.5

RIsland Mean -- 81.6 81.6

RIsland Median -- 78.3 78.3

RIsland Std. Dev. -- 37.5 37.5

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,rms= root-mean-square sound pressure level; re = with 

reference to; RIsland = Rhode Island site; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - Greater Atlantic Regional Fisheries Office (2016).
147

Figure 6-33. Environmental influence on impact volumes for small fish unweighted

LLpp,rrrrrr behavioral.

The LLpp,rms behavioral threshold is 150 dB re 1 μPa and provided basis for impact volumes. μPa = micropascal; 

φφ = grain size; dB = decibel; km3 = cubic kilometer; re = with reference to; RIsland = Rhode Island site; 

VAbeach = Virginia Beach site.

6.3.3.7 Impulsive Signals, Mortal Injury, Physiological, and Behavior Volumes for Sea 

Turtles

The impact volumes for sea turtles based on various single-strike metrics are shown in Table 6-12, 

following the four acoustic criteria categories put forth by Popper et al. (2014) and GARFO (2016). The 

volumes in the LLpp,pk column are smaller than those in the LLEE,12h column and therefore not color-coded.
148

Table 6-12. Sea turtle mortal injury LLEE,11 hh, physiological LLpp,pppp, and behavioral LLpp,rrrrrr impact 

volumes.

Sea turtles, Multiple metrics

Thresholds (mixed metrics) 210a >207a 180b 166b

Site Season Grain 

size (φφ)

Mortal Injury 

LLEE,11 hh

(dB re 1 μPa2 s, 

unweighted)

(km3)

Mortal Injury

LLpp,pppp 

(dB re 1 μPa, 

unweighted)

(km3)

Physiological

LLpp,rrrrrr 

(dB re 1 μPa, 

unweighted)

(km3)

Behavior

LLpp,rrrrrr 

(dB re 1 μPa 

unweighted)

(km3)

VAbeach Summer 0.5 5.41 0.0133 1.369 21.14

VAbeach Summer 1.5 4.73 0.0129 1.248 17.90

VAbeach Summer 2.5 4.08 0.0122 1.152 14.93

VAbeach Winter 0.5 6.27 0.0118 1.572 25.03

VAbeach Winter 1.5 5.54 0.0119 1.459 21.39

VAbeach Winter 2.5 4.85 0.0113 1.365 18.15

RIsland Summer 0.5 2.26 0.0113 0.744 9.55

RIsland Summer 1.5 1.62 0.0097 0.625 6.65

RIsland Summer 2.5 1.07 0.0079 0.528 4.35

RIsland Winter 0.5 2.61 0.0124 0.807 12.82

RIsland Winter 1.5 2.01 0.0108 0.666 9.07

RIsland Winter 2.5 1.39 0.0088 0.556 6.36

VAbeach Mean -- 5.15 0.0122 1.36 19.76

VAbeach Median -- 5.41 0.0122 1.37 21.14

VAbeach Std. Dev. -- 0.69 0.0007 0.14 3.21

RIsland Mean -- 1.82 0.0101 0.65 8.13

RIsland Median -- 2.01 0.0108 0.67 9.07

RIsland Std. Dev. -- 0.52 0.0015 0.10 2.72

-- = not applicable; μPa = micropascal; dB = decibel; km = kilometer; LLpp,pk= peak sound pressure level;

LLpp,rms= root-mean-square sound pressure level; PTS = permanent threshold shift; SEL12h = LLEE,12h = 12-hour sound exposure level; 

re = with reference to; RIsland = Rhode Island site; s = second; Std. Dev. = standard deviation; VAbeach = Virginia Beach site. 

Volumes defined as: green = <0.032 km3

, yellow = 0.032 to 0.32 km3; orange = 0.32 to 3.2 km3; red = 3.2 to 32 km3; 

purple = >32 km3 a - Popper et al. (2014).

b - Greater Atlantic Regional Fisheries Office (2016).
149

The LLpp,rrrrrr behavioral impact volumes for a single strike over the environmental parameters of site,

season, and sediment grain size are compared in Figure 6-34.

Figure 6-34. Environmental influence on impact volumes for sea turtles unweighted

LLpp,rrrrrr behavioral.

The LLpp,rms behavioral threshold is 166 dB re 1 μPa and provided basis for impact volumes. μPa = micropascal; 

φφ = grain size; dB = decibel; km3 = cubic kilometer; re = with reference to; RIsland = Rhode Island site; 

VAbeach = Virginia Beach site.

