Comparison of Sound Speed from Measurements and Models in the Waters of Bunaken National Park, North Sulawesi

Comparison of Sound Speed from Measurements and Models in the Waters of Bunaken National Park, North Sulawesi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of Sound Speed from Measurements and Models in the Waters of Bunaken National Park, North Sulawesi Wawan Hidayat, Lufti Rangga Saputra, Rizqi Rizaldi Hidayat, Amron Amron This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653653/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study aims to investigate the Sound Velocity Profile (SVP) in the waters of Bunaken National Park using advanced bathymetric survey methods to enhance the accuracy of depth measurements obtained from Multibeam Echosounder (MBES). A bathymetric survey is critical for navigation, resource management, and environmental monitoring due to providing essential data about underwater terrain. The importance of correcting MBES data is clarified using SVP, which is influenced by temperature, salinity, and pressure variations. Field observations conducted between June and September 2022 included the use of CTD instruments to measure environmental parameters, although limitations in instrument reach led to unmeasured depths. To address these gaps, SVP was modeled using empirical equations and global databases, comparing the results with in-situ measurements to assess accuracy through Root Mean Square Error (RMSE). The results showed significant variations in SVP across different depths, signifying the need for reliable data in hydrographic surveys. In conclusion, empirical models, particularly Leroy's equation, provided the most accurate predictions. Further investigation should be conducted to refine modeling methods and integrate comprehensive datasets. This study provided a foundation for better marine resource management and conservation strategies in the face of continual environmental changes. Speed velocity profile multibeam echosounder temperature salinity depth Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction A bathymetric survey is an essential hydrographic survey that measures sea depth and underwater terrain, providing critical information about the topography of the seafloor using sound waves. This process is fundamental for understanding marine resource activities, as the depth information is crucial in navigation, fishing, and environmental monitoring. The technology used in the survey has evolved, with advanced equipment designed to capture both visual and quantitative depth data in aquatic environments. Among the various tools available, the Multibeam Echosounder (MBES) attracts attention because of the ability to emit and receive sound waves through a transducer, enabling the acquisition of numerous connected depth points to design detailed profiles of the seabed (Gaida et al., 2019 ). The advancement of MBES technology has significantly improved the accuracy and efficiency of bathymetric mapping, leading to more reliable data for marine resource management (Brown et al., 2012 ; Li et al., 2023 ; Wanda et al., 2023 ). Accurate bathymetric data promote navigation safety as well as inform decisions related to marine biodiversity conservation, fisheries management, and coastal development. These show that bathymetric survey implementation is a critical element in sustainable marine resource utilization. The data integration into marine spatial planning can enhance the understanding of underwater ecosystems and dynamics. A vital component in MBES data processing is the Sound Velocity Profile (SVP), which is essential for correcting measurement results. The differences between corrected and uncorrected MBES data can be substantial, particularly in deeper waters where variations in temperature, salinity, and pressure significantly affect sound speed. The accuracy of depth measurements depends on these factors, as sound speed in seawater is controlled by environmental conditions varying with depth (Gulin & Yaroshchuk, 2014 ; Makar, 2022 ; Zhang et al., 2024 ). Therefore, understanding the SVP is important to ensure that the data collected from the bathymetric survey is reliable. This study was conducted in the waters of Bunaken National Park, a region in the Sulawesi Sea known for the rich marine biodiversity. The Sulawesi Sea acts as a primary conduit for the Indonesian Throughflow (ITF), which transports warm water from the Pacific Ocean to the Indian Ocean. ITF significantly impacts water mass transport in the Sulawesi Sea, particularly affecting the surface layer and thermocline. Considering the vertical variations in physical and chemical properties in this region, sound speed is expected to display different profiles with increasing depth (Wu et al., 2022 ; Zhao et al., 2025 ). A bathymetric survey performed by the Geospatial Information Agency through the Center for Marine and Coastal Environment Mapping used CTD instruments to measure SVP values. Limitations in the use of these instruments, specifically the cable length not reaching the seabed, led to SVP values that did not represent the maximum depth. Consequently, there is a pressing need for more investigation to model the SVP values not captured by in-situ observations (Zhao et al., 2023 , 2025 ). The objective of the modeling endeavor is to accurately represent the vertical SVP that cannot be directly measured by existing instruments. Simulation of natural conditions through the model will enable predictions using various algorithms, facilitating comprehensive SVP analyses. Therefore, this study aims to determine the distribution of SVP values based on temperature, pressure, and salinity at different depths, assess the accuracy of the modeled SVP compared to in-situ results, as well as identify the extent of depth differences derived from the model (Yuan et al., 2023 ). Methods Field observations were conducted from June to September 2022 in the waters of Bunaken National Park, North Sulawesi, measuring temperature, salinity, pressure, and depth (Fig. 1 ). Furthermore, the SVP data processing was performed in March and April 2023 at the Marine Technology Laboratory, Jenderal Soedirman University, as well as the Marine and Coastal Mapping Center, Geospatial Information Agency. This quantitative study with a deductive method used observation and survey to collect primary data, which included the environmental parameters later converted into SVP. The primary instruments used were the CTD Midas SVX2 and Valeport Datalog X2 for data collection, while analysis was performed with MATLAB, Ocean Data View, and Microsoft Excel. Secondary data on temperature and salinity were sourced from the HYCOM and Copernicus Marine Environmental Monitoring Service (CMEMS) databases for comparison with in-situ measurements (Sam-Khaniani, 2022 ; Sammartino et al., 2022 ). Data collection included using the CTD instrument at predetermined locations to gather detailed SVP. Modeling of the SVP was carried out using empirical equations such as those proposed by Leroy, Mackenzie, and Medwin to calculate sound speed based on recorded data (Leroy, 1969 ; Mackenzie, 1981 ; Medwin, 1975 ). The processed data was plotted to compare modeled values against in-situ measurements, allowing for the selection of the most accurate empirical model. Global database methods from hycom.org and marine.copernicus.eu provided supplementary data for further validation of sound speed calculations. Statistical methods including exponential, linear, and polynomial regression were applied to model SVP at maximum depths, facilitating a comprehensive analysis of sound speed variations across different layers in the water column. The results were visually represented through graphs and images analyzed descriptively to determine differences across various depths and study stations. Validation of the model required by comparing the SVP results against in-situ measurements, with statistical methods applied to assess accuracy using the Root Mean Square Error (RMSE) metric. Results and Discussion Spatial Distribution of SVP The in-situ measurements of sound velocity conducted in the waters of Bunaken National Park provided critical insights into the complex interplay between environmental factors and seawater acoustic properties. According to Fig. 2 and Table 1 , the bathymetric survey carried out from June to September 2022 showed significant variations in sound velocity across different depths, primarily driven by fluctuations in