Key information

Title
System and Method for Detection, Early Warning, and Precise Localization of Threats or Damage to Subsea Infrastructure
Application number
20250183
Case type
National
Status
18.02.2025 Første realitetsuttalelse foreligger
Filed
18.02.2025
Divided to
20250322
Effective date
18.02.2025
Publicly available
19.08.2026
Next annual fee due
28.02.2027
Applicant
VESPER AS (NO)
Inventor
ALEXANDER M RAFAELSEN (NO)

Abstract and drawing

Disclaimer: This text has been machine-scanned and may contain errors – please refer to "Publications" for legally binding content.
The invention provides a system and method for real-time detection, early warning, and precise localization of threats or damage to subsea infrastructure, including but not limited to oil and gas pipelines, fiber-optic cables, and power transfer lines. The system employs coiled wires integrated into or mounted near subsea infrastructure, leveraging pulse induction, beat frequency oscillation, and low-frequency oscillation techniques. A segmented detection network, combined with machine learning algorithms, enables realtime threat identification, material discrimination, and damage localization. Communication uses CAN-bus or similar protocols, and power can be drawn from subsea powerlines, ensuring efficient operation.

Publications

A1
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Documents

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Date
 Date Doc. No. Process Case number In/Out Journal description To/from
 06.01.2026 04-03 Saksbehandling 20250183 INN Response to Office Action – NIPO VESPER AS
 06.01.2026 04-02 Saksbehandling 20250183 INN Bilag til Tilsvar - Claims Redline VESPER AS
 06.01.2026 04-01 Saksbehandling 20250183 INN Korrespondanse (Hovedbrev inn) VESPER AS
 18.09.2025 03-02 Saksbehandling 20250183 UT PT report 01:54:37
18.09.2025 03-01 Saksbehandling 20250183 UT Substantive examination VESPER AS
 19.02.2025 02-01 Saksbehandling 20250183 UT Informasjon til oppfinner ALEXANDER M RAFAELSEN
 18.02.2025 01-06 Saksbehandling 20250183 INN Rafale only Illustrations VESPER AS
 18.02.2025 01-05 Saksbehandling 20250183 INN Rafale Final illustrated 18feb25 VESPER AS
 18.02.2025 01-04 Saksbehandling 20250183 INN Rafale Final illustrated 18feb25 VESPER AS
 18.02.2025 01-03 Saksbehandling 20250183 INN Rafale Final illustrated 18feb25 VESPER AS
 18.02.2025 01-02 Saksbehandling 20250183 INN erklaering_rett_oppfinnelsenb VESPER AS
 18.02.2025 01-01 Saksbehandling 20250183 INN Søknadsskjema Patent VESPER AS

