Knowledge-grounded Dialogue Response Generation (KRG) can facilitate informative and fidelity dialogues using external knowledge. Prior monolingual works can only use the knowledge of the corresponding native language. Thus, due to the prohibitive costs of collecting and constructing external knowledge bases, the limited scale of accessible external knowledge always constrains the ability of KRG, especially in low-resource language scenarios. To this end, we propose a new task, Multi-Source Multilingual Knowledge-Grounded Response Generation (MMKRG), which simultaneously uses multiple knowledge sources of different languages. We notice that simply combining knowledge of different languages is inefficient due to the Cross-Conflict issue and Cross-Repetition issue. Thus, we propose a novel approach MMK-BART, which uses a simple but elegant Estimate-Cluster-Penalize mechanism to overcome the mentioned issues and adopts the multilingual language model mBART as the backbone. Meanwhile, based on the recent multilingual corpus XDailyDialog, we propose an MMKRG dataset MMK-DailyDialog, which has been aligned to the large-scale multilingual commonsense knowledge base ConceptNet and supports four languages (English, Chinese, German, and Italian). Extensive experiments have verified the effectiveness of our dataset and approach in monolingual, cross-lingual, and multilingual scenarios.

Prestack depth migration (PSDM) has been widely employed in oil and gas exploration, demonstrating its potential to greatly enhance the image of complex geological structures. Its application in continental deep seismic reflection, however, is rare due to the low signal-to-noise ratio data and difficulties in accurately constructing velocity models, even its feasibility is controversial. In this study, we apply the PSDM technique to the high-quality deep seismic reflection data collected in the Kumkol Basin on the northern margin of the Tibetan Plateau. During the PSDM processing, we obtain the interval velocity in depth which is consistent with the stratigraphic trend. The PSDM image reveals the detailed structure from the surface to the 20 km depth of the crust. Specifically, we find the deep structure of the basin is consistent with the surface geology, with three deformation styles in different surface structures. Moreover, the PSDM image shows high-angle thrusts and fault-propagation folds developed in the middle uplift area, which are not discernable in conventional processing. PSDM can reveal intricate structures such as high-angle thrusts in complex plateau basins without distortion, which confirms the feasibility and necessity of PSDM in deep continental seismic reflection data.

Conventional least-squares reverse time migration (LSRTM) generally involves a de-migration operator based on the first-order scattering approximation (Born modeling), which can only simulate the seismograms containing the primary reflected wave. When the input observed seismograms contain “redundant information” (especially multiples), crosstalk may occur in the imaging results. Therefore, we develop a least-squares full-wavefield reverse time migration (LSFWM), which is implemented based on a two-way modeling engine with vector reflectivity and the corresponding adjoint sensitive kernel. This modeling engine is modified from the variable density acoustic wave equation and can simulate the subsurface wavefield containing the primaries and multiples only by giving the accurate or estimated subsurface reflectivity and velocity. Theoretically, this LSFWM approach can eliminate the influence of “redundant information” on imaging and provide higher-quality imaging results compared to conventional LSRTM. In addition, since the modeling engine is based on vector reflectivity, the imaging results produced by the LSFWM are also vectorized, which can give more information about the subsurface structures, especially steep structures. And the imaging results produced by the LSFWM can accurately depict the subsurface reflectivity. These are helpful to obtain the information on subsurface structure and physical properties more clearly.