Optimal seismic survey line planning method and device based on ocean current prediction

Abstract

The application discloses a kind of optimal survey line planning method and device for seismic exploration based on ocean current prediction, method includes: obtaining the candidate survey line set in exploration area, obtain the ocean current prediction data in preset planning time window;Establish towed bird angle prediction model, according to ocean current prediction data, determine the expected feather angle and risk index of each candidate survey line in candidate survey line set at each time by using towed bird angle prediction model;According to the expected feather angle and risk index of each candidate survey line in candidate survey line set, screening is carried out according to the first preset constraint condition, determine the feasible time window set of each candidate survey line;According to the feasible time window set of each candidate survey line, the optimization result containing the execution order and time window of each candidate survey line is determined by using the second preset constraint condition and multi-objective cost function, and the survey line pushing instruction set of ship is generated according to the optimization result. The expected feather angle and risk index are actively predicted before operation using ocean current prediction data, and the operation safety is improved.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of marine geophysical exploration and marine environmental forecasting, specifically to a method and apparatus for optimal seismic survey line planning based on ocean current forecasting. Background Technology

[0002] In recent years, as offshore oil and gas exploration has expanded into deeper waters, marine seismic exploration technology has become a crucial means of supporting energy security and strategic resource development. The planning and design of seismic exploration routes plays a vital role in exploration efficiency, data quality, and operational cost control. Traditional route planning primarily relies on geological structural inferences and empirical layout, often failing to fully consider the impact of dynamic marine environmental factors. Especially in areas with complex currents, the source vessel and towed cable system are easily disturbed by the current field, leading to exploration path deviations, uneven coverage, or rework.

[0003] In practical operations, ocean currents have a particularly significant impact on towed cable systems. Ocean currents can cause deviations between the seismic source vessel's trajectory and the pre-set seismic survey lines, reducing efficiency and potentially leading to serious incidents such as towed cable entanglement and equipment damage. Therefore, incorporating ocean current forecast information into the seismic survey line planning process has become one of the key technical problems that urgently need to be solved.

[0004] Currently, ocean current numerical prediction technologies (such as HYCOM (HYbrid Coordinate Ocean Model), ROMS (Regional Ocean Modeling System), and NCODA (Navy Coupled Ocean Data Assimilation)) have achieved high spatiotemporal accuracy, providing gridded prediction data for near-surface and deep current fields over the next few days, thus offering an objective basis for survey line optimization. Meanwhile, marine seismic acquisition operations widely employ GNSS navigation, INS inertial navigation systems, and tail-cable positioning technology, providing data support for survey line execution error modeling and feedback optimization.

[0005] However, there is currently a lack of a systematic method that closely integrates ocean current forecasting with seismic survey line deployment, capable of predicting and assessing potential migrations before actual operations and adjusting survey lines to achieve the goals of "shortest path, minimum migration, and optimal imaging." This technological gap restricts the ability to acquire high-quality seismic data in deep-water and complex sea state areas. Summary of the Invention

[0006] In view of the above problems, embodiments of the present invention are proposed to provide a method and apparatus for optimal seismic exploration line planning based on ocean current forecasting that overcomes or at least partially solves the above problems.

[0007] According to one aspect of the present invention, a method for optimal seismic exploration line planning based on ocean current prediction is provided, the method comprising: Acquire a set of candidate survey lines within the exploration area, and acquire ocean current forecast data within a pre-defined planning time window; A towline plume angle prediction model was established, and based on ocean current forecast data, the expected plume angle and risk index of each candidate survey line in the candidate survey line set at each time was determined using the towline plume angle prediction model. Based on the first preset constraints, the expected feather angle and risk indicators of each candidate survey line in the candidate survey line set are screened to determine the feasible time window set of each candidate survey line. Based on the feasible time window set of each candidate survey line, the second preset constraint condition and multi-objective cost function are used to determine the optimization result including the execution order and time window of each candidate survey line, so as to generate the survey line push instruction set of the ship according to the optimization result.

