High-Precision Depth Domain Migration Method in Imaging of 3D Seismic Data in Coalfield

Fault structures developed in coal seams, which are often associated with roof collapse, water inrush, gas outburst, and other accidents, are common geological hazards in coal exploration and development. The accurate detection of micro-structures such as small faults has always been a research focus to ensure safety in coalfields. Three-dimensional (3D) seismic research is one of the most efficient methods for obtaining the structural characteristics of coal areas and identify small faults in coal seams, but it is difficult for traditional seismic data imaging technologies to meet the high-precision demand of current coal exploration. Aiming at the characteristics of 3D seismic data in coalfields, we calculated the difference coefficients based on the optimized inversion algorithm and proposed a variable-density acoustic equation optimized with a temporal–spatial staggered-grid finite difference forward algorithm. On this basis, by combining normalized cross-correlation imaging conditions and GPU/CPU collaborative parallel processing technology, we developed an efficient and high-precision 3D reverse time migration method suitable for 3D seismic data in coalfields. Numerical tests verified the accuracy and efficiency of the proposed migration method for the imaging of coal-measure strata with small fault structures and could effectively identify 5 m small faults in the coal seam. The migration test of 3D seismic data in real coalfields showed that our 3D reverse time migration method has good practicability for high-precision imaging of 3D seismic data in coalfields and is an effective method for the precise imaging of small faults in coal measures

A Novel Bearing Fault Diagnosis Method Based on GL-mRMR-SVM

A convolutional neural network (CNN) has been used to successfully realize end-to-end bearing fault diagnosis due to its powerful feature extraction ability. However, the CNN is prone to focus on local information, ignoring the relationship between the whole and the part of the signal due to its unique structure. In addition, it extracts some fault features with poor robustness under noisy environment. A novel diagnosis model based on feature fusion and feature selection, GL-mRMR-SVM, is proposed to address this problem in this paper. First, the model combines the global features in the time-domain and frequency-domain of the raw data with the local features extracted by CNN to make full use of the signal information and overcome the weakness of traditional CNNs neglecting the overall signal. Then, the max-relevance min-redundancy (mRMR) algorithm is used to automatically extract the discriminative features from the fused features without any prior knowledge. Finally, the extracted discriminative features are input into the SVM for training and output the fault recognition results. The proposed GL-mRMR-SVM model was evaluated through experiments on bearing data of Case Western Reserve University (CWRU) and CUT-2 platform. The experimental results show that the proposed method is more effective than other intelligent diagnosis methods

Impact of Reclaimed Water Irrigation on Rice Yields and Quality

Poultry and livestock farming wastewater is one of the main sources of agricultural non-point source pollution. Using reclaimed poultry and livestock farming wastewater for rice irrigation can not only provide nutrition for rice, but also reduce agricultural water consumption, and also further control non-point source pollution through the self-purification of rice ecosystem. It has been considered to be environmentally friendly and reliable for poultry and livestock farming wastewater treatment. However, the effect of reclaimed water irrigation on rice yield and quality is still unclear. In order to explore the effects of reclaimed water irrigation on rice yield and quality, we conducted a rice reclaimed irrigation experiment in the Erhai lake basin in 2017. The rice yield and quality were compared among different irrigation modes, different irrigation water quality and different fertilization schemes of reclaimed water. The results showed that the yield of W1F2, under intermittent irrigation with reclaimed water, was the highest, reaching 11.7t/hm2. The differences of rice yield and its components between different irrigation and fertilization methods were not significant. The yield and its components of rice under different irrigation and fertilization treatments were not significant. In terms of yield, the intermittent irrigation was 7.6% higher than that of flood irrigation. The amount of fertilizer applied by intermittent irrigation of reclaimed water was 7.0% less than that of clear water, and the yield increased by 3.5%. Correspondingly, the amount of fertilization of flood irrigation of reclaimed water is equivalent to that of clear water, which increases yield by 1.9%. In summary, the effect of reclaimed water irrigation on yield increase is obvious. There were significant differences in brown rice rate and chalky grain rate between different irrigation modes, which showed that the brown rice rate of intermittently irrigated rice was 0.7% smaller than that of flooded rice, and the white grain of intermittently irrigated rice was 45% larger than that of flooded irrigation. Intermittent irrigation has an adverse effect on the processing quality and appearance quality of rice. There were significant differences in brown rice rate, whole milled rice rate, gel consistency and depletion value under different irrigation water quality. The results showed that the brown rice rate and the depletion value of reclaimed water irrigation were smaller than those of clear water, and the whole milled rice rate and gel consistency were larger than that of clear water. Regularity, reclaimed water irrigated rice processing quality and cooking taste quality is better. In terms of nutritional quality of rice, the crude protein content in flooding treatment was 4.2% higher than that in intermittent irrigation, and the irrigation treatment of reclaimed water was 13.8% higher than that in clear water irrigation

Artificial Neurons Based on Ag/V₂C/W Threshold Switching Memristors

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems