Quantifying the Effect of Geological Factors on Distribution of Earthquake Occurrences by Inhomogeneous Cox Processes

Choiruddin, Achmad; Aisah, Aisah; Trisnisa, Finola; Iriawan, Nur

doi:10.1007/s00024-021-02713-2

Cited by 20 publications

(4 citation statements)

References 28 publications

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“…6) and presents the clustering pattern described by ω and κ in Table 4 (see e.g. Baddeley et al, 2015;Choiruddin, Aisah, Trisnisa, & Iriawan, 2021, for the interpretation of the clustering estimates). Again, this overall model can not offer two-weeks snapshots of the spatial distribution of infected cases in relation to some spatial covariates, aspect that was mainly needed by authorities to drive their restrictions to society.…”

Section: Methodological Considerationmentioning

confidence: 99%

COVID-19 transmission risk in Surabaya and Sidoarjo: An inhomogeneous marked Poisson point process approach

Choiruddin

Hannanu

Mateu

et al. 2022

Preprint

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Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.

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Section: Methodological Considerationmentioning

confidence: 99%

COVID-19 transmission risk in Surabaya and Sidoarjo: An inhomogeneous marked Poisson point process approach

Choiruddin

Hannanu

Mateu

et al. 2022

Preprint

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“…ISSN: 0216-308X (Print) 2721-3862 (Online) DOI: 10.12962/j27213862.v5i2.12307 mendeteksi random process dari persebaran gempabumi dengan asumsi tidak ada keterkaitan antar kejadian gempabumi. Untuk merevisi model independen, beberapa model cluster juga dipertimbangkan pada analisis kejadian gempabumi diantaranya yaitu Hawkes poin process ([10]- [12]), Thomas process ( [13]- [15]), Gibbs process ( [16], [17]), log-Gaussian Cox processes (LGCP) ( [18]- [20]), dan Mixture Poisson Process [21]. Sebagian besar penelitian mengasumsikan bahwa proses stasioner atau mendeteksi non-stationarity dengan kernel smoothing.…”

Section: Pendahuluanunclassified

“…Pemodelan intensitas gempabumi menggunakan regularisasi ridge regression digunakan pada penelitian ini karena adanya korelasi yang tinggi antarkovariat sehingga. Model terbaik dalam pemodelan kasus gempabumi di Sumatera adalah Cauchy cluster [15], sehingga pada penelitian ini Cauchy cluster process dipilih untuk memodelkan efek cluster aktivitas gempabumi yang diawali gempa utama (mainshocks) dan diikuti gempa susulan (aftershocks). Penelitian ini diharapkan mampu menghasilkan informasi mengenai karakteristik persebaaran gempabumi berdasarkan kondisi geografis wilayah Sumatera dan memodelkan kejadian gempabumi sehingga dapat berguna sebagai upaya mitigasi dan mengurangi kerugian akibat gempabumi.…”

