Visualization method for marine survey shallow stratum profile data

Abstract

The invention discloses a visualization method for marine survey shallow stratum profile data, which is applied to the field of marine survey, and comprises the following steps: extracting acoustic echo samples, navigation data and attitude data from JSF format data collected by a shallow stratum profiler, analyzing the JSF format data into a JSON format containing the original echo intensity, geographic position and attitude information of each Ping, and analyzing the JSON format data to obtain the marine survey shallow stratum profile data. The method comprises the following steps of: firstly, dividing a local sub-block, mapping the local sub-block into an initial gray level image matrix, then enhancing the initial gray level image matrix by adopting CLAHE, and generating a high-contrast profile image capable of clearly identifying the characteristics of the seabed shallow layer structure by dividing local sub-blocks, calculating a local histogram cumulative distribution function and redistributing pixel values. According to the method, the data accuracy is ensured, the accuracy and the efficiency of interpreting the submarine pipeline burial depth and the stratum structure by interior interpretation personnel are improved, the data processing cost is reduced, the readability, the transportability and the compatibility of the data are improved, and a standardized interface can be provided for long-term management of ocean survey data and application of different platforms.

Description

Technical Field

[0001] This invention relates to a method for processing marine geophysical exploration data, and more particularly to a method for obtaining clearer shallow seismic profile images by parsing raw data files in JSF format obtained by a shallow seismic profiler and enhancing weak signal contrast using the CLAHE algorithm. Background Technology

[0002] In activities such as subsea pipeline surveying, marine resource development, seabed geological exploration, seabed topography mapping, and environmental resource monitoring, measurement results are generally presented using shallow seismic profile images. Equipment used for seabed exploration typically includes side-scan sonar systems (SSS), multibeam bathymetry systems (MBES), synthetic aperture sonar (SAS), sub-bottom profilers (SBP), and other acoustic imaging equipment, as well as marine magnetometers.

[0003] A shallow seismic profiler (SPR) is a geophysical exploration device based on acoustic principles to detect shallow geological structures below the seabed. It is primarily used to detect shallow geological information such as submarine pipeline routes, seabed surface sediment stratification, fault structures, and the distribution of buried objects. The SPR works based on the propagation and reflection characteristics of sound waves in different media. By analyzing the echo signals from the stratigraphic interfaces, it images the underground structures, detecting shallow geological structures below the seabed and buried submarine pipelines. The device mainly includes a deck unit (containing transmitting / receiving circuits and a data acquisition computer), an underwater towed fish (containing a transducer array), and positioning and attitude sensors. The transducers generate low-frequency, wide-bandgap acoustic pulses. These low-frequency, wide-bandgap acoustic pulses attenuate slowly when propagating through water, sediments, and strata, allowing them to penetrate strata several meters to hundreds of meters below the seabed. The acoustic waves emitted by a seismic profiler cover a certain width of the stratigraphic profile in a cylindrical or fan-shaped waveform, propagating downwards approximately perpendicular to the seabed. When the acoustic waves encounter interfaces with differences in acoustic impedance within the strata, some energy is reflected back to the transducer of the seismic profiler. As the survey vessel navigates along the survey line, it uses the seismic profiler and its accompanying data acquisition software to conduct marine measurements. The seismic profiler and its accompanying data acquisition software continuously transmit and acquire data, obtaining raw data including acoustic echo data and auxiliary sensor data, forming the raw data file for each survey line. The time delay and intensity variation of the echo signal received by the transducer reflect the depth and medium characteristics of the stratigraphic interface. The signal sequence acquired in one transmission and reception process is called a Ping echo. The profile data from consecutive Ping echoes are stitched together in chronological order to form the seismic profile record data of the survey vessel along the route (survey line).

[0004] EdgeTech's shallow seismic profilers, equipped with data acquisition software such as Discover, store the acquired shallow seismic profile data in the proprietary JSF (Joint Sonar File) format. The JSF format employs a dynamic message stream structure, fundamentally different from the fixed header structure of the traditional SEGY format. JSF files contain multi-source heterogeneous data, composed of a series of tightly connected messages. Each message begins with a 16-byte header identifying the message type and length. Message types include:

[0005] Type 80: Acoustic data message, storing echo signal samples;

[0006] Type 2002: NMEA navigation data message, which records geographical location information such as latitude and longitude;

[0007] Type 2020: Attitude sensor data message, storing carrier attitude parameters such as pitch and roll.

[0008] The shallow seismic profile record contains parameters such as WGS-84 coordinate system latitude and longitude, UTC timestamp, flight speed and heading, time-depth sequence, sampling interval, and reflection coefficient. During data processing, the echo time series is converted into depth coordinates based on the real-time sound velocity profile, establishing the spatial relationship of the stratigraphic interface. The echo amplitude is then normalized and mapped to grayscale values ​​(strong echo → bright pixels, weak echo → dark pixels), forming a two-dimensional profile image.

