Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill monitoring.However,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater samples.Moreover,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and sunglint.To address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various classes.Using the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill detection.Based on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment experiments.In contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss functions.In comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods.To verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two studies areas(study area 2 and study area 3).The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88,which is 0.16–0.24 higher than that of other methods,and the F1-Score of the detection result of oil slicks is 0.96–0.97,which is 0.06–0.23 higher than that of other methods.Based on all the above experiments,we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills,and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

Research on polarization of oil spill and detection

The SAR（Synthetic Aperture Radar） has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD（Co-polarized Phase Difference） of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

A new oil spill detection algorithm based on Dempster-Shafer evidence theory

Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.

Adulteration detection of edible oils with castor oil by gas chromatography

Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.

A method for coastal oil tank detection in polarimetric SAR images based on recognition of T-shaped harbor

To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

An interdigitated impedance microsensor for detection of moisture content in engine oil

To address the need for the on-site measurement of aging oil, in this paper, we propose an impedance-based microsensor for analyzing the moisture content in engine oil. Using a microfabrication process, we fabricated an interdigitated microelectrode and integrated it with a 3 D-printed microcontainer to produce a microsensor that can detect changes in the permittivity of oil. When the moisture content in oil increases, this sensor can detect the resulting change in the oil impedance, which is related to its permittivity, and then determine the degree to which the oil has aged. The test results show that the proposed microsensor has the advantages of being small and having high sensitivity, good accuracy, and the ability to be combined with hand-held instruments.The proposed method is expected to be used for the rapid, low cost, on-site determination of oil aging.

3-D Fiber Optic Fluorescent Detection System for Mineral Oilin Water

Parameter quantification principle of three-dimensional(3-D) fluorescent spe ctrum and its realization technique are studied. The portab le fiber-optic fluorescent measurer for mineral oil is given. Thr ee parameters of the exciting light wavelength, the wavelength and the intensity of the fluorescent light excited by the measured oil are expressed with 3-D co ordinates and are processed so as to be sampled again and to be curve fitted.

Common Transceiver LIF⁃Lidar Based on Y⁃Type Optical Fiber for Marine Oil Spill Detection

This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.

Research on Oil Pipeline Leak Detection and Location system

Because pipeline has large pipe diameter, large throughout and high pressure, once pipeline leakage accident happens, the damage is quite serious. In addition, pipeline leakage accident caused by man-made drilling oil stolen every year results in huge economic losses on oilfield. Therefore, a real-time and accurate pipeline leak detection and location system not only can effectively decrease leakage loss and reduce the waste of manpower and material resources in patrolling work, but also is conductive to the management of oil pipeline and improvement of economic efficiency of enterprise. The paper determines leak detection and location project giving priority to negative pressure wave and supplemented by flow parameter analysis. The method not only can judge the accidence of leakage timely and accurately, but also can effectively avoid leakage false alarm caused by start or stop pumps in pipeline.

A proposed NMR solution for multi-phase flow fluid detection

In the petroleum industry,detection of multi-phase fluid flow is very important in both surface and down-hole measurements.Accurate measurement of high rate of water or gas multi-phase flow has always been an academic and industrial focus.NMR is an efficient and accurate technique for the detection of fluids;it is widely used in the determination of fluid compositions and properties.This paper is aimed to quantitatively detect multi-phase flow in oil and gas wells and pipelines and to propose an innovative method for online nuclear magnetic resonance(NMR)detection.The online NMR data acquisition,processing and interpretation methods are proposed to fill the blank of traditional methods.A full-bore straight tube design without pressure drop,a Halbach magnet structure design with zero magnetic leakage outside the probe,a separate antenna structure design without flowing effects on NMR measurement and automatic control technology will achieve unattended operation.Through the innovation of this work,the application of NMR for the real-time and quantitative detection of multi-phase flow in oil and gas wells and pipelines can be implemented.

Pipeline Potential Leak Detection Technologies: Assessment and Perspective in the Nigeria Niger Delta Region

This paper examines the advances in pipeline third party encroachment alert systems and leak control methods in the oil/gas industry. It also highlights the extent of spill/pollution issues in the Niger Delta region due to intended/unin- tended damages and suggests a possible method of control. It is believed that the best option to avoid pollution due to pipeline failure is to ensure that hydrocarbon does not exit from the pipeline. With the different methods considered in this review, acoustic monitoring of change in the operational sound generated from a given pipeline section is suggested to be practicable to identifying sound abnormalities of third party encroachments. One established challenge of the acoustic system for buried pipelines protection is attenuation of acoustic transmission. An attempt to check the performance of an acoustic transmission on steel pipelines submerged in water points to a similar research on plastic water pipelines that attenuation is small compared with pipe buried in soil. Fortunately, Niger Delta of Nigeria is made of wetland, swamps and shallow water and could therefore offer an opportunity to deploy acoustic system for the safety of pipelines against third party attacks in this region. However, the numerous configuration and quantity of oil installation in this region imply that cost of application will be enormous. It is therefore suggested that a combination of impressed alternating cycle current (IACC) which traces encroachment on the pipeline coating and an acoustic system be used to manage intended and unintended pipeline potential damages. The IACC should be used for flow lines and other short distance delivery lines within the oilfield, while the relatively large diameter and long length delivery, trunk and transmission lines should be considered for acoustic protection. It is, however, noted that further efforts are required to reduce cost and improve effectiveness of these systems.