temperature and salinity. The results corresponded with the conclusions by Makar ( 2022 ), and Affatati et al. ( 2022 ) which reported variations in these parameters to be crucial in determining sound velocity distributions across marine environments. Understanding the dynamics of temperature and salinity is essential not only for advancing the knowledge of marine biology but also for practical applications in underwater acoustics, sonar technology, and environmental monitoring. The implications of the results extend to various fields, including fisheries management, marine conservation efforts, and oceanographic study. Sound velocity measurements ranged from 1544.02 to 1541.86 m/s in the mixed layer, defined as the uppermost 60 m of the water column. The mixed layer is characterized by relatively uniform temperature and salinity because of the effects of solar radiation and wind-induced mixing. The stability observed in this layer is significant for marine ecosystems due to promoting acoustic environments that benefit many marine organism dependents on sound for communication and navigation. According to Wang et al. ( 2024 ), the mixing effects caused by wind and solar radiation produce conditions conducive to marine life proliferation. The described stability is crucial for species such as dolphins and certain fish that depend on echolocation as well as other sound-based communication methods to locate prey and maintain social structures. The presence of a well-mixed upper layer enhances the acoustic signals facilitating the interactions and supports complex ecological dynamics. A marked decrease in sound velocity from 1541.37 to 1503.61 m/s was observed along the thermocline spanning across depths of 61 to 255 m. This significant decline reflects the transition from the mixed layer to deeper waters where the temperature drops more rapidly, providing a pronounced stratification. The thermocline serves as a critical boundary in the water column, influencing sound propagation and marine species distribution. Weidner & Weber ( 2021 ) reported how rapid temperature changes in this layer could significantly influence sound velocity, suggesting the importance of the thermocline in acoustic profiling. The thermocline acoustic characteristics affect the traveling of sound waves through the water, which is crucial for species using sound to navigate and communicate. Many fish species adjust behavior based on sound propagation characteristics, which can influence feeding strategies and reproductive success. Table 1 , Sound velocity profile based on layer for each station Station Layer Range of depth (m) Range of sound velocity (m s − 1) Average change of sound velocity (m s − 1 ) S-1 Mixed Thermocline Deep 0–68 69–132 133–210 1543.41–1538.30 1537.89–1512.91 1512.91–1508.26 0.04 .021 0.03 S-2 Mixed Thermocline Deep 0–54 55–151 152–190 1542.46–1541.41 1541.25–1511.96 1511.98–1511.10 0.02 0.16 0.01 S-3 Mixed Thermocline Deep 0–37 38–201 202–318 1544.38–1543.41 1543.41–1510.49 1510.21–1503.64 0.02 0.08 0.02 S-4 Mixed Thermocline Deep 0–56 57–219 220–288 1544.67–1541.12 1541.56–1503.19 1503.21–1494.51 0.06 0.11 0.02 S-5 Mix Thermocline Deep 0–36 37–284 285–313 1544.18–1543.97 1543.70–1497.67 1497.61–1495.17 0.05 0.08 0.02 S-6 Mixed Thermocline Deep 0–40 41–205 206–273 1543.31–1542.71 1541.86–1507.49 1507.49–1505.59 0.01 0.11 0.02 S-7 Thermocline Deep 0–154 155–292 1543.77–1520.82 1520.78–1497.67 0.11 0.04 S-8 Mixed Thermocline Deep 0–74 75–186 187–266 1546.15–1544.25 1543.59–1511.26 1511.18–1508.24 0.02 0.14 0.01 S-9 Thermocline Deep 0–45 46–238 1543.90–1542.10 1541.83–1509.47 0.01 0.08 S-10 Mixed Thermocline Deep 0–30 31–210 211–312 1545.22–1542.98 1542.14–1510.49 1510.50–1499.21 0.05 0.09 0.03 S-11 Mixed Thermocline Deep 0–35 36–172 173–218 1540.89–1544.45 1544.14–1512.60 1512.59–1509.16 0.05 0.15 0.03 S-12 Mixed Thermocline Deep 0–75 76–200 201–316 1545.07–1543.39 1542.60–1508.71 1508.66–1495.41 0.03 0.13 0.03 S-13 Mixed Thermocline Deep 0–72 73–233 234–288 1544.99–1543.88 1543.68–1515.07 1514.25–1502.49 0.01 0.09 0.06 S-14 Mixed Thermocline Deep 0–82 83–219 220–304 1543.89–1539.49 1538.42–1502.68 1502.65–1498.53 0.06 0.12 0.01 Sound velocity was recorded between 1511.84 and 1498.53 m/s in the deep layer with depths from 220 to 256 m. The consistent decline in sound velocity at these depths is attributed to reduced temperatures associated with diminishing solar heat absorption. The study by Rohling et al. ( 2022 ) confirms that deep ocean temperatures, particularly in tropical regions such as the Sulawesi Sea, tend to be stable and low. This stability has profound implications for the behavior and ecology of deep-sea organisms, among which many have adapted to the conditions. The acoustic environment in the deep layer can influence predator-prey interactions and habitat selection, as sound plays a crucial role in how organisms communicate and locate resources. Understanding sound velocity dynamics in the region can help inform conservation strategies aimed at protecting vulnerable deep-sea ecosystems, which are often threatened by human activities such as fishing and mining. The analysis of spatial distribution using Ocean Data View software showed minimal changes in sound velocity at depths of 0 and 50 m, where values ranged from 1546 to 1538 m/s (Fig. 3 ). support the results by stating that surface layers often show stability in sound speed due to the homogeneity of physical conditions. Stability is essential for various marine applications, including sonar technology and environmental monitoring systems. The ability to predict sound propagation accurately is critical for naval operations, fisheries management, and monitoring marine biodiversity. Moreover, understanding how sound travels through different layers of the ocean can enhance the ability to track marine mammals and study the behaviors in relation to changing environmental conditions. This study generally clarifies the significant influence of temperature and salinity on SVP in the waters of Bunaken National Park. The trend of decreasing sound velocity with increasing depth shows the intricate relationship between physical water conditions and acoustic behavior. Additionally, this study enhances the understanding of the acoustic dynamics in tropical waters and serves as a crucial reference for future investigations in oceanography. The knowledge gained can inform management practices aimed at preserving marine biodiversity in the face of climate change, which poses a growing threat to oceanic ecosystems worldwide. Continued exploration of the dynamics will be essential for effective marine resource management and conservation efforts, particularly as environmental conditions continue to evolve. Comprehensive sound velocity analysis in Bunaken National Park provides essential insights into the complex interplay of environmental factors influencing marine acoustics. This study shows that temperature and salinity are not merely physical parameters because both play a critical role in shaping the acoustic environment of marine ecosystems. Continual exploration of these dynamics will help to understand the implications for marine life and the broader ecological context. This study provides the foundation for future investigations into the effects of climate change on marine acoustics, emphasizing the need for persistent monitoring and adaptive management strategies in the biodiverse region. The results show the importance of integrating acoustic investigations into broader marine study frameworks to promote sustainable practices and ensure environmental protection. Accuracy of the SVP Models Compared to In-situ The development of SVP models is crucial for various applications in oceanography, acoustics, and marine biology. Microsoft Excel and Matlab were used in this study to develop SVP models through three distinct methods, including empirical equations (Leroy, Mackenzie, and Medwin) using CTD data, Leroy’s model using the global databases (Hycom and Marine Copernicus), and Leroy’s model using the predicted data (extrapolation from CTD data). The Medwin, Leroy, and McKenzie empirical equations used have been widely recognized in the literature for the ability to estimate sound velocity based on temperature, salinity, and pressure. Le Menn & Naïr ( 2022 ) corroborated the effectiveness of the empirical models through the report of the utility in various marine settings where in-situ measurements may be limited. The most accurate method for estimating