Radar Sounders (RSs) are sensors operating in the nadir-looking geometry (with HF or VHF bands) by transmitting modulated electromagnetic (EM) pulses and receiving the backscattering response from different subsurface targets. Recently, convolutional neural network (CNN) architectures were established for characterizing RS signals under the semantic segmentation framework. In this paper, we design a Fast Fourier Transform (FFT) based CNN-Transformer encoder to effectively capture the long-range contexts in the radargram. In our hybrid architecture, CNN models the high-dimensional local spatial contexts, and the Transformer establishes the global spatial contexts between the local spatial ones. To overcome Transformer complex self-attention layers by reducing learnable parameters; - we replace the self-attention mechanism of the Transformer with unparameterized FFT modules as depicted in FNet architecture for Natural Language Processing (NLP). The experimental results on the MCoRDS dataset indicate the capability of the CNN-Transformer encoder along with the unparameterized FFT modules to characterize the radargram with limited accuracy cost and by reducing the time consumption. A comparative analysis is carried out with the state-of-the-art Transformer-based architecture.
Estimating probability of strata convergence in relation to the distribution of stresses around the excavated wall as well as quantifying the time-dependent displacement are of paramount importance. Convergence is defined as the gradual decrease in the interval between to specified rock units or geological horizons as a result of thinning of intervening strata. The heterogeneous distribution of induced stresses with time resulting into simultaneous strata displacement which is extremely difficult to predict under the robust mining environment. As a result of unpredictable stresses, during the time of subsequent excavation around a specified geologic condition, significant amount of gravitational potential energy decreases due to increasing kinematic energy of overburden. Furthermore, the equilibrium of underground mining conditions is disturbed subsequently with time after the initial uncovering of side wall and roof top. This paper proposes an approach to construct a functional equation of convergence purely based on idealized geological conditions. There are several implications which are associated with the prediction of convergence such as reducing health risk of mining workers, contributions of fractures over the dynamic convergence mechanism and so on.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.