...Anne S. Kiremidjian; Thalia Anagnos Abstract A slip-predictable stochastic model is presented based on Markov renewal theory. Times between successive events are assumed to be Weibull-distributed with an increasing hazard rate. The model forecasts probabilities of earthquake occurrences conditional...

... Power Research Institute , Research Project P101-38 . Cox D. R. (1962) . Renewal Theory , Methuen & Co. Ltd. , New York . Der Kiureghian A. Ang...

.... (1993) . Seismic hazard induced by mechanically interactive fault segments , Bull. Seism. Soc. Am. 83 , 436 - 449 . Cox D. R. (1962) . Renewal Theory , Methuen & Co. Ltd. , New York...

... A. L. , and Baglivo J. A. 2007 . Non‐Poissonian earthquake clustering and the hidden Markov model as bases for earthquake forecasting in California , Seismol. Res. Lett. 78 , 57 – 65 , doi: 10.1785/gssrl.78.1.57 Feller W. 1968 . An Introduction to Probability Theory and Its...

... . Fishman P. M. Snyder D. L. (1976) . The statistical analysis of space-time point processes , IEEE Trans. Inf. Theory IT-22 , 257 - 274 . Kagan Y. Y...

... is simplified by lithofacies classification, and the random multifacies heterogeneous model which is derived from known formation based on transfer probability theory are established, respectively. Numerical simulation is carried out to study the migration law of CO 2 injected into the above three heterogeneous...

.... The sizes of successive earthquakes are assumed to be independent and identically distributed random variables. The interevent times are random variables with a distribution that is conditional on the size of the last event. A Markov renewal process describes the visits to the earthquake states in time...

... theory and methods for model parameter estimation : Society of Industrial and Applied Mathematics . Tjelmeland H. , and Besag J. , 1998 , Markov Random Fields with Higher-order Interactions : Scandinavian Journal of Statistics , 25 , no. 3 , 415 – 433 , 10.1111/1467-9469.00113...

... of a virtual “active source” experiment (i.e., the empirical Green’s function, abbreviated as EGF) without the actual deployment of physical sources. Sources of the ambient background may be either naturally occurring or engineered. Regardless, the theory for diffusive systems requires them...

... and near the sandy belt. To separate random depositional events from normal ordered events, a Markov analysis was performed using seven characteristics. Lithologies tend to succeed one another in a consistent order. From the base up, these are: irregular surface, sandstone, siltstone, shale, carbonaceous...

...Javier B. Gómez; Amalio F. Pacheco Abstract Renewal models are usually applied to seismic regions where large earthquakes occur repeatedly at approximately regular time intervals. In most cases, these models lack a seismological basis and are just well known statistical distributions rooted...

... approaches, in which scientists develop theories based on physical laws to compare with real‐world observations, ML approaches learn directly from data without explicitly reasoning about the underlying physical mechanisms. ML algorithms are often categorized into supervised and unsupervised learning (see...

... ; Bräunig et al., 2020 ; Markovic et al., 2020 ) have focused on recent 2D seismic data from Blötberget. Together with other geophysical and petrophysical studies in the area ( Maries et al., 2017b ; Yehuwalashet and Malehmir, 2018 ; Almqvist et al., 2019 ), these studies provide essential information...

... be interpreted as the number of earthquakes between times t and t + Δ t , inclusive. For a renewal model with density f , the conditional rate is given by (8) \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty...

... of the primary data (e.g. temperature data) changes in space. Like stochastic simulation for Kriging, cokriging can be used to produce stochastic results. This is called cosimulation. The theory and Kriging expressions are flexible to allow the data T ( u α ) and the unknown T S K...

... resources and the lack of an acceleration technique, we selected a 1D MT inversion to test DQN performance. This paper is organized as follows. In the “Basic theory” section, we provide a brief introduction to the Markov decision process, Q -learning algorithm, DQN algorithm, and design of MT inversion...

... application is earthquake forecasting based on data assimilation, in which sequential MC methods have been used for the renewal processes ( Werner et al., 2011 ). We demonstrate the application of EKFs, unscented KFs (UKFs), and PFs to nonvolcanic tremor (NVT) tracking ( Obara, 2002 ; Rogers and Dragert...

... effectively frame our problem within queue theory. A well‐known result in queue theory is Burke’s theorem ( Burke, 1956 ), stating that a Markov stationary process is reversible in time if the forward‐ and backward‐transition rates are equal, as it is always verified in a stationary Poisson process. Hence...

... 2018: nature-based solutions for water. ISBN: 9789210450645 , https://doi.org/10.18356/dc00fb4d-en Borovic , S. and Markovic , I . 2015 . Utilization and tourism valorisation of geothermal waters in Croatia . Renewable & Sustainable Energy Reviews , 44 , 52 – 63 , https...

... renewal process or Markov process models (Aki, 1956; Vere-Jones, 1966). For engineering purposes the Poisson results are con- sidered adequate for numerous reasons (Rosenblueth, 1966; Lomnitz, 1966). When swarms and aftershocks are excluded, data does not clearly reject the Poisson assump- tion (Lomnitz...