Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysi...Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic data, the authors present a set of data attributes that can be used to train a SOM in such a way that zones of interest can be automatically identified or segmented, reducing time in the interpretation process. The authors show how to associate SOM to 2D color maps to visually identify the clustering structure of the input seismic data, and apply the proposed technique to a 2D synthetic seismic dataset of salt structures.展开更多
The temporal and spatial distribution characteristics of earthquakes in the Ordos block are studied by using historical earthquake data,instrument data of the regional seismic network around the Ordos block and the hi...The temporal and spatial distribution characteristics of earthquakes in the Ordos block are studied by using historical earthquake data,instrument data of the regional seismic network around the Ordos block and the historical felt earthquake data,and the relationship between seismicity in the Ordos block and seismicity around the Ordos block is discussed. The result shows that the Ordos block is a typical moderate-strong earthquake active region where many M_S≥5.0 destructive earthquakes have occurred. The temporal and spatial distribution of earthquakes in the Ordos block is asymmetrical. The temporal distribution of earthquakes shows a periodic characteristic and the activity of earthquakes in the southeastern Ordos block is higher than in the northwest Ordos block. The M_S≥5.0 moderate size earthquakes in the Ordos block are controlled by the M_S≥6.0 earthquake around the Ordos block.展开更多
文摘Unsupervised neural networks such as the Kohonen Self-Organizing Maps （SOM） have been widely used for searching natural clusters in multidimensional and massive data. One example where the data available for analysis can be extremely large is seismic interpretation for hydrocarbon exploration. In order to assist the interpreter in identifying characteristics of interest confined in the seismic data, the authors present a set of data attributes that can be used to train a SOM in such a way that zones of interest can be automatically identified or segmented, reducing time in the interpretation process. The authors show how to associate SOM to 2D color maps to visually identify the clustering structure of the input seismic data, and apply the proposed technique to a 2D synthetic seismic dataset of salt structures.
基金funded by the project of"Study on the key techniques of strong earthquake risk zoning"under the National Science and Technology Support Program of China,Grant No.2006BAC13B01
文摘The temporal and spatial distribution characteristics of earthquakes in the Ordos block are studied by using historical earthquake data,instrument data of the regional seismic network around the Ordos block and the historical felt earthquake data,and the relationship between seismicity in the Ordos block and seismicity around the Ordos block is discussed. The result shows that the Ordos block is a typical moderate-strong earthquake active region where many M_S≥5.0 destructive earthquakes have occurred. The temporal and spatial distribution of earthquakes in the Ordos block is asymmetrical. The temporal distribution of earthquakes shows a periodic characteristic and the activity of earthquakes in the southeastern Ordos block is higher than in the northwest Ordos block. The M_S≥5.0 moderate size earthquakes in the Ordos block are controlled by the M_S≥6.0 earthquake around the Ordos block.
