Interview Questions for SideScan Sonar Interpretation

SideScan Sonar operates on the principle of acoustic reflection. A transducer emits acoustic pulses (sound waves) perpendicularly to the direction of the vessel’s movement. These pulses fan out, creating a swath of coverage along the seabed. As the sound waves encounter objects or changes in the seabed’s composition, they reflect back to the transducer. The time it takes for the sound wave to return, along with the signal’s strength, provides information about the distance and reflectivity of the object or feature. Stronger reflections show up as brighter pixels on the sonar image, indicating harder or more reflective surfaces, while weaker reflections appear darker, showing softer or more absorbent materials. Think of it like shining a flashlight underwater – the brighter the reflection, the more solid or closer the object.

SideScan Sonar systems vary in frequency and their application depends on the desired resolution and penetration depth.

• High-Frequency Systems (e.g., 500 kHz – 1 MHz): These systems provide high-resolution imagery ideal for detecting small objects and features close to the seabed. They’re commonly used for detailed seabed mapping, wreck surveys, and pipeline inspections, where the focus is on precision. The limited penetration makes them unsuitable for deep sediment analysis.
• Medium-Frequency Systems (e.g., 100 kHz – 500 kHz): Offer a balance between resolution and penetration depth, making them versatile for a range of applications. They can detect larger objects and features at deeper water depths and can be used for geological surveys, habitat mapping, and search and recovery operations.
• Low-Frequency Systems (e.g., below 100 kHz): These systems penetrate deeper into the seabed, ideal for geological investigations or detecting deeply buried objects. Resolution is typically lower than high-frequency systems. Examples of this use include sub-bottom profiling and searching for large geological structures.

While powerful, SideScan Sonar has limitations.

• Range Limitations: Signal strength diminishes with distance, limiting the effective swath width and maximum depth. The further away the target, the poorer the resolution.
• Water Column Effects: Water conditions like turbidity, salinity, and temperature influence sound wave propagation, degrading image quality and introducing noise. Strong currents can also distort the acoustic path.
• Resolution Limitations: While high-frequency systems offer high resolution, even these have limits in their ability to distinguish closely spaced objects or finely detailed features. For instance, two very close rocks might appear as one.
• Interpretation Challenges: The data needs expert interpretation. Acoustic shadows and multiple reflections can create ambiguities, requiring experienced analysts to distinguish true targets from artifacts.
• Bottom Type Effects: The type of seabed (e.g., sand, rock, mud) significantly affects the backscatter strength and image appearance. Differentiating between similar bottom types based solely on backscatter strength can be challenging.

SideScan Sonar data processing and interpretation typically involves several steps.

1. Data Acquisition: The raw data is collected and stored, often using specialized software.
2. Data Cleaning: This step involves removing noise and correcting for various effects such as vessel motion and water column variations. This often includes applying gain and filtering to improve image quality.
3. Mosaicing: Individual sonar pings are assembled into a continuous image, creating a comprehensive map of the surveyed area. This often involves geometric corrections to account for vessel movement and water depth variations.
4. Image Enhancement: Techniques such as contrast enhancement, filtering, and edge detection are used to improve the visibility of features.
5. Target Identification & Classification: This is the most crucial step. Analysts use their knowledge of sonar imagery, the survey area, and the nature of potential targets to identify and classify objects and features seen on the image.
6. Reporting: Finally, a report is generated summarizing the findings, usually including images, annotations, and interpretations.

Acoustic shadowing occurs when a sound wave encounters a large, relatively solid object that obstructs its path. This results in an area behind the object that receives little or no reflected sound energy, appearing as a dark area on the SideScan image. The size and shape of the acoustic shadow provide important information about the target’s size, shape, and height. For example, a large rock projecting from the seabed will produce a distinct acoustic shadow that extends downstream behind the rock.

Imagine throwing a pebble into a calm pond – the waves don’t go directly behind the pebble, creating a quiet zone, much like an acoustic shadow.

Identifying targets and features in SideScan Sonar data involves a combination of visual inspection, knowledge of the area, and understanding sonar principles.