[0009] However, due to seabed scattering, multiple reflections, and environmental noise, raw shallow seismic profile images often suffer from noise interference and weak signal problems. In particular, the hyperbolic reflection characteristics of targets such as submarine pipelines (formed by the path difference of sound waves at different parts of a cylindrical pipe) are not clearly displayed in the images, resulting in a low signal-to-noise ratio and making them difficult to interpret manually. Therefore, it is necessary to enhance the shallow seismic profile data to obtain higher-quality visualization images.

[0010] When processing data, the shallow seismic profiling instrument (SPR) converts the echo time series into depth coordinates based on real-time sound velocity profiles, establishes the spatial relationship of stratigraphic interfaces, and normalizes the echo signal amplitude before mapping it to grayscale values. Strong echoes correspond to bright pixels, and weak echoes correspond to dark pixels, thus forming a two-dimensional profile image reflecting the reflection intensity of stratigraphic interfaces. During SPR surveys, the propagation of long-distance echoes is susceptible to interference from seabed scattering, multiple reflections, and environmental noise. Furthermore, due to the imaging mechanism of the SPR, system parameter settings, and the complex marine environment, the obtained shallow seismic profile images suffer from noise interference and weak local echo signals. The hyperbolic features of weak targets (such as deeply buried subsea pipelines) in the raw acoustic images generated by the SPR are not clearly displayed, resulting in a low signal-to-noise ratio and making manual interpretation difficult. Therefore, to obtain higher quality images, further processing of the shallow seismic profile data and images generated by the SPR is necessary.

[0011] In processing shallow seismic profile data, the first step is to accurately resolve the raw data to obtain the corresponding element information. Then, the obtained element data is reproduced in a clearly visible image format. Based on the reproduced image results, the location and burial depth of the subsea pipeline are analyzed and interpreted. Regarding image processing of shallow seismic profiles, mainstream commercial processing software (such as SonarWiz) mainly provides data visualization playback and basic processing functions. Its closed architecture does not expose the programming interface (API) for underlying data parameters. Users cannot directly obtain raw parameters such as acoustic samples and navigation data, making it difficult for users to access these underlying parameters and limiting secondary development of subsequent image processing functions. Furthermore, in complex marine environments, the raw shallow seismic profile images generated by shallow seismic profilers often suffer from significant noise interference and weak echo signals from deeply buried pipelines and other weak targets. Traditional global histogram equalization or simple linear stretching methods are insufficient to effectively highlight the hyperbolic characteristics of subsea pipelines, leading to difficulties in data interpretation.

[0012] Adaptive Histogram Equalization (AHE) is an algorithm that enhances local contrast and reveals details in smooth areas by performing histogram equalization within a rectangular region surrounding the currently processed pixel. AHE processes local areas where small rectangular regions result in strong local contrast, and large rectangular regions in weak local contrast. If the image blocks within the rectangular region are relatively flat and have similar gray levels, their gray-level histograms will be pointed, potentially leading to excessive noise amplification during histogram equalization. Contrast-limited adaptive histogram equalization (CLAHE), however, effectively limits noise amplification. While the basic AHE algorithm tends to over-amplify noise in flat regions, the contrast-limited CLAHE algorithm effectively suppresses noise amplification by introducing contrast limiting, enhancing weak signals while maintaining image naturalness. Summary of the Invention

[0013] The technical problem this invention aims to solve is to provide a visualization method for shallow seismic profile data from marine surveys. This method directly parses the JSF binary stream obtained from a shallow seismic profiler survey, utilizes a JSON file as an intermediate carrier to achieve standardized data storage, and combines a contrast-limited adaptive histogram equalization algorithm (CLAHE) to significantly improve the quality of shallow seismic profile images.

[0014] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0015] A method for visualizing shallow seismic profile data from marine exploration includes the following steps:

[0016] Step 1: Set up survey lines and observation systems according to marine survey specifications, collect raw data of the target sea area using a shallow seismic profiler, and store the data in JSF format to form raw data files for each survey line;

[0017] Step 2: Write a parsing program according to the JSF format specification, and use the parsing program to read the raw data file in JSF format. Parse the binary data stream of the JSF file according to the file header structure and message type definition of the JSF format; identify and extract echo samples in acoustic data message type 80, as well as NMEA navigation data in message type 2002 and attitude sensor data in message type 2020, thereby parsing the raw echo intensity, geographic location coordinates and attitude information corresponding to each Ping;

[0018] Step 3: Construct a structured data model from the parameters of each survey line and the acoustic echo data obtained in Step 2, and store it as an intermediate data of the analysis results in JSON format.