6.3.4 Impact Versus Vibratory Piles

The primary focus of this work was the impact pile, which has an impact strike at the top of the pile and 

transmits a traveling pressure wave into the pile. This wave travels back and forth along the pile several 

times, each pass radiating into the water column. Much of this radiated energy goes into the Mach cone, 

which enters the water at an angle determined by the ratio of the sound speed in the metal pile and the 

sound speed in the water. Signals from impact pile driving were treated as impulsive, regardless of the 

distance from the source. The time spread for all signals and all sites is on the order of 2 seconds or less,

even for ranges of 60 km. This means that a pile struck every 3 seconds or more will be received as a 

discrete pulse of energy.

For a vibratory pile, no hammer directly hits the top of a pile and there is no coherent traveling wave 

down the pile. It is this coherent acoustic structure of the impact strike on a pile that leads to bands of

energy at specific angles that radiate into the seafloor, as evidenced in Figure 6-35. To model the 

vibratory pile, the same acoustic field generated 5 m from the pile was used in the FE model, but the 

phases of each frequency and depth component were randomized. The randomization of the phases 
150

destroys the coherent nature of the propagating wave but maintains the physics of the sound in the pile 

and the water. No publicly available measured data were available to compare the vibratory pile with the 

impact pile, so it was assumed they have the same LLEE (pressure squared) at each depth and frequency.

This is a considerable assumption, based on the requirement that the same amount of work must be done 

to insert a pile into the seafloor whether by hitting it or shaking it. Note that when computing LLEE, the 

integration time has not been specified. 

A comparison of the decidecade band (band index 3: 1,778.279 to 2,238.721 Hz) LLEE for the impact and 

vibratory pile at RIsland during winter is shown in Figure 6-35. The coherent energy from the Mach cone 

is clearly visible in the upper panel, and it radiates out to nearly 2 km. The vibratory pile has no such 

coherent wave, and in fact has energy entering the water column at all vertical angles. The energy leaving 

the pile at short range (total LLEE for an integration time of 1 second, and at 3 m range from the pile axis) is 

the same for both models. The impact pile (top panel) shows the coherent multi-path resulting from the 

sound traveling from the impact through the pile into the water. This energy interacts with the seafloor 

and surface repeatedly to 2 km. For the vibratory pile, this coherent field is eliminated and the sound 

enters the water column at all angles, including much higher ones that are rapidly attenuated.

Figure 6-35. Modeled impact (top) and vibratory (bottom) pile comparison.

The piles were modeled in the decidecade band (band index 3: 1778 to 2239 Hz) for unweighted, single-strike sound 

exposure level (LLEE,ss) at RIsland during winter. The LLEE,ss is in dB re 1 μPa2 s. μPa = micropascal; dB = decibel; 

m = meter; re = with reference to; RIsland = Rhode Island site; s = second.

The unweighted, depth-averaged LLEE,ss was computed for the impact and vibratory piles for RIsland north 

(0°) and east (90°), shown in Figures 6-36 and 6-37, respectively.
151

Figure 6-36. Modeled impact (black) and vibratory (blue) pile comparison of unweighted, single

strike SEL, bearing north.

The piles were modeled in the decidecade band (band index 3: 1,778 to 2,239 Hz) for SELss at RIsland during 

summer, bearing north (0°). The LLEE,ss = SELss is in dB re 1 μPa2 s. The sound exposure was integrated over a time 

duration of 1 second. μPa = micropascal; dB = decibel; km = kilometer; re = with reference to; RIsland = Rhode 

Island site; s = second.

Figure 6-37. Modeled impact (black) and vibratory (blue) pile comparison of unweighted, single

strike SEL bearing east.

The piles were modeled in the decidecade band (band index 3: 1,778 to 2,239 Hz) for LLEE,ss = SELss at RIsland during 

summer, bearing east (90°). The SELss is in dB re 1 μPa2 s. The sound exposure was integrated over a time duration 

of 1 second. μPa = micropascal; dB = decibel; km = kilometer; re = with reference to; RIsland = Rhode Island site;

s = second.
152

The unweighted, depth-averaged LLpp,pk was computed for the impact and vibratory piles for RIsland north 

(0°) and east (90°), shown in Figures 6-38 and 6-39, respectively.

Figure 6-38. Modeled impact (black) and vibratory (blue) pile comparison of LLpp,pppp, bearing north.