The influence factors of gas-bearing and geological characteristics of Niutitang Formation shale in the southern margin of Xuefeng Mountain ancient uplift: A case of Well Huangdi 1

In order to evaluate the geological characteristics and gas-bearing factors of Niutitang Formation within the Lower Cambrian of northern Guizhou,the Huangping area located at the southern edge of the ancient uplift belt of Xuefeng Mountain was selected as the target area,and Well Huangdi 1 was drilled for the geological survey of shale gas.Through geological background analysis and well logging and laboratory analysis such as organic geochemical test,gas content analysis,isothermal adsorption,and specific surface area experiments on Well Huangdi 1,the results show that the Niutitang Formation is a deep-water shelf,trough-like folds and thrust fault.The thickness of black shale is 119.95 m,of which carbonaceous shale is 89.6 m.The average value of organic carbon content is 3.55%,kerogen vitrinite reflectance value is 2.37% and kerogen type is sapropel-type.The brittle mineral content is 51%(quartz 38%),clay mineral content is 38.3%.The value of porosity and permeability are 0.5%and 0.0014 mD,which the reservoir of the Niutitang Formation belongs to low permeability with characteristics of ultra-low porosity.The gas content is 0.09‒1.31 m^3/t with a high-value area and a second high-value area.By comparing with the geological parameters of adjacent wells in the adjacent area,the accumulation model of“sediment control zone,Ro control zone,structure controlling reservoir”in the study area is proposed.Therefore,deep-water shelf-slope facies,Ro is between high maturity-early stage of overmaturity and well-preserved zones in the Niutitang Formation in this area are favorable direction for the next step of shale gas exploration.

Three-Dimensional Finite Element Numerical Simulation and Physical Experiment for Magnetism-Stress Detecting in Oil Casing

The casing damage has been a big problem in oilfield production. The current detection methods mostly are used after casing damage, which is not very effective. With the rapid development of China's offshore oil industry, the number of offshore oil wells is becoming larger and larger. Because the cost of offshore oil well is very high, the casing damage will cause huge economic losses. What's more, it can also bring serious pollution to marine environment. So the effective methods of detecting casing damage are required badly. The accumulation of stress is the main reason for the casing damage. Magnetic anisotropy technique based on counter magnetostriction effect can detect the stress of casing in real time and help us to find out the hidden dangers in time. It is essential for us to prevent the casing damage from occurring. However, such technique is still in the development stage. Previous studies mostly got the relationship between stress and magnetic signals by physical experiment, and the study of physical mechanism in relative magnetic permeability connecting the stress and magnetic signals is rarely reported. The present paper uses the ANSYS to do the three-dimensional finite element numerical simulation to study how the relative magnetic permeability works for the oil casing model. We find that the quantitative relationship between the stress' s variation and magnetic induction intensity's variation is: Δδ =K* ΔB, K = 8.04×109, which is proved correct by physical experiment.

Theory analysis and experimental research on on-line contamination detecting technology in hydraulic oil

A system of on-line contamination detecting in hydraulic oil based on silting principle is accomplished, where, metal filter membrane as detector, solenoid as active force to propel piston to blotter and gain differential pressure, step motor drives the membrane to filtrate and counter-flush, LabVIEW as detecting software platform, oil's contamination detecting indirectly by gauging differential pressure. Based on theory analysis, accomplished is relation between contamination level and differential pressure, realizing polynomial curve fitting, and calibration experiment. Field experiment is simulated in the condition of experimental laboratory, has credible precision and real-time performance, which can popularize to the field of production.

TEST STUDY OF DETECTING OIL AND GAS BY USING REMOTE SENSING TECHNIQUE

Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process

Discrimination of pressed sesame oil:A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints.The goal of this study was to provide a reliable tool for assessing the quality of sesame oil.The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200-330 nm.Various classification and prediction methods,including linear discrimination analysis(LDA),support vector machines(SVM),soft independent modeling of class analogy(SIMCA),partial least squares regression(PLSR),support vector machine regression(SVR),and back-propagation neural network(BPNN),were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil.The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil,achieving an accuracy of 96.15%,a sensitivity of 97.87%,and a specificity of 80%.For quantitative analysis,BPNN yielded the best prediction results,with an R^(2) value of 0.99,RMSEP of 2.34%,and RPD value of 10.60(LOD of 8.60%and LOQ of 28.67%).Overall,the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.

The polarimetric features of oil spills in full polarimetric synthetic aperture radar images

Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.