Professor Amit Das, The ICFAI University, Dehradun, India
The Indian Ocean (Figure 1) holds vast strategic, economic, and ecological importance in the modern world. It is a vital platform for global trade routes, energy transport, blue economy, and military operations, required to maintain the comprehensive surveillance over its vast expanse. This region is becoming a top priority for coastal and island nations. However, the Indian Ocean’s unique geography, including deep trenches, rugged seafloor topography, and dynamic current systems, presenting significant challenges for traditional monitoring techniques. In this context, acoustic surveillance has emerged as a powerful tool, exploiting the ability of sound to travel long distances underwater with relatively low attenuation.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into acoustic surveillance systems has revolutionized the ability to detect, classify, and predict underwater signatures and activities across the Indian Ocean. It enhances both security and environmental in that region.
The Acoustic Surveillance relies on the deployment of sequence of hydrophones, sonar arrays, and autonomous underwater vehicles (AUVs) to capture sounds produced by natural phenomena, marine life, and human-made objects such as ships and submarines.
This process generates the high volume complex acoustic data accumulated from different sources and dimensions. It is very tough or impossible for the human analyst to intercept the hidden information or knowledge from the gathered acoustics data in real-time approach. This challenge could be addressed with the help of AI (Artificial Intelligence) and ML (Machine Learning) technologies. The effective integration of advanced intelligent algorithms and AI Systems can autonomously or independently intercept the high-volume acoustics data. The AI-Enabled Systems distinguish data collected from the between different sound sources and identify the encapsulated patterns otherwise those patterns will be hidden in the provided data. In ML, the supervised learning models can be trained to recognize the acoustic signatures of specific vessel classes or submarines or marine species by analysing labelled datasets. Once trained, these models can rapidly classify and report the new acoustic events in real time. It provides the active and effective situational awareness occurred across the Indian Ocean.
One of the most significant advantages of applying AI/ML to acoustic surveillance is anomaly detection. The Indian Ocean is known for the diverse range of acoustic sources, from migratory whales to deep-sea earthquakes, to fishing fleets and naval vessels. ML models, particularly unsupervised learning techniques, can be used to establish baseline acoustic profiles for different regions of the ocean. Any deviation from this baseline can be easily identified and flagged automatically by the deployed ML Models.
It enables early detection of unusual activities or potentially threatening activities in Indian Ocean such as the movement of unauthorized submarines or illegal fishing operations. The signature of unauthorised submarines could be traced in real-time from the gathered acoustics data. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are particularly effective in parsing time-series acoustic data, recognizing subtle features that are often imperceptible to traditional analysis methods.
AI-powered acoustic surveillance contributes significantly to the broader effort of maritime domain awareness (MDA) in the Indian Ocean. Day-by-Day the geopolitical competition is exponentially growing in this region and ability of early detection and monitor of unusual naval movement are required for the strategic implementations and national security.
AI-enhanced acoustic sensors deployed on seabed arrays or mobile platforms can create a distributed surveillance network capable of continues monitoring of the different parts of the Indian Ocean. These smart networks can autonomously prioritize sensor data and reduce the communication burdens and forward critical information to be relayed promptly to active command centres simultaneously. This real-time intelligence-gathering capability is vital for countries bordering the Indian Ocean, many of which lack the resources to maintain large naval fleets for patrolling their waters.
The AI/ML is creating a transformative effect for the acoustic source localization. The traditional approach of triangulating the position of a sound source require sophisticated sensor deployments and extensive computational resources with huge programming codes. Machine learning models, however, can infer source locations with fewer measurements by learning the statistical relationships between sound arrival times and geographical positions. This has direct applications in tracking the movement of submarines, surface ships, and even autonomous underwater vehicles across vast stretches of the Indian Ocean. ML algorithms can adapt dynamically to suddenly occurred environmental changes, such as varying salinity and temperature profiles, which affect sound propagation and often degrade the performance of classical localization techniques.
Environmental monitoring also benefits enormously from AI/ML-driven acoustic surveillance in the Indian Ocean. Marine ecosystems in the region face threats from shipping noise, oil exploration, and climate change. Passive acoustic monitoring (PAM) systems equipped with AI models can continuously track the presence and migration patterns of various marine species. By automating the identification of species-specific vocalizations, conservationists can obtain high-resolution datasets on population dynamics and habitat use without intrusive survey methods. The AI models can detect seismic activity on the ocean floor, providing valuable early warnings for natural disasters like tsunamis, which can devastate coastal communities bordering the Indian Ocean.
Despite its enormous potential, integrating AI/ML into acoustic surveillance systems across the Indian Ocean presents big technical and operational challenges. Training robust machine learning models requires access to large, high-quality datasets that capture the diversity of acoustic environments found across the ocean’s many sub-regions. However, labelled datasets for underwater acoustics are often limited, and gathering new data is complex and resource-intensive activity. Transfer learning and data augmentation techniques are being explored to overcome these limitations, allowing models trained in one region or on one type of sound to generalize to new conditions with minimal retraining. In addition, maintaining a distributed network of acoustic sensors in a harsh marine environment requires innovative solutions for power supply, data transmission, and sensor maintenance, many of which are being addressed through advances in energy-harvesting technologies and satellite communications.
Ethical and legal considerations also arise with the deployment of AI/ML-based acoustic surveillance. There is a delicate balance between ensuring national security and respecting the rights of other nations to freedom of navigation in international waters. Furthermore, monitoring marine life raises questions about data privacy and the unintended consequences of constant human observation of natural ecosystems. Addressing these concerns will require clear policies, international cooperation, and the responsible use of AI technologies in line with globally recognized frameworks.
The future of AI/ML in acoustic surveillance across the Indian Ocean is incredibly promising. Advances in edge computing will allow more data processing to be done directly on sensing platforms, reducing latency, and increasing the autonomy of underwater surveillance systems. The continues learning approaches, where AI models are trained collaboratively without centralized data sharing, could protect sensitive information while still enhancing model performance. As acoustic sensor technology continues to evolve and AI algorithms become more sophisticated, it is foreseeable that the Indian Ocean will be mapped and monitored with unprecedented resolution, ushering in a new era of maritime situational awareness. In the long term, these innovations will not only strengthen the security posture of Indian Ocean nations but will also contribute to the sustainable management of one of the world’s most important and dynamic marine regions.