[0019] Step 4: Read the echo intensity sequence from the JSON file, and after denoising and linear stretching, map the original signal into an initial grayscale image matrix in the range of 0-255.

[0020] Step 5: The initial grayscale image matrix is ​​enhanced by using an adaptive histogram equalization algorithm with limited contrast. By dividing the image into local sub-blocks, calculating the local histogram cumulative distribution function, and redistributing pixel values, the contrast of weak signals is enhanced while suppressing noise, and finally a high-contrast profile image is generated for clearly identifying the features of shallow seabed structures.

[0021] A further improvement to the technical solution of this invention lies in the following: the process of parsing the binary data stream of the JSF format raw data file in step 2 includes:

[0022] First, read the message header of the raw JSF data file and verify the start synchronization flag 0x1601 to confirm the file's validity;

[0023] The data type is determined by the message type field in the message header. When the message type is 80, the subsequent 240-byte message body header and acoustic echo data are parsed. When the message type is 2002 or 2020, NMEA navigation data or attitude sensor data are parsed respectively.

[0024] For acoustic data messages of message type 80, the weighting factor N is read from byte offset 168-169 in the Type 80 message body header, and the stored 16-bit signed integer DataSample is processed using the formula...

[0025]

[0026] Restored to floating-point echo intensity value.

[0027] A further improvement of the technical solution of the present invention is that: when decoding the binary data stream of the JSF format raw data file, the acoustic echo intensity data of all Pings of the survey line are extracted with Ping as the horizontal axis and time depth as the vertical axis, and the geographical location and attitude information are obtained.

[0028] A further improvement to the technical solution of this invention lies in the following steps: Step 2, the process of parsing the binary data stream of the JSF format raw data file, includes the following steps:

[0029] Step 2-1: Use file operation functions to import the JSF format raw data file with read-only permissions;

[0030] Step 2-2: Read each message in the JSF file in a loop and perform the following verification and parsing for each message: a) Read the first 16 bytes of each message as the message header; b) Verify the synchronization flag at byte offset 0-1 in the message header to confirm whether the 16-bit integer value of the synchronization flag is 0x1601. If it does not meet the requirement, skip the message; c) Read the message type field at byte offset 4-5 in the message header and determine the message type based on the message type.

[0031] Steps 2-3: When the value of the message type field is 80, the data packet is determined to be an acoustic data message. The following extraction and restoration operations are performed: a) Read the 240-byte message body header immediately following the message header, and extract the Ping number and total number of sampling points; b) Read the weighting factor with a byte offset of 168-169 in the 240-byte message body header, and store the weighting factor as a 16-bit unsigned integer; c) Read the subsequent acoustic echo data DataSample, which is stored as a 16-bit signed integer; d) Utilize the weighting factor and formula... This restores the compressed 16-bit integer data to the actual floating-point echo intensity value ScaledDataSample.

[0032] Steps 2-4: When the message type is 2002 or 2020, determine that the message is an auxiliary sensor data message, and extract the longitude, latitude, pitch and roll information of the Ping respectively.

[0033] A further improvement of the technical solution of the present invention is that: in step 3, the JSON format file uses the Ping number as the main index key, and the internal structure of the JSON format file includes a Header object that stores static metadata information of the JSF file and a Payload object that stores dynamic data of each Ping.

[0034] The Header object includes metadata such as message synchronization flag, protocol version, and subsystem number;

[0035] The payload object includes a Unix timestamp, sound speed, sampling frequency, number of sampling points, geographic coordinates including longitude and latitude, attitude information including pitch and roll, and a sequence of floating-point echo intensity arrays parsed in step 2.

[0036] A further improvement to the technical solution of this invention lies in the following: In step 4, the specific process of converting the echo intensity sequence into an initial grayscale image matrix is ​​as follows:

[0037] a) Perform statistical analysis on the echo intensity sequence to determine the lower limit MinIntensity and upper limit MaxIntensity of its dynamic range;

[0038] b) Using the linear mapping formula

[0039]

[0040] Map the floating-point echo intensity value ScaledDataSample to the grayscale space GrayValue (0-255);

[0041] c) Arrange the grayscale values ​​in rows according to Ping number and in columns according to time depth to form a two-dimensional grayscale image matrix.

[0042] A further improvement to the technical solution of this invention lies in the following: In step 5, the processing procedure of the adaptive histogram equalization algorithm with contrast limitation is as follows:

[0043] The initial grayscale image is divided into several non-overlapping local sub-blocks;

[0044] Calculate the histogram cumulative distribution function for each local sub-block;

[0045] Set a contrast limit threshold L, crop the histogram frequencies that exceed the threshold, and distribute the cropped portion evenly across all gray levels;

[0046] By using the modified histogram generated after cropping and redistribution, its cumulative distribution function is calculated, and pixel values ​​are remapped, thereby improving the recognizability of hyperbolic features of subsea pipelines and stratigraphic interfaces.

