Eliminated aliasing effect on wavelet transform based multiresolution analysis

Analysis of pre‐stack seismic data is important for seismic interpretation and geological features classification. However, most classification analyses are based on post‐stack data, which ignores pre‐stack information, and it may be disadvantageous for complex geological description. In this work, we propose a method to address the classification of pre‐stack seismic data decomposed using the wavelet transform to spread the amplitude and frequency seismic attributes at the same time, which are then classified by a self‐organizing map. The resulting classes constitute an attribute constructed by the joint amplitude–frequency components of the transformed pre‐stack seismic gathers, which create a multi‐dimensional set defined through a given metric. Tests on a real seismic cube revealed that the method can identify patterns observed on the seismic images, which agree with our current knowledge of the seismic data. The method can be used as a complementary tool to identify features and structures in seismic signals.

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Event Detection and Classification for Fiber Optic Perimeter Intrusion Detection System

Wang

Peng

et al. 2019

International Journal of Cognitive Informatics and Natural Intelligence

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A perimeter intrusion detection system (PIDS) is critical for the security of a shale gas field. Among many technologies, the fiber optic sensor-based method is the most widely used, due to its passive, low-cost, long-life, and strong anti-interference ability and strong environmental adaptability. This article proposes an event detection and classification method for a fiber optic PIDS. In general, three types of features are extracted for an improved double-threshold method to improve the probability of detection. Also, the detected intrusion events are distinguished by a support vector machine with wavelet features to reduce the nuisance alarm rate. Experiments on the PIDS in Chongqing Fuling's shale gas field show that detection algorithms based on the feature of short-time energy and short-time wavelet coefficient energy are much better, and the performance of event classification is satisfactory.

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Eliminated aliasing effect on wavelet transform based multiresolution analysis

Cited by 3 publications

References 2 publications

Multi-Feature-Based Intrusion Detection for Optical Fiber Perimeter Security System: A Aase Study

Multi-Feature-Based Intrusion Detection for Optical Fiber Perimeter Security System: A Aase Study

A method for classifying pre‐stack seismic data based on amplitude–frequency attributes and self‐organizing maps

Event Detection and Classification for Fiber Optic Perimeter Intrusion Detection System

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