Modeling of the seismotectonic provinces of Iran using the self-organizing map algorithm

Mojarab, Masoud; Memarian, Hossein; Zaré, Mehdi; Morshedy, Amin Hossein; Pishahang, Mohammad Hossein

doi:10.1016/j.cageo.2013.12.007

Cited by 18 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The same method was tested in Zamani et al [19], in which Wilk's Lambda criterion was used to select an optimum number of clusters. Mojarab et al [20] discussed the effect of SOM input parameters and proposed a combined model to select a cluster number for Iran seismicity analysis. Besheli et al [21] performed a K-means clustering along with a SOM on an Iranian foreshock database and they found a foreshock zone which has a very close relation with large earthquakes.…”

Section: Related Workmentioning

confidence: 99%

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

Shang

Morales‐Esteban

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts' seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.

show abstract

Section: Related Workmentioning

confidence: 99%

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

Shang

Morales‐Esteban

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The convergence of these plates shows the emergence of faults and different seismotectonic regions in Iran (Figure 1). Different seismotectonic regions have been identified for Iran, typically from the geographical and historical distributions of active faults and structural trends [26][27][28][29]. The main seismotectonic regions in Iran are as follows: (1) Alborz-Azarbaijan, (2) Central Iran, (3) Kopet Dagh, (4) Lut, (5) Makran, and (6) Zagros.…”

Section: Seismotectonic Settingmentioning

confidence: 99%

DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

Karimzadeh

Feizizadeh

Matsuoka

2019

IJGI

View full text Add to dashboard Cite

Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes.

show abstract

“…However, the hierarchical cluster has a tendency to form seismic events into linear groups [12]. SOM is another widely used seismic cluster method, and Zamani and Hashemi [5] introduced a self-organized tectonic zoning for Iran, and then Zamani et al [18] chose Wilk's Lambda criterion and a relative discrepancy of Wilk's Lambda to determine the optimum tectonic zoning number; and Mojarab et al [19] discussed the effect of SOM input parameters. Fuzzy clusters have also been used in seismic clusters, Ansari et al [4] and Benitez et al [21] used the Gath and Giva (GG) fuzzy cluster for Iran and South West Colombia, respectively; while Monem and Hashemy [20] applied the fuzzy -means (FCM) and the GustafsonKessel (GK) clusters to the Ghazvin canal irrigation network.…”

Section: State Of the Artmentioning

confidence: 99%

K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)

Shang

Morales‐Esteban

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

Seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location ( , , ) and Euclidean distance were selected as the -Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input features and cluster determination measurement, respectively, and 1516 seismic events ( > −1.5) obtained from the Yongshaba mine (China) were chosen for the cluster analysis. In addition, a Silhouette and Krzanowski-Lai-(KL-) combined S-KL index was proposed to obtain the possible optimum cluster number and to compare the cluster methods. Results show that the -Means cluster obtains the best cluster "quality" with higher S-KL indexes on the whole and meaningful clusters. Furthermore, the optimal number for detailed geological structure interpretation is confirmed as eleven clusters, and we found that two areas probably have faults or caves, and two faults may be falsely inferred by mine geologists. Seismic hazard assessment shows that C5 and C7 ( = 11) have a high mean moment magnitude ( ) and C1, C2, C3, and C4 ( = 11) have a relatively high , where special attention is needed when mining. In addition, C7 ( = 11) is the most shear-related area with a mean S-wave to P-wave energy ratio ( Es/Ep ) of 41.21. In conclusion, the -Means cluster provides an effective way for mine seismicity partitioning, geological structure interpretation, and seismic hazard assessment.

show abstract

Modeling of the seismotectonic provinces of Iran using the self-organizing map algorithm

Cited by 18 publications

References 30 publications

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran

K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)

Contact Info

Product

Resources

About