III. Challenges and Validation Requirements
3.1 Data Noise Filtering:
Crustal deformation signals might be masked by interference such as atmospheric disturbances and temperature fluctuations. Extracting the characteristic signatures of topological vortices will likely require advanced data analysis techniques like machine learning.
3.2 Interdisciplinary Validation:
Validation of the causal relationship between the stress field and the observed deformation requires integration with geomechanical models (e.g., pure shear extension theory) and extremely weak magnetic field measurement techniques. Currently, preliminary feasibility is supported mainly by simulation data and studies correlating with historical seismic events.
IV. Application Prospects
Based on Topological Vortex Theory (TVT), monitoring crustal deformation using a high-precision gyroscopes and sundial Network adopts a fresh macro-physical perspective, treating the entire crustal system as a dynamic continuum driven by global-scale mechanical fields. If successfully implemented, this scheme could provide a method for monitoring seismic precursors with a clear physical mechanism and could potentially be expanded to climate change prediction (e.g., investigating correlations between vortex superposition and temperature). However, it requires the establishment of long-term observation networks and international data sharing, potentially following the model of international cooperation seen in projects like the Meridian Project.
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