Computer Programs In Seismology PORTABLE Download

With the installation of a PDP 11/70 minicomputer in 1981 for use in seismic network analysis, a persistent effort was made to use that tool for research. Because of the uniqueness of a dedicated minicimputer for research at that time as well as a desire to do something for the world wide seismological community, a set of documented computer programs emerged. The programs developed actually reflect research interests of the department as well as a desire to look at data in the manner of other investigators. In doing so, the hope is that these tools will permit research to progress rapidly.

These programs focus on the understanding and interpretation of seismic wave propagation in the crust and upper mantle of the Earth. Synthetic seismograph code is provided for sources and receivers at arbitrary positions in the plane layered media. Programs are provided for determination of crustal structure through the inversion of surface-wave dispersion and teleseismic P-wave receiver functions. Inversion of broadband recordings of regional earthquakes for source depth, focal mechanism and seismic moment is also provided. Finally, a new tool, gsac, is provided to permit interactive and script based manipulation of seismic traces. The entire package is well integrated in that the same Earth velocity model, waveform and graphics formats are used by all programs. A complete interactive graphics package is also provided.

Computer Programs In Seismology Download

Download File 🔥 https://fancli.com/2y3AHl 🔥

This document shows how to install VirtualBox on your Windows, Mac or Linux system, and then install a version of LINUX with required compilers, and finally Computer Programs in Seismology. CPS_VirtualBoxInstall.pdf. The advantage of this approach is that the computer disk does not have to be partitioned in the presence of a Windows operating system, and that all compilers are easily obtained.

This report provides Fortran source code and program manuals for HYPOELLIPSE, a computer program for determining hypocenters and magnitudes of near regional earthquakes and the ellipsoids that enclose the 68-percent confidence volumes of the computed hypocenters. HYPOELLIPSE was developed to meet the needs of U.S. Geological Survey (USGS) scientists studying crustal and sub-crustal earthquakes recorded by a sparse regional seismograph network. The program was extended to locate hypocenters of volcanic earthquakes recorded by seismographs distributed on and around the volcanic edifice, at elevations above and below the hypocenter. HYPOELLIPSE was used to locate events recorded by the USGS southern Alaska seismograph network from October 1971 to the early 1990s. Both UNIX and PC/DOS versions of the source code of the program are provided along with sample runs.

AmaSeis was the orignal software used to record earthquakes with the AS-1 Seismometer. It can also be used to view AS-1 seismic data from other schools by simply acquiring their files and saving them on your computer. To understand how to use the software there is an accompanying AmaSeis Manual.

IRIS no longer maintains a license agreement with Boulder Real Time Technologies (BRTT) providing Antelope realtime software to IRIS member institutions. However, BRTT now provides Antelope to all educational institutions within the United States (details). PASSCAL has a separate license specifically for the archiving of PASSCAL experiments, and we install Antelope on all field computers. If you need Antelope for archiving your PASSCAL project, please data_group [at] passcal [dot] nmt [dot] edu (email the Data Group).

A few trends served as the backdrop for the meeting. Seismic data processing software and numerical codes for HPC simulation of the seismic wavefield have evolved substantially in recent years. The volume of seismic data has greatly increased as instrumentation and technology have advanced and barriers to deploying sensors and transmitting data have fallen. Capabilities for HPC simulation of seismic waves in realistic 3-D Earth models have also greatly increased for source and Earth structure studies, driven by advances in numerical methods and computer programs as well as the inexorable growth in computing power. These trends indicate that HPC simulations of seismic waves will become more common in research and seismic network operations.

Workshop tutorials gave participants hands-on experience using four open-source codes for waveform processing and simulation. ObsPy is a Python-based software package for accessing, processing, and visualizing seismic waveforms, event data, and metadata. Three methods for computing synthetic seismograms were covered. Instaseis computes seismograms for radially symmetric models and runs on a laptop. Two codes compute seismograms in 3-D Earth models on parallel computers: SW4, a Cartesian finite difference code developed at LLNL, and SPECFEM3D, a spectral element code developed by a large team led by Princeton University. Participants learned how to run these codes and then processed and visualized the results.