• Visual Inspection: Experienced interpreters carefully examine the sonar imagery for deviations from the surrounding seabed. They look for changes in backscatter strength, shapes, patterns, and the presence of acoustic shadows.
• Contextual Information: Information about the survey area, such as bathymetry (water depth), geology, and historical data, is crucial for interpretation. Knowing the expected seabed characteristics helps in discriminating between real targets and artifacts.
• Pattern Recognition: Identifying recurring patterns helps differentiate natural formations from man-made objects. For instance, pipelines typically show up as consistent linear features with characteristic acoustic shadows.
• Software Tools: Several software packages allow analysts to measure features, overlay different data layers, and improve image quality, thus aiding in identification.

SideScan Sonar can identify a wide range of targets and features, including:

• Wreckage: Ships, airplanes, and other submerged objects.
• Obstacles: Rocks, boulders, pipelines, cables, and other potential hazards.
• Geological Features: Sand waves, canyons, reefs, outcrops, and other seabed formations.
• Man-made Structures: Foundations, debris fields, and other artifacts.
• Biological Features: Large aggregations of organisms or specific habitats, such as kelp forests (though often requires other sensors for definitive identification).

The ability to identify a specific target often depends on the sonar frequency, water conditions and the size and reflectivity of the target itself.

Distinguishing between natural and man-made objects in SideScan Sonar imagery relies on understanding their characteristic acoustic signatures. Natural features like rock formations, reefs, or sand waves typically exhibit irregular, organic shapes and textures. Their acoustic returns often show gradual changes in reflectivity. In contrast, man-made objects like shipwrecks, pipelines, or debris fields tend to have sharp, geometric shapes and well-defined boundaries. Their returns are often more uniform and intense, depending on the material composition.

For example, a naturally occurring rock outcrop might appear as a vaguely defined area of high backscatter with irregular edges, whereas a sunken barge would show up as a distinctly rectangular or box-like shape with strong, consistent backscatter. The key is to analyze the shape, size, texture, and acoustic intensity of the target within the context of the surrounding seabed. Sometimes, further investigation like high-resolution sonar or even ROV inspection might be necessary for positive identification.

• Shape and Geometry: Regular shapes often indicate man-made objects.
• Acoustic Shadowing: Significant shadowing behind an object suggests a relatively tall, three-dimensional structure, more common with man-made features.
• Internal Structure: Man-made objects can sometimes show internal structures or compartments, unlike natural formations.

Gain settings in SideScan Sonar are crucial for obtaining high-quality, interpretable data. Gain controls the amplification of the received acoustic signal. Improper gain settings can lead to either over- or under-representation of the seabed features.

Too low gain: Weak signals from the seabed are not amplified enough, resulting in a ‘washed-out’ image with poor detail and potentially missing subtle features. Think of it like trying to photograph a dimly lit scene with a camera set to a low ISO – you get a dark, grainy image.

Too high gain: Weak signals get amplified too much, leading to noise saturation and the obscuring of real features. The image becomes cluttered with noise, making interpretation nearly impossible. Imagine trying to photograph a bright sunny scene with an extremely high ISO – the image is blown out and loses detail.

Optimal gain settings are usually determined through a combination of pre-survey planning (considering water depth, expected bottom type), real-time monitoring during the survey, and post-processing adjustments. The goal is to achieve a balance that clearly shows the seabed features without excessive noise. Modern SideScan Sonar systems often have automatic gain control (AGC) to help with this, but manual adjustments might still be required for optimal results depending on the survey environment. Careful attention to gain levels is vital for ensuring the accuracy and reliability of your interpretation.

Water column characteristics significantly impact SideScan Sonar imagery. The sound waves used by the sonar interact with water particles, temperature gradients, salinity variations, and the presence of suspended sediments, significantly affecting the quality and interpretation of the data.

• Suspended Sediments: High concentrations of suspended sediment (like silt or clay in turbid water) scatter the sound waves, causing signal attenuation and reducing image clarity. The result is a hazy or blurry image, obscuring details on the seabed.
• Temperature and Salinity Gradients: Variations in water temperature and salinity create sound speed gradients within the water column, leading to refraction and distortion of the acoustic signals. This can cause apparent shifts in the position of features or create false echoes.
• Water Absorption: Water itself absorbs acoustic energy, leading to signal attenuation, particularly at higher frequencies. This results in a loss of resolution and detail at greater ranges from the sonar towfish.

Understanding these effects is crucial for proper data interpretation. For example, a seemingly blurry area in the imagery might not necessarily represent a featureless seabed but could be due to high turbidity. Similarly, apparent distortions of seabed features could be a result of refractive effects rather than actual seabed morphology. This is why thorough understanding of the water column is important during the planning, execution and post-processing stages of a sidescan survey.