SVP in the study area was established by comparing these models to in-situ measurements. Table 2 shows that Leroy's empirical equation produced the lowest RMSE values near zero, which signifies a high level of accuracy in the predictions compared to the in-situ data. This result was consistent with the report by Huang et al. ( 2024 ) that Leroy's equation often outperformed other empirical models in tropical marine environments. Conversely, the RMSE values for Medwin's and McKenzie's equations were near 1, representing less precision. These discrepancies suggest the importance of selecting appropriate models based on local environmental conditions, as the performance of the equations can vary significantly depending on the specific characteristics of the water body being studied. Table 2 , RMSE of SPV models compared to the in-situ measurement Station Leroy (CTD data) Medwin (CTD data) Mackenzie (CTD data) Leroy (Hycom data) Leroy (Mar. Coper. data) Leroy (interpolation data) S-1 0.023 0.106 0.169 7.116 - 0.889 S-2 0.024 0.098 0.158 4.763 1.184 2.424 S-3 0.040 0.086 0.140 2.358 0.940 2.300 S-4 0.057 0.079 0.123 3.204 1.002 3.426 S-5 0.056 0.085 0,130 2.460 0.328 2.012 S-6 0.042 0.114 0.153 2.811 - 8.953 S-7 0.042 0.098 0.127 - - 5.168 S-8 0.039 0.081 0.140 - 2.115 2.276 S-9 0.023 0.113 0.152 - - 2.507 S-10 0.050 0.080 0.122 3.142 - 2.022 S-11 0.031 0.110 0.164 - 1.799 1.441 S-12 0.060 0.096 0.153 2.927 3.248 3.337 S-13 0.041 0.115 0.167 5.352 - 1.050 S-14 0.058 0.085 0.133 4.210 0.418 4.555 The second modeling method used global databases obtained from hycom.org and marine.copernicus.eu. The databases offer valuable data for sound velocity modeling, but this study has significant limitations. For example, the Hycom database only contains data up to 2018, which poses challenges when comparing against more current in-situ data from 2023. Marine Copernicus provides data only to a maximum depth of 130 m, while the current in-situ measurements extend beyond 300 m. The limitations can cause substantial inaccuracies, as shown by the RMSE values reported for these models, which were not near zero. Robbani & Pratomo ( 2023 ) reported similar challenges when using global datasets for SVP modeling in the Madura Strait. Corresponding in-situ data for certain locations were occasionally absent in the two global databases, leading to gaps in the analysis. This absence is critical because of the tendency to generate incomplete models and potentially misleading conclusions about SVP. The importance of comprehensive and up-to-date databases was emphasized by Fleishman et al. ( 2023 ) which stated how missing data could significantly impact acoustic studies and the assessment of marine ecosystems. The integration of robust in-situ measurements with global datasets is essential for enhancing the accuracy of sound velocity models. Graphical representations of the results in Fig. 4 showed substantial differences among the Leroy’s models using CTD, Hycom, and interpolation data. The Hycom database model at Station 2 significantly deviated from others, while the empirical model closely corresponded with in-situ data. At Stations 3 and 4, the CTD and Interpolation data models performed best, reflecting the utility in accurately predicting SVP. The effectiveness of the CTD data can be attributed to the dependency on direct measurements as well as established relationships between temperature, salinity, and sound velocity. This was supported by the work of Le Menn & Naïr ( 2022 ), which emphasized the relevance of empirical equations in marine acoustics. Statistical equations provided valuable insights for extrapolating sound velocity models, but careful consideration of the modeled parameters is needed to ensure accuracy. Comparison of Water Depth Obtained by the SVP Models This study shows the critical role of the SVP in correcting MBES data and emphasizes that accurate depth measurements are essential in marine surveys, particularly in deep-water environments. According to Šiljeg et al. ( 2022 ), the discrepancies between corrected and uncorrected MBES data can be substantial, with implications for navigation, resource management, and environmental monitoring. The sound waves emitted by MBES systems travel through the water column, where the speed is influenced by temperature, salinity, and depth parameters, which often have significant spatial and temporal variability. This variability should be considered to ensure that depth calculations, represented by the formula D = 12(v × Δt), maintain accuracy. Liu et al. ( 2022 ) show the necessity of considering sound speed as an independent variable in the calculations, which directly affects the reliability of depth data. Sound velocity effects on depth accuracy were investigated by using several modeling schemes and conducting simulations to compare calculated depths with in-situ measurements. The results presented Table 3 showed that certain points had minor depth differences, particularly in the mixed layer and thermocline, but more significant discrepancies were evident in the deep layer. The applied CTD and interpolation data models showed an average depth difference of about 2 m in the upper layers, while larger variations occurred in deeper waters. The results suggest that the models may not adequately capture the complexities of acoustic propagation in the water environments, specifically as the depth increases. The importance of accurate modeling in these scenarios was supported by Li et al. ( 2023 ) which found depth discrepancies in deep water to be capable of initiating substantial errors in navigational charts and resource assessments. Table 3 , Water depth estimation (m) obtained by in-situ measurement and models. The value in parentheses represent the difference in measurements between the model and the in-situ data’ Station Measurement CTD data Hycom data Mar. Coper. data Interpolation data S-2 214,082 214.609 (+ 0.527) 214,778 (+ 0.696) 216.004 (+ 1.922) 216,921 (+ 2.839) S-3 409,288 405.216 (-4.072) 405,214 (-4.074) 405.361 (-3.927) 411,603 (+ 2.315) S-4 323,048 319.939 (-3.109) 320,371 (-2.677) 320.502 (-2.546) 325,294 (+ 2.246) These models had significant depth variations across all layers, with RMSE values representing substantial inaccuracies. The observed discrepancies can be attributed to the propagation characteristics of acoustic waves, which are affected by the local SVP. According to Makar ( 2022 ), the refraction or bending of sound waves can lead to significant errors in depth estimation, particularly in regions where temperature and salinity gradients are pronounced. This shows the necessity for local calibration of SVP data against in-situ measurements to enhance hydrographic survey accuracy. Considering the results, sound velocity values derived from modeling can be beneficial for refining depth measurements in Bunaken National Park, but further investigations are essential to improve model accuracy. The integration of continuous monitoring and updating of SVP data is crucial, particularly in dynamic marine environments where conditions change rapidly. The use of secondary data sources, such as those provided by Marine Copernicus, presents a viable alternative for obtaining sound velocity values when direct measurements are not feasible. Previous exploration found that using comprehensive databases significantly enhanced the reliability of hydrographic survey, as reported by Robbani & Pratomo ( 2023 ) which achieved an improved accuracy in the modeling efforts by incorporating secondary SVP data. This study generally shows the integral relationship between sound velocity and depth accuracy in hydrographic survey, particularly in challenging environments such as deep waters. The results advocate for a multidisciplinary method that combines empirical modeling, in-situ measurements, and reliable secondary data sources to achieve higher precision in marine survey. As the field of hydrography continues to evolve, the incorporation of advanced modeling methods and robust data sources will be essential for supporting sustainable marine resource management and conservation efforts in sensitive ecosystems such as the type found in Bunaken National Park. Conclusion In conclusion, this study showed the critical role of SVP in enhancing the accuracy of the bathymetric survey in Bunaken National Park. Furthermore, MBES data correction with SVP was found to be essential for reliable depth measurements, particularly in deep waters where environmental factors