Land subsidence over oilfields in the Yellow River Delta

Subsidence in river deltas is a complex process that has both natural and human causes. Increasing human activities like aquaculture and petroleum extraction are affecting the Yellow River delta, and one consequence is subsidence. The purpose of this study is to measure the surface displacements in the Yellow River delta region and to investigate the corresponding subsidence source. In this paper, the Stanford Method for Persistent Scatterers (StaMPS) package was employed to process Envisat ASAR images collected between 2007 and 2010. Consistent results between two descending tracks show subsidence with a mean rate up to 30 mm/yr in the radar line of sight direction in Gudao Town (oilfield), Gudong oilfield and Xianhe Town of the delta, each of which is within the delta, and also show that subsidence is not uniform across the delta. Field investigation shows a connection between areas of non-uniform subsidence and of petroleum extraction. In a 9 km2 area of the Gudao Oilfield, a poroelastic disk reservoir model is used to model the InSAR derived displacements. In general, good fits between InSAR observations and modeled displacements are seen. The subsidence observed in the vicinity of the oilfield is thus suggested to be caused by fluid extraction

A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification

Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and phenotypes. Many methods have been proposed to find subsets of genes with maximum relevance and minimum redundancy, which can distinguish accurately between samples with different labels. To find the minimum subset of relevant genes is often referred as biomarker discovery. Two main approaches, filter and wrapper techniques, have been applied to biomarker discovery. In this paper, we conducted a comparative study of different biomarker discovery methods, including six filter methods and three wrapper methods. We then proposed a hybrid approach, FR-Wrapper, for biomarker discovery. The aim of this approach is to find an optimum balance between the precision of the biomarker discovery and the computation cost, by taking advantages of both filter method’s efficiency and wrapper method’s high accuracy. Our hybrid approach applies Fisher’s ratio, a simple method easy to understand and implement, to filter out most of the irrelevant genes, then a wrapper method is employed to reduce the redundancy. The performance of FR-Wrapper approach is evaluated over four widely used microarray datasets. Analysis of experimental results reveals that the hybrid approach can achieve the goal of maximum relevance with minimum redundancy

Arbitrarily Oriented Dense Object Detection Based on Center Point Network in Remote Sensing Images

Arbitrarily oriented object detection has recently attracted increasing attention for its wide applications in remote sensing. However, it is still a challenge for detection algorithms because of complex scenes, small size, rotation, densely parked. And angle discontinuity at the boundary is an important factor restricting model performance. In this paper, we propose an anchor-free approach for the high-precision detection of rotated objects. Firstly, our model achieves the classification and localization of an object by detecting its center point. Secondly, we convert the regression task of angle into a classification task and utilize the periodicity and symmetry of the transformation function to eliminate the disturbance of angle discontinuity. Thirdly, the dynamic gradient adjustment method is applied to suppress the negative effects of sample imbalance on the classification task. In addition, we proposed a union loss function to achieve accurate and stable regression of the rotated bounding box. We perform a series of ablation experiments to validate the effectiveness of the improvements. The experimental results obtained on several publicly available remote sensing datasets show that the proposed method has a higher detection accuracy, and it can be applied to efficiently identify rotated objects in remote sensing images

A New Processing Chain for Real-Time Ground-Based SAR (RT-GBSAR) Deformation Monitoring

Due to the high temporal resolution (e.g., 10 s) required, and large data volumes (e.g., 360 images per hour) that result, there remain significant issues in processing continuous ground-based synthetic aperture radar (GBSAR) data. This includes the delay in creating displacement maps, the cost of computational memory, and the loss of temporal evolution in the simultaneous processing of all data together. In this paper, a new processing chain for real-time GBSAR (RT-GBSAR) is proposed on the basis of the interferometric SAR small baseline subset concept, whereby GBSAR images are processed unit by unit. The outstanding issues have been resolved by the proposed RT-GBSAR chain with three notable features: (i) low requirement of computational memory; (ii) insights into the temporal evolution of surface movements through temporarily-coherent pixels; and (iii) real-time capability of processing a theoretically infinite number of images. The feasibility of the proposed RT-GBSAR chain is demonstrated through its application to both a fast-changing sand dune and a coastal cliff with submillimeter precision