Variations in water depth and bottom type cause significant changes in the received acoustic signal strength and image characteristics. To compensate, several techniques are employed:

• Gain Control: As previously discussed, gain settings can be adjusted to compensate for variations in water depth and bottom type. Higher gains may be needed for deeper water or harder bottoms to enhance weaker returns.
• Post-Processing Techniques: Software can apply corrections to account for variations in water column properties and bottom reflectivity. These corrections can improve the consistency of the imagery across different areas.
• Time-Varied Gain (TVG): This technique automatically adjusts the gain based on the range (distance) from the sonar. It accounts for the fact that signals weaken with distance due to both spherical spreading and absorption.
• Beam-forming and processing algorithms: Advanced signal processing algorithms in modern sonar systems account for the varying acoustic properties of water and seabed during data acquisition and processing, improving overall image quality.

In essence, the goal is to create a consistent image despite these variations. This allows for a more reliable comparison of different areas of the survey area and ensures accurate interpretation of seabed features.

Mosaicking SideScan Sonar data is the process of combining multiple overlapping sonar images to create a larger, continuous image of the seabed. It’s essential for covering large areas because a single sonar run only covers a limited swath.

The process typically involves these steps:

1. Data Acquisition: Multiple overlapping sonar runs are conducted, ensuring sufficient overlap (typically 20-30%) for seamless integration.
2. Georeferencing: Each sonar image needs to be georeferenced, meaning its location is accurately determined using GPS or other positioning systems.
3. Image Alignment: Software aligns the overlapping images based on common features to create a coherent mosaic. This may involve manual or automated adjustments, depending on the level of overlap and the accuracy of the georeferencing.
4. Mosaicking: The aligned images are then stitched together to create a seamless composite image of the surveyed area.
5. Quality Control: The final mosaic is checked for errors, such as misalignments or artifacts, before final presentation.

Specialized software packages are used for these tasks. The outcome is a high-resolution, comprehensive map of the seabed covering a large area, allowing for a much broader understanding of the surveyed region. Mosaicking is critical in many applications, including seabed mapping, pipeline inspection, and shipwreck detection.

Assessing SideScan Sonar data quality is crucial for ensuring the reliability of interpretations. Several factors need to be considered:

• Image Clarity: A high-quality image will show clear, distinct features with minimal noise and artifacts. Poor image clarity might indicate problems with gain settings, water column conditions, or system malfunctions.
• Geometric Accuracy: The accuracy of the positioning system (GPS) directly impacts the geometric accuracy of the sonar data. Errors in positioning can lead to misinterpretations of feature locations and distances.
• Signal-to-Noise Ratio (SNR): A high SNR indicates a strong signal relative to noise, resulting in a cleaner image. A low SNR can make it difficult to distinguish real features from noise.
• Coverage and Overlap: Sufficient coverage and overlap are essential for creating complete mosaics and ensuring that no areas are missed. Inadequate coverage can result in gaps and incomplete maps.
• System Calibration: Regular calibration of the sonar system ensures that the data is accurate and consistent over time. Poorly calibrated systems can lead to systematic errors and inaccurate interpretations.

Quality assessment often involves visual inspection of the imagery, analysis of system parameters, and comparison to other data sources (e.g., bathymetry). Proper quality control is essential for ensuring the reliability of the data and the validity of any conclusions drawn from the interpretation.

Several common artifacts can appear in SideScan Sonar imagery. Understanding these artifacts is essential for accurate interpretation. Some common examples include:

• Reverberation: Multiple reflections of the sound waves within the water column or off the seabed surface can create false echoes. These appear as ‘clutter’ in the image. Minimizing reverberation can be achieved through proper gain settings and signal processing techniques.
• Acoustic Shadows: Objects that obstruct the sound waves create acoustic shadows behind them. These appear as dark areas in the image. While a shadow indicates a significant object, it does not provide detail about the object’s features.
• Side Lobes: Side lobes are weaker acoustic signals radiated from the sides of the transducer. They can produce false echoes and reduce the clarity of the primary signal. These artifacts are often minimized by careful transducer design and signal processing.
• Multiple Paths: Sound waves traveling along different paths can reach the receiver at slightly different times. This can cause image blurring and distortion. The impact of multipath propagation can be reduced using sophisticated signal processing techniques.
• Fish Schools: Fish schools or other marine life can produce strong acoustic returns, resulting in false targets in the image. Understanding the characteristics of real objects versus these types of artifacts is crucial for correct interpretation.

