Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network

Monem, M J; Hashemy, S. M.

doi:10.2166/hydro.2010.058

Cited by 18 publications

(4 citation statements)

References 20 publications

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“…Some other cluster methods are proposed for seismicity analysis. Ansari et al [23], Benitez et al [24] and Monem and Hashemy [25] used fuzzy clusters, including Gath-Geva clustering, proposed Gath-Geva clustering, fuzzy c-means clustering and Gustafson-Kessel (GK) clustering, to study seismic spatial pattern recognition in Iran, south-west Colombia and the Ghazvin canal irrigation network. The fuzzy clusters can be used for a large dataset cluster; however, its cluster result is sensitive to the initial cluster centers.…”

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

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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.

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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

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“…aftershocks, e.g. Hartigan & Wong 1979;Baiesi & Paczuski 2004;Ansari et al 2009;Mukhopadhyay et al 2010;Monem & Hashemy 2011;Benitez et al 2013;Georgoulas et al 2013;Martínez-Álvarez et al 2015). However, the ability of performing a systematic cluster analysis is hampered by the lack of a formal definition of seismic clusters.…”

Section: Introductionmentioning

confidence: 99%

Clustering characteristics of gas-extraction induced seismicity in the Groningen gas field

Muntendam-Bos

2020

Geophysical Journal International

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SUMMARY The Groningen gas field in the north of the Netherlands is one of the largest gas fields in the world. Since the early 1990s induced seismicity has been recorded. The largest magnitude event observed so far was a Mw = 3.6 event at the town of Huizinge in 2012. The risk posed by the induced events urged the necessity to build comprehensive seismological models capable of explaining the spatial-temporal distribution of the recorded seismicity and evaluating the regional seismic hazard and risk. The link between the occurrence of the seismicity and pressure depletion due to the production of the gas has been firmly established. However, the construction of comprehensive seismological models as well as hazard assessment is complicated by the fact that it is difficult to distinguish between induced and clustered events (events triggered by stress transfer of preceding, neighbouring events). This paper explores the contribution of clustered populations (i.e. aftershocks) to the Groningen induced seismic catalogue based on a statistical methodology in the time–space–magnitude domain. Specifically, the distributions of space–time distances between pairs of nearest-neighbour earthquakes, referred to as cluster style, is analysed. The cluster style of the Groningen induced seismicity is found to be very diffuse and characterized by a very low proportion of fore-/aftershock sequences and swarms (∼5 per cent) and a large proportion of repeater events (∼10 per cent). In contrast to human-induced seismicity in other regions, the background seismicity rate of Groningen is very low. Temporal variations in background seismicity rates can be related to changes in fault loading rates induced by gas production. Furthermore, a significant amount of independent, coincidental events (events occurring very close in time, but long distances apart) are observed. As the large gas field is fully connected, loading of the faults occurs roughly simultaneously throughout the field. Hence, the statistical probability of events occurring very close in time, but spatially far apart is significantly larger than in areas of fluid-injection induced seismicity The significant amount of repeaters and coincidental events cause an overabundance of events at intermediate time- and space-distances. This is further enhanced by the larger location errors in the catalogue increasing the estimated space-distance for non-relocated events. The diffusivity due to this overabundance of events at intermediate time- and space-distances, and the low-proportion of true fore-/aftershocks renders the statistical method used incapable of deriving a proper mode-separation value. However, this is not unique to this method. Any statistical method aimed at resolving two populations will break down if one of the populations analysed is too small. Hence, it is advisable to use caution when distinguishing fore-/aftershocks sequences or swarms for induced seismicity where the relative proportion of clustered events may be significantly lower than for tectonic events. In addition, given the small proportion of clustering and the general uncertainty in earthquake statistics, the results of this paper indicate that a distinction for earthquake risk modelling in Groningen is unnecessary.

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“…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. In addition, the density-based algorithms [22][23][24] and the TriGen-based method [25] have also been applied for the seismic cluster.…”

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

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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.

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Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network

Cited by 18 publications

References 20 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

Clustering characteristics of gas-extraction induced seismicity in the Groningen gas field

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

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