RNA sampling time on postmortem avian carcasses in the wild

Genetic sampling, especially high‐quality RNA from wild avian populations, is challenging in wildlife biology due to rapid RNA degradation. Although carcasses could be a potential RNA source, the optimal postmortem sampling time on the avian carcasses under field conditions remains unclear. Here, we carried out a field experiment on the Qinghai‐Tibet Plateau (QTP) and evaluated the relationship between PMI and RNA degradation in three tissue types (muscle, brain, and liver) of the domestic chicken Gallus gallus domesticus carcasses. In the muscle and brain tissues, we found that the RNA Integrity Number (RIN) of samples collected within 60 h postmortem was more than 7.0, suggesting a high RNA extract quality. The following RNA‐seq experiment demonstrated that gene expression profiles of the samples collected within 36 h postmortem were comparable to those of fresh samples (i.e. 0 h), with a low percentage of differentially expressed genes (< 3.0%) observed between samples at 0 and 36 h postmortem. However, in the liver tissue, RNA samples already degraded at 12 h postmortem, showing low RIN values (< 7.0), different gene expression profiles from fresh samples, and a high percentage of differentially expressed genes (15.6%). Therefore, our study suggests that samples from muscle and brain tissues collected within 36 h postmortem are qualified for RNA‐seq analyses. In contrast, only the fresh RNA samples from liver tissue are qualified. Our study provides a practicable and efficient sampling strategy for the transcriptome study on avian populations under extreme environment such as the QTP

Noise attenuation of seismic data from simultaneous-source acquisition

Simultaneous shooting achieves a much faster seismic acquisition but poses a challenging problem for subsequent processing because of the interference from the neighbor crews. Separation of different sources, also called deblending, becomes important for the overall success of this acquisition technology. I propose a novel iterative estimation scheme for separating the blended simultaneous source seismic data to produce separate-source data as if they were acquired independently. I construct an augmented estimation problem, then use shaping regularization to constrain the characteristics of the model during the inversion and to obtain a suitable estimation result. The data reconstruction and source separation problems can be combined into one problem in order to make the future acquisition more flexible and efficient. In order to best utilize the capability of median filtering in attenuating spike-like noise, I also propose to use a new type of median filter (MF), termed as space-varying median filter (SVMF) to remove blending noise. SVMF can be regionally adaptive, instead of rigidly using a constant window length through the whole profile for MF. Simultaneous-source seismic data may also contain strong ambient random noise, so traditional denoising is still an important step. One of the most widely used approaches for removing random noise is using a sparse-transform thresholding strategy. I propose a double sparsity dictionary (DSD) for seismic data in order to combine the benefits of both analytic transform and learning-based dictionary. In the DSD framework, data-driven tight frame (DDTF) obtains an extra structure regularization when learning dictionaries, while the seislet transform obtains a compensation for the transformation error caused by slope dependency. DSD aims to provide a sparser representation than the individual transform and dictionary and therefore can help achieve better performance in denoising applications. Finally, considering that signal loss sometimes cannot be avoided in nearly all the existing denoising or deblending approaches. I propose a novel approach to retrieve the leakage energy from the initial noise section using local signal-and-noise orthogonalization. The proposed denoising approach corresponds to orthogonalizing the initially denoised signal and noise in a local manner. I evaluate denoising performance by using local similarity. The local signal-and-noise orthogonalization algorithm can also be used in the iterative deblending framework for obtaining better performance.Geological Science

stemflow: A Python Package for Adaptive Spatio-Temporal Exploratory Model

A Python Package for Adaptive Spatio-Temporal Exploratory Model (AdaSTEM)</p

Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works

Thrust Ripple Force Minimization and Efficiency Analysis of Electromagnetic Actuator on Active Suspension

A novel electromagnetic actuator for active suspension is designed on an in-wheel motor electric vehicle in this paper. Aiming at reducing thrust ripple force and improving stability of the actuator, a method of calculating the optimum slot width and optimizing edge radian of end tooth is proposed. Firstly, a finite element model (FEM) of the actuator is modeled, and the correctness of FEM is verified through comparisons of simulation results and analytical ones, including counterelectromotive force of coil winding and force characteristic test of the actuator. Based on the FEM, the influence of slot width on electromagnetic thrust and total harmonic distortion (THD) is analyzed, and the slot width is improved. The side effect of the actuator is considered. By improving the edge radian, the fluctuation of the cogging force and thrust ripple is reduced. In addition, output efficiency and energy feed efficiency of the actuator after reducing thrust ripple are studied. The results show the minimum THD is 4.2%, which is obtained at the slot width 4.3 mm, and thrust ripple is 36.5 N. When the edge radian is 60°, the thrust ripple decreases to only 15.7 N, which is reduced by 57.0%. The maximum output efficiency and energy feedback efficiency of the actuator are 87.5% and 27.1%, respectively. Finally, according to actuator characteristic tests of two working modes, it is concluded that the maximum energy feedback efficiency is 25.6%. The input current and current frequency should be gradually increased with the increase of suspension speed under active mode, and the maximum output efficiency is 80.2%. The test results are basically consistent with the FEM analysis values, which verify the correctness of the FEM analysis

Automatic high-resolution microseismic event detection via supervised machine learning

Microseismic methods are crucial for real-Time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-To-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2∗wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-The-Art short-Term-Average over long-Term-Average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.</p

Full waveform inversion and joint migration inversion with an automatic directional total variation constraint

As full waveform inversion (FWI) is a non-unique and typically ill-posed inversion problem, it needs proper regularization to avoid cycle-skipping. To reduce the non-linearity of FWI, Joint Migration Inversion (JMI) is proposed as an alternative, explaining the reflection data with decoupled velocity and reflectivity parameters. However, the velocity update may also suffer from being trapped in local minima. To optimally include geologic information, we propose FWI/JMI with directional total variation as an L1-norm regularization on the velocity. We design the directional total variation operator based on the local dip field, instead of ignoring the local structural direction of the subsurface and only using horizontal- and vertical- gradients in the traditional TV. The local dip field is estimated using plane-wave destruction based on a raw reflectivity model, which is usually calculated from the initial velocity model. With two complex synthetic examples, based on the Marmousi model, we demonstrate that the proposed method is much more effective compared to both FWI/JMI without regularization and FWI/JMI with the conventional TV regularization. In the JMI-based example, we also show that L1 directional TV works better than L2 directional Laplacian smoothing. In addition, by comparing these two examples, it can be seen that the impact of regularization is larger for FWI than for JMI, because in JMI the velocity model only explains the propagation effects and, thereby, makes it less sensitive to the details in the velocity model.ImPhys/Acoustical Wavefield Imagin