Description, claims and drawings

Disclaimer: This text has been machine-scanned and may contain errors – please refer to "Publications" for legally binding content. Description
(Title:)System and Method for Detection, Early Warning, and Precise Localization of Threats or Damage to Subsea Infrastructure1. Field of the Invention:The present invention relates generally to the field of subsea infrastructure monitoring.More particularly, it relates to a system and method for real-time detection of threats or damage to subsea pipelines, fiberoptic communication lines, and power transmission cables collectively referred to as “subsea infrastructure” involving a detection network using coiled wires and advanced electromagnetic techniques.The invention provides an early warning mechanism, precise positioning of potential damage points or threats, and immediate notification to shoreside controllers.2. Background of the Invention:Subsea infrastructure is critical for global energy and communication needs. Examples include oil and gas pipelines, fiberoptic cables, and subsea power cables.These infrastructures are often and increasingly subject to various threats, both accidental (e.g., ship anchors or fishing gear dragging along the seabed) and intentional (e.g., sabotage, explosive charges).Damage to such infrastructure can result in massive financial losses, environmental hazards, and disruption of essential services.Conventional monitoring techniques rely primarily on indirect indicators such as pressure loss (for pipelines), signal degradation (for fiberoptic cables), or power fluctuations (for electrical lines). These methods often fail to provide immediate alerts and exact coordinates of a threat or a breach. Furthermore, they do not consistently distinguish between benign marine operations and actual malicious or accidental interference close to the subsea infrastructure.Therefore, there is a need for an advanced, real-time monitoring system that provides early warning, identifies the nature of the threat, locates the precise point of damage or intrusion, and integrates smoothly with existing subsea infrastructure installations while leveraging advanced electromagnetic techniques, modern data communication methods and machine learning to enhance system capabilities. 3. Summary of the Invention:The invention disclosed herein addresses the above-mentioned challenges by introducing a novel method and system for real-time detection and monitoring, employing coils, sensors, and advanced signal processing techniques to sense metallic or inductive objects and detect physical damage along subsea infrastructure. The system provides:1. Real-Time Detection & Threat Classification:A network of sensors wrapped around, molded into, or placed in proximity to pipelines, power cables, or fiberoptic cables. These sensors use pulse induction, beat frequency oscillation (BFO), and/or multi-frequency electromagnetic signals (ELF/ULF/VLF/MF) to detect anomalies. Machine learning algorithms classify the type of threat—e.g., anchor, submarine, diver, or ROV/AUV.2. Precise Localization of Damage:The system’s segment-based architecture allows immediate identification of the location where the sensor circuit is broken or disturbed. Each segment is calibrated to a baseline electromagnetic value; deviations generate real-time alarms.3. Networked Segments:Individual sensor segments are connected in a continuous network. A broken or damaged segment pinpoints a breach location instantly, as signals from shore-based controllers cannot pass beyond the compromised segment.4. Self-Powered or Externally Powered:The sensor system can draw power from existing subsea power lines where feasible, or operate via dedicated communication and power lines. Communication may employ CAN-bus or other protocols over the detection circuit itself, reducing the need for separate infrastructure.5. Scalability & Flexibility:A single detection segment can simply indicate intact/broken status, while multiple segments significantly enhance resolution, enabling high-precision positioning of threats. The system can be integrated into new installations from the outset, be retrofitted on top of existing infrastructure or installed as a standalone structure.6. Machine Learning:Algorithms improve detection sensitivity, enabling object identification, material classification, and determination of proximity, velocity, and trajectoryBy providing immediate alerts, classifying the type of threat, and determining the exact breach location, the invention mitigates risks, aids rapid response, simplifies attribution efforts and reduces downtime in critical subsea infrastructure. 4. Brief Description of the Drawings:• FIG. 1: Schematic view of a subsea pipeline or cable with an integrated sensor coil system. -Page 11• FIG. 2: Block diagram illustrating signal flow, from sensor coils to shoreside controller. -Page 12• FIG. 3: Diagram depicting segment-based architecture and methods of identifying the precise break or intrusion location. -Page 13• FIG. 4: Flowchart summarizing the threat detection and classification process using machine learning algorithms. -Page 14• FIG. 5: Diagram of powerline-integrated detection, where sensors draw power directly from the subsea electrical cable. -Page 15(Preliminary drawings are included here but may be subject to change with formal patent filing.) 