The piles were modeled in the decidecade band (band index 3: 1,778 to 2,239 Hz) for LLpp,pk at RIsland during

summer, bearing north (0°) where the seafloor shallows at 48 km. The LLpp,pk is in dB re 1 μPa2 s. μPa = micropascal;

dB = decibel; km = kilometer; re = with reference to; RIsland = Rhode Island site; s = second.

Figure 6-39. Modeled impact (black) and vibratory (blue) pile comparison of LLpp,pppp, bearing east.

The piles were modeled in the decidecade band (band index 3: 1,778 to 2,239 Hz) for LLpp,pk at RIsland during

summer, bearing east (90°). The LLEE,ss = SELss is in dB re 1 μPa2 s. μPa = micropascal; dB = decibel; km = kilometer; 

re = with reference to; RIsland = Rhode Island site; s = second.
153

6.4 Sensitivity Study Conclusions 

The validated FE/PE model was used to compute the sound pressure time series (and subsequent) LE, 

LLpp,pk, and LLpp,rms metrics. Model sensitivity to environmental parameters was evaluated across season 

(SSP), site, bearing (range-dependent bathymetry), and grain size (sediment type). To investigate the

overall sensitivity of impact volumes from the environment, the impact volumes were computed based on 

acoustic criteria. The introduction of energy above 2 kHz leads to impact volumes exceeding those that 

might be expected from LF energy alone. Regardless of size, impact volumes at both sites varied 

noticeably with season, sediment type, and bathymetry. How the relative contributions of these 

parameters occur at any pile-driving site, therefore, is unknown and creates a need to model multiple sites 

individually. How much an impact volume was shaped like a circular cylinder versus an amoeboid 

depended greatly on proximity to shore and bathymetry. 

As a proxy for water depth and oceanography, two sites off the U.S. Atlantic Coast, RIsland and 

VAbeach, were chosen for the modeling study. The environmental variable with the greatest impact on 

acoustic propagation, and therefore on impact volume, was the site and, more specifically, the 

bathymetry. RIsland was closer to land than VAbeach, leading to differences of 40 dB at ranges beyond 

20 km due to variability in water depth. Changes in sediment type were the next largest influence on 

impact volume, particularly in summer when warm surface waters led to increased refraction towards the 

seafloor and thus more absorption of sound into the seafloor. The environmental driver with less impact 

was season (SSP), with better propagation in winter than in summer, which led to higher received levels 

at all ranges and, therefore, larger impact volumes in winter. The effect of modeling sea surface 

scattering, though outside the project scope, would have been to decrease the predicted impact volumes, 

especially for MF and HF cetaceans in the winter.

There was substantial variability in the impact volumes, varying for marine mammals between 

0.0001 km3 (PTS in MF cetaceans; Table 6-1) and 282 km3 (TTS in LF cetaceans; Table 6-3). In fishes,

based on GARFO (2016) the impact volumes varied between 17 km3 (injury, large fish; Table 6-9) and 

221 km3 (behavior, any fish; Table 6-11). For comparison, application of the Popper et al. (2014) 

guidelines resulted in volumes between 0.04 km3 (recoverable injury, fishes with no swim bladder; 

Table 6-6) and 127 km3 (TTS, fishes with a swim bladder involved in hearing; Table 6-8). In sea turtles, 

the spread was between 0.5 km3 (physiological; Table 6-12) and 25 km3 (behavior; Table 6-12).

For fishes, the calculated median impact volumes for recoverable injury based on the criteria of Popper 

et al. (2014) varied from 0.06 to 3.2 km3 for VAbeach, and from 0.04 to 1.8 km3 for RIsland (Table 6-6). 

The calculated median impact volumes for injury based on the criteria of Stadler and Woodbury (2009) 

varied from 76 to 130 km3 for VAbeach, and from 40 to 63 km3 for RIsland (Table 6-9). On average the 

impact volumes of Table 6-9 were 30x and 1,600x larger than those of Table 6-6 for fishes with and 

without a swim bladder, respectively.

For marine mammals, fishes, and sea turtles, the largest impact volumes were predicted at VAbeach in 

winter with coarse sand (0.5 φφ). Whereas, the smallest impact volumes were predicted at RIsland with 

fine sand (2.5 φφ). For fishes and sea turtles, the smallest impact volumes always occurred in summer; for 

marine mammals the impact volumes in winter were slightly larger than during the summer at VAbeach 

(Figures 6-22 to 6-24).
154

The largest predicted impact volumes were for TTS in LF cetaceans (282 km3

; Table 6-3) and behavior in 

small or large fish 221 km3

; Table 6-11), both at VAbeach in summer with coarse sand (0.5 φφ). For these 

impacts, the predicted impact volumes for summer and fine sand decrease by 33% to 55% at VAbeach 

and by 77% to 78% at RIsland relative to those for winter and coarse sand.

