Seismic and Geodetic Observations of Accelerated Sliding at Haupapa/Tasman Glacier, New Zealand

<p>Rain-induced accelerations of Haupapa/Tasman Glacier are accompanied by abundant seismicity. This seismicity reveals some of the glacial processes occurring at times of accelerated glacier sliding and those related directly to surficial water inputs.To study the processes occurring during rain-induced accelerations a network of seismic and geodetic sensors was deployed on the lower Haupapa/Tasman Glacier for four months in 2016. Seven categories of seismicity were defined during the study period. Glacier source processes were inferred for these categories based on their waveform characteristics, and each source was then compared to meteoric and geodetic data to discern spatial and temporal relationships. Of the seven categories of seismicity only the seismic events associated with crevasse opening were found to correlate with rain rate. Increased crevassing rate likely results from two factors: 1) increased extensional strain rates following the propagation of a subglacial cavitation front during transient accelerations and 2) hydrofracture due to the accumulation of rain in crevasses. Strain-driven crevassing is associated only with glacier acceleration, but crevasse opening via hydrofracture is inferred to occur independently of strain changes such that it is an active process at any point following heavy rainfall. Basal seismicity was not observed to respond to changes in glacier velocity or inferred subglacial water pressure, although this may be due to limitations in the seismic event detection technique.</p>

Seismic and Geodetic Observations of Accelerated Sliding at Haupapa/Tasman Glacier, New Zealand

<p>Rain-induced accelerations of Haupapa/Tasman Glacier are accompanied by abundant seismicity. This seismicity reveals some of the glacial processes occurring at times of accelerated glacier sliding and those related directly to surficial water inputs.To study the processes occurring during rain-induced accelerations a network of seismic and geodetic sensors was deployed on the lower Haupapa/Tasman Glacier for four months in 2016. Seven categories of seismicity were defined during the study period. Glacier source processes were inferred for these categories based on their waveform characteristics, and each source was then compared to meteoric and geodetic data to discern spatial and temporal relationships. Of the seven categories of seismicity only the seismic events associated with crevasse opening were found to correlate with rain rate. Increased crevassing rate likely results from two factors: 1) increased extensional strain rates following the propagation of a subglacial cavitation front during transient accelerations and 2) hydrofracture due to the accumulation of rain in crevasses. Strain-driven crevassing is associated only with glacier acceleration, but crevasse opening via hydrofracture is inferred to occur independently of strain changes such that it is an active process at any point following heavy rainfall. Basal seismicity was not observed to respond to changes in glacier velocity or inferred subglacial water pressure, although this may be due to limitations in the seismic event detection technique.</p>

MCA-Unet: Multi-class Attention-aware U-net for Seismic Phase Picking

&lt;p&gt;This study presents a deep learning based algorithm for seismic event detection and simultaneous phase picking in seismic waveforms. U-net structure-based solutions which consists of a contracting path (encoder) to capture feature information and a symmetric expanding path (decoder) that enables precise localization, have proven to be effective in phase picking. The network architecture of these U-net models mainly comprise of 1D CNN, Bi- &amp; Uni-directional LSTM, transformers and self-attentive layers. Althought, these networks have proven to be a good solution, they may not fully harness the information extracted from multi-scales.&lt;/p&gt;&lt;p&gt;&amp;#160;In this study, we propose a simple yet powerful deep learning architecture by combining multi-class with attention mechanism, named MCA-Unet, for phase picking. &amp;#160;Specially, we treat the phase picking as an image segmentation problem, and incorporate the attention mechanism into the U-net structure to efficiently deal with the features extracted at different levels with the goal to improve the performance on the seismic phase picking. Our neural network is based on an encoder-decoder architecture composed of 1D convolutions, pooling layers, deconvolutions and multi-attention layers. This architecture is applied and tested to a field seismic dataset (e.g. Wenchuan Earthquake Aftershocks Classification Dataset) to check its performance.&lt;/p&gt;

Proposal for a System Model for Offline Seismic Event Detection in Colombia

This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system.