Bayesian optimal experimental design for fracture imaging

Fracture imaging is a pivotal technique in a variety of fields including Carbon Capture, Utilization, and Storage (CCUS), geothermal exploration, and wasterwater disposal, essential for the success of the field operation and seismic hazard mitigation. However, accurate fracture imaging is challenging due to accurate fracture imaging is challenging due to the complex nature of subsurface geology, the presence of multiple overlapping signals, and the variability of fracture sizes and orientations. Additionally, limitations in the resolution of current imaging technologies and the need for high-quality data acquisition further complicate the process.

To address these challenges, we have conducted fracture imaging experiments utilizing acoustic sensors in laboratory-scale specimens with varied fracture geometries.A dynamic acquisition system involving robotic arms have been developed, enabling the flexible positioning of sensors on any part of the specimen's surface.This not only significantly reduces the time and resources required for experiments but also increases the adaptability of the process to different surface topography of specimens and fracture geometries.

In addition, we employ Bayesian optimization algorithms to enhance the efficiency of sensor placement in laboratory-scale specimens, aiming to achieve precise fracture imaging with the least number of measurements necessary. This algorithmic approach optimizes the data collection process, ensuring that we gather the most relevant and accurate information with minimal intrusion. The collected data is then rigorously compared and calibrated against findings from numerical simulations, which helps in refining the algorithm for broader applications.