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Exploration of geothermal resources has become one of the major drivers for urbanization and economic transformation in southeast China, and it calls for temporal and spatial resolution improvements on conventional geophysical techniques under complex near-surface conditions. A dense seismic network including 192 sensors, with its aperture around 4.8 km, was deployed to record ambient noise for local-scale geothermal reservoir imaging in central Zhejiang Province, southeast China. With the ultrashort (less than 5 days) observation, a subset of the total station pairs was extracted to conduct seismic interferometry and dispersion measurements. This customized subnet was qualified to be an overdetermined system to achieve similar performance of entire network for the eikonal tomography with higher efficiency. The recorded high-frequency noise sources turned out to be rather homogeneous distributions, except for the dominant direction of northern Jinhua urban area. Azimuthal effects and wavelength restriction of dispersion measurements were quantified by simulating source–receiver responses under non-equipartitions of energy. The azimuth–velocity variations of field dataset verified that phase velocity measurements using the tested wavelength restriction were robust under different source–receiver orientations. The obtained phase velocity maps were comparable with the geological map. Furthermore, the inverted body wave velocities coincide well with the borehole data. Shear wave velocity profiles revealed corresponding fracture and depression structures of the reservoir. Finally, multiple geophysical properties delineate the shallow geothermy to be karst fissure water heated at certain earth temperature gradient. [ABSTRACT FROM AUTHOR]

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) in the extraction of dispersion curves and inversion can release a tremendous workload brought by dense seismic arrays. We presented a case study of imaging shear-wave velocity (Vs) structure and detecting low-velocity layer (LVL) in the Hangzhou urban area (eastern China). We used traffic-induced passive surface-wave data recorded by dense linear arrays. We extracted phase-velocity dispersion curves from noise recordings using seismic interferometry and multichannel analysis of surface waves. We adopted a convolutional neural network to estimate near-surface Vs models by inverting Rayleigh-wave fundamental-mode phase velocities. To improve the accuracy of the inversion, we utilized the sensitivities to weight the loss function. The average root mean square error from the weighted inversion is 46% lower than that from the unweighted DL inversion. The estimated pseudo-2D Vs profiles correspond to the velocities obtained from downhole seismic measurements. Compared with an investigation on the same survey area, our inversion results are more consistent with the Vs provided by downhole seismic measurements within 50–60 m where the LVL exists. The trained neural network successfully identified that the LVL is located at 50–60 m deep. To check the applicability of the trained neural network, we applied it to a nearby passive surface-wave survey and the inversion results agree with the existing investigation results. The two applications demonstrate the accuracy and efficiency of delineating near-surface Vs structures with the LVL from traffic-induced noise using the DL technique. The DL inversion has great potential for monitoring subsurface medium changes in urban areas. • We used passive surface-wave data and deep learning to image near-surface structures. • Our results are more accurate compared to previous studies. • The neural network and the weighted loss function can successfully detect the LVL. [ABSTRACT FROM AUTHOR]

Near-surface (<200 m) stratigraphic identification is of scientific and engineering significance to urban sustainable development and expansion. Complex urbanized environments, however, limit the application of traditional active-source seismic surveys. Passive seismic surveys provide an alternative for investigating near-surface structures by utilizing abundant traffic noise. We presented a case study of fine-scale shear-wave velocity (Vs) structure imaging in the Hangzhou urban area (eastern China) and compared inversion results of ambient noise tomography and multichannel analysis of passive surface wave (MAPS). Noise data were recorded within one day by a dense seismic network (130 m × 120 m). The average adjacent station interval is about 10 m. Our goal is to investigate the top 100 m of the subsurface at the survey area. The two inverted Vs models by tomography and MAPS are similar and they both tally with the logging data. The subsurface geology structures are analyzed based on tomography inversion results and borehole data. The bedrock surface is determined at about 60 m depth and a high-velocity layer is found at around 45– 50 m depth. The sedimentary formation and bedrock in the survey area are further stratified under the control of borehole data. • Ambient noise tomography and MAPS were utilized for urban subsurface imaging. • The two inverted Vs models are similar and agree with borehole datasets. • The comprehensive interpretations reveal local geologies. [ABSTRACT FROM AUTHOR]

Background: The Adverse Drug Event Reports (ADERs) from the spontaneous reporting system are important data sources for studying Adverse Drug Reactions (ADRs) as well as post-marketing pharmacovigilance. Apart from the conventional ADR information contained in the structured section of ADERs, more detailed information such as pre- and post- ADR symptoms, multi-drug usages and ADR-relief treatments are described in the free-text section, which can be mined through Natural Language Processing (NLP) tools., Objective: The goal of this study was to extract ADR-related entities from free-text section of Chinese ADERs, which can act as supplements for the information contained in structured section, so as to further assist in ADR evaluation., Methods: Three models of Conditional Random Field (CRF), Bidirectional Long Short-Term Memory-CRF (BiLSTM-CRF) and Lexical Feature based BiLSTM-CRF (LF-BiLSTM-CRF) were constructed to conduct Named Entity Recognition (NER) tasks in free-text section of Chinese ADERs. A semi-supervised learning method of tri-training was applied on the basis of the three established models to give un-annotated raw data with reliable tags., Results: Among the three basic models, the LF-BiLSTM-CRF achieved the highest average F1 score of 94.35%. After the process of tri-training, almost half of the un-annotated cases were tagged with labels, and the performances of all the three models improved after iterative training., Conclusions: The LF-BiLSTM-CRF model that we constructed could achieve a comparatively high F1 score, and the fusion of CRF, while BiLSTM-CRF and LF-BiLSTM-CRF in tri-training might further strengthen the reliability of predicted tags. The results suggested the usefulness of our methods in developing the specialized NER tools for identifying ADR-related information from Chinese ADERs., (Copyright © 2019 Elsevier Inc. All rights reserved.)