[0047] A further improvement to the technical solution of this invention lies in the following: In step 5, the specific processing procedure of the adaptive histogram equalization algorithm for limiting contrast is as follows:

[0048] Step 5-1: Divide the initial grayscale image matrix into M×N non-overlapping local sub-blocks, where M≥2 and N≥2;

[0049] Step 5-2: Calculate the grayscale histogram for each local sub-block and set the contrast limit threshold L based on the total number of pixels in the local sub-block;

[0050] Step 5-3: Crop the gray levels in the local histogram whose frequency exceeds the contrast limit threshold L, and evenly redistribute the sum of all the frequencies that have been cropped out to the other gray levels in the histogram to form a corrected histogram to prevent excessive amplification of noise.

[0051] Step 5-4: Calculate the local cumulative distribution function based on the modified histogram and use it as the mapping transformation function for the region block;

[0052] Step 5-5: Use the mapping transformation function to remap the pixel values ​​in the local area to enhance image details;

[0053] Steps 5-6: Use bilinear interpolation to smooth and fuse the pixel values ​​at the edges of adjacent blocks to eliminate the boundary effect caused by the division of blocks, and finally generate a high-contrast shallow seismic profile image.

[0054] A further improvement to the technical solution of the present invention is that the calculation formula for the contrast limiting threshold L is as follows:

[0055]

[0056] Where TotalSamples is the total number of pixels in the local sub-block, NumberOfGrayLevels is the total number of gray levels, which is 256, and ClipFactor is an empirical coefficient between 1.0 and 10.0.

[0057] The technological advancements achieved by this invention due to the adoption of the above technical solutions are as follows:

[0058] This invention addresses the challenge of low signal-to-noise ratio and difficulty in manual interpretation of weak targets such as submarine pipelines in raw acoustic images. Based on the JSF communication protocol, the method identifies and extracts Type 80 acoustic data messages and Type 2002 / 2020 assisted navigation attitude messages. It utilizes the weighting factor defined in the protocol to accurately reconstruct compressed 16-bit integer echo samples, obtaining the true acoustic intensity physical quantity. This method employs JSON files as a universal data carrier for JSF file parsing results, storing the parsed timestamps, latitude and longitude, attitude information, and acoustic echo intensity sequences in standardized key-value pairs. This significantly improves cross-platform compatibility and readability, providing underlying data support for subsequent data mining and secondary development, facilitating data management and further development. This invention applies an adaptive histogram equalization algorithm (CLAHE) with contrast limitation to the generated base grayscale image matrix for local image enhancement. By calculating the cumulative distribution function (Clipping Limit) in the local region, it effectively avoids excessive amplification of noise and significantly improves the local contrast of the image, making the hyperbolic reflection characteristics of the submarine pipeline and the shallow subsurface interface clearly prominent.

[0059] This invention uses JSON files as the data carrier for the parsed results, offering excellent cross-language compatibility and readability. It enables cross-platform data exchange and subsequent processing program calls, facilitating data exchange between different systems and subsequent web-based or cloud-based data management and applications. This invention achieves completely autonomous parsing of JSF data, eliminating reliance on expensive commercial software. Through structured storage and CLAHE enhancement technology, it generates high-resolution, high-contrast shallow seismic profile images, significantly improving the image's signal-to-noise ratio and making the hyperbolic reflection characteristics of buried subsea pipelines clearer. This effectively improves the accuracy and efficiency of subsea pipeline burial depth detection in marine engineering surveys, and significantly enhances the efficiency and accuracy of data interpretation, demonstrating significant technological advancement and practical application value.

[0060] The final shallow seismic profile image processed by the method of the present invention has the following characteristics:

[0061] 1. Breakthrough in format barriers: This method achieves independent decoding of EdgeTech's proprietary JSF format, especially accurately restoring the acoustic intensity value based on weighted factor compression, ensuring the physical accuracy of the data, completely eliminating dependence on expensive commercial software, and reducing data processing costs.

[0062] 2. Data Standardization and Generalization: The system innovatively uses JSON files as an intermediate carrier to structure and text-ify the parsed heterogeneous binary data. This significantly improves data readability, portability, and compatibility, providing a standardized interface for the long-term management of marine survey data and its application on different platforms (such as web interfaces and GIS systems).

