Combining Graphic Data Analysis and Mechanical-Statistical-Physical Models in Overcoming the Uncertainties of Laboratory Rock Mechanics Investigation

This research looks at how energy is released when rocks crack, using both graphic data analysis and mechanical‑statistical‑physical models. When rocks fail, they emit measurable energy bursts that follow scale‑invariant patterns. Those patterns help link small‑scale lab tests to large‑scale field observations. Each energy burst is turned into a node in a complex network, applying graph theory. This creates a continuous picture of where and when the rock degrades. The study examines critical behaviors such as the buildup of large‑magnitude clusters and the relationship between focal mechanisms of event pairs, all through the network’s shape. To reduce the usual uncertainties in lab rock‑mechanics work, the project looks at higher‑order connections

in the network. This checks whether the physical trends we see are reliable. The framework gives a clear structure for: – modeling how fractures grow, – interpreting acoustic‑emission data, and – building predictive tools for failure in heterogeneous geological materials. Overall, the approach supports the creation of advanced statistical‑physical models that overcome experimental limits and improve the trustworthiness of lab‑scale fracture‑mechanics results.

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