Application on Polynomial Approximation Method in Airborne Geological Exploration

Coil motion noise is the main noise source in AEM data caused by the movement of the receiver coil in the Earth’s magnetic field. The characteristics of both time domain and frequency domain of motion noise are analyzed. In this paper we present a scheme of removing motion noise which employs polynomial to represent motion noise for each half cycle. A set of equations are set up according to the characteristics of AEM data, and Lagrange method is employed to solve minimum problem with two constraint conditions together. The polynomial coefficients are calculated by matrix inverse and finally motion noise is subtracted from the AEM data half cycle by half cycle. Polynomial approximation method is applied to the synthetic data contaminated by low frequency motion noise comparing with traditional high pass filter method. The corrected AEM data shows polynomial approximation method is more effective in dealing with motion noise

An Optimized Air-Core Coil Sensor with a Magnetic Flux Compensation Structure Suitable to the Helicopter TEM System

The air-core coil sensor (ACS) is widely used as a transducer to measure the variation in magnetic fields of a helicopter transient electromagnetic (TEM) system. A high periodic emitting current induces the magnetic field signal of the underground medium. However, such current also generates a high primary field signal that can affect the received signal of the ACS and even damage the receiver. To increase the dynamic range of the received signal and to protect the receiver when emitting current rises/falls, the combination of ACS with magnetic flux compensation structure (bucking coil) is necessary. Moreover, the optimized ACS, which is composed of an air-core coil and a differential pre-amplifier circuit, must be investigated to meet the requirements of the helicopter TEM system suited to rapid surveying for shallow buried metal mine in rough topography. Accordingly, two ACSs are fabricated in this study, and their performance is verified and compared inside a magnetic shielding room. Using the designed ACSs, field experiments are conducted in Baoqing County. The field experimental data show that the primary field response can be compensated when the bucking coil is placed at an appropriate point in the range of allowed shift distance beyond the center of the transmitting coil and that the damage to the receiver induced by the over-statured signal can be solved. In conclusion, a more suitable ACS is adopted and is shown to have better performance, with a mass of 2.5 kg, resultant effective area of 11.6 m2 (i.e., diameter of 0.496 m), 3 dB bandwidth of 66 kHz, signal-to-noise ratio of 4 (i.e., varying magnetic field strength of 0.2 nT/s), and normalized equivalent input noise of 3.62 nV/m2

Life-threatening bleeding from gastric dieulafoy’s lesion in a pregnant woman with hellp syndrome: a case report and literature review

Abstract Background Dieulafoy’s lesion (DL) is one of the rare causes of upper gastrointestional bleeding. This disease is characterized by small sub-mucosal arteriole that eroded the stomach mucosa and cause severe upper GI bleeding without obvious ulceration. The most common location is fundus area of stomach and usually affects patients over 50 years of age with multiple comorbidities. Case presentation We report a case of life-threatening bleeding from DL during late pregnancy 31 weeks. Hemoclips were used twice through upper endoscopy with successful hemostasis. Unfortunately, she developed HELLP syndrome diagnosed 5 days after the GI bleeding was stopped. Her pregnancy had to be terminated with delivery of a premature infant. She recovered from her illness and discharged from hospital uneventfully. There is no current report in literature of DL in pregnant woman subsequently suffered HELLP syndrome. Conclusion Endoscopic hemoclip application is an effective technique in the treatment of upper GI bleeding from DL. For this patient, laparoscopic surgery or combination therapy before pregnancy may have been a suitable treatment on preventing rebleeding

The characteristics analysis of strain variation associated with Wenchuan earthquake using principal component analysis

Borehole strainmeters that are installed deeply into bedrock are capable of recording both continuous stress and strain measurements, and have consequently become an important tool for monitoring crustal deformation. A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Wenchuan earthquake on May 12, 2008 (UTC) (=8.0). We apply principal component analysis (PCA) to analyze borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors are calculated. The fitted results of the cumulative number of anomalous eigenvalues demonstrate that an acceleration occurred approximately 4 months before the earthquake (from January 2008). The results of the combined eigenvalue and eigenvector analyses show that the spatial distribution of eigenvectors and accelerated occurrence of eigenvalue anomalies represents the stress evolution characteristics of the fault from a steady state to a sub-instability state in rock experiments. We tentatively infer that this process may also be linked to the preparation phase of a large earthquake

Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm

Aerosol loadings and their spatial distribution are among the most important atmospheric information needed for a range of applications such as air quality monitoring, climate research, and public health. A key measure of aerosol quantity is aerosol optical depth (AOD) and it has been routinely observed from space by Earth observing satellites/instrument, especially the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite its global coverage and daily temporal resolution, the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product is fraught with missing values, severely limiting its use. A gap-filling method which is suitable for large-area application with high efficiency to obtain gapless AOD with reasonable spatial pattern and complete coverage is still lacking. Here, we proposed a novel spatiotemporal fitting algorithm to gap-fill the daily MODIS AOD product. Our algorithm is a multi-stage method aimed to address the non-stationary nature of AOD time series. First, the trend of daily AOD in a year in each pixel was fitted via smoothing splines and the residual was derived based on the original data and the trend. Second, the residual was spatially interpolated, leveraging the spatial correlation between the target pixel and the neighboring pixels. Third, the actual AOD was calculated as the sum of the trend and interpolated residual. We tested the algorithm against ground-based AOD data from 2011 to 2018 in China and further evaluated it via cross-validation at the global scale based on 10 selected MODIS tiles. Compared to the ground-reference AOD, the RMSE of our gapless datasets were 0.24 and 0.27 for Terra and Aqua, respectively; and the cross-validation showed a RMSE ranging from 0.045 to 0.055 (Terra) and 0.047 to 0.057 (Aqua) under different missing ratios. The novel gap-filling method outperforms the Interpolation-based Correlation Weighting (ICW) and Inverse Distance Weighting (IDW) algorithms in accuracy. Meanwhile, the gapless AOD using the novel algorithm shows lower accuracy than original MAIAC AOD, similar accuracy with the AOD from the Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset, higher accuracy than the AOD from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, the accuracy of gapless AOD using this algorithm meets the need of typical applications in relevant studies. The proposed algorithm is transferable to other regions, with the potential to be used even operationally and efficiently for generating accurate gapless global daily AOD datasets with the input of only MODIS MAIAC AOD data

Proteomics investigation of the changes in serum proteins after high- and low-flux hemodialysis

Purpose: This study aimed to use proteomics methods to investigate the changes in serum protein levels after high- and low-flux hemodialysis (HD). Methods: Before and after HD, serum samples were obtained from two selected patients who were treated with a Polyflux 140 H high-flux dialyzer and a Polyflux 14 L low-flux dialyzer during two continuous therapy sessions. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to identify the proteins. Results: A total of 212 and 203 serum proteins were identified after high-flux and low-flux HD, respectively. After high-flux HD, 21 proteins increased, and 132 proteins decreased. After low-flux HD, 87 proteins increased, and 45 proteins decreased. High-flux HD led to a significantly greater reduction in protein levels than low-flux HD (0.73 ± 0.13 vs. 0.84 ± 0.18, p = .00). Among the increased and decreased proteins, the isoelectric point (pI) values mainly ranged from 5 to 7, and the molecular weights (Mws) were mostly smaller than 30 kDa. The serum proteins showed no difference in pI or Mw for high- and low-flux HD. Gene ontology (GO) analysis showed that the detected proteins were related to immune system processes and complement activation. Conclusions: Serum protein levels differentially changed after high- and low-flux HD. Long-term effects should be observed in future studies

Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis

A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Lushan earthquake on April 20, 2013 (UTC) ( =7.0). To identify earthquake-induced abnormal strain changes, we apply principal component analysis (PCA) for the first time to analyse the borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors demonstrate that the anomalous days are mainly concentrated within two time periods:1) October 25-December 30, 2012, and 2) April 15-19,2013. A combined eigenvalue and eigenvector analysis reveals that the abnormal days exhibit a clustered distribution that is aggregated in the same location for both periods, intuitively indicating that there is a forceful correlation between the two anomalies. We infer that a similar process contributed to the formation of both anomalies and that these two anomalies are both earthquake precursors associated with the Lushan earthquake. These findings also indicate that the PCA approach exhibits potential for the extraction of earthquake precursor anomalies

Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve

In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, Nano, and the length of alarm window, Talm. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, tpr, and false alarm rate, fpr, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting