Abstract

The Southern Baikal region is located within the limits of the actively developing Baikal rift zone (BRZ). Its seismic potential is significant, and M>7 earthquakes occur periodically with intensive shaking in the epicenters (up to 10 points). The problem of prediction and forecasting of strong earthquakes has always been critical for this region, considering its increasing urbanization, industrial clusters and transport systems. The article describes the methodology based on rock deformation monitoring data, which aims at developing a technology capable of efficient prediction and forecasting of strong earthquakes. The technology is based on the stick-slip model developed by of W. Brace and J. Byerlee and its synergetic interpretation proposed by Ma J. et al. This model shows the preparation of the earthquake source in stages, which is reflected in deformation monitoring data. An integral property of the deformation process is its self-organization right before an earthquake. The self-organization, that always takes place before a seismic event, is considered by the authors as an inevitable shortterm precursor. All other indicators that occur randomly are considered occasional precursors. The article is focused on the methodology aspects and describes the technical details of measuring the rock deformation. It presents the proven methods of data processing with the major goal of detecting the inevitable precursor. Described in detail are the structural-geodynamic conditions of the locations of rock deformation monitoring points in the study area. The main results are based on the rock deformation records taken before three strong earthquakes in the study area – Kultuk (August 27, 2008), Bystrinskoe (September 21, 2020), and Kudara (December 10, 2020) earthquakes. The study shows that, in contrast to the inevitable precursor, the occasional precursors are manifested depending on the geodynamic conditions of the earthquake source preparation, the position of the monitoring point relative to the source, and the structural conditions of the monitoring point location.

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