[0063] 3. Significantly Improved Image Quality: By employing the CLAHE algorithm for local adaptive enhancement, the local contrast of the image is significantly improved, especially for the hyperbolic reflectance features of weak targets such as deeply buried subsea pipelines, making these features clearly stand out from the low signal-to-noise ratio background. Compared with traditional methods, the cross-sectional images generated by this invention have sharper stratigraphic interfaces and clearer weak signals, greatly improving the accuracy and efficiency of interpretation of subsea pipeline burial depth and stratigraphic structure by field interpreters. Attached Figure Description

[0064] Figure 1 This is an overall flowchart of the method of the present invention; it includes data parsing, JSON conversion and CLAHE enhancement steps;

[0065] Figure 2 This is a schematic diagram of the JSF data structure; it includes the JSF message header and message type 80 structure.

[0066] Figure 3 This is an example of the JSON file structure generated by this invention;

[0067] Figure 4 This is a comparison image of a shallow seismic profile generated using the method of the present invention and the image display effect. In this image, the upper image was generated by EDGETECH software, and the lower image was obtained using the method of the present invention. Detailed Implementation

[0068] A visualization method for shallow seismic profile data in marine surveys involves equipment such as a shallow seismic profiler, positioning and navigation equipment, and a computer.

[0069] like Figure 1 As shown, the processing flow of the visualization method for shallow seismic profile data in marine surveys according to the present invention is as follows: Figure 1 As shown, it includes the following steps:

[0070] Step 1: Set up survey lines and observation systems according to marine survey specifications, collect raw data of the target sea area using a shallow seismic profiler, and store the data in JSF format to form raw data files for each survey line;

[0071] Step 2: Write a parsing program based on EdgeTech's JSF format specification. Use this program to read the raw JSF data file. Parse the binary data stream of the JSF file according to the JSF file header structure (e.g., a 16-byte message header) and message type definitions. Identify and extract echo samples from acoustic data message type 80 (Type 80 message), as well as NMEA navigation data (Message Type 2002) and attitude sensor data (Message Type 2020), thereby reconstructing the raw echo intensity, geographic coordinates, and attitude information for each Ping. This parsing program needs to identify the message type field, extract echo samples from Type 80 messages, and parse auxiliary messages such as Type 2002 NMEA navigation data and Type 2020 attitude sensor data to reconstruct the raw echo intensity value, geographic coordinates (e.g., latitude and longitude), and attitude information (e.g., pitch and roll) for each Ping.

[0072] Step 3: Construct a structured data model from the parameters of each survey line and the acoustic echo data obtained in Step 2, and store it as an intermediate data of the analysis results in JSON format.

[0073] Step 4: Read the echo intensity sequence from the JSON file, and after denoising and linear stretching, map the original signal into an initial grayscale image matrix in the range of 0-255.

[0074] Step 5: The initial grayscale image matrix is ​​enhanced using the contrast-limited adaptive histogram equalization (CLAHE) algorithm. By dividing the image into local sub-blocks, calculating the local histogram cumulative distribution function, and redistributing pixel values, the contrast of weak signals is enhanced while suppressing noise, ultimately generating a high-contrast profile image for clearly identifying shallow seabed structural features.

[0075] Example:

[0076] Taking a submarine water pipeline detection project in a certain sea area as an example, the EdgeTech 3400 shallow seismic profiler was used to collect data. The specific steps are as follows:

[0077] Step 1: Acquiring Raw Data

[0078] The EdgeTech shallow seismic profiler was used to conduct marine surveys in the survey area, collecting shallow seabed geological information through a towed operation. The water depth in the survey area was less than 100m; the survey lines were laid out as perpendicularly as possible to the general trend of the seabed contour lines; the towed fish was kept at a relatively stable distance from the seabed (e.g., 5m); and the navigation speed was kept steady (e.g., below 5 knots).

[0079] The system utilizes accompanying data acquisition software such as Discover to acquire and display raw echoes in real time. The software records and stores the raw data in JSF format binary files. These files contain not only acoustic echo data but also encapsulated GPS positioning data and attitude sensor data.

[0080] Step 2: JSF format parsing and data extraction

[0081] According to EdgeTech's JSF format specification, a parsing program is written using programming languages ​​such as Python or Matlab. The raw JSF data file is imported into the computer, and the parsing program is run on the computer. This program reads the binary stream from the raw JSF data file in a loop, processes the raw JSF data file of the survey line, and extracts signal data such as the raw echo intensity of each ping, as well as geographical location and attitude information.

