Reconstruction of structural health monitoring data is a challenging task, since it involves
time series data forecasting especially in the case with a large block of missing data. In this …

Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong
noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon …

Seismic data interpolation is an effective way of recovering missing traces and obtaining
enough information for subsequent processing. Unlike traditional methods, deep neural …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

In structural health monitoring, data quality is crucial to the performance of data-driven
methods for structural damage identification, condition assessment, and safety warning …

In recent years, sparse representation is seeing increasing application to fundamental signal
and image-processing tasks. In sparse representation, a signal can be expressed as a linear …

Simultaneous-source acquisition, breaking the limit of conventional seismic acquisition, is a
rapidly evolving research field, due to its advantage in reducing survey time and improving …

Acquisition technology advances, as well as the exploration of geologically complex areas,
are pushing the quantity of data to be analyzed into the “big-data” era. In our related work …

近年来, 随着压缩感知技术在信号处理领域的巨大成功, 由其衍生而来的矩阵补全技术也日益
成为机器学习领域的研究热点, 诸多研究者针对矩阵补全问题展开了大量卓有成效的研究 …

This comprehensive survey addresses the notable yet relatively uncharted territory of
machine learning (ML) applications within the realm of earthquake engineering. While …