Submarine Signal Identification via Image Classification Applications

Based on Patent Research | CN-116699703-A (2024)

Submarine electromagnetic exploration relies on constant data acquisition. This continuous operation creates massive data redundancy and drains battery life from subsea stations. Image classification addresses this by using a convolutional neural network to identify start and stop signals in data patterns. This artificial intelligence method categorizes entire visual spectra to automate system triggers. This approach reduces unnecessary power use. It also extends deployment times for remote sensors and improves the efficiency of data collection processes.

SOLUTION OVERVIEW

Modernizing Manual Identification with AI Classification

Image classification technology serves as a vital tool for oil and gas professionals by automating the detection of critical data signals. The process begins when sensors capture raw underwater electronic activity and convert it into a visual waterfall pattern. A convolutional neural network then examines these visual representations to identify specific signal markers. By categorizing the entire visual field into predefined classes, the system determines exactly when to activate or deactivate data recording without requiring human intervention.

This automated approach integrates seamlessly with subsea exploration workflows to eliminate manual monitoring and excessive battery drain. It functions much like a smart thermostat that only activates a heating system when it senses a specific temperature drop, ensuring energy is never wasted on an empty room. By focusing power usage on relevant events, the technology facilitates longer sensor deployments and higher quality data sets. This shift toward intelligent sensing provides a more sustainable and reliable method for deep-sea resource mapping.

HOW IT WORKS

Submarine Image Processing for Classification

Transforming Raw Signals Into Visual Patterns

Subsea sensors continuously monitor the ocean floor to capture raw electromagnetic activity during resource mapping. This raw data is immediately converted into a two-dimensional waterfall spectrum pattern, which effectively turns invisible electronic signals into a visual representation for processing.

Identifying Strategic Features Using Neural Networks

The system employs a convolutional neural network to scan these visual waterfall patterns for specific start and stop markers. This advanced classification process detects unique signatures within the visual field, allowing the AI to distinguish between background noise and critical data signals without human oversight.

Automating Sensor Activation To Conserve Energy

Once the software identifies a signal marker, it triggers the subsea station to either begin or end the data recording process. This automated control ensures that energy is used only for relevant events, preventing unnecessary battery drain and allowing sensors to remain deployed in remote locations for much longer periods.

Potential Benefits

Extended Remote Sensor Life

By automating the start and stop of recording, the system significantly reduces battery drain on subsea stations. This efficiency allows sensors to remain deployed in deep-sea environments for much longer durations.

Optimized Data Storage Usage

The AI filters out redundant noise and only captures high-value electromagnetic signals. This targeted approach prevents massive data redundancy and ensures that storage capacity is dedicated to relevant exploration data.

Automated Operational Decision Making

Image classification removes the need for constant human monitoring of signal patterns. The neural network identifies critical triggers automatically, streamlining subsea workflows and reducing the potential for manual oversight errors.

Higher Quality Resource Mapping

Focusing data acquisition on specific visual markers leads to cleaner and more accurate datasets. These high-quality results provide oil and gas professionals with more reliable information for mapping underwater resources.

Implementation

1 Deploy Subsea Sensors. Install electromagnetic sensors on the ocean floor to monitor raw electronic activity and establish constant data streams.

2 Configure Waterfall Processing. Set up the software interface to convert raw electromagnetic signals into two dimensional waterfall spectrum patterns for analysis.

3 Integrate Neural Network. Load the pre-trained convolutional neural network onto the subsea station to identify specific start and stop markers.

4 Automate Trigger Systems. Connect the AI classification output to the recording hardware to enable automatic system activation and deactivation.

5 Establish Power Protocols. Define energy conservation parameters to ensure the system remains in a low power state when no markers are detected.

Source: Analysis based on Patent CN-116699703-A "Submarine electromagnetic acquisition station awakening method based on convolutional neural network" (Filed: August 2024).

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