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Machine Learning Applications in Seismology
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Machine Learning Applications in Seismology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 24716

Special Issue Editors

Prof. Dr. Shiyong Zhou

E-Mail Website
Guest Editor
School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: seismology; seismicity; machine learning; processing of seismic data
Dr. Ke Jia

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
Interests: statistical seismology; machine learning

Special Issue Information

Dear Colleagues,

In recent years, machine-learning-based artificial intelligence technology has been rapidly applied to digital seismic data processing and developing a structured seismic catalog. Artificial intelligence methods hold significant promise for solving fundamental scientific problems in seismology; AI technology can carry out multiple geophysical observations, so as to identify signals or patterns that cannot be captured by traditional methods unable to easily generate information about strong earthquakes. AI can help us further understand the physical process of earthquakes.

This Special Issue will present innovative ideas and the latest findings in earthquake monitoring, early warning and forecasting systems as developed through different machine-learning-related methods, theories and applications. The scope of this Special Issue includes, but is not limited to: seismic data processing, event location and discrimination, early warning, forecasting, machine learning, deep learning and other applications in seismology.

Prof. Dr. Shiyong Zhou
Dr. Ke Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • processing of seismic data
  • phase picking
  • denoising of seismic data
  • earthquake location
  • earthquake detection
  • earthquake early warning
  • earthquake forecast and prediction

Published Papers (15 papers)

