Speech Recognition based Automatic Earthquake Detection and Classification

Die moderne Seismologie zeichnet die Bodenbewegungen mit einem weltweit verteilten Stationsnetz kontinuierlich auf und gilt damit als datenreiche Wissenschaft. Die Extraktion der im Moment interessierenden Daten aus diesen kontinuierlichen Aufzeichnungen, seien es Erdbebensignale oder Nuklearsprengungen oder ä.m., ist eine Herausforderung an die bisher verwendeten Detektions- und Klassifizierungsalgorithmen

Curvelet processing and imaging: 4D adaptive subtraction

With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields. 4D, or time-lapse, seismic imaging holds great promise to better monitor and optimise reservoir production. The basic idea behind 4D seismic is that when multiple 3D surveys are acquired at separate calendar times over a producing field, the reservoir geology will not change from survey to survey but the state of the reservoir fluids will change. Thus, taking the difference between two 3D surveys should remove the static geologic contribution to the data and isolate the time-varying fluid flow component. However, a major challenge in 4D seismic is that acquisition and processing differences between 3D surveys often overshadow the changes caused by fluid flow. This problem is compounded when 4D effects are sought to be derived from legacy 3D data sets that were not originally acquired with 4D in mind. The goal of this study is to remove the acquisition and imaging artefacts from a 4D seismic difference cube using Curvelet processing techniques. The denoising problem In this paper, we argue that computing 4D difference cubes can be recast into the framework of solving a generic denoising problem that estimates the model m from noisy data d = m + n with Gaussian noise n. The solution of this inverse problem can be written in terms of the following variational problem mˆ mi

ObsPy – What can it do for data centers and observatories?

Data acquisition by seismic centers relies on real-time systems, like SeisComP3, Antelope and Earthworm. However, these are complex systems that are designed for fast and precisely defined standard real-time analyses. Therefore, it is not a simple task to access or modify internal routines, and to integrate them into custom-processing workflows or to perform in-depth data analyses. Often a library is necessary that provides convenient access to data and allows easy control over all of the operations that are to be performed on the data. ObsPy is such a library, which is designed to access and process seismological waveform data and metadata. We use short and simple examples here to demonstrate how effective it is to use Python for seismological data analysis. Then, we illustrate the general capabilities of ObsPy, and highlight some of its specific aspects that are relevant for seismological data centers and observatories, through presentation of real-world examples. Finally, we demonstrate how the ObsPy library can be used to develop custom graphical user interface applications.<br /&gt

ObsPy : a bridge for seismology into the scientific Python ecosystem

info:eu-repo/semantics/publishe

Data publication: SAPPHIRE - Establishment of small animal proton and photon image-guided radiation experiments

This repository contains the data shown in the results part of the paper entitled: SAPPHIRE - Establishment of small animal proton and photon image-guided radiation experiments

Data publication: SAPPHIRE - Establishment of image-guided small animal proton and photon irradiation experiments

This repository contains the data shown in the results part of the paper entitled: SAPPHIRE - Establishment of image-guided small animal proton and photon irradiation experiments