The dilatancy-diffusion hypothesis, earthquake prediction, and operational earthquake forecasting: In memory of Professor Amos Nur on the 50th Anniversary of the 1975 Haicheng Earthquake

Abstract

Dilatancy is referred to the phenomenon of volume increase that occurs when a material is deformed. Dilatancy theory originated in geomechanics for the study of the behavior of granular materials. Later it is expanded to the case of more brittle materials like rocks when it is subjected to the load of varying effective stress and starts to crack and deform, then named the dilatancy-diffusion hypothesis. This hypothesis was developed to explain the changes in rock volume and pore pressure that occur prior to and during fault slip, which can influence earthquake dynamics. Dilatancy-fluid diffusion is a significant concept in understanding the seismogenic process and has served as the major theoretical pillar for earthquake prediction by its classic definition. This paper starts with the recount of fundamental laboratory experiments on granular materials and rocks, then conducts review and examination of the history for using the dilatancy-diffusion hypothesis to interpret the ‘prediction’ of the 1975 Haicheng Earthquake and other events. The Haicheng Earthquake is the first significant event to be interpreted with the dilatancy-diffusion hypothesis in the world. As one pivotal figure in the development of the dilatancy-diffusion hypothesis for earthquake prediction Professor Amos Nur of Stanford University worked tirelessly to attract societal attention to this important scientific and humanistic issue. As a deterministic physical model the dilatancy-diffusion hypothesis intrinsically bears the deficit to interpret the stochastic seismogenic process. With the emergence of deep learning and its successful applications to many science and technology fields, we may see a possibility to overcome the shortcoming of the current state of the theory with the addition of empirical statistics to push the operational earthquake forecasting approach with the addition of the physically-informed neural networks which adopt the dilatancy-diffusion hypothesis as one of its embedded physical relations, to uplift the seismic risk reduction to a new level for saving lives and reducing the losses.