Reverse time migration (RTM) is a seismic imaging method to map the subsurface reflectivity using recorded seismic waveforms. The practice in exploration seismology has long established a two-fold ...approach of seismic imaging: Using velocity modeling building to establish the long-wavelength reference velocity models, and using seismic migration to map the short-wavelength reflectivity structures. Among various seismic migration methods for different situations, RTM is the only method that is capable to use all seismic wave types that can be computed numerically. Being initiated in early 1980's, RTM seeks an image of the subsurface reflectivity as the best match in an image space between the extrapolation of time-reversed waveform data and the prediction based on estimated velocity model and source parameters. Judging the image quality in the same space of forming the images is more advantageous than the approaches of modeling and inversion which seek the solution in the model space but judge its fitness in data space. Considering that most seismic migration applications today still use primary reflection as the only signal, the capability of RTM to use all computable wave types is unique and helpful reducing the imaging artifacts due to mistaking non-primary waves as primary reflections. Hence, we refer to those RTM algorithms using only primary reflections as the first-generation RTM methods, and the RTM algorithms making a full use of primary reflections, multiple reflections and other non-primary waveform data as the second-generation RTM methods. This paper reviews the development history of the RTM along with its major challenges, current solutions, and future perspectives.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The Tan-Lu Fault Zone (TLFZ) is the largest strike-slip system in East Asia. The central TLFZ has four main branch faults across a width of 20– 40 km, and both the great 1668 Tancheng Earthquake ...(M8.5) and the Anqiu Earthquake (M7.0) occurred on its two eastern branch faults. Due to the lack of recent earthquakes and stations around a seismic-quiescent segment of the TLFZ centered near the 70 BCE Anqiu Earthquake, previous tomographic images have resolutions coarser than 20 km in the middle and lower crust, unable to show how the crust is faulted along the TLFZ. To improve the resolution, we deployed a 70-km-long W-E-trending seismic array with 38 portable seismographs across the TLFZ at 36.1°N. Using one month of teleseismic records and a newly developed multiscale teleseismic tomography method, our P-wave velocity profile has achieved a resolution of 5 km × 5 km in shallow crust, and 10 km × 10 km in deep crust, as shown by the resolution tests. The profile's velocity variation correlates well with surface geology and crustal electrical resistivity profiles. The new profile provides clues for the depth distribution of major TLFZ branches, and indicates the two eastern TLFZ branch faults cut through the crust along a column-shaped low-velocity anomaly. This low-velocity column is associated with low-resistivity anomalies in the upper and lower crust, indicating a likely presence of fluids in the faulted rocks. This crustal faulting interpretation is consistent with the Moho geometry of previous receiver function studies, with an alignment between a receiver function Moho dome and a large low-velocity anomaly. Though seismicity is sparse near the profile, our interpreted crust-cutting location is in line with a linear cluster of recent TLFZ earthquakes about 20 km south of the profile, indicating that this quiescent segment could be a 150 km seismic gap along the central TLFZ.
•The multiscale teleseismic tomography is revised and used to construct a high-resolution velocity model.•High resolution Vp model reveals the depth distribution of the TLFZ.•The eastern TLFZ branches cut through the crust along a vertical low-velocity zone.•A 150 km seismic-quiescent segment of the central TLFZ is recognized to be a seismic gap.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. ...Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis.
Offshore freshened groundwater (OFG) has been encountered in continental margins around the world and identified as a potential unconventional water resource. In China, coastal areas and islands face ...limited freshwater resources. The East China Sea, specifically the region north of Shengsi islands, may contain an OFG system hosted in buried paleochannels associated with the ancient Yangtze river. To assess the OFG potential, characteristics, and controls in this region, we employed an integrated modeling approach. We constructed a 2D geological model of Quaternary sediments based on data from two well sites. By considering sea-level fluctuations over the past 200,000 years, we conducted a paleo-reconstruction of groundwater flow and solute transport conditions on the 2D transect. We compared the simulated present-day distribution of OFG in the model with borehole observations. Our findings indicate that the region was mostly sub-aerially exposed during the simulated period, allowing for potential meteoric recharge. Numerical results demonstrate a high likelihood of a laterally extensive OFG system existing today. The mechanism responsible for its formation appears to be meteoric recharge and offshore directed groundwater flow caused by increased hydraulic gradients during sea-level lowstand. The model suggests that the OFG system forms an oceanward dipping wedge, with the top occurring approximately 50–100 m below the seafloor. Freshwater is likely present down to the basement at around 250 m. The geometry and volume of the OFG system are strongly influenced by the shelf stratigraphy. We estimate the volume of freshwater in the region to range from 0.5 to 1.6 km
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, indicating a viable potential freshwater resource for the Shengsi region and coastal city of Shanghai. To gain further insights, we recommend conducting additional investigations using geophysical techniques.