Addressing these artifacts often involves careful data acquisition (e.g., optimizing gain settings, choosing appropriate frequencies), and advanced signal processing techniques applied during post-processing. Experience and knowledge of the survey area are crucial for differentiating between real targets and artifacts.

Accurate navigation is paramount in SideScan Sonar surveys because the sonar’s data is georeferenced; meaning its position in the real world is recorded. Without precise navigation, the sonar imagery becomes useless, essentially a pretty picture with no context. Imagine trying to find a lost item in a room with only a blurry image and no idea where the image was taken! The position data allows us to accurately locate features detected by the sonar, creating a map showing the location of objects, changes in seabed morphology, or pipeline routes.

For example, a slight navigational error in a pipeline survey could lead to misidentification of the pipeline’s exact location, jeopardizing subsequent maintenance or repair efforts. The more accurate the navigation, the more precise our interpretation and the greater the value of the survey data.

Several methods exist for positioning in SideScan Sonar surveys, each with its own strengths and weaknesses. The choice depends on factors like water depth, survey area size, budget, and required accuracy.

• GPS (Global Positioning System): A common method, especially in shallow to moderate water depths. GPS provides latitude and longitude coordinates, but its accuracy can be affected by atmospheric conditions and multipath signal interference in coastal areas.
• DGPS (Differential GPS): Improves GPS accuracy by using a base station with a known precise location to correct for GPS errors. This increases positional accuracy significantly.
• Real-Time Kinematic (RTK) GPS: Offers the highest precision among GPS-based methods, typically within centimeters. It involves a network of base stations and is ideal for high-accuracy surveys requiring detailed positioning information.
• Inertial Navigation Systems (INS): Often used in conjunction with GPS. INS continuously tracks position, heading, and velocity using accelerometers and gyroscopes. This is particularly valuable where GPS signals are weak or unavailable. However, INS can accumulate errors over time, and its accuracy relies on the initial positioning.
• Ultra-Short BaseLine (USBL) and Long BaseLine (LBL): Acoustic positioning systems using transponders deployed on the seabed. They provide highly accurate positioning, especially in areas with GPS challenges, but require more setup time and are more expensive.

Often a combination of these methods is used to ensure redundancy and enhance overall accuracy. For instance, RTK GPS might be the primary method supplemented by INS as a backup.

Integrating SideScan Sonar data with other geophysical data dramatically enhances the overall understanding of the subsurface. SideScan Sonar provides a detailed image of the seafloor, but other data types reveal information not accessible to sonar alone.

For example, combining SideScan Sonar with sub-bottom profiler data can provide a three-dimensional understanding of the seabed. The SideScan reveals surface features, while the sub-bottom profiler shows layering and buried objects. Similarly, integrating magnetic data can help identify metallic objects, like shipwrecks, and this information can then be corroborated by the imagery of the wreck from the SideScan Sonar.

The integration process usually involves georeferencing all data sets to a common coordinate system. This allows for overlaying the data in GIS (Geographic Information System) software. This is extremely useful for creating a comprehensive interpretation of the survey area.

SideScan Sonar plays a crucial role in pipeline inspection by providing a detailed image of the pipeline’s route and surrounding seabed. This is essential for identifying potential hazards and assessing the pipeline’s condition.

Inspectors use SideScan Sonar to:

• Detect pipeline burial depth: Assessing the extent of the pipeline’s burial and identifying any areas of shallow burial or exposure.
• Locate scour: Scour is the erosion of sediment around the pipeline, creating instability and potential damage. SideScan Sonar is extremely effective in detecting scour patterns.
• Identify pipeline free spans: These are sections of pipeline that are not properly supported and can be subject to failure. SideScan Sonar can highlight free spans via their altered acoustic shadow.
• Detect obstructions and debris: Rocks, debris, or other objects near the pipeline can damage it or interfere with its operation. SideScan allows for visual identification.
• Assess the integrity of pipeline coatings: Changes in the acoustic reflection can, under certain conditions, be indicative of pipeline coating damage. This needs to be further confirmed using other methods.