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Research

0 pages, 9536 KiB  
Article
Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
by Xian Lu, Qiong Wang, Xiaodong Zhang, Wei Yan, Lingyuan Meng and Haitao Wang
Appl. Sci. 2024, 14(6), 2401; https://doi.org/10.3390/app14062401 - 13 Mar 2024
Viewed by 556
Abstract
The application of certain mathematical–statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, [...] Read more.
The application of certain mathematical–statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, the machine learning-based detection of seismic multi-parameters using the sliding extreme value relevancy method, based on the earthquake-corresponding relevancy spectrum, was calculated in the tectonic regions in the western Chinese mainland, and the R-value evaluation was completed. Multi-parameter data included the b value, M value (missing earthquakes), ƞ value (the relationship between seismic magnitude and frequency), D value (seismic hazard), Mf value (intensity factor), N value (earthquake frequency), and Rm value (modulation parameter). The temporal results showed that the high-value anomalies appeared before most target earthquakes during the training period. Moreover, some target earthquakes also occurred during the advantageous extrapolation period with high-value anomalies. The spatial results showed that some months before the target earthquakes, there was indeed a significant abnormal enhancement area that appeared near the epicenter, and the anomaly gradually disappeared after the earthquakes. This study demonstrated that machine learning techniques for detecting earthquake anomalies using seismic multi-parameter data were feasible. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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16 pages, 6166 KiB  
Article
Randomly Distributed Passive Seismic Source Reconstruction Record Waveform Rectification Based on Deep Learning
by Binghui Zhao, Liguo Han, Pan Zhang, Qiang Feng and Liyun Ma
Appl. Sci. 2024, 14(5), 2206; https://doi.org/10.3390/app14052206 - 6 Mar 2024
Viewed by 521
Abstract
In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent [...] Read more.
In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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14 pages, 4487 KiB  
Article
Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
by Lei Li, Xiaobao Zeng, Xinpeng Pan, Ling Peng, Yuyang Tan and Jianxin Liu
Appl. Sci. 2024, 14(5), 2194; https://doi.org/10.3390/app14052194 - 6 Mar 2024
Viewed by 678
Abstract
Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and [...] Read more.
Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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15 pages, 5138 KiB  
Article
A High-Resolution Aftershock Catalog for the 2014 Ms 6.5 Ludian (China) Earthquake Using Deep Learning Methods
by Jun Li, Ming Hao and Zijian Cui
Appl. Sci. 2024, 14(5), 1997; https://doi.org/10.3390/app14051997 - 28 Feb 2024
Viewed by 518
Abstract
A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm [...] Read more.
A high-resolution catalog for the 2014 Ms 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short–long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao–Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao–Xiaohe Fault, and they also intersected with the Zhaotong–Ludian Fault. (3) This suggests that the NE-trending Zhaotong–Ludian Fault may have been intersected by the NW-trending Baogunao–Xiaohe Fault, indicating that the Baogunao–Xiaohe Fault is likely a relatively young Neogene fault. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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12 pages, 12079 KiB  
Article
A Seismic Inversion Method Based on Multi-Scale Super-Asymmetric Cycle-JNet Network
by Mingming Tang, Boyang Huang, Rong Xie and Zhenzhen Chen
Appl. Sci. 2024, 14(1), 242; https://doi.org/10.3390/app14010242 - 27 Dec 2023
Viewed by 735
Abstract
In order to improve the resolution and accuracy of seismic inversion, this study constructs a multi-scale super-asymmetric network (Cycle-JNet). In this model, wavelet analysis is used to capture the multi-scale data characteristics of well-seismic data, thereby improving the machine’s ability to learn details. [...] Read more.
In order to improve the resolution and accuracy of seismic inversion, this study constructs a multi-scale super-asymmetric network (Cycle-JNet). In this model, wavelet analysis is used to capture the multi-scale data characteristics of well-seismic data, thereby improving the machine’s ability to learn details. Using the UNet neural network from Convolutional Neural Network (CNN), we modified the network structure by adding several convolution kernel layers at the output end to expand generated data, solving the problem of mismatched resolutions in well-seismic data, thus improving the resolution of seismic inversion and achieving the purpose of accurately identifying thin sandstone layers. Meanwhile, a cycle structure of Recurrent Neural Network (RNN) was designed for the secondary learning of the seismic data generated by JNet. By comparing the data transformed through inverse wavelet transform with the original data again, the accuracy of machine learning can be improved. After optimization, the Cycle-JNet model significantly outperforms traditional seismic inversion methods in terms of resolution and accuracy. This indicates that this method can provide more precise inversion results in more complex data environments, providing stronger support for seismic analysis. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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19 pages, 1639 KiB  
Article
Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
by Leonidas Agathos, Andreas Avgoustis, Nikolaos Avgoustis, Ioannis Vlachos, Ioannis Karydis and Markos Avlonitis
Appl. Sci. 2023, 13(19), 10884; https://doi.org/10.3390/app131910884 - 30 Sep 2023
Viewed by 828
Abstract
The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant [...] Read more.
The importance of monitoring earthquakes for disaster management, public safety, and scientific research can hardly be overstated. The emergence of low-cost seismic sensors offers potential for widespread deployment due to their affordability. Nevertheless, vehicular noise in low-cost seismic sensors presents as a significant challenge in urban environments where such sensors are often deployed. In order to address these challenges, this work proposes the use of an amalgamated deep neural network constituent of a DNN trained on earthquake signals from professional sensory equipment as well as a DNN trained on vehicular signals from low-cost sensors for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. To this end, we present low-cost seismic sensory equipment and three discrete datasets that—when the proposed methodology is applied—are shown to significantly outperform a generic stochastic differential model in terms of effectiveness and efficiency. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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21 pages, 10243 KiB  