[0082] (1) Read the file header: Read the first 16 bytes of each message (i.e., the message header), check whether the synchronization flag at offset 0-1 bytes is 0x1601 to confirm whether the message is valid and synchronized; read the message length at byte offset 2-3 to determine the starting position of the next message; read 4-5 bytes to get the message type;

[0083] (2) Parsing Type 80 Messages: When the message type is 80, read the 240-byte message body header immediately following the 16-byte message header to obtain the key configuration and calibration parameters contained in the message body header. Extract information such as Ping Number, Samples, Sample Interval, and Velocity of Sound from the 240-byte message body header. Specifically, read the weighting factor N at offsets 168-169 (starting from the message body header). This weighting factor N is usually stored as a 16-bit unsigned integer; to save storage space, the JSF format usually stores the actual floating-point echo intensity. Compressed into a 16-bit signed integer format Therefore, the acoustic echo data following the weighting factor N is read. According to the JSF protocol formula Calculate the true floating-point acoustic echo intensity, which has the correct physical dynamic range;

[0084] Extracting the echo sequence: Continue to read subsequent binary data blocks according to the number of sampling points and data type (16-bit signed integer), and apply the above formula to restore them one by one to form the echo sequence of the side line;

[0085] The key fields of the Type 80 message involved in this step are shown in Table 1 below:

[0086] Table 1: Key Fields of Message Type 80

[0087]

[0088] (3) Parsing auxiliary messages: Auxiliary messages or data generally refer to positioning and attitude information, which are used for subsequent static correction and georeferencing. When the message type is 2002, the NMEA string is parsed to obtain the longitude and latitude of the GPS positioning; if the message type is 2020, the attitude data is parsed to obtain the pitch and roll information, which are used for subsequent image correction.

[0089] Step 3: Generating JSON file

[0090] To facilitate subsequent use and transmission of the data, the parameters of each survey line and the acoustic echo data after step 2 were constructed into a structured data model and stored as a JSON (JavaScript Object Notation) file serialized as intermediate data of the analysis results, thus uniformly converting heterogeneous data into JSON format.

[0091] Construct a list of JSON objects ordered by Ping number (primary index key), generating a JSON file for each test line. The internal structure of the JSON file is stored either as an array of Ping objects or indexed by Ping number. Specifically, the internal structure includes a Header object storing static metadata information from the JSF file and a Payload object storing dynamic data for each Ping. In other words, for each Ping, a JSON object containing both a "header" and a "payload" is created.

[0092] The header contains metadata such as file creation time, synchronization flag, protocol version, subsystem number, and original filename.

[0093] The payload includes: a Unix timestamp, sound speed (SoundSpeed), sampling frequency, number of sampling points, the geographical coordinates (longitude and latitude) of the Ping, attitude information including the Ping's pitch and roll, and the floating-point echo intensity array ProfileData parsed in step 2. sequence.

[0094] This JSON-based approach enables the data to be self-descriptive, standardizes and generalizes the raw JSF data, and provides excellent readability and program call compatibility, greatly facilitating subsequent data quality control, image processing, and network transmission.

[0095] Step 4: Basic Image – Grayscale Image Matrix Generation

[0096] Read the floating-point echo intensity sequence (i.e., ProfileData acoustic echo data) from the JSON file generated in step 3. Because the original acoustic data has a wide dynamic range (potentially from 0 to tens of thousands) and contains outliers, it needs to be denoised and normalized to convert the echo intensity sequence into an initial grayscale image matrix, so that it can be mapped to a grayscale space (0-255) that is recognizable by the human eye. The specific process of converting the echo intensity sequence into an initial grayscale image matrix is ​​as follows:

[0097] 4.1 Outlier Truncation: To eliminate the impact of a very small number of high-intensity noise values ​​on the overall grayscale stretching, statistical methods (such as the 1%-99% quantile truncation method) are used to remove outliers and determine the effective dynamic range of the data. The 1st quantile of the echo intensity sequence, The 99th percentile of the echo intensity sequence; anything exceeding this value... The range of values ​​will be clipped to the corresponding boundary values;

[0098] 4.2 Linear Mapping: Then, using the linear mapping formula...

[0099] The trimmed floating-point strength value Linear mapping to grayscale values ​​of 0-255, i.e., grayscale space. ;

[0100] 4.3 Matrix Construction: The calculated matrix... The images are arranged by Ping number as columns (horizontal axis) and by time depth (sampling points) as rows (vertical axis), thus constructing the initial grayscale image matrix of the initial two-dimensional shallow bottom profile.

[0101] Step 5: Enhance visualization using the CLAHE algorithm

[0102] To address the issues of weak targets, low signal-to-noise ratio, severe signal attenuation in the deep seabed, and blurred pipeline features in the raw shallow seismic profile images from the EdgeTech shallow seismic profiler, this step employs the contrast-limited adaptive histogram equalization (CLAHE) algorithm for local enhancement processing of the initial grayscale image matrix generated in step 4.

[0103] 5.1 Image segmentation: Divide the initial grayscale image matrix into non-overlapping local sub-blocks of equal size (e.g., 8×8 or 16×16 blocks).

