AI-assisted triage frameworks to rank suspected subsurface flow incidents

AI-assisted triage frameworks bring structured, data-driven methods to rank suspected subsurface flow incidents so that field crews and asset managers can focus limited resources on the most credible anomalies. These frameworks ingest heterogeneous inputs — from acoustic leak noise and thermal gradients to fiberoptic temperature profiles and pressure excursions — then score and rank incidents through calibrated models. The approach reduces redundant surveys and clarifies when targeted excavation or prolonged monitoring is warranted, without relying on any single sensor type.

Acoustics and localization

Acoustic sensors remain a primary input because leak-generated flow noise is often one of the earliest detectable signals. Arrays of microphones or pipe-mounted accelerometers can localize a sound source by time-of-arrival and amplitude differences. When integrated into an AI triage layer, acoustic detections are weighted against background noise patterns and historical false positives. Localization outputs are combined with mapping data to propose candidate excavation locations and to reduce the search area for follow-up surveys.

Thermography and mapping

Thermography offers a complementary view by highlighting temperature anomalies caused by moisture movement or fluid escape. Aerial or ground-based thermal surveys can be overlaid on GIS maps to reveal linear patterns consistent with subsurface flow. AI models trained on thermographic signatures can help differentiate thermal noise from meaningful anomalies, and mapping layers provide context such as pipeline routes, soil types, and surface drainage that affect interpretation and prioritization.

Fiberoptic monitoring

Fiberoptic sensing technologies, such as distributed temperature sensing (DTS) and distributed acoustic sensing (DAS), provide continuous, high-resolution profiles along assets. These data streams detect localized temperature shifts and acoustic patterns over long distances, enabling time-series analysis and anomaly detection. In a triage framework, fiberoptic alerts can trigger rapid re-scoring of suspected incidents and guide teams to precise segments for targeted inspection, reducing the need for broad-area surveys.

Pressure sensors and surveys

Pressure transients and gradual deviations in network pressure can signal flow anomalies in buried systems. AI-assisted triage incorporates pressure trends from remote telemetry with periodic survey results to refine incident scores. Field surveys — whether handheld instruments, drone inspections, or targeted ground-penetrating radar — are scheduled based on ranked urgency. Combining pressure metrics with acoustic and thermal evidence improves confidence in prioritization and helps plan the scope and timing of excavation when required.

Sensor calibration and data fusion

Reliable triage depends on calibrated sensors and robust data fusion. Calibration aligns sensor outputs with known baselines and accounts for environmental factors, reducing false positives. Data fusion algorithms normalize inputs from acoustics, thermography, fiberoptic systems, pressure sensors, and surveys, then apply weighting rules and learned models to generate a unified risk score. Regular recalibration and validation against confirmed excavation results strengthen model performance over time and support adaptive thresholds for monitoring.

Triage, excavation, and monitoring

The final stage translates ranked incidents into action: immediate excavation for high-confidence events, targeted surveys for medium-priority cases, and continued monitoring for low-scoring anomalies. Excavation remains the definitive validation step; triage frameworks aim to minimize unnecessary digs by improving localization and confidence before committing crews. Post-excavation findings feed back into the AI models to refine calibration, reduce repeat false positives, and enhance long-term monitoring strategies across the asset base.

In summary, AI-assisted triage frameworks provide a structured path from raw sensor outputs to prioritized field actions. By combining acoustics, thermography, fiberoptic data, pressure measurements, mapping, and calibrated sensor fusion, these systems support efficient surveys, more accurate localization, and better-informed excavation decisions. Continuous monitoring and feedback loops help maintain model accuracy and adapt priorities as conditions change.