Article
Anomalies in Infrared Outgoing Longwave Radiation Data before the Yangbi Ms6.4 and Luding Ms6.8 Earthquakes Based on Time Series Forecasting Models
by Junqing Zhu, Ke Sun and Jingye Zhang
Appl. Sci. 2023, 13(15), 8572; https://doi.org/10.3390/app13158572 - 25 Jul 2023
Cited by 2 | Viewed by 978
Abstract
Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance [...] Read more.
Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of 5° × 5° at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi Ms6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding Ms6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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18 pages, 15108 KiB  
Article
A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
by Jinjun Hu, Yitian Ding, Shibin Lin, Hui Zhang and Chaoyue Jin
Appl. Sci. 2023, 13(14), 8232; https://doi.org/10.3390/app13148232 - 15 Jul 2023
Cited by 1 | Viewed by 1509
Abstract
Ground-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfied. A ground-motion simulation [...] Read more.
Ground-motion simulations provide input time history data required for designing and assessing structures; however, the simulations conducted by the currently available tools only match the design spectrum without verifying if the statistical characteristics of the spectrum and duration are satisfied. A ground-motion simulation software was developed to resolve these issues. The developed software employs machine learning methods to match the amplitude, spectrum, and duration features of the target region. Principal component analysis is employed to extract features from the actual ground-motion database to detect characteristic ground motions and predict the target acceleration amplitude, response spectrum, and duration, based on the response spectrum and duration prediction equations. The results show that the simulated ground motion can match the amplitude, spectrum, and duration characteristics well. Therefore, the simulated ground motion can provide more reasonable input for the structure. Moreover, the developed software provides visualization functions that enable the user to determine the target area and obtain the amplitude field intuitively. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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19 pages, 5754 KiB  
Article
Small Earthquakes Can Help Predict Large Earthquakes: A Machine Learning Perspective
by Xi Wang, Zeyuan Zhong, Yuechen Yao, Zexu Li, Shiyong Zhou, Changsheng Jiang and Ke Jia
Appl. Sci. 2023, 13(11), 6424; https://doi.org/10.3390/app13116424 - 24 May 2023
Cited by 1 | Viewed by 2132
Abstract
Earthquake prediction is a long-standing problem in seismology that has garnered attention from the scientific community and the public. Despite ongoing efforts to understand the physical mechanisms of earthquake occurrence, there is no convincing physical or statistical model for predicting large earthquakes. Machine [...] Read more.
Earthquake prediction is a long-standing problem in seismology that has garnered attention from the scientific community and the public. Despite ongoing efforts to understand the physical mechanisms of earthquake occurrence, there is no convincing physical or statistical model for predicting large earthquakes. Machine learning methods, such as random forest and long short-term memory (LSTM) neural networks, excel at identifying patterns in large-scale databases and offer a potential means to improve earthquake prediction performance. Differing from physical and statistical approaches to earthquake prediction, we explore whether small earthquakes can be used to predict large earthquakes within the framework of machine learning. Specifically, we attempt to answer two questions for a given region: (1) Is there a likelihood of a large earthquake (e.g., M ≥ 6.0) occurring within the next year? (2) What is the maximum magnitude of an earthquake expected to occur within the next year? Our results show that the random forest method performs best in classifying large earthquake occurrences, while the LSTM method provides a rough estimation of earthquake magnitude. We conclude that small earthquakes contain information relevant to predicting future large earthquakes and that machine learning provides a promising avenue for improving the prediction of earthquake occurrences. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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17 pages, 6999 KiB  
Article
Development of the Algorithmic Basis of the FCAZ Method for Earthquake-Prone Area Recognition
by Sergey M. Agayan, Boris A. Dzeboev, Shamil R. Bogoutdinov, Ivan O. Belov, Boris V. Dzeranov and Dmitriy A. Kamaev
Appl. Sci. 2023, 13(4), 2496; https://doi.org/10.3390/app13042496 - 15 Feb 2023
Viewed by 932
Abstract
The present paper continues the series of publications by the authors devoted to solving the problem of recognition regions with potential high seismicity. It is aimed at the development of the mathematical apparatus and the algorithmic base of the FCAZ method, designed for [...] Read more.
The present paper continues the series of publications by the authors devoted to solving the problem of recognition regions with potential high seismicity. It is aimed at the development of the mathematical apparatus and the algorithmic base of the FCAZ method, designed for effective recognition of earthquake-prone areas. A detailed description of both the mathematical algorithms included in the FCAZ in its original form and those developed in this paper is given. Using California as an example, it is shown that a significantly developed algorithmic FCAZ base makes it possible to increase the reliability and accuracy of FCAZ recognition. In particular, a number of small zones located at a fairly small distance from each other but having a close “internal” connection are being connected into single large, high-seismicity areas. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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13 pages, 5499 KiB  
Article
FocMech-Flow: Automatic Determination of P-Wave First-Motion Polarity and Focal Mechanism Inversion and Application to the 2021 Yangbi Earthquake Sequence
by Shuai Li, Lihua Fang, Zhuowei Xiao, Yijian Zhou, Shirong Liao and Liping Fan
Appl. Sci. 2023, 13(4), 2233; https://doi.org/10.3390/app13042233 - 9 Feb 2023
Cited by 5 | Viewed by 2645
Abstract
P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism [...] Read more.
