Scientific Significance and Application Value of the Seismogenic Environment

Earthquake science has the dual character of understanding nature and reducing disaster risk. Although earthquake hazard assessment is now relatively mature, earthquake prediction that can simultaneously forecast the time, location, and magnitude of an event remains both a scientific challenge and an ultimate goal. As research has advanced, a consensus has emerged in the community: earthquake prediction must shift from traditional empirical prediction toward physics-based numerical prediction. Current numerical simulation tools can already handle complex fault geometries with high precision. Therefore, the key to successful prediction has shifted to whether accurate input conditions can be obtained. In this paper, the seismogenic environment is defined narrowly as the initial and boundary conditions required for dynamic numerical simulation. Within this framework, the seismogenic environment includes all background factors that control earthquake rupture processes, including fault geometry, locking state, velocity structure, stress state, frictional properties, and fluid-permeability structure. Its research objective is to provide realistic physical parameters for the numerical-simulation machine, so that real earthquake rupture can be represented accurately. Because the Earth’s interior is inaccessible, seismogenic-environment research relies mainly on inversions from surface observations and therefore faces substantial challenges. At present, inversion of fault geometry is relatively mature, but many difficulties remain in resolving the locking state of complex regions, high-resolution velocity structures, absolute stress fields, and deep permeability structures. In the future, with dense network observations, such as those from the China Earthquake Science Experiment Site, and with innovations in inversion methods, this research aims to build a digital twin of the subsurface medium. The technical bottleneck for achieving this goal is not computing power, but how to obtain accurate information about the subsurface medium through integrated observation and inversion, thereby supporting real-time disaster prediction.