Academic literature on the topic 'Seismic instability'

Taking the landslides triggered by the 2008 Wenchuan Earthquake as examples, their dynamic responses with different epicenter distances due to single and combined action with regionality and spatial heterogeneity of the Primary and Secondary waves were simulated by applying the Universal Distinct Element Code software. The results shows that the slope suffered from the combined action between P and S waves appears instability prior to the slope under single action of P wave. With the epicenter distance increasing, the key controlling factor resulting in the slope failure varies from the combined seismic action between P and S waves to the single seismic action of the P wave. As for the formation mechanism of slope instability, coupled action between the vertical and horizontal seismic forces results in the slope dynamic failure with key action varying from the vertical to the horizontal one. Finally, the initial instability originates always at slope shoulder due to the peak ground acceleration amplification effect and the variation trend of the slope mechanical parameters on its fracturing of the seismic action.

The paper emphasizes the advantages of employing multiple data techniques—geology, GPS, surveys of cracking, boreholes, seismic refraction and electrical resistivity tomography—to image the shallow stratigraphy and hypothesize the cause of instability of an urban area. The study is focused on the joint interpretation of the crack pattern, topographic monitoring and the features of the underground, to define the area affected by instability and the direction of ground motion with the objective to advance a hypothesis on the cause of the instability of the area and to depict the main features. Borehole stratigraphies for a univocal interpretation of the lithology of electrical and seismic data and electrical resistivity tomography to constrain the interpretation of the lateral velocity variations and thickness of seismic bedrock were used. The geophysical surveys reveals to be complementary in the depicting of underground features. The study is approached at small and medium scale.

Q-compensated reverse time migration (Q-RTM) is a crucial technique in seismic imaging. However, stability is a prominent concern due to the exponential increase in high-frequency ambient noise during seismic wavefield propagation. The two primary strategies for mitigating instability in Q-RTM are regularization and low-pass filtering. Q-RTM instability can be addressed through regularization. However, determining the appropriate regularization parameters is often an experimental process, leading to challenges in accurately recovering the wavefield. Another approach to control instability is low-pass filtering. Nevertheless, selecting the cutoff frequency for different Q values is a complex task. In situations with low signal-to-noise ratios (SNRs) in seismic data, using low-pass filtering can make Q-RTM highly unstable. The need for a small cutoff frequency for stability can result in a significant loss of high-frequency signals. In this study, we propose a multi-task learning (MTL) framework that leverages data-driven concepts to address the issue of amplitude attenuation in seismic records, particularly when dealing with instability during the Q-RTM (reverse time migration with Q-attenuation) process. Our innovative framework is executed using a convolutional neural network. This network has the capability to both predict and compensate for the missing high-frequency components caused by Q-effects while simultaneously reconstructing the low-frequency information present in seismograms. This approach helps mitigate overwhelming instability phenomena and enhances the overall generalization capacity of the model. Numerical examples demonstrate that our Q-RTM results closely align with the reference images, indicating the effectiveness of our proposed MTL frequency-extension method. This method effectively compensates for the attenuation of high-frequency signals and mitigates the instability issues associated with the traditional Q-RTM process.