Pattern Recognition for Earthquake Detection
نویسنده
چکیده
The detector algorithms in use at date rely on negative decision logic: based on a model of the ambient noise process they detect all deviations, but many of them are false alarms. The principal alternative to this approach is pattern recognition, which tests on positive correlation with some known signal patterns. The Sonogram-detector realizes this scheme for single seismogram traces. Sonograms display spectral energy versus time. Suitably scaled, these images display only information which is significant to the detection process. Patterns of known earthquakes and noise signals are defined by means of these images. Event detection is performed by recognizing one of the patterns in the actual sonogram. The overall processing scheme is similar to the visual inspection of seismograms by the human observer. An off-line test installation for detecting local earthquakes proves the expected low false alarm rate, high timing accuracy and good detection probability of the Sonogram-detector.
منابع مشابه
A neuro-fuzzy approach to the reliable recognition of electric earthquake precursors
Electric Earthquake Precursor (EEP) recognition is essentially a problem of weak signal detection. An EEP signal, according to the theory of propagating cracks, is usually a very weak electric potential anomaly appearing on the Earth’s electric field prior to an earthquake, often unobservable within the electric background, which is significantly stronger and embedded in noise. Furthermore, EEP...
متن کاملDetection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...
متن کاملDetection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...
متن کاملSeismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/r...
متن کاملApplication of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation
This paper reports the experimental results on the application of different pattern recognition algorithms to the evaluation of earthquake risk for real geological structures. The study area used for the experiments is related to a well-known geological structure representing a “triangular valley over bedrock”. Performances obtained by two neural networks and two statistical classifiers are rep...
متن کاملLIQUEFACTION POTENTIAL ASSESSMENT USING MULTILAYER ARTIFICIAL NEURAL NETWORK
In this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, Dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1987