At RIsland, season and grain size each led, on average, to a 45% reduction separately and a 77% 

reduction when combined. In other words, for both LF cetacean TTS and fish behavior, predicted impact 

volumes at RIsland were more than four times larger for coarse sand in winter than for fine sand in 

summer. The impact volume for RIsland in summer with fine sand (i.e., changing site, season, and grain 

size) was just 33 km3

, only 12% of the impact volume relative to VAbeach in winter with coarse sand 

(Table 6-3).

At VAbeach, the trend was similar, but the percentage differences were consistently smaller. The 

combined reduction of predicted impact volume for fine sand in summer relative to coarse sand in winter 

was 33% for LF cetacean TTS and 55% for fish behavior. The effect of grain size only (12% and 32%) 

was larger than for season only (5% and 26%).

Propagation has been shown to vary across season and grain size, and the impact volumes for each animal 

group also showed variability. For example, when considering sensitivity of impact volume to grain size 

and season, TTS impact volumes for LF cetaceans had a standard deviation of 73 km3 at RIsland 

(Table 6-3). Multiple factors create a variety of propagation differences at a single site, so it would 

constitute good practice to acoustically model each site separately, in lieu of assuming another 

pile-driving site accurately represents environmental influences of SSP, range-dependent bathymetry, and 

sediment types.

The sensitivity studies showed a constant and reasonable trend for both predicted acoustic propagation 

modeling and predicted impact volumes that matched the expected physics of the two study scenarios 

(RIsland and VAbeach) for the following ranges of critical acoustic parameters: 34 m and 24 m at the 

pile, respectively; winter and summer SSPs; and sediment grain size from coarse to fine sediment. This 

indicates the reliability of the algorithm for the entire range of parameter values considered, which is a 

realistic representation for the U.S. Atlantic Coast.

Most pile-driving sites are not expected to occur in water deeper than 50 m, but variation in sound 

propagation exists across water columns from 0 to 50 m deep. For identical propagation conditions, a 

change of 50% in water depth should change the impact volume by 50%. To avoid changes in impact 

volume exceeding 50%, new models would be needed for sites that are 50% deeper (or 33% shallower) 

than each other (i.e., 5, 7, 11, 16, 23, 34 or 50 m deep) (This sequence is similar to the Fibonacci 

sequence of ...5, 8, 13, 21, 34, and 50.)
155

7 Overall Conclusions

This section summarizes the overall conclusions of this report. Specifically, conclusions for DCSiE

(Section 3), the model verification and validation results (Section 5), and the sensitivity study (Section 6)

7.1 Damped Cylindrical Spreading in Excel

A recently developed DCS model permits extrapolation of acoustic metrics from LLEE based on physical 

principles, leading to an improved understanding and prediction of the propagation mechanism in the first 

few kilometers from the pile. The form of TL in DCS is 10 log10

rr

rr1

dB + αα(rr − rr1), which performs 

well out to 5-6 km for the measurements and test scenarios available.

DCS enables estimation of LLEE at ranges that satisfy αα < 20 dB. For ranges that do not satisfy this 

inequality, the DCS model is expected to overestimate TL and underestimate LLEE. Therefore, it remains 

necessary to supplement the DCS model with an alternative method at long ranges, where the field 

appears to follow a power law instead of an exponential decay. Figure 3-5 suggests the possibility of 

extrapolating for αα > 20 dB using the PSLM with FF between 25 and 45 (based empirically), although 

caution is warranted and validation is needed for this untested approach. In Section 3.5.1, extrapolation 

using FF = 25 was recommended as a precautionary approach.

While systematic validation of the empirical correlations for LLpp,pk and LLpp,rms to LLEE is needed,

extrapolation of LLEE to estimates of LLpp,pk and LLpp,rms may be permitted. In this way, impact assessments

involving the metrics LLEE, LLpp,pk, and LLpp,rms can be carried out based on a single measurement of LLEE near 

the pile, without requiring measurements of LLpp,pk or LLpp,rms. 

Conversion from LLEE to LLpp,pk (and LLpp,rms) remains an empirical correlation and is limited to the range of 

parameters (water depth, sediment type, hammer energy, and pile diameter) considered during the 

regression analysis. Therefore, the approach described in this project is applicable, but the values are site 

specific. A validation of the recommended method at a suitable U.S. site, though outside this project’s 

scope, is needed to test the accuracy of the predictions of LLEE, LLpp,pk, and LLpp,rms. A validation study likely 

would lead to improved understanding of the observed correlations and the possibility of further 

improvement.

Finally, the concept of LLpp,eff was introduced alongside LLpp,90% as an alternative metric to represent LLpp,rms. 

Both LLpp,eff and LLpp,90% are measures of the LLpp,rms, but they are calculated in different ways. Using LLpp,eff

leads to stronger (more robust) correlations with LLEE than does using LLpp,90%. Therefore, the LLpp,eff metric is 

suggested as a supplement to LLpp,90%.

7.2 Model Verification and Validation Methods 

7.2.1 Verification

To address the issue of sound radiation from pile driving, an FE model was used to generate the sound 

field close to the pile. This model handles the complexity of the hammer strike on a large solid object 
156

penetrating the seafloor and the transfer function from the pile to the sea. This sound field was matched to 

a PE propagation model, and the pile signals were propagated out to distances of interest. Using this 

combination of models (FE/PE), the results from the COMPILE I workshop were successfully 

reproduced, which verified the approach used for this project. 

7.2.2 Validation

When validating the combined FE/PE model prediction by comparing it to measurements from 

construction of the Gemini wind farm (ITAP, 2015b), differences were identified. To address these 

differences, a layered sediment was introduced. With sediment layering included, validation was carried 

out against two Gemini pile measurements: U8 and Z2. To get good agreement, a simple inversion for the 

seafloor was performed, and the sediment was determined to be a fine sand (0.0 φφ), which is slightly 

softer than the 0.5 φφ used as the softest sediment in the U.S. Atlantic continental shelf region for this 

study.

For U8, good agreement occurred at all frequencies above approximately 30 Hz. Frequencies lower than 

30 Hz did not contribute substantially to the broadband LLEE, even before frequency weighting. After 

weighting, they were negligible. The level of the model at 3 m from the pile axis was chosen to match the

LE U8 measurements at 732 m. The agreement between these predictions and the U8 measurements at 

ranges 7, 32, and 62 km was excellent at all frequencies.

For Z2, at 3 m from the pile axis of symmetry, the LLEE spectrum was assumed to be the same as for U8. 

Under this assumption, the same LF (<30 Hz) discrepancies as with U8 were identified, but these were 

unimportant as they did not contribute substantially to the broadband LLEE. 

7.3 Sensitivity Study

The validated FE/PE model was used to compute the sound pressure time series (and subsequent) LLEE,

LLpp,pk, and LLpp,rms metrics at two representative sites for pile driving along the U.S. Atlantic Coast. The 

model’s sensitivity was evaluated across four environmental parameters: site (depth), season (SSP), 

sediment type (grain size), and bearing (range-dependent bathymetry). To investigate the overall 

sensitivity of impact volumes from the environment, the impact volumes were computed based on 

previously published acoustic criteria for PTS, TTS, and behavioral categories. 

The environmental variable with the greatest impact on acoustic propagation, and thereby on impact 

volume, was site (water column depth), which inherently incorporated general bathymetry characteristics 

not completely represented in the bearing parameter. RIsland was 45% deeper than VAbeach but resulted 

in most impact volume differences exceeding 50%. Therefore, water depth is not the only explanatory 

variable for impact volume size because surrounding bathymetry and propagation conditions also play a 

role. 

RIsland was closer to land than VAbeach, leading to differences of 40 dB at ranges beyond 20 km due 

largely to variability in water depth. Change in sediment type was the next largest influence on impact 

volume. The environmental driver with the least impact was season (SSP). Better propagation occurs in 

the winter than in the summer because warm surface waters lead to a refraction of the sound waves 

towards the seafloor where more absorption can occur, especially with softer sediments. In general, 
157

higher received levels, and therefore larger impact volumes, occurred in colder waters over harder 

sediments. Including sea surface scattering in the model, though outside the project scope, would have 

caused a decrease in the predicted impact volumes, especially for MF and HF cetaceans in the winter.

For marine mammals, fishes, and sea turtles, the largest impact volumes always were predicted at

VAbeach in winter with coarse sand (0.5 φφ) except when values for 0.5 φφ and 1.5 φφ at VAbeach in the 

summer were slightly larger (Figure 6-29). The smallest impact volumes always were predicted at 

RIsland in summer with fine sand (2.5 φφ). 