[0008] According to another aspect of the present invention, a seismic exploration optimal survey line planning device based on ocean current prediction is provided, comprising: The acquisition module is suitable for acquiring a set of candidate survey lines within the exploration area, as well as acquiring ocean current forecast data within a preset planning time window; The feather angle module is suitable for establishing a towed feather angle prediction model and, based on ocean current forecast data, using the towed feather angle prediction model to determine the expected feather angle and risk index of each candidate survey line in the candidate survey line set at each time. The window module is suitable for filtering the expected feather angle and risk indicators of each candidate survey line in the candidate survey line set according to the first preset constraint conditions, and determining the feasible time window set of each candidate survey line. The results module is adapted to determine the optimization results, which include the execution order and time window of each candidate survey line, based on the set of feasible time windows for each candidate survey line, using the second preset constraint conditions and multi-objective cost function, so as to generate a set of survey line push instructions for the ship based on the optimization results.

[0009] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described optimal seismic survey line planning method based on ocean current prediction.

[0010] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the above-described method for optimal seismic exploration line planning based on ocean current prediction.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising at least one executable instruction that causes a processor to perform operations corresponding to the above-described method for optimal seismic survey line planning based on ocean current prediction.

[0012] The optimal seismic survey line planning method and apparatus based on ocean current forecasting provided by embodiments of the present invention utilizes ocean current forecasting data to quantitatively predict the expected plume angle and risk indicators of each candidate survey line before operation. Compared with traditional operations involving passive correction and simple velocity threshold pausing, this method can proactively avoid inoperable periods or survey line directions with strong crosscurrents or excessive plume angles, significantly reducing the risks of tow cable deviation, uneven coverage, and equipment entanglement, thus ensuring data acquisition quality and operational safety from the planning and design level. Furthermore, it eliminates the need for reliance on human experience, reducing the risk of human error and facilitating standardized management among different operational teams.

[0013] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of an optimal seismic survey line planning method based on ocean current prediction according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of ocean current decomposition is shown; Figure 3 A schematic diagram of a seismic exploration optimal survey line planning device based on ocean current prediction according to an embodiment of the present invention is shown; Figure 4 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0015] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0016] Figure 1 A flowchart of an optimal seismic survey line planning method based on ocean current prediction according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps: Step S101: Obtain the set of candidate survey lines in the exploration area, and obtain ocean current forecast data within the preset planning time window.

[0017] For seismic exploration survey lines, most existing methods rely on experience for selection, which cannot achieve quantitative assessment and reproduction. Alternatively, during the operation, towed cable rudders or bird control are used, which are local controls and cannot avoid inoperable periods from the source. Simply pausing the current velocity will lead to wasted time and will not allow for finer-grained pushing or changing of survey lines. Overall, there is a lack of a survey line pushing mechanism that is spatiotemporally coupled with ocean current forecast data.

[0018] Based on the above issues, this embodiment first acquires a set of candidate survey lines within the exploration area, along with corresponding ocean current forecast data, to filter candidate survey lines based on the ocean current forecast data. For example, the exploration area boundaries, no-navigation zones, and restricted navigation zones can be imported using a shipborne mission planning terminal or a shore-based planning terminal to determine the exploration area. Original design survey lines (such as existing design survey lines), backup survey lines, and regular grid survey lines are acquired. For regular grid survey lines, candidate survey lines can be automatically generated based on the exploration area boundaries, main survey line direction, and line spacing. Corresponding attribute information is also acquired for each type of survey line, including attributes such as length, azimuth, priority, and coverage requirements. All acquired survey lines can be converted to the same geodetic coordinate system and projected coordinate system for unified management. They are stored using fields such as line number, start and end point coordinates, azimuth, line length, line spacing, turning end extension distance, priority, and coverage type. Storage can be achieved through methods such as a survey line database, which are not limited here. Based on the attribute information of each candidate survey line, elimination can be performed, such as eliminating survey lines that conflict with the boundaries of the exploration area, no-navigation zones, and restricted navigation zones (here, line segments in the candidate survey lines that conflict with the boundaries of the exploration area, no-navigation zones, and restricted navigation zones can be eliminated, while non-conflicting line segments are retained, etc.). Each candidate survey line after elimination can be discretized into sampling points along the line to obtain a set of candidate survey lines containing information such as survey lines, spatial sampling points, and attribute parameters.

