Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

• Published in: Journal of geophysical research. Machine learning and computation Vol. 2; no. 3
• Main Authors: Nolte, David D., Pyrak‐Nolte, Laura J., Hopp, Chet
• Format: Journal Article
• Language: English
• Published: 01.09.2025
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2025JH000657

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation. The transmission and reflection of seismic waves propagating across fractures in rock depend on fracture properties related to how easily fluids flow through the fractures. Therefore, by monitoring seismic data, it may be possible to predict fluid flow in geothermal systems. However, the seismic signatures are complicated, which require novel data analysis to extract evolving fracture properties, such as a new machine learning technique developed for continuous active seismic monitoring. Seismic waves reflecting or transmitting across fractures provide information on changing fluid saturations within the fractures Machine learning is applied to continuous active seismic source monitoring of evolving fracture properties Evolving fracture properties can be detected using unsupervised machine learning with dimensionality reduction