The high-resolution imagery provided by SideScan allows for precise identification and measurement of these features, facilitating effective pipeline maintenance and risk mitigation.

SideScan Sonar is a powerful tool for wreck detection and identification. Its ability to create high-resolution images of the seafloor allows for the detection of even partially buried or obscured wrecks. The distinctive acoustic shadows cast by wrecks, combined with their unique shapes, make them easily identifiable on the sonar imagery.

The process typically involves:

• Initial survey: A wide-area search to locate potential wreck sites.
• Detailed investigation: Once a potential wreck is identified, a higher-resolution survey is conducted to create a detailed image of the wreck.
• Image interpretation: Experts analyze the SideScan data to identify the wreck’s type, size, orientation, and condition.
• Data integration: Often, other data, such as magnetic and sub-bottom profiler data, are integrated to enhance the understanding of the wreck and surrounding environment.

For instance, a SideScan sonar survey might reveal a distinctive acoustic signature indicative of a shipwreck. Further analysis might reveal the wreck’s size, shape, and features suggestive of its type (e.g., a sailing vessel versus a steamboat).

SideScan Sonar plays a vital role in environmental monitoring, particularly in assessing the condition of sensitive marine habitats and monitoring the impact of human activities. It’s useful for mapping and characterizing seafloor features such as:

• Coral reefs: Mapping the extent and health of coral reefs, and identifying areas affected by damage or disease.
• Seagrass beds: Assessing the distribution and density of seagrass, important indicators of coastal ecosystem health.
• Habitat mapping: Creating detailed maps of different seafloor habitats to aid in conservation efforts.
• Sediment transport: Monitoring changes in sediment distribution patterns, which can be indicators of erosion or pollution.
• Pollution detection: Identifying areas of pollution based on changes in seafloor morphology or the presence of debris.

The high-resolution images produced by SideScan Sonar provide detailed information about seafloor habitats, allowing scientists to monitor changes over time and assess the effectiveness of conservation measures.

SideScan Sonar is frequently used in the search and recovery of lost objects, from small items to large vessels. Its ability to create detailed images of the seafloor allows for the efficient and effective search of large areas.

The process often involves:

• Defining the search area: This is based on available information about the lost object, such as its last known position.
• Systematic search pattern: A planned search pattern, such as a grid or parallel lines, is used to ensure complete coverage of the search area.
• Data analysis: The SideScan data is analyzed to identify potential targets, which are then further investigated.
• Target confirmation: Additional surveys, such as Remotely Operated Vehicle (ROV) dives, are often needed to confirm the identity of the target.

For example, SideScan Sonar has been successfully used to locate lost fishing gear, sunken aircraft, and even small personal items. The high-resolution imagery is key to effectively finding and pinpointing the location of these objects even in challenging environments.

Post-processing software is absolutely crucial in SideScan Sonar interpretation. Raw SideScan data is often noisy and requires significant enhancement before meaningful interpretation can be done. Think of it like developing a photograph – the raw image needs adjustments to reveal the details. Post-processing software allows us to perform several vital tasks, including:

• Noise Reduction: Filtering out unwanted background noise to improve the clarity of targets.
• Gain and Contrast Adjustment: Optimizing the image’s brightness and contrast to highlight subtle features and improve target visibility. This is similar to adjusting the brightness and contrast on your computer screen.
• Geometric Corrections: Correcting distortions caused by variations in water depth, vessel speed, and sonar towfish altitude. Accurate geometric correction is essential for precise measurements.
• Mosaicking: Combining multiple sonar images into a larger, continuous image for a more comprehensive view of the survey area. This is particularly important for large surveys.
• Target Identification and Classification: Some advanced software allows for automated or semi-automated target identification based on shape, size, and other characteristics. While not foolproof, it can significantly aid in the initial analysis.

For example, I once worked on a project where the raw data was incredibly noisy due to strong currents. By using specialized noise-reduction filters within the software, we were able to clearly identify a previously obscured shipwreck.

Depth calculation in SideScan Sonar is indirect; the sonar doesn’t directly measure depth like a single-beam echo sounder. Instead, we rely on other data and calculations. Common methods include:

• Using a separate depth sounder (e.g., single-beam echo sounder): This is the most accurate method. The depth sounder provides the depth at each point along the survey line. This depth information is then used to correct geometric distortions in the SideScan data. Think of it like having a map to guide your interpretation of the picture.
• Using the towfish altitude and water column information from the SideScan data itself: High-end SideScan sonars record the towfish depth. By carefully examining the water column return (the signal reflecting from the seafloor), we can estimate the depth, although this is less accurate than using a separate depth sounder.
• Inferred depth from known features: If the survey area includes features with known depths (e.g., previously surveyed areas or man-made structures), this can provide depth context for the SideScan data.

