A dual instrument is assembled to investigate the usefulness of optical coherence tomography (OCT) imaging in an ear, nose and throat (ENT) department. Instrument 1 is dedicated to in vivo laryngeal investigation, based on an endoscope probe head assembled by compounding a miniature transversal flying spot scanning probe with a commercial fiber bundle endoscope. This dual probe head is used to implement a dual channel nasolaryngeal endoscopy-OCT system. The two probe heads are used to provide simultaneously OCT cross section images and en face fiber bundle endoscopic images. Instrument 2 is dedicated to either in vivo imaging of accessible surface skin and mucosal lesions of the scalp, face, neck and oral cavity or ex vivo imaging of the same excised tissues, based on a single OCT channel. This uses a better interface optics in a hand held probe. The two instruments share sequentially, the swept source at 1300 nm, the photo-detector unit and the imaging PC. An aiming red laser is permanently connected to the two instruments. This projects visible light collinearly with the 1300 nm beam and allows pixel correspondence between the en face endoscopy image and the cross section OCT image in Instrument 1, as well as surface guidance in Instrument 2 for the operator. The dual channel instrument was initially tested on phantom models and then on patients with suspect laryngeal lesions in a busy ENT practice. This feasibility study demonstrates the OCT potential of the dual imaging instrument as a useful tool in the testing and translation of OCT technology from the lab to the clinic. Instrument 1 is under investigation as a possible endoscopic screening tool for early laryngeal cancer. Larger size and better quality cross-section OCT images produced by Instrument 2 provide a reference base for comparison and continuing research on imaging freshly excised tissue, as well as in vivo interrogation of more superficial skin and mucosal lesions in the head and neck patient.
A lateral soft tissue neck radiograph is a useful adjunct in diagnosing and managing the patient presenting with upper airway symptoms but is often inadequately reviewed. We present some common findings and robust systems to improve analysis of these radiographs.
In the recent decades, passive surface wave methods have gained much attention in the near-surface community due to their ability to retrieve low-frequency surface wave information. Temporal averaging over a sufficiently long period of time is a crucial step in the workflow to fulfill the randomization requirement of the stationary source distribution. Because of logistical constraints, passive seismic acquisition in urban areas is mostly limited to short recording periods. Due to insufficient temporal averaging, contributions from non-stationary sources can smear the stacked dispersion measurements, especially for the low-frequency band. We formulate a criterion in the tau-p domain for selective stacking of dispersion measurements from passive surface waves and apply it to high-frequency (> 1 Hz) traffic noise. The criterion is based on the automated detection of input data with a high signal-to-noise ratio in a desired velocity range. Modeling tests demonstrate the ability of the proposed criterion to capture the contributions from the non-stationary sources and classify the passive surface wave data. A real-world application shows that the proposed data selection approach improves the dispersion measurements by extending the frequency band below 5 Hz and attenuating the distortion between 6 and 13 Hz. Our results indicate that significant improvements can be obtained by considering tau-p-based data selection in the workflow of passive surface wave processing and interpretation.
Geothermal energy is one of the most promising renewable energy sources, particularly within the context of China's energy structure optimization, environmental protection measures, energy conservation, and rising pressure on emission reduction. By the end of 2020, renewable energy facilities, including solar, wind, geothermal and other types of energy, in China will supply 27% of total power generation, according to the government's 2016-2020 plan for renewable energy. However, geothermal resources accounted for only 0.6
Accurate understanding of near-surface structures of the solid Earth is challenging, especially in urban areas where active source seismic surveys are constrained and difficult to perform. The analysis of anthropogenic seismic noise provides an alternative way to image the shallow subsurface in urban environments. We present an application of using traffic noise with seismic interferometry to investigate near-surface structures in Hangzhou City, eastern China. Noise data were recorded by dense linear arrays with approximately 5 m spacing deployed along two crossing roads. We analyze the characteristics of traffic-induced noise using 36 hr continuous recordings. Coherent Rayleigh surface waves between 2 and 20 Hz are retrieved based on crosscorrelations within 1 hr time windows. Robust phase-velocity dispersion curves are extracted from virtual shot gathers using multichannel analysis of surface waves and coincide with the results from active seismic data, noise beamforming analysis, and measurements with the spatial autocorrelation method (SPAC). Shear-wave velocity profiles are derived for the top 100 m of the subsurface at the array locations. The estimated shear-wave velocities from traffic noise correspond to the velocities estimated from logging data. The 2D shear-wave velocity maps reveal different soil deposits and bedrock structures in the estuarine sedimentary area. The results demonstrate the accuracy and efficiency of delineating near-surface structures from traffic-induced noise, which has great potential for monitoring subsurface changes in urban areas.
Summary With the emergence of massive seismic datasets, surface wave methods using deep learning (DL) can effectively obtain shear-wave velocity (Vs) structure for noninvasive near-surface investigations. Previous studies on DL inversion for deep geophysical investigation have a reference model to generate the training dataset, while near-surface investigations have no model. Therefore, we systematically give a set of training dataset generation processes. In the process, we use both prior information and the observed data to constrain the dataset so that the DL inversion model can learn the local geological characteristics of the survey area. Because the space of inverted Vs models is constrained and thus narrowed, the inversion nonuniqueness can be reduced. Furthermore, the mean squared error, which is commonly used as loss function, may cause a poor fitting accuracy of phase velocities at high frequencies in near-surface applications. To make the fitting accuracy evenly in all frequency bands, we modify the loss function into a weighted mean squared relative error. We designed a convolutional neural network (CNN) to directly invert fundamental-mode Rayleigh-wave phase velocity for 1D Vs models. To verify the feasibility and reliability of the proposed algorithm, we tested and compared it with the Levenberg-Marquardt (L-M) inversion and neighborhood algorithm (NA) using field data from the Lawrence experiment (USA) and the Wuwei experiment (China). In both experiments, the inverted Vs models by CNN are consistent with the borehole information and are similar to that from existing methods after fine tuning of model parameters. The average root mean squares errors (RMSE) of the CNN, NA, and L-M methods are also similar, except in the Lawrence experiment, the RMSE of CNN is 17.33 m/s lower than previous studies using the L-M method. Moreover, the comparison of different loss functions for the Wuwei experiment indicates the modified loss function can achieve higher accuracy than the traditional one. The proposed CNN is therefore ideally suited for rapid, repeated near-surface subsurface imaging and monitoring under similar geological settings.
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