Section: Pendahuluanunclassified

Analisis Risiko Gempabumi di Sumatera dengan Cauchy Cluster Process

Kartikasari

Choiruddin

2022

Inferensi

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Sumatera adalah salah satu wilayah di Indonesia yang rawan terhadap gempabumi. Kondisi ini dikarenakan geografisnya yang dilalui oleh sesar aktif, zona subduksi, dan gunung berapi. Pada penelitian ini, persebaran gempabumi di Sumatera dimodelkan dengan mempertimbangkan efek spatial trend yang disebabkan oleh keberadaan zona subduksi, sesar aktif, dan gunung berapi dan efek cluster yang disebabkan aktivitas mainshock dan aftershocks menggunakan inhomogeneous Cauchy cluster process. Pada pemodelan spatial trend, terdapat indikasi bahwa terjadi multikolinearitas ditandai dengan korelasi yang tinggi antar faktor geografis, sehingga penelitian ini mempertimbangkan regularisasi ridge untuk mengatasi hal tersebut. Hasil eksplorasi data menunjukkan bahwa gempabumi di Sumatera tidak homogen dan membentuk cluster yang disebabkan karena faktor geologis seperti keberadaan gunung berapi, zona subduksi, dan sesar aktif. Pemodelan intensitas gempabumi dengan regularisasi ridge menghasilkan nilai AIC sebesar -2280648 yang mana lebih kecil daripada model tanpa regularisasi. Model Cauchy cluster dengan mempertimbangkan regularisasi ridge menghasilkan estimasi jumlah gempabumi utama sebanyak 63 dengan standar deviasi gempabumi susulan disekitar gempabumi utama sebesar 17,685 km. Semakin dekat suatu lokasi ke sesar aktif, risiko terjadinya gempabumi di lokasi tersebut meningkat 1,6972 kali. Semakin dekat suatu lokasi ke zona subduksi, risiko terjadinya gempabumi di lokasi tersebut meningkat sebesar 1,25899 kali, dan semakin dekat suatu lokasi ke gunung berapi risiko terjadinya gempabumi di lokasi tersebut meningkat sebesar 1,55910 kali. Kata Kunci⎯ spatial trend, faktor geologis, multikolinearitas, intensitas, risiko.ABSTRACT ⎯ Sumatra is one of the prone areas in Indonesia to earthquakes. This condition is due to its geography which is traversed by active faults, subduction zones, and volcanoes. In this study, the distribution of earthquakes occurrences in Sumatra is modeled by considering the effects of spatial trends due to subduction zones, active faults, and volcanoes and also considering the cluster effects caused by mainshock and aftershock activities using the inhomogeneous Cauchy cluster process. In spatial trend modeling, there are indications that there is multicollinearity issue characterized by a high correlation among geographical factors, so this study considers ridge regularization to overcome this problem. The results of data exploration show that the earthquakes in Sumatra are not homogeneous and form clusters due to geological factors such as the presence of volcanoes, subduction zones, and active faults. Earthquake intensity modeling with ridge regularization produces an AIC value of -2280648 which is smaller than the model without regularization. The Cauchy cluster model by considering ridge regularization resulted in an estimated number of 63 mainshocks with a standard deviation of aftershocks around the mainshocks of 17.685 km. The closer a location to a fault, the risk of an earthquake occurring at that locat...

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“…To represent uncertainty in the intensity of the spatial point process, we select a log-Gaussian Cox process (LGCP), which is a well-known method for modeling spatially Poisson-distributed targets that is used in many different applications. 2,6,[10][11][12][13] Our paper examines the relationship between target arrivals modeled by LGCP and sensor network performance constructed by the estimated intensity function by LGCP. Our approach is to estimate uncertain models from real historical data, select sensor locations based on the a model, and then assess the performance of the sensor network.…”

Section: Introductionmentioning

confidence: 99%

On the role of uncertainty in poisson target models used for placement of spatial sensors

Kim¹,

Yetkin²,

Stilwell³

et al. 2023

Ocean Sensing and Monitoring XV

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This paper addresses the role of uncertainty in spatial point-process models, such as those that might arise in modelling ship traffic. We consider a doubly stochastic Poisson point process where the intensity function is uncertain. To assess the role of uncertainty, we conduct a large set of numerical trials where we estimate a doubly stochastic Poisson point-process model from historical target data, and the evaluate the model by assessing the target detection performance of a set of sensors whose locations are selected using the model. Our work is motivated by seabed sensors that detect ship traffic, and we conduct numerical trials using historical ship traffic data near the mouth of the Chesapeake Bay, Virginia, USA, that was recorded by the Automated Identification System.

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Quantifying the Effect of Geological Factors on Distribution of Earthquake Occurrences by Inhomogeneous Cox Processes

Cited by 20 publications

References 28 publications

COVID-19 transmission risk in Surabaya and Sidoarjo: An inhomogeneous marked Poisson point process approach

COVID-19 transmission risk in Surabaya and Sidoarjo: An inhomogeneous marked Poisson point process approach

Analisis Risiko Gempabumi di Sumatera dengan Cauchy Cluster Process

On the role of uncertainty in poisson target models used for placement of spatial sensors

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