Real-Time Classification of Earthquake using Deep Learning

Küyük, H. Serdar; Ohno, Susumu

doi:10.1016/j.procs.2018.10.316

Cited by 56 publications

(23 citation statements)

References 15 publications

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“…In the case of sound, LSTM has been used in the problem of speech recognition (Sak et al, 2014) and the classification of laryngitis (Guedes et al, 2018). In addition, in 2018, LSTM was used to detect earthquakes in Japan (Kuyuk and Susumu, 2018) with good results. Zhou et al (2015) introduced a new method of classification combining the Convolutional Neural Network (CNN) and LSTM called C-LSTM.…”

Section: A Long Short Term Memory (Lstm)mentioning

confidence: 99%

Email Spam Classification Using Gated Recurrent Unit and Long Short-Term Memory

Basyar¹,

Adiwijaya²,

Murdiansyah³

2020

Journal of Computer Science

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High numbers of spam emails have led to an increase in email triage, causing losses amounting to USD 355 million per year. One way to reduce this loss is to classify spam email into categories including fraud or promotions made by unwanted parties. The initial development of spam email classification was based on simple methods such as word filters. Now, more complex methods have emerged such as sentence modeling using machine learning. Some of the most well-known methods for dealing with the problem of text classification are networks with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). This study focuses on the classification of spam emails, so both the LTSM and GRU methods were used. The results of this study show that, under the scenario without dropout, the LSTM and GRU obtained the same accuracy value of 0.990183, superior to XGBoost, the base model. Meanwhile, in the dropout scenario, LSTM outperformed GRU and XGboost with each obtaining an accuracy of 98.60%, 98.58% and 98.52%, respectively. The GRU recall score was better than that of LSTM and XGBoost in the scenario with dropouts, each obtaining values of 98.98%, 98.92% and 98.15% respectively. In the scenario without dropouts, LSTM was superior to GRU and XGBoost, with each obtaining values of 98.39%, 98.39% and 98.15% respectively.

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Section: A Long Short Term Memory (Lstm)mentioning

confidence: 99%

Email Spam Classification Using Gated Recurrent Unit and Long Short-Term Memory

Basyar¹,

Adiwijaya²,

Murdiansyah³

2020

Journal of Computer Science

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“…In civil engineering, classification is preferred for finding vehicle types in traffic by using deep learning algorithms, in natural disasters and accidents [78] for damage assessment of settlements or solving other infrastructure problems [83]. Classification from real-time images or visuals in the dataset is done by CNN architecture [84]. The classification does with the identification of the object from the visual in some applications.…”

Section: Classificationmentioning

confidence: 99%

İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

Çakiroğlu¹,

Süzen²

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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In this study, the applicability of deep learning algorithms in the field of civil engineering has been investigated. Firstly, the information that is about deep learning algorithms has been given. Additionally, deep learning applications, which are made, in subjects such as classification, estimation, and interpretation in the field of civil engineering, have been examined. The applications are elaborated according to civil engineering's sub-branches that transportation, geotechnical and construction. The contributions of the realized applications' in view of success rates to civil engineering that were analyzed. As a result of the study, it is foreseen that in the studies where the number of data is high, high performance will be achieved in the use of deep algorithms.

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“…Applications of RNN and LSTM to solve geophysical problems are recent and date back to the very last years. These examples refer to earthquake classification (Kuyuk & Susumu, 2018), detection of earthquake precursors (Cai et al ., 2019), earthquake magnitude prediction (Gonzales et al ., 2019), facies classification from post‐stack seismic data (Grana et al., 2020), seismic velocity analysis (Fabien‐Ouellet & Sarkar, 2020), well log generation (Zhang et al ., 2018), well production prediction (Jie et al ., 2020), seismic data interpolation (Yoon et al ., 2020) and porosity estimation from well log data (Chen et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

Elastic properties and litho‐fluid facies estimation from pre‐stack seismic data through bidirectional long short‐term memory networks

Aleardi

2022

Geophysical Prospecting

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One of the main goals of pre‐stack seismic inversion is the estimation of elastic properties (i.e. P‐, S‐ wave velocities and density) and litho‐fluid classes in the investigated area. To this end, many inversion strategies have been proposed, but the most popular is based on a two‐step inversion approach: First, elastic properties are inferred from pre‐stack data, and then a classification algorithm is used to convert the outcomes of the first stage into litho‐fluid facies. In this work, we propose an alternative approach based on recurrent neural networks. We train two bidirectional long short‐term memory networks to predict the inverse mappings from pre‐stack seismic data to elastic properties, and litho‐fluid classes. In the elastic inversion, we also use a Monte Carlo simulation approach to properly propagate onto the model space both the uncertainties related to noise contamination in the data and to the modelling error introduced by the network approximation. One crucial aspect of any machine‐learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are drawn according to a previously defined elastic and facies prior models derived from actual well log recordings. In particular, we assume a Gaussian‐mixture elastic prior, and we also take into account the uncertainties affecting the estimation of the transition probabilities of facies. We invert each seismic gather independently, and in this context, the generation of the training set, and the learning process can be accomplished with a very limited computational effort on a common notebook. Once trained, the networks estimate the elastic properties, the litho‐fluid facies and the related uncertainties from the pre‐stack data in near real‐time. Synthetic and field data inversions are used to validate the proposed method. The network predictions are also benchmarked against the outcomes of a more standard two‐step approach that combines a linear elastic inversion and a subsequent point‐wise Bayesian classification. Our results demonstrate that the implemented algorithm guarantees more accurate elastic property estimations and facies predictions than the standard inversion strategy. In particular, the predictions provided by the long short‐term memory network are less affected by erroneous assumptions on the noise statistics and prior model and by errors in the estimated source wavelet.

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Real-Time Classification of Earthquake using Deep Learning

Cited by 56 publications

References 15 publications

Email Spam Classification Using Gated Recurrent Unit and Long Short-Term Memory

Email Spam Classification Using Gated Recurrent Unit and Long Short-Term Memory

İnşaat Mühendisliğinde Derin Öğrenme Algoritmalarının Değerlendirilmesi ve Uygulanması

Elastic properties and litho‐fluid facies estimation from pre‐stack seismic data through bidirectional long short‐term memory networks

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