There was substantial variability in the impact volumes, varying for marine mammals between 

0.0001 km3 (PTS in MF cetaceans; Table 6-1) and 282 km3 (TTS in LF cetaceans; Table 6-3). This 

largest predicted impact volume (282 km3

) occurred at VAbeach in winter with coarse sand (0.5 φφ). In 

fishes, the impact volumes varied between 17 km3 (injury, large fish; Table 6-9) and 221 km3 (behavior, 

any fish; Table 6-11) (GARFO, 2016). For comparison, application of the Popper et al. (2014) guidelines 

resulted in volumes between 0.04 km3 (recoverable injury, fishes with no swim bladder; Table 6-6) and 

127 km3 (TTS, fishes with a swim bladder involved in hearing; Table 6-8). In sea turtles, the spread was 

between 0.5 km3 (physiological; Table 6-12) and 25 km3 (behavior; Table 6-12).

For fishes, the median impact volumes for recoverable injury calculated using the criteria of Popper et al. 

(2014) varied from 0.06 to 3.2 km3 for VAbeach, and from 0.04 to 1.8 km3 for RIsland (Table 6-6). The 

median impact volumes for injury calculated using the criteria of Stadler and Woodbury (2009) varied

from 76 to 130 km3 for VAbeach, and from 40 to 63 km3 for RIsland (Table 6-9). On average the impact 

volumes of Table 6-9 were 30x and 1,600x larger than those of Table 6-6 for fishes with and without a 

swim bladder, respectively.

The largest predicted impact volumes were for TTS in LF cetaceans (282 km3

; Table 6-3) and behavior in 

small or large fish (221 km3

; Table 6-11), both at VAbeach in summer with coarse sand (0.5 φφ). For 

these impacts, the predicted impact volumes for summer and fine sand decreased by 33% to 55% at 

VAbeach and by 77% to 78% at RIsland, relative to those for winter and coarse sand. 

At RIsland, season and grain size each led, on average, to a 45% reduction separately and a 77% 

reduction when combined. In other words, for LF cetacean TTS and fish behavior, predicted impact 

volumes at RIsland were more than four times larger for coarse sand in winter than for fine sand in 

summer. The impact volume for RIsland in summer with fine sand (i.e., changing site, season, and grain 

size) was just 33 km3

, only 12% of the impact volume at VAbeach in winter with coarse sand 

(Table 6-3).

At VAbeach, the trend was similar, but the percentage differences were consistently smaller. The 

combined reduction of predicted impact volume for fine sand in summer relative to coarse sand in winter 

was 33% for LF cetacean TTS and 55% for fish behavior. The effect of grain size only (12% and 32%) 

was larger than for season only (5% and 26%).

Propagation has been shown to vary across season and grain size, and the impact volumes for each animal 

group also showed variability. For example, when considering sensitivity of impact volume to grain size 

and season, TTS impact volumes for LF cetaceans had a standard deviation of 73 km3 at RIsland 

(Table 6-3). Multiple factors create a variety of propagation differences at a single site, so it would 

constitute good practice to acoustically model each site separately, in lieu of assuming another 
158

pile-driving site accurately represents environmental influences of SSP, range-dependent bathymetry, and 

sediment types.

The sensitivity study showed a constant and reasonable trend for both predicted acoustic propagation 

modeling and predicted impact volumes. These trends matched the expected physics of the two study sites 

(RIsland and VAbeach) for the ranges of acoustic environmental parameters employed. This indicates the 

reliability of the PE/FE model algorithm used in this study and suggests it should perform just as well 

across similar sites that are typical for the U.S. Atlantic Coast (i.e., have similar bathymetries, sediment 

types, and distances to land).

8 Recommendations for Practical Application of Sensitivity Results

To help understand and apply recommendations based on the results and findings of this study, an 

example narrative scenario is provided. Consider 10 piles being driven for a wind farm off the coast of 

Atlantic City, New Jersey. The piles will be placed 2 km apart in a straight line going west to east, 

starting at 39.44448° N, 73.88525° W in a water depth of 30 m during March. Also assume the same 

specific starter field (based on a pile size and forcing function, among other parameters) that was used for 

this project applies to the following example scenario. The following suggestions aim to provide a clearer 

explanation of how the sensitivity results could be applied.