REF TEK software enhances the ability to retrieve and analyze data captured by the REF TEK Seismic Recorders and Sensors. From utilities to configure in the field to comprehensive network analytics programs, REF TEK software programs will maximize your use of your REF TEK products.

We seek a student interested in using computer vision methods to resolve time-varying ground deformation and fracturing from the caldera collapse video. The student will work with geophysics postdoc Josh Crozier, geophysics professor Paul Segall, and Hawaiian Volcano Observatory geologist Matt Patrick. This will be a self-contained project with opportunities for producing a first-author publication and contributing to multiple ongoing research projects. The student should be proficient in at least one programing language, and familiarity with geodesy or computer vision would be beneficial but not required.

This project highlights the critical issues facing bofedales, unique high-altitude peatland ecosystems across the Andes, in the face of climate change. These ecosystems, characterized by cushion plants and intricate mat-like structures, play a vital role in purifying water, regulating regional water flow, supporting biodiversity, and mitigating climate change through carbon sequestration. The symbiotic relationship between bofedales and traditional ecological knowledge (TEK) of indigenous communities underscores their cultural and social significance. However, these ecosystems confront challenges such as altered hydrological cycles through glacial retreat, overgrazing, and mineral extraction, jeopardizing their survival and ecosystem services. This research project involves utilizing Google Earth Engine (GEE) and aims to leverage remote sensing technology, focusing on Terra and Aqua MODIS data. The student will assess spatial and temporal patterns in bofedales from 2001 to 2022, shedding light on climate change and human-induced stressors. This summer research opportunity welcomes students to delve into wetland and peatland stress, climate change, ecology, hydrology, remote sensing, spatial analysis, and computer programming, offering a chance to contribute to the conservation and sustainable management of these vital ecosystems. No prior experience is required, making it an ideal opportunity for those passionate about terrestrial landscapes, freshwater ecosystems, and advancing their coding and remote sensing skills.

We propose to use Machine Learning techniques and empirical signal detectors to search for unknown earthquakes in the vicinity of Stanford. We are seeking for self motivated and independent learners with some experience in seismology and earthquakes. Coding skills in Python and shell scripting are preferred but not required. Students will be exposed and become familiar with forefront seismological research. The computations will be performed in a shared computing cluster. After assembling the earthquake catalog, this will be used to draw interpretations on the tectonic setting, patterns of seismicity and faults and interactions between populations of earthquakes.

Agricultural sustainability in California is faced by both nutrient and water management challenges, which are highly interlinked. This project will work towards combining satellite data and field-level datasets in California to investigate and monitor the effects of state-wide agricultural programs that aim to improve water efficiency and soil health. This would be a good project for students with a strong coding background (especially python) and an interest in learning about remote sensing data and its applications towards agricultural intervention policies.

Machine learning, particularly in the realms of mathematics and physical sciences, has seen a significant increase in application. Physics-Informed Neural Networks have become a prominent new method for solving differential equations in scientific machine learning. One challenge in this domain, especially in studies like turbulence, is the need for higher precision than what is typically required in areas like computer vision. Existing neural networks often encounter limitations in training loss, which plateaus over time, leading to less accurate predictions. Our project aims to develop enhanced PINN-based algorithms capable of solving 2-D turbulence governing equations with greater efficiency and accuracy than existing methods. The project will involve reviewing recent PINN advancements, adapting these techniques for turbulence equations, and evaluating the performance of our new algorithm against classical numerical methods.

We are seeking an enthusiastic undergraduate researcher to join us in this venture. The role offers an opportunity to develop coding skills and apply machine learning techniques to complex fluid dynamics problems. Students with background in math, computer science, statistics, applied math, and physics are all welcomed to apply.

Machine learning shows great power to automatically learn complex relations between variables from big data and achieves superior performance than traditional approaches. In this project, we will build machine learning models to integrate microwave remote sensing data from different sources, using field measurements for training and validation. We seek applicants with strong interests in working with big remote sensing data and applying machine learning methods to environmental issues. Previous experience in remote sensing is not required, but students should have some experience with computer programming. Experience with machine learning is preferred, but not necessary. The students will gain skills in applying machine learning models to analyzing remote sensing data. 2351a5e196