Formation conditions and distribution patterns of N-1 tight oil in Zhahaquan Area, Qaidam Basin, China

A systematic study on the formation conditions and distribution patterns of N-1 tight oil in Zhahaquan Area of the Qaidam Basin was conducted by summarizing and analyzing documents related to geochemistry, sedimentary petrology, petroleum geology, lithofacies paleogeography, oil production test, well drilling and the well logging, etc. The four favorable conditions for the formation, migration, and accumulation of tight oil in the Zhahaquan Area are as follows: good source rocks, high quality reservoirs, display of the reservoirs, and abnormal high pressure of the stratum. Firstly, there are three sets of high quality source rocks that lay a good foundation for the formation of tight oil in this area. Secondly, high quality reservoirs consisting of multiple types of sand, such as beach sand, gravity current sand, and beach-dam sand are widely distributed, thickly accumulated and continuous, thus providing reserving space for the formation of tight oil in this area. Thirdly, the reservoirs of beach-bar sand links with the source rocks directly or links with the source rocks like fingers distributed in the deep or semi-deep lake or are interbedded with source rocks. Above source and reservoir dispositions are favorable for the formation of tight oil. Lastly, in the N-1 downstream, tight oil reservoirs are at an abnormal high pressure of 1.3-1.5, which provides enough pressure for tight oil charging and reservoir forming. The study shows that tight oil is mainly distributed in the sag center and the slopes around the sag and has a huge resource potential.National Science and Technology Major Special Projects [2011ZX05009-002-403, 2011ZX05004-004-005]; National Key Basic Research Development Planned Project [2012CB214801]SCI(E)[email protected]

Impacts of Sulfur and Microalgae Co-fertilization on Saline-alkaline Soil of Sunflower Field in Hetao Irrigation District

[Objectives]To evaluate the impacts of the elemental sulfur (S0) and micro-algae (MA) co-fertilization on saline-alkaline soil of sunflower field in the Hetao Irrigation District (HID). [Methods]The greenhouse pot experiment was conducted with four treatments: control (CK), single S0fertilization (S), single MA fertilization (A), and S0and MA co-fertilization (SA) for comparing the selected soil properties and sunflower plant heights and weights in different treatments. [Results]The results showed that the mean soil organic matter (SOM) under the SA (25.08 g/kg) was significantly higher than that for the CK (20.59 g/kg), S (22.47 g/kg), and A (22.95 g/kg). The mean pH under the SA (7.75) was significantly lower than that for the CK (8.14), S (7.82), and A (7.96). The mean soil exchangeable Na+concentration under the SA was significantly lower than that for the S. The mean soil electrical conductivity (EC) under the SA was 9.76% lower than that for the S. The means of Cl-(1.22 g/kg) and width=31,height=15,dpi=110(1.90 g/kg) in soil under the SA were lower than that for the S (1.30, 2.06 g/kg)and A (1.31, 1.97 g/kg), respectively. For plant height 3 at the late stage of plant growth, the mean plant height 3 under the SA (89.00 cm) was higher than that of the CK (69.60 cm) and A (74.33 cm). The total weights of the fresh sunflower heads, fresh stems, and dry seeds under the SA were higher than that for the CK, S, and A. [Conclusions]In conclusion, the S0and MA co-fertilization had positive effects on improving saline-alkaline soils, the soil under the S0and MA co-fertilization could be better conditions for promoting sunflower growth than that for the S, Z, and CK, and thereby the S0and MA co-fertilization could be a new idea to improve saline-alkaline soil in the cold and arid regions

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a &ldquo;spurious&rdquo; heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in &ldquo;spurious&rdquo; heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.<br /