significantly influence sound speed. The results showed that Leroy's empirical equation produced the most accurate predictions, but there were limitations, such as insufficient depth coverage from in-situ measurements and challenges posed by outdated global databases including Hycom and Marine Copernicus. These discrepancies signified the need for continuous monitoring and integration of robust in-situ data with reliable secondary sources to improve precision in marine survey. The results advocated for a multidisciplinary method combining empirical modeling and statistical methods to capture the complexities of acoustic propagation. This suggested the importance of persistent investigation to understand sound velocity dynamics in relation to environmental changes, particularly in the context of climate change and the impact on marine ecosystems. Declarations Author Contribution Conceptualization, AA, WH, LRS and RRH; methodology, AA, WH and LRS; validation, AA and WH; formal analysis, AA, WH and LRS; investigation, AA, WH, LRS and RRH; resources, AA , WH and LRS; data curation, AA, WH, LRS and RRH; writing original draft preparation, AA, WH, LRS and RRH; writing review and editing, AA and WH; supervision, AA and LRS; project administration, WH; funding acquisition, -. All authors have read and agreed to the published version of the manuscript. Funding Funding of this work is not available. References Affatati, A., Scaini, C., & Salon, S. (2022). Ocean sound propagation in a changing climate: Global sound speed changes and identification of acoustic hotspots. 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IEEE Transactions on Geoscience and Remote Sensing , 61 , 1–12. https://doi.org/10.1109/TGRS.2023.3327282 Zhang, W., Jin, S., Bian, G., Peng, C., & Xia, H. (2024). A Method for Full-Depth Sound Speed Profile Reconstruction Based on Average Sound Speed Extrapolation. Journal of Marine Science and Engineering , 12 (6), 930. https://doi.org/10.3390/jmse12060930 Zhao, S., Liu, H., Xue, S., Wang, Z., & Xiao, Z. (2023). Two-step correction based on in-situ sound speed measurements for USBL precise real-time positioning. Remote Sensing , 15 (20), 5046. https://doi.org/10.3390/rs15205046 Zhao, S., Xue, S., Li, B., Zhou, J., Liu, H., & Ma, Y. (2025). Temporal Sound Speed Error Correction Model for Underwater Acoustic Positioning Based on In Situ Measurements. Journal of Surveying Engineering , 151 (2), 4025002. https://doi.org/10.1061/JSUED2.SUENG-1528 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers invited by journal 15 May, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 13 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6653653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457322690,"identity":"f8eeabc8-af94-4e51-9a9f-62773eaaa472","order_by":0,"name":"Wawan Hidayat","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Wawan","middleName":"","lastName":"Hidayat","suffix":""},{"id":457322691,"identity":"aa8b6a9d-6a9f-4f84-874f-787dacc48ede","order_by":1,"name":"Lufti Rangga Saputra","email":"","orcid":"","institution":"Geospatial Information Agency of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Lufti","middleName":"Rangga","lastName":"Saputra","suffix":""},{"id":457322692,"identity":"4e8d30b4-57a8-4ce2-95d7-aaf1d4ad0aba","order_by":2,"name":"Rizqi Rizaldi Hidayat","email":"","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":false,"prefix":"","firstName":"Rizqi","middleName":"Rizaldi","lastName":"Hidayat","suffix":""},{"id":457322693,"identity":"50d43144-145e-43a2-b843-a7eded7cd9ba","order_by":3,"name":"Amron Amron","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYNCCCgglASLYGBKI0XKGZC2MbUhaGAhp4Z929uHjwnmH8+Tbmw/eYKixY+BjJ6BF4na6sfHMbYeLDc4cS7ZgOJbMwMbzgICjbqexSfNuO5y4QSLHTIKB7QADmwQBW+TBWuYcTpw/I/+bBMM/IrQYgLU0HE5suJHDJsHYRoQWw9tpzMY8x9ITN5w5ZmyR2JfMQ9AvcrfTGB/z1Fgnzm9vfnjjwzc7Ofl2AragAqBiHlLUj4JRMApGwSjAAQCyND1XCKrSgAAAAABJRU5ErkJggg==","orcid":"","institution":"Jenderal Soedirman University","correspondingAuthor":true,"prefix":"","firstName":"Amron","middleName":"","lastName":"Amron","suffix":""}],"badges":[],"createdAt":"2025-05-13 09:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6653653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6653653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83103909,"identity":"55176893-ab68-4a56-a35e-540a20756860","added_by":"auto","created_at":"2025-05-20 05:43:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321528,"visible":true,"origin":"","legend":"\u003cp\u003eResearch station\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6653653/v1/e307c8f87f6329dffce93dfc.png"},{"id":83103233,"identity":"14866245-66bd-4f47-90c2-5cdb0b9d0d35","added_by":"auto","created_at":"2025-05-20 05:35:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58221,"visible":true,"origin":"","legend":"\u003cp\u003eSound velocity profile for each station\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6653653/v1/c7d7ffe9e5c803e33cef8e2c.png"},{"id":83103245,"identity":"63e7c772-9322-46fc-a1e9-bcd40784b18c","added_by":"auto","created_at":"2025-05-20 05:35:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":779567,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of sound velocity based on depth. (A-F) represent the water depth with 50 m interval (50 – 300 m, respectively).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6653653/v1/f96642dd4c0e362693b31ccb.png"},{"id":83103236,"identity":"952928df-6269-40fc-9cc0-6521351dd16f","added_by":"auto","created_at":"2025-05-20 05:35:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149746,"visible":true,"origin":"","legend":"\u003cp\u003eSVP in-situ vs models at maximum depth for three stations. (A-C) represent the S-2, S-3, and S-4 respectively\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6653653/v1/a35944540d8ec2e265cd9dc0.png"},{"id":83105212,"identity":"50b5cfdf-748d-41e4-b9bb-8510ab7dcb1c","added_by":"auto","created_at":"2025-05-20 06:07:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2067825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6653653/v1/43f528cf-95a5-4979-913a-29b8e90e8093.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Sound Speed from Measurements and Models in the Waters of Bunaken National Park, North Sulawesi","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA bathymetric survey is an essential hydrographic survey that measures sea depth and underwater terrain, providing critical information about the topography of the seafloor using sound waves. This process is fundamental for understanding marine resource activities, as the depth information is crucial in navigation, fishing, and environmental monitoring. The technology used in the survey has evolved, with advanced equipment designed to capture both visual and quantitative depth data in aquatic environments. Among the various tools available, the Multibeam Echosounder (MBES) attracts attention because of the ability to emit and receive sound waves through a transducer, enabling the acquisition of numerous connected depth points to design detailed profiles of the seabed (Gaida et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe advancement of MBES technology has significantly improved the accuracy and efficiency of bathymetric mapping, leading to more reliable data for marine resource management (Brown et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wanda et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accurate bathymetric data promote navigation safety as well as inform decisions related to marine biodiversity conservation, fisheries management, and coastal development. These show that bathymetric survey implementation is a critical element in sustainable marine resource utilization. The data integration into marine spatial planning can enhance the understanding of underwater ecosystems and dynamics.\u003c/p\u003e \u003cp\u003eA vital component in MBES data processing is the Sound Velocity Profile (SVP), which is essential for correcting measurement results. The differences between corrected and uncorrected MBES data can be substantial, particularly in deeper waters where variations in temperature, salinity, and pressure significantly affect sound speed. The accuracy of depth measurements depends on these factors, as sound speed in seawater is controlled by environmental conditions varying with depth (Gulin \u0026amp; Yaroshchuk, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Makar, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, understanding the SVP is important to ensure that the data collected from the bathymetric survey is reliable.