Time-lapse seismic monitoring of dynamic rock property changes due to an explosive event at depth

Abstract

After the second world war and the cold war, nuclear weapon disarmament and the prohibition of nuclear tests has become a significant global security issue. The United Nations established the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) in 1996 to develop verification regimes to monitor nuclear weapon testing. Characterising seismic events as underground nuclear explosions is a challenging task. The On-Site Inspection (OSI), carried out by a trained Inspection Team at the suspected nuclear test location, is the most invasive verification method under the Comprehensive Test-Ban Treaty (CTBT). The team verifies the suspicious event using a suite of scientific techniques such as visual observation, radio-nuclide (RN) measurement techniques, geophysical methods including seismic monitoring, etc., to link post-explosion phenomena to potential sources. The main challenges in seismic monitoring involve accurately locating the test location and separating underground variations in seismic properties due to the explosion from naturally occurring variations. Moreover, the OSI verification operations occur several days/months after the explosion, which would lead to possible variation in the detectability of nuclear-explosion related anomalies due to their decay over time. This study explores the potential of implementing seismic time-lapse monitoring techniques to locate underground explosion zone by detecting the explosion related seismic property variation. This project’s scope is drawn within the framework of the CTBTO treaty to develop a monitoring technique that can be acquired, processed, and interpreted within the 130 days post-explosion On-Site Inspection period. An underground nuclear explosion (UNE) produces characteristic static and dynamic signatures due to the sudden release of intense pressure and thermal energy. Geological deformations, such as a cavity, rubble chimney, rock compaction, and fracturing, collectively known as the ‘cavity system’, are the characteristic static signatures associated with UNE. The P-wave velocity of the cavity system is 50 – 80 % lower than the initial background velocity. Certain dynamic signatures, such as groundwater mounding, are observable for a prolonged period, even for several decades, due to the redistribution of residual stress, temperature, and saturation. These dynamic phenomena are most prominent near the source origin, while their spatial and temporal behaviour depends on the explosive yield strength, detonation depth, and geology of the test site. This study was carried out in four stages. The first stage investigates the characteristic phenomena associated with the UNE and their impact on the emplacement rock’s seismic property. The dynamic phenomena of groundwater mound (GWM) has a very high dissipation rate during the first 100 days following the explosion. The numerical modelling study carried out for isotropic alluvium rock medium shows that the GWM dissipation can cause about 40 % velocity change, which can be detected by time-lapse seismic. The target for active seismic survey in OSI is a cavity (low velocity zone) created by UNE and associated GWM dynamic phenomena. In the second stage of the research, a seismic processing workflow for detecting subsurface cavities is presented, and a frequent time-lapse seismic monitoring strategy is proposed. The rate of dissipation of GWM; which is geology, yield strength and rock permeability dependent; is at its peak during the initial days after UNE and gradually drops. Conducting a frequent time-lapse seismic monitoring during the period of high dissipation rate would increase the chance of detecting the time-lapse change associated with GWM. Due to the time constraint, conventional time-lapse seismic processing would not be suitable for the OSI activity. Therefore, machine learning-based multi-vintage cross-equalisation and 4D detection workflow are presented in the third stage of the research. The near-real-time 4D detection workflow using machine learning can be implemented during the OSI to identify the potential source location or ground zero. In the final stage of the research, optimal survey parameters and technologies to be considered for implementing a time-lapse seismic survey during the OSI are discussed .