Semi-supervised seismic event detection using Siamese Networks

Detecting seismic events and their precursors is vital to understand and assess risks in areas of seismic instability. Most recent detection methods are based on supervised learning, where machine learning models are first trained using a labelled dataset, before being deployed. However, seismic sensors are often difficult to install and maintain, and large-scale events are few and far between. Furthermore, labelling collected data requires a great deal of time and effort from seismologists. Noise can vastly increase the difficulty of this task and labels can be highly subjective. Labelled data used for training machine learning models depends on the monitoring setup and geological characteristics of the terrain where the sensors are installed. For example, a dataset of events recorded in the Alps will likely not be representative of events that could be seen in less mountainous regions, meaning that transferability of proposed networks is vital. The Rest and Be Thankful in Scotland is a remote hillside prone to weather-induced seismic events which can cause disruption to the road infrastructure in the valley below, after rockfalls and landslides due to quakes. In this paper we propose a semi-supervised method of clustering these different types of events. Grouping data into categories of both known and unknown event types can reduce the time needed by experts to create labelled datasets via the use of Siamese networks and further understand the dynamics of the slope. We validate results against the BGS earthquake database from within a 50km radius, as well as human induced rockfalls. Grouping across around 100 days of data has detected a possible 10 earthquakes, 82 rockfalls, and 137 micro-quakes

High-resolution seismic event detection using local similarity for Large-N arrays

We develop a novel method for seismic event detection that can be applied to large-N arrays. The method is based on a new detection function named local similarity, which quantifies the signal consistency between the examined station and its nearest neighbors. Using the 5200-station Long Beach nodal array, we demonstrate that stacked local similarity functions can be used to detect seismic events with amplitudes near or below noise levels. We apply the method to one-week continuous data around the 03/11/2011 Mw 9.1 Tohoku-Oki earthquake, to detect local and distant events. In the 5–10 Hz range, we detect various events of natural and anthropogenic origins, but without a clear increase in local seismicity during and following the surface waves of the Tohoku-Oki mainshock. In the 1-Hz low-pass-filtered range, we detect numerous events, likely representing aftershocks from the Tohoku-Oki mainshock region. This high-resolution detection technique can be applied to both ultra-dense and regular array recordings for monitoring ultra-weak micro-seismicity and detecting unusual seismic events in noisy environments

The Viking seismometry

Efforts were made to determine the seismicity of Mars as well as define its internal structure by detecting vibrations generated by marsquakes and meteoroid impacts. The lack of marsquakes recognized in the Viking data made it impossible to make any direct inferences about the interior of Mars and only allowed the setting of upper bounds on the seismic activity of the planet. After obtaining more than 2100 hours worth of data during the quite periods at rates of one sample per second or higher, the Viking 2 seismometer was turned off as a consequence of a landing system failure. During the periods when adequate data were obtained, one event of possible seismic or meteoroid impact origin was recognized; however, there is a significant probability that this event was generated by a wind gust

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world. Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively

Integrated Seismic Event Detection and Location by Advanced Array Processing

The principal objective of this two-year study is to develop and test a new advanced, automatic approach to seismic detection/location using array processing. We address a strategy to obtain significantly improved precision in the location of low-magnitude events compared with current fully-automatic approaches, combined with a low false alarm rate. We have developed and evaluated a prototype automatic system which uses as a basis regional array processing with fixed, carefully calibrated, site-specific parameters in conjuction with improved automatic phase onset time estimation. We have in parallel developed tools for Matched Field Processing for optimized detection and source-region identification of seismic signals. This narrow-band procedure aims to mitigate some of the causes of difficulty encountered using the standard array processing system, specifically complicated source-time histories of seismic events and shortcomings in the plane-wave approximation for seismic phase arrivals at regional arrays