\u003c/p\u003e \u003cp\u003eThis study was conducted in the waters of Bunaken National Park, a region in the Sulawesi Sea known for the rich marine biodiversity. The Sulawesi Sea acts as a primary conduit for the Indonesian Throughflow (ITF), which transports warm water from the Pacific Ocean to the Indian Ocean. ITF significantly impacts water mass transport in the Sulawesi Sea, particularly affecting the surface layer and thermocline. Considering the vertical variations in physical and chemical properties in this region, sound speed is expected to display different profiles with increasing depth (Wu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA bathymetric survey performed by the Geospatial Information Agency through the Center for Marine and Coastal Environment Mapping used CTD instruments to measure SVP values. Limitations in the use of these instruments, specifically the cable length not reaching the seabed, led to SVP values that did not represent the maximum depth. Consequently, there is a pressing need for more investigation to model the SVP values not captured by in-situ observations (Zhao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objective of the modeling endeavor is to accurately represent the vertical SVP that cannot be directly measured by existing instruments. Simulation of natural conditions through the model will enable predictions using various algorithms, facilitating comprehensive SVP analyses. Therefore, this study aims to determine the distribution of SVP values based on temperature, pressure, and salinity at different depths, assess the accuracy of the modeled SVP compared to in-situ results, as well as identify the extent of depth differences derived from the model (Yuan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eField observations were conducted from June to September 2022 in the waters of Bunaken National Park, North Sulawesi, measuring temperature, salinity, pressure, and depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the SVP data processing was performed in March and April 2023 at the Marine Technology Laboratory, Jenderal Soedirman University, as well as the Marine and Coastal Mapping Center, Geospatial Information Agency. This quantitative study with a deductive method used observation and survey to collect primary data, which included the environmental parameters later converted into SVP.\u003c/p\u003e \u003cp\u003eThe primary instruments used were the CTD Midas SVX2 and Valeport Datalog X2 for data collection, while analysis was performed with MATLAB, Ocean Data View, and Microsoft Excel. Secondary data on temperature and salinity were sourced from the HYCOM and Copernicus Marine Environmental Monitoring Service (CMEMS) databases for comparison with in-situ measurements (Sam-Khaniani, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sammartino et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data collection included using the CTD instrument at predetermined locations to gather detailed SVP.\u003c/p\u003e \u003cp\u003eModeling of the SVP was carried out using empirical equations such as those proposed by Leroy, Mackenzie, and Medwin to calculate sound speed based on recorded data (Leroy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1969\u003c/span\u003e; Mackenzie, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Medwin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The processed data was plotted to compare modeled values against in-situ measurements, allowing for the selection of the most accurate empirical model. Global database methods from hycom.org and marine.copernicus.eu provided supplementary data for further validation of sound speed calculations.\u003c/p\u003e \u003cp\u003eStatistical methods including exponential, linear, and polynomial regression were applied to model SVP at maximum depths, facilitating a comprehensive analysis of sound speed variations across different layers in the water column. The results were visually represented through graphs and images analyzed descriptively to determine differences across various depths and study stations. Validation of the model required by comparing the SVP results against in-situ measurements, with statistical methods applied to assess accuracy using the Root Mean Square Error (RMSE) metric.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Distribution of SVP\u003c/h2\u003e \u003cp\u003eThe in-situ measurements of sound velocity conducted in the waters of Bunaken National Park provided critical insights into the complex interplay between environmental factors and seawater acoustic properties. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the bathymetric survey carried out from June to September 2022 showed significant variations in sound velocity across different depths, primarily driven by fluctuations in temperature and salinity. The results corresponded with the conclusions by Makar (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Affatati et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) which reported variations in these parameters to be crucial in determining sound velocity distributions across marine environments. Understanding the dynamics of temperature and salinity is essential not only for advancing the knowledge of marine biology but also for practical applications in underwater acoustics, sonar technology, and environmental monitoring. The implications of the results extend to various fields, including fisheries management, marine conservation efforts, and oceanographic study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSound velocity measurements ranged from 1544.02 to 1541.86 m/s in the mixed layer, defined as the uppermost 60 m of the water column. The mixed layer is characterized by relatively uniform temperature and salinity because of the effects of solar radiation and wind-induced mixing. The stability observed in this layer is significant for marine ecosystems due to promoting acoustic environments that benefit many marine organism dependents on sound for communication and navigation. According to Wang et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the mixing effects caused by wind and solar radiation produce conditions conducive to marine life proliferation. The described stability is crucial for species such as dolphins and certain fish that depend on echolocation as well as other sound-based communication methods to locate prey and maintain social structures. The presence of a well-mixed upper layer enhances the acoustic signals facilitating the interactions and supports complex ecological dynamics.\u003c/p\u003e \u003cp\u003eA marked decrease in sound velocity from 1541.37 to 1503.61 m/s was observed along the thermocline spanning across depths of 61 to 255 m. This significant decline reflects the transition from the mixed layer to deeper waters where the temperature drops more rapidly, providing a pronounced stratification. The thermocline serves as a critical boundary in the water column, influencing sound propagation and marine species distribution. Weidner \u0026amp; Weber (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported how rapid temperature changes in this layer could significantly influence sound velocity, suggesting the importance of the thermocline in acoustic profiling. The thermocline acoustic characteristics affect the traveling of sound waves through the water, which is crucial for species using sound to navigate and communicate. Many fish species adjust behavior based on sound propagation characteristics, which can influence feeding strategies and reproductive success.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Sound velocity profile based on layer for each station\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLayer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange of\u003c/p\u003e \u003cp\u003edepth (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange of\u003c/p\u003e \u003cp\u003esound velocity (m s\u003csup\u003e\u0026minus;\u0026thinsp;1)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage change of\u003c/p\u003e \u003cp\u003esound velocity (m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;68\u003c/p\u003e \u003cp\u003e69\u0026ndash;132\u003c/p\u003e \u003cp\u003e133\u0026ndash;210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1543.41\u0026ndash;1538.30\u003c/p\u003e \u003cp\u003e1537.89\u0026ndash;1512.91\u003c/p\u003e \u003cp\u003e1512.91\u0026ndash;1508.