[0019] For ocean current forecast data, data containing the horizontal velocity vector of seawater can be obtained based on a preset spatial grid. The preset spatial grid includes the discretization granularity of the data in time, horizontal space, and vertical directions, such as the time resolution Δt (the time interval between two adjacent forecast fields), spatial grids Δx and Δy (the distance between adjacent grid points in the longitude (east-west) and latitude (north-south) directions), and depth layer Δz (the spacing between adjacent data layers in the vertical direction). Ocean current forecast data contains the horizontal velocity vector of seawater, with u and v components, representing the east-west and north-south components of the velocity. The preset planning time window T can be [t0, t0+L], where t0 is the start time of the planning time window for acquiring ocean current forecast data, L is the preset step size, and t0+L is the end time. The start and end times are set according to the implementation situation and are not limited here. Ocean current forecast data can be obtained from multiple sources, such as ocean current forecast systems or local forecast systems. For ocean current forecast data from different sources, processing such as time unification, spatial resampling, and coordinate transformation can be performed. For the depth range where towed cables are significantly affected by currents, the near-surface or target depth layer velocity can be extracted based on the towed cable depth, seismic source depth, empirical weights, etc., to obtain the equivalent operational flow field.

[0020] Furthermore, after obtaining ocean current forecast data, it can be corrected using methods such as tidal models and current velocity devices (e.g., ADCP (Acoustic Doppler Current Profiler), shipborne current meters, drifting buoys, etc.). For example, bias correction can be used to correct ocean current forecast data by constructing a spatiotemporal bias field using observation data and forecast differences, and then fusing it using inverse distance weighting, optimal interpolation, or Kalman filtering. After fusion, a corrected flow field is generated that covers the exploration area and satisfies a unified time step and spatial grid. The forecast lead time, data source, and reliability of each grid point are recorded to obtain more accurate ocean current forecast data.

[0021] Step S102: Establish a towline plume angle prediction model, and based on ocean current forecast data, use the towline plume angle prediction model to determine the expected plume angle and risk index of each candidate survey line in the candidate survey line set at each time.

[0022] A towing feather angle prediction model is established to obtain the expected feather angle and risk indicators. The towing feather angle prediction model can be trained based on historical sample data. In this embodiment, the towing feather angle prediction model is used to interpolate ocean current forecast data to the corresponding positions and operation times of each candidate survey line in the candidate survey line set, based on the sampling points along the line and the candidate opening time. The unit vectors of the survey line direction and the lateral unit vectors of each candidate survey line are calculated, and the current velocity is decomposed into along-line components. (i.e., the longitudinal component along the survey line) and the transverse component (i.e., the component perpendicular to the survey line direction), based on the survey vessel's speed relative to the water, the expected feather angle of each candidate survey line in the candidate survey line set at each time is obtained, as shown below:

[0023] Where t represents each time point, and Vs represents the speed of the ship relative to the water.

[0024] like Figure 2 As shown, the massive vector U represents the ocean current forecast data, and its survey line direction vector... u ∥ That is, the component along the line Components perpendicular to the survey line direction u ⊥ That is, the horizontal component .

[0025] Furthermore, the towline feather angle prediction model can also accept parameters such as towline length, towline burial depth, number of towlines, empirical coefficients for feather angle, and allowable fine-tuning range of heading for operations to more accurately determine the expected feather angle under crossflow. When calculating the expected feather angle, the lateral offset (e.g., calculated based on towline length and expected feather angle) and the expected feather angle at different positions along the candidate survey line can also be calculated to obtain the maximum, average, and high quantile values ​​of the expected feather angle for the candidate survey line. Risk indicators can include lateral offset and the maximum value of the expected feather angle. When multiple towlines exist, corresponding risk indicators can be determined based on the changes in feather angle and spacing between the inner and outer towlines. For example, the maximum value or weighted calculated value of the changes in feather angle and spacing between the inner and outer towlines can be used as the risk indicator for the candidate survey line and the corresponding time combination; that is, risk indicators also include spacing, etc.