5. Detailed Description of the Invention:5a: System OverviewThe invention comprises a sensor network arranged along subsea pipelines, fiberoptic communication lines, or power cables. The core components include:• Sensor Coils: Coiled conductive wires (e.g., copper or other suitable alloys) that are either molded into or wrapped around the pipeline or cable. They can also be added as a superstructure positioned in close proximity, especially for retrofitting on existing infrastructure.• Segmented Architecture: The sensor coils are divided into multiple segments. Each segment can act independently, providing localized data on environmental electromagnetic variations. When arranged end-to-end along the subsea infrastructure, these segments form a continuous detection system that identifies the precise segment experiencing a disturbance or break.• Signal Generation & Detection: Pulse induction, beat frequency oscillation (BFO), and/or low-frequency to multi-frequency electromagnetic signals (e.g., ELF, ULF, VLF, MF) are employed to detect the presence of metallic or otherwise inductive objects. Each sensor segment is calibrated in situ to a local baseline. Deviations from this baseline trigger alerts.• Local Electronics Module: Each segment contains (or is connected to) a small electronics package comprising:• An exciter/receiver unit to send out the detection signals.• A controller unit (microcontroller or similar computing element) that processes data locally, adjusts calibration, and communicates with the larger network.• A communication interface (wired CAN-bus, Ethernet, or similar protocol) that sends real-time information to a shoreside controller and/or remote monitoring stations.• Power Supply: Power can be derived from the infrastructure itself, such as in the case of subsea power cables, or from dedicated auxiliary lines in the case of oil/gas pipelines. If neither is feasible, battery or renewable energy solutions (e.g., wave or current-based energy harvesters) may be employed. 5b: Calibration and Machine Learning• Baseline Calibration: During installation, each sensor segment is “ground balanced” and calibrated in situ to account for local magnetic or electromagnetic influences. This process ensures that each segment knows its “normal” electromagnetic environment.• Machine Learning Algorithms: Data collected from each segment is analyzed by a machine learning model (e.g., a neural network or random forest algorithm) that has been trained to distinguish between benign marine objects (e.g., fish schools, drifting debris) and potential threats (e.g., anchors, AUV/ROVs, divers, torpedoes). Enhanced analysis of signal patterns may also determine the type of object, material composition, distance, speed, and trajectory. The system can update itself over time as it gathers more data and refines its threat classification accuracy5c: Threat Detection• Real-Time Monitoring: The segments continuously emit and receive signals, comparing real-time readings to baseline values. Significant deviations trigger alerts. • Threat Classification: The onboard processing or a centralized shoreside server classifies the detected object. For example, if the system detects a slow-moving metallic signature that matches that of an anchor, it raises an “anchor threat” alert.• Immediate Action: If a threat is recognized, the system can notify operators, automatically shut down the pipeline (if applicable), or prompt emergency procedures such as adjusting anchor drop zones, dispatching protective measures, or activating assets to immediately investigate and identify surface vessels or other perpetrators.5d: Damage Localization• Broken Segment Identification: In the event of physical damage (e.g., a pipeline rupture, cable cut), the sensor circuit in that segment is interrupted. Shoreside controllers receive data from each consecutive segment. Because signals cannot propagate beyond the break, the specific damaged segment is immediately identified.• Coordination & GPS: The known location of each sensor segment, combined with GPS coordinates at the start and end of each segment (stored during installation and calibration), allows for precise localization of the break or threat within a short distance. 5e: Communication Methods• Integrated Communication Channel: Communication between segments and the main control center can be carried out on the same conductive lines used for detection signals, using a robust data protocol (e.g., CAN-bus, powerline communication, or other specialized protocols).• High-Resolution Data Reporting: Each segment periodically reports status data (operational, calibration level, detected anomalies). If an anomaly is detected, it immediately switches to high-frequency reporting mode for prompt analysis.5f: Retrofit Installation vs. New Construction• Retrofit: For existing pipelines or cables, the coiled wires can be placed within a secondary protective sheath, or mounted externally using clamps or protective housings. Segments are then joined via specialized connectors that maintain watertight integrity.• New Construction: For newly constructed pipelines or cables, the sensor coils and electronics can be integrated directly into the layers of the pipeline or cable. In this scenario, the invention benefits from maximum protection and ease of calibration during the manufacturing process.
Claims