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e.021\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;54\u003c/p\u003e \u003cp\u003e55\u0026ndash;151\u003c/p\u003e \u003cp\u003e152\u0026ndash;190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1542.46\u0026ndash;1541.41\u003c/p\u003e \u003cp\u003e1541.25\u0026ndash;1511.96\u003c/p\u003e \u003cp\u003e1511.98\u0026ndash;1511.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.16\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;37\u003c/p\u003e \u003cp\u003e38\u0026ndash;201\u003c/p\u003e \u003cp\u003e202\u0026ndash;318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1544.38\u0026ndash;1543.41\u003c/p\u003e \u003cp\u003e1543.41\u0026ndash;1510.49\u003c/p\u003e \u003cp\u003e1510.21\u0026ndash;1503.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;56\u003c/p\u003e \u003cp\u003e57\u0026ndash;219\u003c/p\u003e \u003cp\u003e220\u0026ndash;288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1544.67\u0026ndash;1541.12\u003c/p\u003e \u003cp\u003e1541.56\u0026ndash;1503.19\u003c/p\u003e \u003cp\u003e1503.21\u0026ndash;1494.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMix\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;36\u003c/p\u003e \u003cp\u003e37\u0026ndash;284\u003c/p\u003e \u003cp\u003e285\u0026ndash;313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1544.18\u0026ndash;1543.97\u003c/p\u003e \u003cp\u003e1543.70\u0026ndash;1497.67\u003c/p\u003e \u003cp\u003e1497.61\u0026ndash;1495.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;40\u003c/p\u003e \u003cp\u003e41\u0026ndash;205\u003c/p\u003e \u003cp\u003e206\u0026ndash;273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1543.31\u0026ndash;1542.71\u003c/p\u003e \u003cp\u003e1541.86\u0026ndash;1507.49\u003c/p\u003e \u003cp\u003e1507.49\u0026ndash;1505.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;154\u003c/p\u003e \u003cp\u003e155\u0026ndash;292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1543.77\u0026ndash;1520.82\u003c/p\u003e \u003cp\u003e1520.78\u0026ndash;1497.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;74\u003c/p\u003e \u003cp\u003e75\u0026ndash;186\u003c/p\u003e \u003cp\u003e187\u0026ndash;266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1546.15\u0026ndash;1544.25\u003c/p\u003e \u003cp\u003e1543.59\u0026ndash;1511.26\u003c/p\u003e \u003cp\u003e1511.18\u0026ndash;1508.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;45\u003c/p\u003e \u003cp\u003e46\u0026ndash;238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1543.90\u0026ndash;1542.10\u003c/p\u003e \u003cp\u003e1541.83\u0026ndash;1509.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;30\u003c/p\u003e \u003cp\u003e31\u0026ndash;210\u003c/p\u003e \u003cp\u003e211\u0026ndash;312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1545.22\u0026ndash;1542.98\u003c/p\u003e \u003cp\u003e1542.14\u0026ndash;1510.49\u003c/p\u003e \u003cp\u003e1510.50\u0026ndash;1499.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;35\u003c/p\u003e \u003cp\u003e36\u0026ndash;172\u003c/p\u003e \u003cp\u003e173\u0026ndash;218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1540.89\u0026ndash;1544.45\u003c/p\u003e \u003cp\u003e1544.14\u0026ndash;1512.60\u003c/p\u003e \u003cp\u003e1512.59\u0026ndash;1509.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;75\u003c/p\u003e \u003cp\u003e76\u0026ndash;200\u003c/p\u003e \u003cp\u003e201\u0026ndash;316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1545.07\u0026ndash;1543.39\u003c/p\u003e \u003cp\u003e1542.60\u0026ndash;1508.71\u003c/p\u003e \u003cp\u003e1508.66\u0026ndash;1495.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;72\u003c/p\u003e \u003cp\u003e73\u0026ndash;233\u003c/p\u003e \u003cp\u003e234\u0026ndash;288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1544.99\u0026ndash;1543.88\u003c/p\u003e \u003cp\u003e1543.68\u0026ndash;1515.07\u003c/p\u003e \u003cp\u003e1514.25\u0026ndash;1502.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003eThermocline\u003c/p\u003e \u003cp\u003eDeep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;82\u003c/p\u003e \u003cp\u003e83\u0026ndash;219\u003c/p\u003e \u003cp\u003e220\u0026ndash;304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1543.89\u0026ndash;1539.49\u003c/p\u003e \u003cp\u003e1538.42\u0026ndash;1502.68\u003c/p\u003e \u003cp\u003e1502.65\u0026ndash;1498.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSound velocity was recorded between 1511.84 and 1498.53 m/s in the deep layer with depths from 220 to 256 m. The consistent decline in sound velocity at these depths is attributed to reduced temperatures associated with diminishing solar heat absorption. The study by Rohling et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) confirms that deep ocean temperatures, particularly in tropical regions such as the Sulawesi Sea, tend to be stable and low. This stability has profound implications for the behavior and ecology of deep-sea organisms, among which many have adapted to the conditions. The acoustic environment in the deep layer can influence predator-prey interactions and habitat selection, as sound plays a crucial role in how organisms communicate and locate resources. Understanding sound velocity dynamics in the region can help inform conservation strategies aimed at protecting vulnerable deep-sea ecosystems, which are often threatened by human activities such as fishing and mining.\u003c/p\u003e \u003cp\u003eThe analysis of spatial distribution using Ocean Data View software showed minimal changes in sound velocity at depths of 0 and 50 m, where values ranged from 1546 to 1538 m/s (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). support the results by stating that surface layers often show stability in sound speed due to the homogeneity of physical conditions. Stability is essential for various marine applications, including sonar technology and environmental monitoring systems. The ability to predict sound propagation accurately is critical for naval operations, fisheries management, and monitoring marine biodiversity. Moreover, understanding how sound travels through different layers of the ocean can enhance the ability to track marine mammals and study the behaviors in relation to changing environmental conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study generally clarifies the significant influence of temperature and salinity on SVP in the waters of Bunaken National Park. The trend of decreasing sound velocity with increasing depth shows the intricate relationship between physical water conditions and acoustic behavior. Additionally, this study enhances the understanding of the acoustic dynamics in tropical waters and serves as a crucial reference for future investigations in oceanography. The knowledge gained can inform management practices aimed at preserving marine biodiversity in the face of climate change, which poses a growing threat to oceanic ecosystems worldwide. Continued exploration of the dynamics will be essential for effective marine resource management and conservation efforts, particularly as environmental conditions continue to evolve.\u003c/p\u003e \u003cp\u003eComprehensive sound velocity analysis in Bunaken National Park provides essential insights into the complex interplay of environmental factors influencing marine acoustics. This study shows that temperature and salinity are not merely physical parameters because both play a critical role in shaping the acoustic environment of marine ecosystems. Continual exploration of these dynamics will help to understand the implications for marine life and the broader ecological context. This study provides the foundation for future investigations into the effects of climate change on marine acoustics, emphasizing the need for persistent monitoring and adaptive management strategies in the biodiverse region. The results show the importance of integrating acoustic investigations into broader marine study frameworks to promote sustainable practices and ensure environmental protection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAccuracy of the SVP Models Compared to In-situ\u003c/h3\u003e\n\u003cp\u003eThe development of SVP models is crucial for various applications in oceanography, acoustics, and marine biology. Microsoft Excel and Matlab were used in this study to develop SVP models through three distinct methods, including empirical equations (Leroy, Mackenzie, and Medwin) using CTD data, Leroy\u0026rsquo;s model using the global databases (Hycom and Marine Copernicus), and Leroy\u0026rsquo;s model using the predicted data (extrapolation from CTD data). The Medwin, Leroy, and McKenzie empirical equations used have been widely recognized in the literature for the ability to estimate sound velocity based on temperature, salinity, and pressure. Le Menn \u0026amp; Na\u0026iuml;r (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) corroborated the effectiveness of the empirical models through the report of the utility in various marine settings where in-situ measurements may be limited. The most accurate method for estimating SVP in the study area was established by comparing these models to in-situ measurements.