Deep learning full waveform inversion (DL-FWI) is an end-to-end and time-efficient high-resolution imaging technique for subsurface media. Popular methods are often plagued by location drift and significant velocity misfits at the stratigraphic boundaries. In this study, we propose an augmented boundary attention algorithm (ABA-FWI) to focus on the key boundary information. Regarding network composition, the wavelet convolution (WTconv) layer and the spatial attention module (SAM) are incorporated into the encoder and the decoder, respectively. The WTconv layer captures low frequencies by obtaining a large receptive field without suffering from overparameterization. SAM extracts distinctive information by utilizing the interspatial relationship of features for resolution enhancement. For loss function design, our reflection coefficient tuned boundary (RCTB) loss introduces the reflection coefficient to adjust the gradient map weight of low-contrast areas. It focuses on boundary regions characterized by speed transitions to minimize errors. Results on OpenFWI, the SEG simulation, and the Marmousi II slice datasets show that our method is superior to the state-of-the-art data-driven methods, especially on boundary details. The source code is available at https://github.com/FanSmale/ABA-FWI.