Prior to applying the impact volume and sensitivity results to new sites, the reader should be reminded of 

two limiting facts from this study. First, this study was focused on environmental variability and therefore 

a single type of pile (with a total energy LE set to match the Gemini U8 pile) was used. For a different size 

pile, the total energy in the water will be different and this needs to be measured or modeled in some way.

The second point is that this study was for vertical piles. A tilted pile is significantly different, leading to 

strong azimuthal dependence on the sound exposure levels.

First, any site that has a 50% depth difference should be modeled separately: at 5, 7, 11, 16, 23, 34, and 

50 m depths. For the example Atlantic City site, the depths moving in 2-km increments west to east from 

the starting point above are: 30, 31, 31, 30, 32, 33, 33, 33, 33, and 35 m deep. All the sites fall well within 

50% of RIsland at 35 m and can be represented by that model.

Second, if a pile-driving site occurs between Rhode Island and Virginia Beach (with similar 

environmental parameters), then the winter and summer models results should suffice. The example 

Atlantic City site is closer to RIsland, and if it were to occur in March, the RIsland winter model could 

represent the example piles without need for additional new model calculations. (More generally, sites 

north of RIsland and south of VAbeach should be modeled again for the month of proposed pile driving. 

This is because sites farther north may be colder for more months of the year and tend to have more 

winter-like propagation environments than modeled by RIsland, while sites farther south may be warmer 

for more months of the year and tend to follow more summer-like propagation environments than 

modeled by VAbeach.)

Third, when a pile-driving site occurs in an area with similar sediment types as VAbeach and RIsland, it 

would be well represented by this study’s results. For the purposes of this example, it could be assumed

that USGS data indicate a sediment grain size of 1.5 φφ at the Atlantic City example site. This would fall 

within grain sizes modeled in this project and not require additional models.
159

Fourth, the closest distance from shore to the easternmost pile-driving site is 32 km. This means, for all 

10 piles, the closest distances to shore would be 32, 34, 36, 38, 40, 42, 44, 46, 48, and 50 km. Sites at 

least 15 km closer to or farther from shore than the sites in this study (i.e., closest distances of 11, 41, 71, 

86, etc. km to shore) would require new model runs. The fifth and sixth piles in the line at the Atlantic 

City example site fall on either side of the 41 km recommendation. Therefore, a new model would be 

needed at either the fifth or sixth pile site.

Given these four recommendations for assessing the environmental sensitivity on impact volumes at the 

Atlantic City example site, 9 of the 10 piles could be represented by the models presented in this project.

In general, if a pile site differs by more than 50% in depth, more than 1.0 φφ in grain size, seasons north of 

Rhode Island or south of Virginia Beach, or intervals of 15 km farther away or closer to shore, then new 

models should be generated during the month of proposed activity to appropriately model the acoustic 

propagation and understand how sound may travel through the environment.
160

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A-1

Appendix A: Definitions

Zero-to-peak and peak-to-peak sound pressure level

The zero-to-peak sound pressure level (LLpp,pk) is

LLpp,pk ≡ 10 log10

pppk

2

pp0

2 dB Equation A.1

The peak-to-peak sound pressure level is not defined by ISO (2017). Lucke et al. (2009) defines this 

quantity as

LLpp,pk−pk ≡ 10 log10

pppk−pk

2

pp0

2 dB Equation A.2

This definition is used by Lippert et al. (2015).

Effective signal duration:

The effective signal duration is a robust measure of signal duration used in signal processing 

(Burdic, 1991). It is defined (Figure A-1) in terms of the complex envelope (Figure A-2) of the analytic 

signal (Figure A-3).

Figure A-1. Definition of effective signal duration in terms of complex envelope (From: ISO 

18405:2017).
A-2

Figure A-2. Definition of complex envelope in terms of analytic signal (From: ISO 18405:2017).

Figure A-3. Definition of analytic signal in terms of sound pressure (From: ISO 18405:2017).

References

Burdic, WS. 1991. Underwater acoustic system analysis. Prentice Hall, Englewood Cliffs, NJ.

ISO. 2017. ISO 18405:2017 Underwater Acoustics – Terminology. Geneva, Switzerland.