The accuracy of depth calculation directly impacts the geometric corrections applied to the SideScan image, which is critical for accurate measurements of target size and position.

Interpreting SideScan Sonar data in complex environments presents numerous challenges. These environments introduce significant noise and ambiguities that can make accurate interpretation difficult. Common complexities include:

• Strong currents and tidal influences: These cause variations in towfish position and orientation, introducing distortions into the data.
• Highly variable bottom topography: Uneven seafloor creates acoustic shadowing (areas where the sonar signal doesn’t reach) and makes it hard to reliably identify targets.
• Refraction effects: Changes in water temperature and salinity can bend the sound waves, leading to distortions and inaccurate positioning of targets.
• High levels of backscatter: A highly reflective seabed makes it difficult to discern smaller objects.
• Clutter from marine life and debris: Fish schools, vegetation, and other debris can mask the presence of targets or create false positives.

For instance, in a highly cluttered harbor environment, distinguishing a small, buried object from random debris requires careful analysis and often the integration of other datasets (e.g., magnetometer data).

Ensuring the accuracy and reliability of SideScan Sonar data interpretation involves a multi-faceted approach:

• Proper survey planning and execution: This includes selecting appropriate sonar settings, maintaining consistent towfish altitude, and recording accurate navigational data. Thorough planning is like having a detailed blueprint before constructing a building.
• Careful data processing and quality control: This includes applying appropriate filters, correcting geometric distortions, and performing visual inspections for artifacts or inconsistencies. A meticulous approach helps in avoiding future issues.
• Experienced interpretation: Accurate interpretation relies heavily on the interpreter’s expertise in recognizing artifacts, understanding the effects of environmental factors, and integrating diverse datasets.
• Ground-truthing: Verification of interpretation results by means of diving surveys or other methods serves as confirmation. This is like double-checking your work by comparing it to a known standard.
• Using multiple datasets: Combining SideScan Sonar with other geophysical methods (e.g., magnetometer, sub-bottom profiler) provides cross-validation and reduces uncertainty.

In my experience, adhering to these principles consistently leads to significantly more robust and reliable results.

Throughout my career, I’ve worked extensively with several SideScan Sonar software packages, including SonarWiz, Hypack, and QPS QINSy. Each package has its own strengths and weaknesses. SonarWiz excels in its ease of use and intuitive interface, making it ideal for initial data processing and analysis. Hypack offers powerful post-processing capabilities and is often preferred for complex surveys. QPS QINSy is a robust integrated system excellent for large-scale projects and data management. The choice of software often depends on the specific project requirements and budget.

My experience with these packages spans both routine data processing and advanced analysis techniques. For example, I used QPS QINSy’s sophisticated mosaicking capabilities on a large-scale underwater pipeline inspection project. The ability to seamlessly stitch together hundreds of sonar images was crucial to delivering a comprehensive report.

Ambiguous SideScan Sonar imagery is a frequent challenge. My approach to problem-solving in such situations is systematic and iterative:

1. Careful examination of the raw data: Often overlooked, a thorough review of raw data can reveal subtle clues that were masked during initial processing.
2. Varying processing parameters: Experimenting with different filters and processing techniques may reveal hidden features.
3. Cross-referencing with other data: Integrating information from other sources like bathymetric surveys, navigational charts, or other geophysical surveys significantly aids in the interpretation.
4. Seeking second opinions: Consulting with other experienced interpreters provides a fresh perspective and can identify potential biases.
5. On-site investigation (if feasible): Physical verification through diving, ROV inspections, or dredging can resolve ambiguities in critical areas. This is often the ultimate solution but can be expensive and time-consuming.

For example, during an investigation of a potential underwater hazard, ambiguous sonar returns could have been misinterpreted as a large rock. By carefully reviewing the processing parameters and incorporating data from a sub-bottom profiler, we determined that the feature was actually a group of smaller rocks buried in sediment. This precise understanding was crucial for ensuring safe navigation in the area.