[0104] 5.2 Introduction of Clipping Limit: This invention utilizes CLAHE to calculate the grayscale histogram and cumulative distribution function (CDF) for each local sub-block; by setting a clipping threshold L, the slope of the grayscale histogram is limited to prevent over-enhancement. The formula for calculating L is:

[0105] ,in This represents the total number of pixels in the local sub-block. The total number of gray levels is 256. An empirical coefficient between 1.0 and 10.0 is used to control the degree of contrast limitation;

[0106] 5.3 Frequency clipping and redistribution: Calculate the gray-level histogram of each local sub-block, clip the gray-level parts in the histogram that exceed the threshold L, and distribute the overflow part evenly to other gray-levels in the histogram to form a corrected histogram, so as to avoid excessive amplification of background noise (such as water noise) while enhancing the signal.

[0107] 5.4 Local CDF Calculation and Remapping: The local cumulative distribution function (CDF) is calculated based on the modified histogram. This CDF is used as the mapping transformation function for the local sub-block. The pixel values ​​are then transformed using the processed CDF. The formula remaps each pixel value within the region;

[0108] 5.5 Boundary Blending: Since processing each local sub-block independently may produce obvious artifacts (chessboard effect) at the block boundaries, this invention uses bilinear interpolation to eliminate the chessboard effect between local sub-blocks: bilinear interpolation is used to smooth the pixels between adjacent local sub-blocks (especially the four corner points) to ensure a smooth visual transition and overall continuity of the final image.

[0109] Through the above steps, the final shallow seismic profile image shows clear seafloor interfaces, and in particular, the hyperbolic reflection characteristics of submarine pipelines are significantly enhanced (e.g., Figure 4 As shown in the figure, it greatly facilitates the interpretation and analysis of the burial depth of submarine pipelines by technical personnel.

[0110] The method of this invention not only achieves fully autonomous parsing of JSF data from EdgeTech, but also provides a complete solution from raw data to high-quality visualization through a JSON intermediate layer and the AHE algorithm.

[0111] The JSF format structure used in this embodiment is as follows:

[0112] JSF Message Header Structure: Each JSF message begins with a 16-byte header, which provides key metadata about the message and serves as the entry point for parsing. Table 2 below describes the structure of the 16-byte header:

[0113] Table 2: JSF 16-byte message header structure

[0114]

[0115] This invention primarily focuses on the following core message types:

[0116] Message Type 80: Sonar Data Message, which is the core message for extracting echo signals;

[0117] Message Type 2002: NMEA Navigation Data, used to extract geographic location (latitude and longitude);

[0118] Message Type 2020: Orientation Sensor Data, used to extract pitch and roll information.

Claims

1. A visualization method for shallow seismic profile data in marine exploration, characterized in that... Includes the following steps: Step 1: Set up survey lines and observation systems according to marine survey specifications, collect raw data of the target sea area using a shallow seismic profiler, and store the data in JSF format to form raw data files for each survey line; Step 2: Write a parsing program according to the JSF format specification, and use the parsing program to read the raw data file in JSF format. Parse the binary data stream of the JSF file according to the file header structure and message type definition of the JSF format; identify and extract echo samples in acoustic data message type 80, as well as NMEA navigation data in message type 2002 and attitude sensor data in message type 2020, so as to parse out the raw echo intensity, geographic location coordinates and attitude information corresponding to each Ping; Step 3: Construct a structured data model from the parameters of each survey line and the acoustic echo data obtained in Step 2, and store it as an intermediate data of the analysis results in JSON format. Step 4: Read the echo intensity sequence from the JSON file, and after denoising and linear stretching, map the original signal into an initial grayscale image matrix in the range of 0-255. Step 5: The initial grayscale image matrix is ​​enhanced by using an adaptive histogram equalization algorithm with limited contrast. By dividing the image into local sub-blocks, calculating the local histogram cumulative distribution function, and redistributing pixel values, the contrast of weak signals is enhanced while suppressing noise, and finally a high-contrast profile image is generated for clearly identifying the features of shallow seabed structures.

2. The visualization method for shallow seismic profile data in marine exploration according to claim 1, characterized in that: Step 2, the process of parsing the binary data stream of the JSF format raw data file, includes: First, read the message header of the raw JSF data file and verify the start synchronization flag 0x1601 to confirm the file's validity; The data type is determined by the message type field in the message header. When the message type is 80, the subsequent 240-byte message body header and acoustic echo data are parsed. When the message type is 2002 or 2020, NMEA navigation data or attitude sensor data are parsed respectively. For acoustic data messages of message type 80, the weighting factor N is read from byte offset 168-169 in the Type 80 message body header, and the stored 16-bit signed integer DataSample is processed using the formula... ； Restored to floating-point echo intensity value.

3. The visualization method for shallow seismic profile data in marine exploration according to claim 2, characterized in that: When decoding the binary data stream of the JSF format raw data file, the acoustic echo intensity data of all Pings along the survey line is extracted with Ping as the horizontal axis and time depth as the vertical axis, and the geographical location and attitude information are obtained.

