High-resolution imaging of subsurface infrastructure using deep learning artificial intelligence on drone magnetometry

Abstract

The use of drones fo r geophysical data acquisition and artificial intelligence (AI) for geophysical data processing, imaging, and interpretation are active focus areas in current industry and academic applications. Unlocking their cumulative potential in single-focus applications can have a transformative impact, possibly leading to dramatic cost reductions in key use cases and new application areas for enhanced actionable business intelligence. We present field study results from Texas and California that show the potential for imaging pipelines and other subsurface infrastructure by using AI-based methods on high-resolution aboveground magnetic data. The superior resolution and interpretability over conventional geophysical inversion is demonstrated. The method has the potential to provide actionable intelligence in several business-use cases for detecting and characterizing pipelines, crossing zones for multiple pipes, etc. at dramatically reduced costs. The advanced algorithms and workflows used resulted in a 100-fold increase in efficiency and delivered results in two days compared to what could take several months using generally available open-source deep learning AI workflows and software. Future direction of development is to validate against excavation-/drill-bit-/inline-tool-based ground truth and further extend and develop this process to deliver near real-time results. The techniques used are general and can be applied to other geophysical data including seismic, electromagnetic, and gravity at various scales and resolution.