[0026] The towline feather angle prediction model performs the following operations for each candidate survey line Li and each candidate opening time tj: The candidate survey line is discretized into several sampling points sm along the line, and the ocean current forecast data is interpolated to (tj, sm, z). z represents the depth dimension of the preset ocean current data, and interpolation is performed based on the depth data of z. According to the formula for calculating the expected feather angle, the current velocity and its components at each sampling point are calculated, and the expected feather angle θ(tj, sm) is calculated in conjunction with the candidate speed. The maximum, mean, or high quantile value of the expected feather angle along the entire candidate survey line is taken as the expected feather angle at the candidate opening time, or the 95th quantile value is taken as the expected feather angle to reduce the influence of isolated anomalies. The lateral offset of the towline tail end is estimated based on the towline length Lc for comparison with the maximum permissible lateral offset in the subsequent first preset constraint condition. For multi-cable acquisition scenarios, the expected feather angle and lateral offset, as well as the tow cable spacing, are calculated separately for the inner, middle, and outer cables as risk indicators. This information is then combined with the tow cable spacing safety threshold to determine if there are risks such as reduced cable distance, crossing, or proximity. Furthermore, when there are turning-off or transfer sections at both ends of the candidate survey line, the low-speed phase before and after the turning-off can be treated as a separate sub-process for evaluating the expected feather angle, avoiding loss of tow cable attitude control before or after line opening due to low-speed crossflow.

[0027] Step S103: Based on the first preset constraints, the expected feather angle and risk indicators of each candidate survey line in the candidate survey line set are screened to determine the feasible time window set of each candidate survey line.

[0028] The first preset constraint is determined based on factors such as the feather angle threshold θmax, the maximum permissible lateral offset, the tow cable spacing safety threshold, the construction period, the upper and lower limits of speed, the turning radius, and the no-navigation zone. The specific values ​​of the first preset constraint are set according to the implementation situation and are not limited here.

[0029] Based on the planned time step such as Δt, each candidate survey line is scanned hourly along the time axis. For each candidate line opening time, such as the complete operation process from line opening, stable acquisition to line retrieval or turning around, it is determined whether the candidate line opening time meets the first preset constraint condition, that is, whether the expected feather angle, lateral offset, tow cable spacing, construction period, speed, turning radius and distance to the restricted area within the candidate line opening time all meet the first preset constraint condition. If so, the corresponding time is determined as a feasible time; otherwise, the candidate line opening time is an infeasible time.

[0030] Once feasible moments are determined, consecutive feasible moments are merged into feasible time windows, resulting in a set of feasible time windows for each candidate survey line. This set records the start and end times of the window, as well as parameters such as flight speed, average or maximum feather angle, risk margin (e.g., a safety reserve index for the feasible time window, determined based on maximum feather angle, lateral offset, etc.), and estimated operation time. Based on the set of feasible time windows, windows shorter than the time required to complete a candidate survey line can be eliminated. For windows at critical times, they can be set as low-priority alternative windows based on factors such as the safety margin included in the risk margin, combined with the implementation situation, facilitating selection based on specific implementation circumstances.

[0031] Step S104: Based on the feasible time window set of each candidate survey line, the second preset constraint condition and multi-objective cost function are used to determine the optimization result containing the execution order and time window of each candidate survey line, so as to generate the survey line push instruction set of the ship based on the optimization result.

[0032] A second set of pre-defined constraints is constructed based on the number of survey line executions, the feasible time window for line opening, the time conditions for transfer and turnaround between adjacent survey lines, the speed being within a preset range, and compliance with no-fly zones and safe distance requirements. These second set of constraints are used to optimize the execution order and time window of candidate survey lines.

[0033] After determining the set of feasible time windows, multiple different results can be obtained by cross-combining the execution order and feasible time window set for each candidate survey line. For the execution order and feasible time windows, a multi-objective cost function can be constrained based on a second preset constraint condition, and the results can be sorted and optimized. For example, in this embodiment, the multi-objective cost function is constrained based on the second preset constraint condition, using the candidate survey line execution order, the opening time of each candidate survey line, air speed, and heading fine-tuning as decision variables to establish a multi-objective cost function. This function comprehensively minimizes the total operation time, waiting time, survey line switching distance, turning loss, feather angle exceeding limit penalty, coverage omission penalty, and high-risk window usage penalty. The second preset constraint condition can specifically include conditions such as each mandatory survey line being executed once, the opening time falling within the feasible time window, adjacent survey lines meeting the requirements for transfer and turning times, air speed within a preset range, and compliance with no-fly zones and safety distances.

