arXiv subjects

Pushkar Bharadwaj

Publications and source records attributed to Pushkar Bharadwaj.

2 recordsLinked to original sources

arXiv2025

Coherent Source Subsampling: A Data-Driven Strategy for Restoring Causal-Acausal Symmetry in Ambient Seismic Wavefield Correlations

Ambient noise tomography relies on the assumption that the seismic wavefield is equipartitioned. In practice, ambient noise sources are spatially and temporally heterogeneous, producing biased estimates of the Green's function between stations. We introduce a data-driven method, Coherent Source Subsampling (CSS), which selects and averages only cross-correlation time windows associated with excitation of sources in the stationary zone. By restricting the ensemble average to these windows, CSS mitigates the effects of nonuniform source distribution and restores causal-acausal symmetry in the retrieved interstation response. Applications to regional ambient-noise datasets show that CSS stabilizes surface-wave dispersion measurements even when source statistics violate the assumptions of standard seismic interferometry. For the central California dataset, CSS-derived group-velocity tomograms consistently image a high-velocity block between the Rinconada and San Andreas faults across multiple periods. In comparison, the full-ensemble (linear) average does not capture this block, which is well established. Our approach is particularly useful for short-duration passive surveys.

physics.geo-ph↗

arXiv2022

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi-based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.

cs.CV↗