To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking ...algorithm based on optical flow estimation. The algorithm consists of several steps. Firstly, an infrared image subspace model is established. Secondly, a full convolutional network (FCN) is utilized for local double-threshold segmentation of the target image. Furthermore, a target observation model is established using SIR filtering particles. Lastly, a shift vector sum algorithm is employed to enhance the intensity of the infrared image at a certain time scale in accordance with the relationship between the pixel intensity and the temporal parameters of the detected image. Experimental results demonstrate that the multi-target tracking accuracy (MOTA) reaches 79.7% and that the inference speed frame per second (FPS) reaches 42.3. Moreover, the number of ID switches during tracking is 9.9% lower than that of the MOT algorithm, indicating high recognition of cluster small targets, stable tracking performance, and suitability for tracking weak small targets on the ground or in the air.
A multispectral infrared zoom optical system design and a single-frame hierarchical guided filtering image enhancement algorithm are proposed to address the technical problems of low contrast, ...blurred edges, and weak signal strength of single-spectrum infrared imaging of faint targets, which are easily drowned out by noise. The multispectral infrared zoom optical system, based on the theory of complex achromatic and mechanical positive group compensation, can simultaneously acquire multispectral image information for faint targets. The single-frame hierarchical guided filtering image enhancement algorithm, which extracts the background features and detailed features of faint targets in a hierarchical manner and then weights fusion, effectively enhances the target and suppresses the interference of complex background and noise. Solving multi-frame processing increases data storage and real-time challenges. The experimental verification of the optical system design and image enhancement algorithm proposed in this paper separately verified that the experimental enhancement was significant, with the combined use improving Mean Square Error (MSE) by 14.32, Signal-Noise Ratio (SNR) by 11.64, Peak Signal-to-Noise Ratio (PSNR) by 12.78, and Structural Similarity (SSIM) by 14.0% compared to guided filtering. This research lays the theoretical foundation for the research of infrared detection and tracking technology for clusters of faint targets.
This paper presents a study on a quadrotor unmanned aerial vehicle (UAV) fault-tolerant control scheme. According to the attitude model and safety control of the aircraft under the uncertainty of ...inertial matrix, the attitude state constraint by reinforcement learning is designed to ensure safety. Even if the boundary is crossed, it can be pulled back to the boundary by means of a designed penalty function with reinforcement learning. Meanwhile, in order to inhibit the oscillation caused by immediate reward as usual, an adaptive update law is proposed. Furthermore, considering the coupled actuator fault and system input saturation due to uncertainty of inertial matrix, the Nussbaum-type function is utilized in this work to handle this challenge, which likely causes the singularity of inertia matrix. As a consequence, combined with the Lyapunov stability theory, it is confirmed that the proposed FTC scheme ensures that all the closed-loop signals are bounded. Simulation results are carried out to illustrate the effectiveness and advantage of the proposed control scheme.
The fault safety monitoring of hydrogen sensors is very important for their practical application. The precondition of traditional machine learning methods for sensor fault diagnosis is that enough ...fault data with the same distribution and feature space under the same working environment must exist. Widely used fault diagnosis methods are not suitable for real working environments because they are easily complicated by environmental conditions such as temperature, humidity, shock, and vibration. Under the influence of such complex conditions, the acquisition of sensor fault data is limited. In order to improve fault diagnosis accuracy under complex environmental conditions, a novel method of transfer learning (TL) with LeNet-5 is proposed in this paper. Firstly, LeNet-5 is applied to learn the features of the data-rich datasets of gas sensor faults in a normal environment and to adjust the parameters accordingly. The parameters of the LeNet-5 are transferred from the task in the normal environment to a task in a complex environment by using the TL method. Then, the migrated LeNet-5 is used for the fault diagnosis of gas sensors with a small amount of fault data in a complex environment. Finally, a prototype hydrogen sensor array is designed and implemented for experimental verification. The gas sensor fault diagnosis accuracy of the traditional LeNet-5 was 88.48 ± 1.04%, while the fault diagnosis accuracy of TL with LeNet-5 was 92.49 ± 1.28%. The experimental results show that the method adopted presents an excellent solution for the fault diagnosis of a hydrogen sensor using a small quantity of fault data obtained under complex environmental conditions.
In the logging-while-drilling acoustic measurements, distinguishing and estimating formation acoustic properties from the interfering signals are commonly deemed difficult. In this study, we present ...the results of a new signal processing technique based on the supervirtual interferometry theory for LWD acoustic data processing. Analytical and numerical modeling is used to analyze the waveform characteristics for different collar attention effects. The results show that the formation signal identification can be difficult in the presence of the collar wave, especially in the situation when the collar wave is poorly attenuated. We apply the new technique to the synthetic and field LWD acoustic data to evaluate its validity and effectiveness. The processing results for the interval demonstrate the ability of the technique to extract formation property for the LWD data in the presence of collar wave and drilling-related noise.
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