6. Claims:1. A system for real-time subsea infrastructure monitoring, comprising:a. A plurality of sensor segments, each containing coiled wires configured to detect electromagnetic disturbances;b. A signal generation and detection unit associated with each segment, employing at least one technique selected from the group consisting of pulse induction, beat frequency oscillation (BFO), and multi-frequency electromagnetic signals;c. A communication interface linking said segments in a network, wherein data indicative of electromagnetic deviations is transmitted to a shoreside controller; and d. A processor or machine learning engine that analyzes received signals to classify potential threats.2. The system of claim 1, wherein each sensor segment is calibrated to a baseline electromagnetic profile, and deviations from this profile trigger an alarm indicative of a threat or anomaly.3. The system of claim 1, wherein each sensor segment further comprises a breakdetection mechanism that identifies a physical cut or rupture in the subsea infrastructure by detecting a discontinuity pinpointing the last active segment in the sensor circuit.4. The system of claim 1, further comprising an onboard energy source or means to draw energy from the subsea infrastructure to power the signal generation and detection unit.5. The system of claim 1, wherein the coiled wires are integrated into or wrapped around a subsea pipeline, fiberoptic cable, or power transmission line, thus forming a continuous detection loop from one segment to the next.6. The system of claim 1, wherein the communication interface employs a data protocol selected from the group consisting of CAN-bus, Ethernet, powerline communication, or proprietary protocols capable of operating over the detection circuit itself.7. The system of claim 1, wherein the machine learning engine uses one or more algorithms selected from the group consisting of neural networks, support vector machines, random forest, or gradient boosting techniques to distinguish between benign objects and potential threats.8. The system of claim 1, wherein the threat classification system identifies objects such as ship anchors, submarine vehicles, remotely operated or autonomous vehicles (ROV/AUVs), divers, underwater drones, or explosive devices based on electromagnetic signature patterns.9. The system of claim 1, wherein each sensor segment’s location is tied to GPS coordinates stored in a database, allowing the system to provide precise coordinates for potential damage or threat detection.10. The system of claim 1, further comprising a shoreside controller configured to display real-time sensor data, threat notifications, and damage location, enabling operators to take immediate remedial action.11. The system of claim 1, wherein the system operates in a standalone configuration mounted near or onto existing subsea infrastructure or at significant points of interest where a heightened awareness or surveillance is deemed necessary or beneficial. (i.e. oil rigs, harbor entrance, aquaculture, military installations)12. The system of claim 1, wherein the system components can be utilized as send, and or, receive antennae for underwater radio communication and navigation.13. The system of claim 1, wherein the system components can be utilized as electrical power supply points.14. The system of claim 1, wherein the system components can be utilized as as twoway data communication points.15. A method of detecting threats to and damage in subsea infrastructure, comprising the steps of:a. Integrating or attaching sensor coils to a subsea pipeline, fiberoptic cable, or power cable in a segmented configuration;b. Generating electromagnetic signals and measuring return signals using pulse induction, BFO, or a multi-frequency approach;c. Comparing measured signals against baseline calibration values to detect anomalies indicative of metallic or inductive objects;d. Classifying said detected anomalies using a machine learning algorithm; and e. Identifying and localizing damage by detecting a discontinuity in the segmented sensor circuit.16. The method of claim 15, further comprising the step of alerting a shoreside control station upon detecting a deviation exceeding a predefined threshold.17. The method of claim 15, further comprising the step of adjusting baseline calibrations using updated data from normal operating conditions and recognized benign objects, thereby refining detection sensitivity.18. The method of claim 15, further comprising the step of providing localized power to each segment from the subsea infrastructure’s power lines or an auxiliary power source.19. The method of claim 15, wherein the step of classifying anomalies includes distinguishing between slow-moving objects (e.g., anchors) and fast-moving threats (e.g., torpedoes) based on signal frequency shifts and other discernible electromagnetic parameters.20. The method of claim 15, wherein machine learning algorithms analyze detection data to infer or extrapolate object type, material composition, distance, speed, and trajectory.21. The method of claim 15, wherein coiled conductive structural components in existing subsea infrastructure is repurposed or utilized for monitoring purposes as outlined.
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Patent classes

IPC classesG01N 22/02G01S 5/16G01M 3/24F17D 5/00F17D 5/02H04W 4/38G06N 20/00G01R 31/08CPC classesG01N 22/02G01S 5/16G01M 3/243F17D 5/005F17D 5/02H04W 4/38G06N 20/00G01R 31/08

Citations

CN 118705550 A (A)CN 114236315 A (A)US 2023102846 A1 (A1)

Applicants, owners and inventors

Applicants
VESPER AS
VESPER AS

Org. number: 934376587

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Ekornveien 7B 1444 DRØBAK NO (FROGN Municipality, Akershus county)Org. number: 934376587Inventors
ALEXANDER M RAFAELSEN
Glosliveien 14 1443 DRØBAK NO (FROGN Municipality, Akershus county)

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