前兆观测的方波型异常图像成因分析

文章对前兆观测中记录到的方波型异常图像从仪器标定、环境干扰、高压直流干扰、人为干扰、震兆异常等方面进行成因分析,根据不同成因提出干扰方波资料的处理方法和相应的预防措施,对前兆观测数据预处理及异常判定有一定的参考意义。

Edge enhancement of gravity anomalies and gravity gradient tensors using an improved small sub-domain filtering method

In order to enhance geological body boundary visual effects in images and improve interpretation accuracy using gravity and magnetic field data, we propose an improved small sub-domain filtering method to enhance gravity anomalies and gravity gradient tensors. We discuss the effect of Gaussian white noise on the improved small sub-domain filtering method, as well as analyze the effect of window size on geological body edge recognition at different extension directions. Model experiments show that the improved small sub-domain filtering method is less affected by noise, filter window size, and geological body edge direction so it can more accurately depict geological body edges than the conventional small sub-domain filtering method. It also shows that deeply buried body edges can be well delineated through increasing the filter window size. In application, the enhanced gravity anomalies and calculated gravity gradient tensors of the Hulin basin show that the improved small sub-domain filtering can recognize more horizontal fault locations than the conventional method.

Removal of the airwave effect by main-part decomposition of the anomalous field of MCSEM data

The airwave effect greatly influences the observational data from controlledsource electromagnetic exploration in shallow seas, which obscures the abnormal effects generated by exploration targets and, hence, affects the accuracy of the late exploration data interpretation. In this study, we propose a method to separate the main part from the anomalous field of marine controlled-source electromagnetic method （MCSEM） data based on Stratton-Chu integral transforms to eliminate the airwave effect, which dominates observed electromagnetic （EM） response in shallow seawater. This method of separating the main part from the anomalous field is a type of finite impulse response filter based on a discrete data set. Theoretical analysis proved that the method is stable and able to effectively depress noise. A numerical test indicated that the method could successfully eliminate the airwave effect from the observed EM signals generated by an air water interface and a seawater layer. This technique is applicable for seawater models with either flat or rough seabeds.

Super-resolution least-squares prestack Kirchhoff depth migration using the L_0-norm

Least-squares migration （LSM） is applied to image subsurface structures and lithology by minimizing the objective function of the observed seismic and reverse-time migration residual data of various underground reflectivity models. LSM reduces the migration artifacts, enhances the spatial resolution of the migrated images, and yields a more accurate subsurface reflectivity distribution than that of standard migration. The introduction of regularization constraints effectively improves the stability of the least-squares offset. The commonly used regularization terms are based on the L2-norm, which smooths the migration results, e.g., by smearing the reflectivities, while providing stability. However, in exploration geophysics, reflection structures based on velocity and density are generally observed to be discontinuous in depth, illustrating sparse reflectance. To obtain a sparse migration profile, we propose the super-resolution least-squares Kirchhoff prestack depth migration by solving the L0-norm-constrained optimization problem. Additionally, we introduce a two-stage iterative soft and hard thresholding algorithm to retrieve the super-resolution reflectivity distribution. Further, the proposed algorithm is applied to complex synthetic data. Furthermore, the sensitivity of the proposed algorithm to noise and the dominant frequency of the source wavelet was evaluated. Finally, we conclude that the proposed method improves the spatial resolution and achieves impulse-like reflectivity distribution and can be applied to structural interpretations and complex subsurface imaging.

Monitoring Freeway Incident Detection Using a Hotelling T2 Control Chart

In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.