P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism solutions of small and moderate earthquakes, we can deepen our understanding of fault geometry and the seismogenic environment. In this paper, we propose an automatic workflow, FocMech-Flow (Focal Mechanism-Flow), for identifying P-wave first-motion polarity and focal mechanism inversion with deep learning and applied it to the 2021 Yangbi earthquake sequence. We use a deep learning model named DiTingMotion to detect the P-wave first-motion polarity of 2389 waveforms, resulting in 98.49% accuracy of polarity discrimination compared with human experts. The focal mechanisms of 112 earthquakes are obtained by using the CHNYTX program, which is 3.7 times more than that of the waveform inversion method, and the results are highly consistent. The analysis shows that the focal mechanisms of the foreshock sequence of the Yangbi earthquake are highly consistent and are all of the strike-slip type; the focal mechanisms of the aftershock sequence are complex, mainly the strike-slip type, but there are also reverse and normal fault types. This study shows that the deep learning method has high reliability in determining the P-wave first-motion polarity, and FocMech-Flow can obtain a large number of focal mechanism solutions from small and moderate earthquakes, having promising application in fine-scale stress inversion. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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23 pages, 3790 KiB  
Article
Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis
by Mouna Merdasse, Mohamed Hamdache, José A. Peláez, Jesús Henares and Tarek Medkour
Appl. Sci. 2023, 13(3), 1566; https://doi.org/10.3390/app13031566 - 26 Jan 2023
Cited by 3 | Viewed by 2800
Abstract
This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency and magnitude forecasting of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA) model encompasses the parametric approach, while the non-parametric method employs [...] Read more.
This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency and magnitude forecasting of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA) model encompasses the parametric approach, while the non-parametric method employs the Singular Spectrum Analysis (SSA) approach. The ARIMA and SSA models were then used to train and forecast the annual number of earthquakes and annual maximum magnitude events occurring in Northeastern Algeria between 1910 and 2019, including 287 main events larger than Mw 4.0. The SSA method is used as a forecasting algorithm in this case, and the results are compared to those obtained by the ARIMA model. Based on the root mean square error (RMSE) criterion, the SSA forecasting model appears to be more appropriate than the ARIMA model. The consistency between the observation and the forecast is analyzed using a statistical test in terms of the total number of events, denoted as N-test. As a result, the findings indicate that the annual maximum magnitude in Northeastern Algeria between 2020 and 2030 will range from Mw 4.8 to Mw 5.1, while between four and six events with a magnitude of at least Mw 4.0 will occur annually. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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18 pages, 10175 KiB  
Article
The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
by Bo Liu, Baofeng Zhou, Jingchang Kong, Xiaomin Wang and Chunhui Liu
Appl. Sci. 2023, 13(3), 1500; https://doi.org/10.3390/app13031500 - 23 Jan 2023
Cited by 1 | Viewed by 2086
Abstract
A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is [...] Read more.
A high-pass cut-off frequency in filtering is critical to processing strong-motion records. The various processing procedures available nowadays are based on their own needs and are not universal. Regardless of the methods, a visual inspection of the filtered acceleration integration to displacement is required to determine if the selected filter passband is appropriate. A better method is to use a traversal search combined with visual inspection to determine the cut-off frequency, which is the traditional method. However, this method is inefficient and unsuitable for processing massive strong-motion records. In this study, convolutional neural networks (CNNs) were used to replace visual inspection to achieve the automatic judgment of the reasonableness of the filtered displacement time series. This paper chose the pre-trained deep neural network (DNN) models VGG19, ResNet50, InceptionV3, and InceptionResNetV2 for transfer learning, which are only trained in the fully connected layer or in all network layers. The effect of adding probability constraints on the results when predicting categories was analyzed as well. The results obtained through the VGG19 model, in which all network layers are trained and probability constraints are added to the prediction, have the lowest errors compared to the other models. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are 0.82, 0.038, 0.026, and 2.99%, respectively. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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16 pages, 1477 KiB  
Article
Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
by Fatema Tuz Johora, Craig J. Hickey and Hakan Yasarer
Appl. Sci. 2022, 12(24), 12815; https://doi.org/10.3390/app122412815 - 13 Dec 2022
Cited by 2 | Viewed by 1643
Abstract
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations [...] Read more.
Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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16 pages, 3561 KiB  
Article
Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
by Muhammad Atif Bilal, Yanju Ji, Yongzhi Wang, Muhammad Pervez Akhter and Muhammad Yaqub
Appl. Sci. 2022, 12(15), 7548; https://doi.org/10.3390/app12157548 - 27 Jul 2022
Cited by 11 | Viewed by 2682
Abstract
Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph [...] Read more.
Earthquake is a major hazard to humans, buildings, and infrastructure. Early warning systems should detect an earthquake and issue a warning with earthquake information such as location, magnitude, and depth. Earthquake detection from raw waveform data using deep learning models such as graph neural networks (GNN) is becoming an important research area. The multilayered structure of the GNN with a number of epochs takes more training time. It is also hard to train the model with saturating nonlinearities. The batch normalization technique is applied to each mini-batch to reduce epochs in training and obtain a steady distribution of activation values. It improves model training and prediction accuracy. This study proposes a deep learning model batch normalization graph convolutional neural network (BNGCNN) for early earthquake detection. It consists of two main components: CNN and GNN. Input to the CNN model is multi-station and three-component waveform data with magnitude ≥3.0 were collected from January 2000 to January 2015 for Southern California. The extracted features of CNN are appended with location information and input to GNN model for earthquake detection. After hyperparameter tuning of the BNGCNN, when testing and evaluating the model on the Southern California dataset, our method shows promising results to the baseline model GNN by obtaining a low error rate to predict the magnitude, depth, and location of an earthquake. Full article
(This article belongs to the Special Issue Machine Learning Applications in Seismology)
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