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that Leroy's empirical equation produced the lowest RMSE values near zero, which signifies a high level of accuracy in the predictions compared to the in-situ data. This result was consistent with the report by Huang et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that Leroy's equation often outperformed other empirical models in tropical marine environments. Conversely, the RMSE values for Medwin's and McKenzie's equations were near 1, representing less precision. These discrepancies suggest the importance of selecting appropriate models based on local environmental conditions, as the performance of the equations can vary significantly depending on the specific characteristics of the water body being studied.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, RMSE of SPV models compared to the in-situ measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeroy\u003c/p\u003e \u003cp\u003e(CTD data)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedwin\u003c/p\u003e \u003cp\u003e(CTD data)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMackenzie\u003c/p\u003e \u003cp\u003e(CTD data)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeroy\u003c/p\u003e \u003cp\u003e(Hycom data)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeroy\u003c/p\u003e \u003cp\u003e(Mar. Coper. data)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeroy\u003c/p\u003e \u003cp\u003e(interpolation data)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe second modeling method used global databases obtained from hycom.org and marine.copernicus.eu. The databases offer valuable data for sound velocity modeling, but this study has significant limitations. For example, the Hycom database only contains data up to 2018, which poses challenges when comparing against more current in-situ data from 2023. Marine Copernicus provides data only to a maximum depth of 130 m, while the current in-situ measurements extend beyond 300 m. The limitations can cause substantial inaccuracies, as shown by the RMSE values reported for these models, which were not near zero. Robbani \u0026amp; Pratomo (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported similar challenges when using global datasets for SVP modeling in the Madura Strait.\u003c/p\u003e \u003cp\u003eCorresponding in-situ data for certain locations were occasionally absent in the two global databases, leading to gaps in the analysis. This absence is critical because of the tendency to generate incomplete models and potentially misleading conclusions about SVP. The importance of comprehensive and up-to-date databases was emphasized by Fleishman et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which stated how missing data could significantly impact acoustic studies and the assessment of marine ecosystems. The integration of robust in-situ measurements with global datasets is essential for enhancing the accuracy of sound velocity models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraphical representations of the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed substantial differences among the Leroy\u0026rsquo;s models using CTD, Hycom, and interpolation data. The Hycom database model at Station 2 significantly deviated from others, while the empirical model closely corresponded with in-situ data. At Stations 3 and 4, the CTD and Interpolation data models performed best, reflecting the utility in accurately predicting SVP. The effectiveness of the CTD data can be attributed to the dependency on direct measurements as well as established relationships between temperature, salinity, and sound velocity. This was supported by the work of Le Menn \u0026amp; Na\u0026iuml;r (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which emphasized the relevance of empirical equations in marine acoustics. Statistical equations provided valuable insights for extrapolating sound velocity models, but careful consideration of the modeled parameters is needed to ensure accuracy.\u003c/p\u003e\n\u003ch3\u003eComparison of Water Depth Obtained by the SVP Models\u003c/h3\u003e\n\u003cp\u003eThis study shows the critical role of the SVP in correcting MBES data and emphasizes that accurate depth measurements are essential in marine surveys, particularly in deep-water environments. According to Šiljeg et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the discrepancies between corrected and uncorrected MBES data can be substantial, with implications for navigation, resource management, and environmental monitoring. The sound waves emitted by MBES systems travel through the water column, where the speed is influenced by temperature, salinity, and depth parameters, which often have significant spatial and temporal variability. This variability should be considered to ensure that depth calculations, represented by the formula D\u0026thinsp;=\u0026thinsp;12(v\u0026thinsp;\u0026times;\u0026thinsp;Δt), maintain accuracy. Liu et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show the necessity of considering sound speed as an independent variable in the calculations, which directly affects the reliability of depth data.\u003c/p\u003e \u003cp\u003eSound velocity effects on depth accuracy were investigated by using several modeling schemes and conducting simulations to compare calculated depths with in-situ measurements. The results presented Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that certain points had minor depth differences, particularly in the mixed layer and thermocline, but more significant discrepancies were evident in the deep layer. The applied CTD and interpolation data models showed an average depth difference of about 2 m in the upper layers, while larger variations occurred in deeper waters. The results suggest that the models may not adequately capture the complexities of acoustic propagation in the water environments, specifically as the depth increases. The importance of accurate modeling in these scenarios was supported by Li et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which found depth discrepancies in deep water to be capable of initiating substantial errors in navigational charts and resource assessments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Water depth estimation (m) obtained by in-situ measurement and models. The value in parentheses represent the difference in measurements between the model and the in-situ data\u0026rsquo;\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"+\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTD data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHycom data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMar. Coper. data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInterpolation data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214.609 (+\u0026thinsp;0.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214,778 (+\u0026thinsp;0.696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e216.004 (+\u0026thinsp;1.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c8\"\u003e \u003cp\u003e216,921 (+\u0026thinsp;2.839)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409,288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e405.216 (-4.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405,214 (-4.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e405.361 (-3.927)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c8\"\u003e \u003cp\u003e411,603 (+\u0026thinsp;2.315)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319.939 (-3.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320,371 (-2.