Classification of Seismic Windows Using Artificial Neural Networks

Diersen, Steve; Lee, En‐Jui; Spears, Diana F.; Chen, Po; Wang, Liqiang

doi:10.1016/j.procs.2011.04.170

Cited by 48 publications

(14 citation statements)

References 29 publications

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“…The same algorithm is also being used in our tomographic inversions. The details of our waveform segmentation and selection algorithm are documented in a separate paper (Diersen et al 2011).…”

Section: Inversion Proceduresmentioning

confidence: 99%

Rapid full-wave centroid moment tensor (CMT) inversion in a three-dimensional earth structure model for earthquakes in Southern California

Lee¹,

Chen²,

Jordan³

et al. 2011

Geophysical Journal International

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S U M M A R YA central problem of seismology is the inversion of regional waveform data for models of earthquake sources. In regions such as Southern California, preliminary 3-D earth structure models are already available, and efficient numerical methods have been developed for 3-D anelastic wave-propagation simulations. We describe an automated procedure that utilizes these capabilities to derive centroid moment tensors (CMTs). The procedure relies on the use of receiver-side Green's tensors (RGTs), which comprise the spatial-temporal displacements produced by the three orthogonal unit impulsive point forces acting at the receivers. We have constructed a RGT database for 219 broad-band stations in Southern California using a tomographically improved version of the 3-D SCEC Community Velocity Model Version 4.0 (CVM4) and a staggered-grid finite-difference code. Finite-difference synthetic seismograms for any earthquake in our modelling volume can be simply calculated by extracting a small, source-centred volume from the RGT database and applying the reciprocity principle. The partial derivatives needed for the CMT inversion can be generated in the same way. We have developed an automated algorithm that combines a grid-search for suitable focal mechanisms and hypocentre locations with a Gauss-Newton optimization that further refines the grid-search results. Using this algorithm, we have determined CMT solutions for 165 small to medium-sized earthquakes in Southern California. Preliminary comparison with the CMT solutions provided by the Southern California Seismic Network (SCSN) shows that our solutions generally provide better fit to the observed waveforms. When applied to a large number of earthquakes, our algorithm may provide a more robust CMT catalogue for earthquakes in Southern California.

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Section: Inversion Proceduresmentioning

confidence: 99%

Rapid full-wave centroid moment tensor (CMT) inversion in a three-dimensional earth structure model for earthquakes in Southern California

Lee¹,

Chen²,

Jordan³

et al. 2011

Geophysical Journal International

Self Cite

View full text Add to dashboard Cite

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“…The confluence of new ML algorithms, fast and inexpensive graphical processing units and tensor processing units, and the availability of massive, often continuous datasets has driven this revolution in data-driven analysis. This rapid expansion has seen application of existing and new ML tools to a suite of geoscientific problems ( 33 – 36 ) that span seismic wave detection and phase identification and location ( 34 , 37 – 45 ), geological formation identification ( 46 , 47 ), earthquake early warning ( 48 ), volcano monitoring ( 49 – 51 ), denoising Interferometric Synthetic Aperture Radar (InSAR) ( 50 , 52 , 53 ), tomographic imaging ( 54 – 57 ), reservoir characterization ( 58 – 60 ), and more. Of particular note is that, over the past 5 y, considerable effort has been devoted to using these approaches to characterize fault physics and forecast fault failure ( 1 – 3 , 13 , 35 , 61 – 63 ).…”

Section: Recent Applications Of ML In Earthquake Sciencementioning

confidence: 99%

Laboratory earthquake forecasting: A machine learning competition

Johnson

Rouet‐Leduc

Pyrak‐Nolte

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

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“…First, the naive Bayes classifier works well because the categorical attributes, bandwidth and job type, are independent. Secondly, compared with other classifiers, the naive Bayes classifier shows efficient and stable classification when the number of features and categories are not large [15]. In our BAPM, we have only two features and two categories.…”

Section: Bayes Compute Unitmentioning

confidence: 99%

Naive Bayes Classifier Based Partitioner for MapReduce

Chen

Lü

Bao

et al. 2018

IEICE Trans. Fundamentals

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MapReduce is an effective framework for processing large datasets in parallel over a cluster. Data locality and data skew on the reduce side are two essential issues in MapReduce. Improving data locality can decrease network traffic by moving reduce tasks to the nodes where the reducer input data is located. Data skew will lead to load imbalance among reducer nodes. Partitioning is an important feature of MapReduce because it determines the reducer nodes to which map output results will be sent. Therefore, an effective partitioner can improve MapReduce performance by increasing data locality and decreasing data skew on the reduce side. Previous studies considering both essential issues can be divided into two categories: those that preferentially improve data locality, such as LEEN, and those that preferentially improve load balance, such as CLP. However, all these studies ignore the fact that for different types of jobs, the priority of data locality and data skew on the reduce side may produce different effects on the execution time. In this paper, we propose a naive Bayes classifier based partitioner, namely, BAPM, which achieves better performance because it can automatically choose the proper algorithm (LEEN or CLP) by leveraging the naive Bayes classifier, i.e., considering job type and bandwidth as classification attributes. Our experiments are performed in a Hadoop cluster, and the results show that BAPM boosts the computing performance of MapReduce. The selection accuracy reaches 95.15%. Further, compared with other popular algorithms, under specific bandwidths, the improvement BAPM achieved is up to 31.31%.

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Classification of Seismic Windows Using Artificial Neural Networks

Cited by 48 publications

References 29 publications

Rapid full-wave centroid moment tensor (CMT) inversion in a three-dimensional earth structure model for earthquakes in Southern California

Rapid full-wave centroid moment tensor (CMT) inversion in a three-dimensional earth structure model for earthquakes in Southern California

Laboratory earthquake forecasting: A machine learning competition

Naive Bayes Classifier Based Partitioner for MapReduce

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