[0034] For situations with a small number of candidate survey lines, algorithms such as dynamic programming, integer programming, or branch and bound can be used to solve the multi-objective cost function. For large-scale work areas with a large number of candidate survey lines, algorithms such as genetic algorithms, simulated annealing, ant colony optimization, or heuristic rolling optimization algorithms can be used. The solved function results are then sorted, for example, by comprehensive cost and risk level, and the candidate survey lines ranked highest are selected as the optimization results. The optimization results can include preferred and backup solutions to address different changing circumstances.

[0035] Based on the candidate survey lines included in the optimization results, a survey line push instruction set for the vessel is generated, which can be displayed graphically on platforms such as the operation command center. The survey line push instruction set includes line number, start coordinates, end coordinates, suggested start time, estimated completion time, suggested course, suggested course correction angle, suggested overwater speed range, expected feather angle, maximum lateral offset, risk level, and backup survey line number. The output format can be tables, JSON / XML interfaces, or route files readable by navigation systems, facilitating integration with shipborne navigation, operation command, and data quality control systems. The displayed results can also overlay feasible windows, risk hotspots, and recommended execution sequences on electronic nautical charts or work area base maps. When multiple feasible windows exist for a candidate survey line, priority and alternative windows are provided based on the preferred and backup options, and their corresponding risk margins can be indicated. The above is an example; specific settings depend on the implementation and are not limited here.

[0036] Furthermore, this embodiment can also update ocean current forecast data based on a preset rolling cycle, such as 1 hour or 3 hours. That is, new ocean current forecast data is acquired every preset rolling cycle, and the feasible time window set for candidate survey lines is redefined. For example, currently executing survey lines and remaining survey lines are considered as candidate survey lines. Completed survey lines can be used as calibration data or historical data. The ship's current position and expected arrival time can be used as new initial conditions, such as re-determining the exploration area, the latitude and longitude address of the ocean current forecast data acquisition, and the time. The feasible time window set for candidate survey lines is redefined, and the results are updated and optimized. For example, the expected plume angle of candidate survey lines is re-evaluated within several future time steps to determine whether there is a trend of increasing plume angle or exceeding limits, so as to determine whether the original survey line navigation can continue, or only minor adjustments to the course and speed are needed. When a reassessment reveals an increased risk or new ocean current forecasts show forecast deviations exceeding thresholds, timely modifications can be made. These modifications include freezing already executed survey lines, locally re-optimizing remaining survey lines and future planning windows (e.g., 6-12 hour intervals), and updating the next survey line, opening time, and backup plans. In case of emergency triggering, suggestions such as delaying survey line opening, reducing or increasing speed, switching to backup survey lines, and pausing data collection can be immediately generated. The preset rolling cycle update optimization method can handle real-time changes in ocean current forecast data and promptly correct survey lines. The preset rolling cycle can also combine short-cycle corrections with long-cycle re-optimization, such as updating ocean current forecast data every 20 minutes, recalculating the set of feasible time windows and survey line push instructions for the next 6-12 hours every hour, and triggering emergency pushes when the predicted plume angle exceeds a threshold within a short period. When selecting a single tidal window, if the crossflow component is large enough to cause the expected yaw angle to exceed θmax, the period is deemed infeasible. After the crossflow weakens during another tidal phase, a suggestion to open the line in that window is provided, along with heading and speed adjustments to further reduce yaw growth. For dynamic line switching across multiple tidal lines, if the forecast indicates a vortex edge on the northern side of the area causing increased crossflow along some lines, high-risk lines are postponed, and low-risk lines are prioritized. If observations reveal an early risk, rolling re-optimization is performed, and the current line is switched to a backup line after completion, while high-risk lines are delayed by several hours. These are just examples; specific settings depend on the implementation and are not limited here.