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e320.502 (-2.546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"+\" colname=\"c8\"\u003e \u003cp\u003e325,294 (+\u0026thinsp;2.246)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese models had significant depth variations across all layers, with RMSE values representing substantial inaccuracies. The observed discrepancies can be attributed to the propagation characteristics of acoustic waves, which are affected by the local SVP. According to Makar (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the refraction or bending of sound waves can lead to significant errors in depth estimation, particularly in regions where temperature and salinity gradients are pronounced. This shows the necessity for local calibration of SVP data against in-situ measurements to enhance hydrographic survey accuracy.\u003c/p\u003e \u003cp\u003eConsidering the results, sound velocity values derived from modeling can be beneficial for refining depth measurements in Bunaken National Park, but further investigations are essential to improve model accuracy. The integration of continuous monitoring and updating of SVP data is crucial, particularly in dynamic marine environments where conditions change rapidly. The use of secondary data sources, such as those provided by Marine Copernicus, presents a viable alternative for obtaining sound velocity values when direct measurements are not feasible. Previous exploration found that using comprehensive databases significantly enhanced the reliability of hydrographic survey, as reported by Robbani \u0026amp; Pratomo (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which achieved an improved accuracy in the modeling efforts by incorporating secondary SVP data.\u003c/p\u003e \u003cp\u003eThis study generally shows the integral relationship between sound velocity and depth accuracy in hydrographic survey, particularly in challenging environments such as deep waters. The results advocate for a multidisciplinary method that combines empirical modeling, in-situ measurements, and reliable secondary data sources to achieve higher precision in marine survey. As the field of hydrography continues to evolve, the incorporation of advanced modeling methods and robust data sources will be essential for supporting sustainable marine resource management and conservation efforts in sensitive ecosystems such as the type found in Bunaken National Park.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study showed the critical role of SVP in enhancing the accuracy of the bathymetric survey in Bunaken National Park. Furthermore, MBES data correction with SVP was found to be essential for reliable depth measurements, particularly in deep waters where environmental factors significantly influence sound speed. The results showed that Leroy's empirical equation produced the most accurate predictions, but there were limitations, such as insufficient depth coverage from in-situ measurements and challenges posed by outdated global databases including Hycom and Marine Copernicus. These discrepancies signified the need for continuous monitoring and integration of robust in-situ data with reliable secondary sources to improve precision in marine survey. The results advocated for a multidisciplinary method combining empirical modeling and statistical methods to capture the complexities of acoustic propagation. This suggested the importance of persistent investigation to understand sound velocity dynamics in relation to environmental changes, particularly in the context of climate change and the impact on marine ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, AA, WH, LRS and RRH; methodology, AA, WH and LRS; validation, AA and WH; formal analysis, AA, WH and LRS; investigation, AA, WH, LRS and RRH; resources, AA , WH and LRS; data curation, AA, WH, LRS and RRH; writing original draft preparation, AA, WH, LRS and RRH; writing review and editing, AA and WH; supervision, AA and LRS; project administration, WH; funding acquisition, -. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding of this work is not available.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAffatati, A., Scaini, C., \u0026amp; Salon, S. (2022). Ocean sound propagation in a changing climate: Global sound speed changes and identification of acoustic hotspots. \u003cem\u003eEarth\u0026rsquo;s Future\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), e2021EF002099. https://doi.org/10.1029/2021EF002099\u003c/li\u003e\n\u003cli\u003eBrown, C. J., Sameoto, J. A., \u0026amp; Smith, S. J. (2012). 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Temporal Sound Speed Error Correction Model for Underwater Acoustic Positioning Based on In Situ Measurements. \u003cem\u003eJournal of Surveying Engineering\u003c/em\u003e, \u003cem\u003e151\u003c/em\u003e(2), 4025002. https://doi.org/10.1061/JSUED2.SUENG-1528\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-systems-and-ocean-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"msot","sideBox":"Learn more about [Marine Systems \u0026 Ocean Technology](http://link.springer.com/journal/40868)","snPcode":"40868","submissionUrl":"https://submission.nature.com/new-submission/40868/3","title":"Marine Systems \u0026 Ocean Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Speed velocity profile, multibeam echosounder, temperature, salinity, depth","lastPublishedDoi":"10.21203/rs.3.rs-6653653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6653653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to investigate the Sound Velocity Profile (SVP) in the waters of Bunaken National Park using advanced bathymetric survey methods to enhance the accuracy of depth measurements obtained from Multibeam Echosounder (MBES). A bathymetric survey is critical for navigation, resource management, and environmental monitoring due to providing essential data about underwater terrain. The importance of correcting MBES data is clarified using SVP, which is influenced by temperature, salinity, and pressure variations. Field observations conducted between June and September 2022 included the use of CTD instruments to measure environmental parameters, although limitations in instrument reach led to unmeasured depths. To address these gaps, SVP was modeled using empirical equations and global databases, comparing the results with in-situ measurements to assess accuracy through Root Mean Square Error (RMSE). The results showed significant variations in SVP across different depths, signifying the need for reliable data in hydrographic surveys. In conclusion, empirical models, particularly Leroy's equation, provided the most accurate predictions. Further investigation should be conducted to refine modeling methods and integrate comprehensive datasets. This study provided a foundation for better marine resource management and conservation strategies in the face of continual environmental changes.\u003c/p\u003e","manuscriptTitle":"Comparison of Sound Speed from Measurements and Models in the Waters of Bunaken National Park, North Sulawesi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 05:35:08","doi":"10.21203/rs.3.rs-6653653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"151776287670870150747604957891799067602","date":"2026-04-09T04:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156079632867879824368185393132773339643","date":"2026-04-08T01:33:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T13:16:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305600366435510223398605579387431610319","date":"2025-05-22T11:27:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T00:31:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-16T00:28:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T08:27:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Systems \u0026 Ocean Technology","date":"2025-05-13T09:04:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-systems-and-ocean-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"msot","sideBox":"Learn more about [Marine Systems \u0026 Ocean Technology](http://link.springer.com/journal/40868)","snPcode":"40868","submissionUrl":"https://submission.nature.com/new-submission/40868/3","title":"Marine Systems \u0026 Ocean Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c170f9fd-1391-46b4-a6f1-e8489b1cd188","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-20T05:35:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 05:35:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6653653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6653653","identity":"rs-6653653","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}