4. The visualization method for shallow seismic profile data in marine exploration according to claim 2, characterized in that: Step 2, the process of parsing the binary data stream of the JSF format raw data file, includes the following steps: Step 2-1: Use file operation functions to import the JSF format raw data file with read-only permissions; Step 2-2: Read each message in the JSF file in a loop and perform the following verification and parsing for each message: a) Read the first 16 bytes of each message as the message header; b) Verify the synchronization flag at byte offset 0-1 in the message header to confirm whether the 16-bit integer value of the synchronization flag is 0x1601. If it does not meet the requirement, skip the message; c) Read the message type field at byte offset 4-5 in the message header and determine the message type based on the message type. Steps 2-3: When the value of the message type field is 80, the data packet is determined to be an acoustic data message. The following extraction and restoration operations are performed: a) Read the 240-byte message body header immediately following the message header, and extract the Ping number and total number of sampling points; b) Read the weighting factor with a byte offset of 168-169 in the 240-byte message body header, and store the weighting factor as a 16-bit unsigned integer; c) Read the subsequent acoustic echo data DataSample, which is stored as a 16-bit signed integer; d) Utilize the weighting factor and formula... This restores the compressed 16-bit integer data to the actual floating-point echo intensity value ScaledDataSample. Steps 2-4: When the message type is 2002 or 2020, determine that the message is an auxiliary sensor data message, and extract the longitude, latitude, pitch and roll information of the Ping respectively.

5. The visualization method for shallow seismic profile data in marine exploration according to claim 1, characterized in that: In step 3, the JSON format file uses the Ping number as the main index key. The internal structure of the JSON format file includes a Header object that stores static metadata information of the JSF file and a Payload object that stores dynamic data of each Ping. The Header object includes metadata such as message synchronization flag, protocol version, and subsystem number; The payload object includes a Unix timestamp, sound speed, sampling frequency, number of sampling points, geographic coordinates including longitude and latitude, attitude information including pitch and roll, and a sequence of floating-point echo intensity arrays parsed in step 2.

6. The visualization method for shallow seismic profile data in marine exploration according to claim 1, characterized in that: In step 4, the specific process of converting the echo intensity sequence into the initial grayscale image matrix is ​​as follows: a) Perform statistical analysis on the echo intensity sequence to determine the lower limit MinIntensity and upper limit MaxIntensity of its dynamic range; b) Using the linear mapping formula ； Map the floating-point echo intensity value ScaledDataSample to the grayscale space GrayValue (0-255); c) Arrange the grayscale values ​​in rows according to Ping number and in columns according to time depth to form a two-dimensional grayscale image matrix.

7. The visualization method for shallow seismic profile data in marine exploration according to claim 1, characterized in that: In step 5, the processing procedure of the contrast-limited adaptive histogram equalization algorithm is as follows: The initial grayscale image is divided into several non-overlapping local sub-blocks; Calculate the histogram cumulative distribution function for each local sub-block; Set a contrast limit threshold L, crop the histogram frequencies that exceed the threshold, and distribute the cropped portion evenly across all gray levels; By using the modified histogram generated after cropping and redistribution, its cumulative distribution function is calculated, and pixel values ​​are remapped, thereby improving the recognizability of hyperbolic features of subsea pipelines and stratigraphic interfaces.

8. A visualization method for shallow seismic profile data in marine exploration according to claim 7, characterized in that: In step 5, the specific processing procedure of the contrast-limited adaptive histogram equalization algorithm is as follows: Step 5-1: Divide the initial grayscale image matrix into M×N non-overlapping local sub-blocks, where M≥2 and N≥2; Step 5-2: Calculate the grayscale histogram for each local sub-block and set the contrast limit threshold L based on the total number of pixels in the local sub-block; Step 5-3: Crop the gray levels in the local histogram whose frequency exceeds the contrast limit threshold L, and evenly redistribute the sum of all the frequencies that have been cropped out to the other gray levels in the histogram to form a corrected histogram to prevent excessive amplification of noise. Step 5-4: Calculate the local cumulative distribution function based on the modified histogram and use it as the mapping transformation function for the region block; Step 5-5: Use the mapping transformation function to remap the pixel values ​​in the local area to enhance image details; Steps 5-6: Use bilinear interpolation to smooth and fuse the pixel values ​​at the edges of adjacent blocks to eliminate the boundary effect caused by the division of blocks, and finally generate a high-contrast shallow seismic profile image.

9. A visualization method for shallow seismic profile data in marine exploration according to claim 8, characterized in that: The formula for calculating the contrast limiting threshold L is as follows: ； Where TotalSamples is the total number of pixels in the local sub-block, NumberOfGrayLevels is the total number of gray levels, which is 256, and ClipFactor is an empirical coefficient between 1.0 and 10.0.

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