[0037] Furthermore, after generating the survey line push command set, it can be sent to systems such as shipboard command and navigation systems so that the ship can execute the corresponding survey line push command set. At the same time, real-time feather angle and towline geometry during actual ship operation can be obtained to correct the parameters of the towline feather angle prediction model and achieve online calibration.

[0038] By utilizing ocean current forecast data, we proactively avoid inoperable time windows and areas, reducing reactive corrections and achieving source avoidance. The feather angle threshold of the first preset constraint serves as a strong constraint, significantly reducing the probability of exceeding limits, improving safety, and mitigating the risks associated with towline crossings and excessive lateral deviation. By rearranging survey lines and utilizing operating windows, we reduce downtime and inefficient countercurrent navigation, improving efficiency. The preset rolling cycle can be continuously pushed, enhancing adaptability to complex sea conditions.

[0039] The optimal seismic survey line planning method based on ocean current forecasting provided by this invention uses ocean current forecasting data to quantitatively predict the expected plume angle and risk indicators of each candidate survey line before operation. Compared with traditional operations involving passive correction and simple velocity threshold pausing, this method can proactively avoid unoperable periods or survey line directions with strong crosscurrents or excessive plume angles, significantly reducing the risks of tow cable deviation, uneven coverage, and equipment entanglement. It ensures data acquisition quality and operational safety from the planning and design level. Furthermore, it eliminates the need for human experience, reducing the risk of human error and facilitating standardized management among different operation teams.

[0040] Figure 3 A schematic diagram of the structure of the optimal seismic survey line planning device based on ocean current prediction provided in an embodiment of the present invention is shown. Figure 3 As shown, the device includes: The acquisition module 310 is suitable for acquiring a set of candidate survey lines within the exploration area, as well as acquiring ocean current forecast data within a preset planning time window; The feather angle module 320 is suitable for establishing a towed feather angle prediction model and, based on ocean current forecast data, using the towed feather angle prediction model to determine the expected feather angle and risk index of each candidate survey line in the candidate survey line set at each time. Window module 330 is adapted to screen the expected feather angle and risk indicators of each candidate survey line in the candidate survey line set according to the first preset constraint conditions, and determine the feasible time window set of each candidate survey line; The results module 340 is adapted to determine the optimization results, which include the execution order and time window of each candidate survey line, based on the set of feasible time windows for each candidate survey line, using the second preset constraint conditions and the multi-objective cost function, so as to generate a set of survey line push instructions for the ship based on the optimization results.

[0041] Optionally, the acquisition module 310 is further adapted to: Obtain the boundaries of the exploration area, no-navigation zones, and / or restricted navigation zones; Obtain the original design survey lines, backup survey lines, and / or regular grid survey lines, as well as the attribute information corresponding to each type of survey line; the attribute information includes length, azimuth, priority, and coverage requirements; The various survey lines obtained are converted to the same geodetic coordinate system and projected coordinate system. Based on the attribute information, survey lines that conflict with the boundaries of the exploration area, no-navigation zones and / or restricted navigation zones are eliminated to obtain a set of candidate survey lines. Ocean current forecast data containing horizontal seawater flow velocity vectors are obtained based on a preset spatial grid, and the ocean current forecast data are corrected according to tidal models and / or flow velocity devices.

[0042] Optionally, the feather module 320 is further adapted to: A tractor feather angle prediction model is established, which is trained based on historical sample data; Using the towline feather angle prediction model, for each candidate survey line in the candidate survey line set, the ocean current forecast data is interpolated to the corresponding positions and operation times of each candidate survey line in the candidate survey line set, based on the sampling points along the line and the candidate opening time. The survey line direction unit vector and lateral unit vector of each candidate survey line are calculated. The expected feather angle of each candidate survey line in the candidate survey line set at each time is obtained according to the survey vessel's speed over water. In addition, the corresponding risk indicators are determined based on the changes in feather angle and spacing on the inner and outer sides of the towline.

[0043] Optionally, window module 330 is further adapted to: The first preset constraint is determined based on the feather angle threshold, the maximum permissible lateral offset, the tow cable spacing safety threshold, the construction period, the upper and lower limits of speed, the turning radius and / or the no-navigation zone. According to the planned time step, each candidate survey line is scanned hourly along the time axis, and for each candidate line opening time, it is determined whether the first preset constraint condition is met. If so, determine the time as the feasible time; By merging consecutive feasible moments into feasible time windows, a set of feasible time windows for each candidate survey line is obtained.

