https://github.com/alicepagano/lcp-a-project-south-california-earthquakes
Analysis of South-California Earthquakes data
https://github.com/alicepagano/lcp-a-project-south-california-earthquakes
analysis-data california earthquakes python
Last synced: 2 months ago
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Analysis of South-California Earthquakes data
- Host: GitHub
- URL: https://github.com/alicepagano/lcp-a-project-south-california-earthquakes
- Owner: AlicePagano
- Created: 2020-03-12T08:23:38.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-16T11:17:01.000Z (over 4 years ago)
- Last Synced: 2025-01-22T23:28:26.697Z (4 months ago)
- Topics: analysis-data, california, earthquakes, python
- Language: Jupyter Notebook
- Homepage:
- Size: 41.9 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Laboratory of Computational Physics - Part A
## South-California Earthquakes Data Analysis
## Authors
* [**Alice Pagano**](https://github.com/AlicePagano) (University of Padova)
* **Alessandro Lambertini** (University of Padova)
* **Michele Puppin** (University of Padova)
* **Michele Guadagnini** (University of Padova)
### Description
The catalog includes the magnitude, time of occurrence (s), and 3D coordinates (m) of earthquakes in about 20 years of recording in South California. Coordinates were converted from latitude, longitude, and depth of events in a seismic catalog. Magnitudes should be within the range $[0,8]$.
* **Waiting time (t)**: time interval between an event and the next one in the sequence.
* **Distance (r)**: Eucledian 3D distance between events. (each 3D set of coordinates refers to the hypocenter, i.e. the point triggering the slip in a fault that forms the earthquake)
### Assignments
1. Deduce what is the variable in each column of the catalog.
2. Visualize the process in space and/or time with suitable time series and/or 3D visualizations of the hypocenters. For instance, plot a space variable (a single coordinate or a nice linear combination of coordinates) as a function of time.
3. Compute the distribution $P_m(t)$ of waiting times for events of magnitude m or above (i.e. do not consider events below $m$). In shaping the bin sizes, take into account that this distribution is expected to have a power-law decay with time (e.g $\sim 1/t$), and that a power-law is well visualized in log-log scale. Do this analysis for many values of $m$, say $m=2,3,4,5$.
4. Compute the distribution $P_m(r)$ of the distance between an event and the next one, considering earthquakes of magnitude m or above. Also here make a clever choice for the bin sizes and try several values of $m$.
5. Compute the distribution $P_{m,R}(t)$ of waiting times for events of magnitude $m$ or above, which are separated by at most a distance $r