Lippert, T, M Galindo-Romero, AN Gavrilov, and O von Estorff. 2015. Empirical estimation of peak 

pressure level from sound exposure level. Part II: Offshore impact pile driving noise. The Journal of 

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Lucke, K, Siebert, U, Lepper, PA, Blanchet, MA. 2009. Temporary shift in masked hearing thresholds in 

a harbor porpoise (Phocoena phocoena) after exposure to seismic airgun stimuli. Journal of the 

Acoustical Society of America. 125(6):4060-4070. DOI: 10.1121/1.3117443.
B-1

Appendix B: Extracting Aggregated Quantities for the Hammer Strikes

For pressure trace segments (between ttstart and ttend), the following quantities are determined:

● integration time ttend − ttstart

● tt5, such that ∫ pp2 tt5

ttstart dtt = 0.05 ∫ pp2 ttendttstart dtt

● tt95, such that ∫ pp2 tt95

ttstart dtt = 0.95 ∫ pp2 ttendttstart dtt

● 90% sound exposure: EE90 = ∫ pp2 tt95

tt5 dtt

● rms sound pressure: pprms = �EE90/(tt95 − tt5)

● sound exposure level (LE): LLEE = 10 log10(EE90/(0.9 pp0

2 1ss) dB

● zero-to-peak sound pressure level: LLpp,pk = 10 log10(max (pp2)/pp0

2) dB

● rms sound pressure level: LLrms,90% = 10 log10(pprms

2 / pp0

2) dB

Finally, the kurtosis ββ was calculated, according to ISO 18405:2017, §3.1.5.5:

ββ = μμ4/μμ2

2, where μμnn = 1

ttend−ttstart

∫ (pp(tt) − pp̅) ttend nn

ttstart dtt, so

ββ =

1

ttend − ttstart ∫ (pp(tt) − pp̅)4 ttend

ttstart dtt

� 1

ttend − ttstart ∫ (pp(tt) − pp̅

)2 ttend

ttstart dtt�

2 = (pp(tt) − pp̅) ��������������4�

�(pp(tt) − pp̅) ��������������2��

2 Equation B-1

References

ISO. 2017. ISO 18405:2017 Underwater Acoustics – Terminology. Geneva, Switzerland.
C-1

Appendix C: Borkum Riffgrund 1 Measurements

The values shown in Table C-1 were used to obtain the circles in Figure 5-18. Specifically, power 

averages were calculated over the two hydrophone positions (2 m and 10 m above the seafloor) of the 

median LE (SEL50 [dB] columns) and of LLpp,pk (LPeak [dB] columns).

Table C-1. Borkum Riffgrund 1 measurements from ITAP (2015; Section 12.1.2, translated into 

English).

Measurement 

position

Distance 

(m)

Hydrophone 2 m height above 

ground Hydrophone 10 m height above ground

SEL90 

(dB)

SEL50 

(dB)

SEL5 

(dB)

LPeak 

(dB)

SEL90 

(dB)

SEL50 

(dB)

SEL5 

(dB)

LPeak 

(dB)

Pile 

strikes

MPS1 726 172 174 174 199 173 173 175 199 1859

MPS2 1,509 169 171 171 192 168 170 170 192 1853

MPS3 783 172 173 174 198 172 174 174 197 1503

MPS4 288 180 182 182 209 179 180 180 208 1859

MPS5 234 178 180 180 207 180 182 182 209 1859

MPS6 499 176 178 178 201 174 176 177 200 1860

MPS7 738 173 174 175 198 173 174 175 197 1859

MPS8 1491 170 171 172 193 169 171 171 192 1856

MPS9 1992 167 169 169 190 172 174 174 195 1859

MPS10 2487 165 167 167 186 165 166 166 186 1859

MPS11 2985 164 165 166 185 164 165 166 184 1859

MPS12 4057 160 162 162 181 161 162 163 182 1859

MPS13 4991 158 160 160 177 no measurable data

MPS14 66 189 190 190 216 187 189 189 215 1859

MPS15 93 190 191 191 217 186 187 188 215 1859

MPS16 70 189 190 191 217 187 188 189 217 1858

MPS17 30 no measurable data no measurable data

MPS18 28 193 193 195 221 189 190 191 218 576

dB = decibel; LPeak = LLpp,pppp or peak sound pressure level; SEL = sound exposure level; SEL90 = 90% exceedance level 

(10th percentile sound exposure level); SEL50 = 50th percentile sound exposure level (median); SEL5 = 5% exceedance level 

(95th percentile sound exposure level).

References

Institut für technische und angewandte Physik (ITAP). 2015. Offshore measurement campaign 3 (OMK3) 

for the project BORA at the offshore-offshore-wind farm Borkum Riffgrund 01, Version 2, Project 

Number: 2271-14-bel.
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