[0044] Optionally, the results module 340 is further adapted to: The second set of preset constraints are constructed based on the number of survey line executions, the feasible time window for line opening, the time conditions for transfer and turnaround between adjacent survey lines, the speed within the preset range, and the requirements for no-navigation zones and safe distances; and a multi-objective cost function is established based on the execution order, line opening time, speed, and heading fine-tuning. Based on the execution order and feasible time window set of each candidate test line, the multi-objective cost function is constrained by the second preset constraint condition. The results of the function are sorted, and the candidate test lines that are sorted first are selected as the optimization results. Based on the candidate survey lines included in the optimization results, a survey line push instruction set for the vessel is generated, and the results can be displayed. The survey line push instruction set includes the line number, start coordinates, end coordinates, suggested start time, expected completion time, suggested course, suggested course correction angle, suggested overwater speed range, expected feather angle, maximum lateral offset, risk level, and backup survey line number.

[0045] Optionally, the device further includes: a rolling module 350, adapted to update ocean current forecast data based on a preset rolling cycle, redetermine the feasible time window set for candidate survey lines, and update and optimize the results.

[0046] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments, and will not be repeated here.

[0047] This invention also provides a non-volatile computer storage medium storing at least one executable instruction that can perform the operation corresponding to the optimal seismic survey line planning method based on ocean current prediction in any of the above method embodiments.

[0048] This application provides a computer program product, which includes at least one executable instruction or computer program that enables a processor to perform the operation corresponding to the optimal seismic survey line planning method based on ocean current prediction in any of the above method embodiments.

[0049] Figure 4 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0050] like Figure 4 As shown, the computing device may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0051] in: The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0052] Communication interface 404 is used to communicate with other network elements such as clients or other servers.

[0053] The processor 402 is used to execute program 410, which can specifically execute the relevant steps in the above embodiment of the optimal survey line planning method for seismic exploration based on ocean current prediction.

[0054] Specifically, program 410 may include program code that includes computer operation instructions.

[0055] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0056] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0057] Specifically, program 410 can be used to cause processor 402 to execute the optimal seismic survey line planning method based on ocean current prediction in any of the above method embodiments. The specific implementation of each step in program 410 can be found in the corresponding steps and units described in the above embodiments of optimal seismic survey line planning based on ocean current prediction, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0058] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the present invention.

[0059] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0060] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0061] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0062] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0063] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0064] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for optimal seismic survey line planning based on ocean current prediction, characterized in that, The methods include: Acquire a set of candidate survey lines within the exploration area, and acquire ocean current forecast data within a pre-defined planning time window; A towline plume angle prediction model is established, and based on the ocean current forecast data, the expected plume angle and risk index of each candidate survey line in the candidate survey line set at each time are determined using the towline plume angle prediction model. Based on the first preset constraint, the expected feather angle and risk index of each candidate survey line in the candidate survey line set are screened to determine the feasible time window set of each candidate survey line. Based on the feasible time window set of each candidate survey line, the optimization result containing the execution order and time window of each candidate survey line is determined using the second preset constraint condition and multi-objective cost function, so as to generate a survey line push instruction set for the ship based on the optimization result.

2. The method according to claim 1, characterized in that, The acquisition of a set of candidate survey lines within the exploration area and the acquisition of ocean current forecast data within a preset planning time window further include: Obtain the boundaries of the exploration area, no-navigation zones, and / or restricted navigation zones; Obtain the original design survey lines, backup survey lines, and / or regular grid survey lines, as well as the attribute information corresponding to each survey line; the attribute information includes length, azimuth, priority, and coverage requirements; The various survey lines obtained are converted to the same geodetic coordinate system and projected coordinate system. Based on the attribute information, survey lines that conflict with the boundaries of the exploration area, no-navigation zones and / or restricted navigation zones are eliminated to obtain a set of candidate survey lines. Ocean current forecast data containing the horizontal flow velocity vector of seawater is obtained based on a preset spatial grid, and the ocean current forecast data is corrected according to a tidal model and / or a flow velocity device.

