New equation helps predict earthquakes better

Researchers in Edinburgh have created a new mathematical model that can improve the forecast of earthquakes. Scientists wanted to use math, rather than resorting to laboratory experiments. The goal was to predict the strength of the type of key rock that underlies the faults in the earth’s crust where earthquakes occur. Details of the study are published by Heriot-Watt University.

Dr. Sabin den Hartog has developed a model that can help geophysicists better predict when and where earthquakes can occur.

Earthquakes occur while moving along faults in the weakest part of the crust, which usually contains phyllosilicates – a type of mineral that consists of tiny, very thin plates.

At the moment, scientists have recreated the movement along the faults in their laboratories in order to try to understand the processes occurring at the micro-level, which lead to earthquakes. Although the experiments helped scientists better understand the processes of faults and earthquakes, they do not provide a complete picture. The reason is simple – in the laboratory, it is difficult to recreate difficult conditions at a depth in the earth’s crust.

Dr. Den Hartog and her associates from the University of Liverpool and Utrecht University wanted to be able to predict the friction force of phyllosilicates, rather than relying on laboratory experiments. Friction is the force required to move along a fault.

Fault zones are usually formed in places with a high concentration of phyllosilicates. Therefore, scientists have created a model so that they can predict the friction force of phyllosilicates under conditions that cannot be achieved in the laboratory.

They analyzed the zones of artificial fractures on a microscopic scale. This was necessary to identify the processes that occurred during the experiment. We are talking about the splitting of platinum phyllosilicate minerals.

Based on the data, scientists formulated a series of equations to predict how the friction force of phyllosilicates changes under different conditions – whether it is humidity or the speed of a fault. This allows you to make predictions in conditions not available in the laboratory, which greatly simplifies the modeling of fault movement in natural conditions, including earthquakes.