Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

The performance of the passing train and the structural state of the track bed are the concerns regarding the safe operation of subways. Monitoring the vibration response of the track bed structure and identifying abnormal signals within it will help address both of these concerns. Given that it is difficult to collect abnormal samples that are symmetric to those of the normal state of the structure in actual engineering, this paper proposes an unsupervised learning-based methodology for identifying the abnormal signals of the track beds detected by the ultra-weak fiber optic Bragg grating sensing array. For an actual subway tunnel monitoring system, an unsupervised learning network was trained by using a sufficient amount of vibration signals of the track bed collected when trains passed under normal conditions, which was used to quantify the deviations caused by anomalies. An experiment to validate the proposed procedures was designed and implemented according to the obtained normal and abnormal samples. The abnormal vibration samples of the track beds in the experiment came from two parts and were defined as three levels. One part of it stemmed from the vibration responses under the worn wheels of a train detected during system operation. The remaining abnormal samples were simulated by superimposing perturbations in the normal samples. The experimental results demonstrated that the established unsupervised learning network and the selected metric for quantifying error sequences can serve the threshold selection well based on the receiver operating characteristic curve. Moreover, the discussion results of the comparative tests also illustrated that the average results of accuracy and F1-score of the proposed network were at least 11% and 13% higher than those of the comparison networks, respectively.

Improving the computational efficiency of 3D FWI is a challenging task in seismic imaging. Using a multi-GPU cluster with acceleration strategy to simulate the wave propagation is an important means to improve its efficiency. We propose a multi-GPU acceleration 3D acoustic FWI algorithm based on the FDTD method in this paper. We improved the parallelism of 3D wavefield simulation algorithm based on a single GPU using a sliding 2D thread block algorithm with three different 2D shared memory stencils. For the multi-node implementation, we achieved bidirectional parallel data transfer between GPUs and used multiple kernels to further overlap the calculation and transfer. Numerical tests verify the validity of our 3D FWI algorithm accelerated with multi-GPU. The strategies used in our algorithm can significantly bring improvement in most cases. And the improvement is strongly related to the model size and the number of GPUs used. In our test, we achieve an acceleration of up to 19% in forward simulation and 25% in gradient calculation, compared with a typical multi-GPU implementation.