3. The method according to claim 1, characterized in that, The step of establishing a towline plume angle prediction model and, based on the ocean current forecast data, using the towline plume angle prediction model to determine the expected plume angle and risk index of each candidate survey line in the candidate survey line set at each time further includes: A tractor feather angle prediction model is established, wherein the tractor feather angle prediction model is trained based on historical sample data; Using the aforementioned towline feather angle prediction model, for each candidate survey line in the candidate survey line set, the ocean current forecast data is interpolated to the corresponding positions and operation times of each candidate survey line in the candidate survey line set, based on the sampling points along the line and the candidate opening time. The survey line direction unit vector and lateral unit vector of each candidate survey line are calculated. The expected feather angle of each candidate survey line in the candidate survey line set at each time is obtained based on the survey vessel's speed over water. Furthermore, the corresponding risk indicators are determined based on the changes in feather angle and spacing on the inner and outer sides of the towline.

4. The method according to claim 1, characterized in that, The step of screening the expected plume angle and risk indicators of each candidate survey line in the candidate survey line set according to the first preset constraint, and determining the feasible time window set of each candidate survey line, further includes: The first preset constraint is determined based on the feather angle threshold, the maximum permissible lateral offset, the tow cable spacing safety threshold, the construction period, the upper and lower limits of speed, the turning radius and / or the no-navigation zone. According to the planned time step, each candidate survey line is scanned hourly along the time axis, and for each candidate line opening time, it is determined whether the first preset constraint condition is met. If so, the stated time is determined as a feasible time; By merging consecutive feasible moments into feasible time windows, a set of feasible time windows for each candidate survey line is obtained.

5. The method according to claim 1, characterized in that, The step of determining the optimization result, which includes the execution order and time window of each candidate survey line, using a second preset constraint condition and a multi-objective cost function based on the feasible time window set of each candidate survey line, and generating a survey line push instruction set for the ship based on the optimization result, further includes: The second set of preset constraints are constructed based on the number of survey line executions, the feasible time window for line opening, the time conditions for transfer and turnaround between adjacent survey lines, the speed within the preset range, and the requirements for no-navigation zones and safe distances; and a multi-objective cost function is established based on the execution order, line opening time, speed, and heading fine-tuning. Based on the execution order and feasible time window set of each candidate test line, the multi-objective cost function is constrained by the second preset constraint condition, and the results of the function are sorted to select the candidate test lines that are sorted first as the optimization result. Based on the candidate survey lines included in the optimization results, a survey line push instruction set for the vessel is generated, and a displayable result is generated. The survey line push instruction set includes the line number, start coordinates, end coordinates, suggested start time, expected completion time, suggested course, suggested course correction angle, suggested overwater speed range, expected feather angle, maximum lateral offset, risk level, and backup survey line number.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Based on the preset rolling cycle of ocean current forecast data, the feasible time window set for candidate survey lines is redefined, and the results are updated and optimized.

7. A seismic exploration optimal survey line planning device based on ocean current prediction, characterized in that, The device includes: The acquisition module is suitable for acquiring a set of candidate survey lines within the exploration area, as well as acquiring ocean current forecast data within a preset planning time window; The feather angle module is suitable for establishing a towed feather angle prediction model, and for using the towed feather angle prediction model to determine the expected feather angle and risk index of each candidate survey line in the candidate survey line set at each time based on the ocean current forecast data. The window module is adapted to filter the expected feather angle and risk index of each candidate survey line in the candidate survey line set according to the first preset constraint conditions, and determine the feasible time window set of each candidate survey line. The results module is adapted to determine the optimization results, which include the execution order and time window of each candidate survey line, based on the feasible time window set of each candidate survey line and using a second preset constraint condition and a multi-objective cost function, so as to generate a survey line push instruction set for the ship based on the optimization results.

8. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the optimal seismic survey line planning method based on ocean current prediction as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the optimal seismic survey line planning method based on ocean current prediction as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes at least one executable instruction that causes the processor to perform the operation corresponding to the optimal seismic survey line planning method based on ocean current prediction as described in any one of claims 1-6.

Images