Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection-identification of seismic events in continuous data-is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection metho...

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. ...

In this paper, we propose a novel framework for behaviors recognition and detection of certain types of abnormal behaviors, capable of achieving high detection rates on a variety of real-life scenes. The new proposed approach here is a combination of the location based methods and the object based ones. First, a novel approach is formulated to use optical flow and binary motion video as the loc...

This report presents an interim appraisal of capabilities of a single Large Aperture Seismic Array system to perform the following functions: (i) preprocess arriving seismic signals to increase their detectability, (ii) use such preprocessed signals to perform on-line automatic detection and location, (iii) process recordings of LASA data off-line, and (iv) use the results of the off-line proce...

Seismic data consist of traces, which contain information about a seismic event, but in some period of time the traces may be just noise. A trace which contains seismic information, is called a seismic signal. Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarisation. Amongst others, a ...

Previous work on novel object detection considers zero or few-shot settings where none few examples of each category are available for training. In real world scenarios, it is less practical to expect that ‘all’ the classes either unseen have few-examples. Here, we propose a more realistic setting termed ‘Any-shot detection’, totally and categories can simultaneously co-occur during inference. ...