These processed data were created to investigate seismic velocity changes associated with the period of extended unrest and eruption at Mauna Loa, Island of Hawaiʻi. Primary data (i.e., seismic waveforms) are hosted at NSF SAGE Facility (https://service.iris.edu/) and were ingested by the code CWIRE Version 1.0.0 (https://doi.org/10.5066/P13BKSJJ) to produce the data analyzed in Hotovec-Ellis 'Seismic velocity changes from repetitive seismicity at Mauna Loa prior to and during its 2022 eruption.' This release contains the inputs and commands used to generate the derived data as text files of the inverted continuous time series, .pdf figures for human visual interpretation of those time series, and text files of the raw velocity measurements for millions of pairs of earthquakes. See README.txt for further documentation.

This paper explores several data mining and time series analysis methods for predicting the magnitude of the largest seismic event in the next year based on the previously recorded seismic events in the same region. The methods are evaluated on a catalog of 9,042 earthquake events, which took place between 01/01/1983 and 31/12/2010 in the area of Israel and its neighboring countries. The data was obtained from the Geophysical Institute of Israel. Each earthquake record in the catalog is associated with one of 33 seismic regions. The data was cleaned by removing foreshocks and aftershocks. In our study, we have focused on ten most active regions, which account for more than 80% of the total number of earthquakes in the area. The goal is to predict whether the maximum earthquake magnitude in the following year will exceed the median of maximum yearly magnitudes in the same region. Since the analyzed catalog includes only 28 years of complete data, the last five annual records of each region (referring to the years 2006–2010) are kept for testing while using the previous annual records for training. The predictive features are based on the Gutenberg-Richter Ratio as well as on some new seismic indicators based on the moving averages of the number of earthquakes in each area. The new predictive features prove to be much more useful than the indicators traditionally used in the earthquake prediction literature. The most accurate result (AUC = 0.698) is reached by the Multi-Objective Info-Fuzzy Network (M-IFN) algorithm, which takes into account the association between two target variables: the number of earthquakes and the maximum earthquake magnitude during the same year.

The raw, pre-processed, and modeled GNSS time-series of the 213 GEONET sites in the Tohoku region, Japan, from Mar. 12, 2011 to Nov. 20, 2021, relative to the Okhotsk plate (Argus et al., 2011, Geochemistry, Geophysics, Geosystems).

The original GNSS time-series are F5 solutions, which are distributed by Geospatial Information Authority of Japan (GSI, https://www.gsi.go.jp/). The details and availability of F5 solutions are written in Takamatsu et al. (2023, Earth, Planets, and Space) https://doi.org/10.1186/s40623-023-01787-7.

The GNSS time-series processing was performed by Tomita (submitted), and the following signals were excluded from the raw time-series: seasonal variation, coseismic step, antenna maintenance offset, and common mode errors. Then, the pre-processed time-series were modeled by a trajectory modeling method considering postseismic deformation of the 2011 Tohoku earthquake, the Boso SSEs, and postseismic deformations due to aftershocks and L-ASE (long-term aseismic slip event) since late 2019.

"sitelist.txt" - Site information file
column 1: Full site ID
column 2: 4digits site ID
column 3: Longitude [deg]
column 4: Latitude [deg]
column 5: Height [m]

"pre-process/xxxx.txt" - Time-series at xxxx (4digits site ID) site
column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)
column 2: raw East-West displacement [m]
column 3: raw North-South displacement [m]
column 4: raw Up-down displacement [m]
column 5: pre-processed East-West displacement [m]
column 6: pre-processed North-South displacement [m]
column 7: pre-processed Up-down displacement [m]

"model/xxxx/prediction_yy.txt" - Time-series for yy component (yy=EW, NS, UD) at xxxx (4digits site ID) site
column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011)
column 2: modeled displacement excluding the Boso SSEs [m]
column 3: modeled displacement excluding the Boso SSEs and postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. [m]
column 4: modeled displacement excluding the Boso SSEs, postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. and the 2019 L-ASE [m]

The displacement on Mar. 12, 2011 was initially set to be zero before the pre-processing, but the removal of the above factors provided some deviation from zero.

The raw time-series excluded outliers from the original F5 solutions, and the raw time-series were transformed into the Okhotsk plate reference.

Contains Datasets for training and testing models for Urban Seismic Event Detection (USED).

1. Strong Dataset: Contains Synthetic Data to be used for supervised learning
2. Unlabel Dataset: Contains unlabeled data to be used for semi supervised (or unsupervised) learning
3. Test Synth: Synthetic Dataset to evaluate models
4. Test Real: Small Real Dataset to evaluate models

The data is in SAC format, with JSON labels. The obspy library in python can be used to read this data.

This dataset is composed of GPS stations (1 file) and seismometers (1 file) multivariate time series data associated with three types of events (normal activity / medium earthquakes / large earthquakes). Files Format: plain textFiles Creation Date: 02/09/2019Data Type: multivariate time seriesNumber of Dimensions: 3 (east-west, north-south and up-down)Time Series Length: 60 (one data point per second)Period: 2001-2018Geographic Location: -62 ≤ latitude ≤ 73, -179 ≤ longitude ≤ 25Data Collection - Large Earthquakes: GPS stations and seismometers data are obtained from the archive [1]. This archive includes 29 large eathquakes. In order to be able to adopt a homogeneous labeling method, dataset is limited to the data available from the American Incorporated Research Institutions for Seismology - IRIS (14 large earthquakes remaining over 29). > GPS stations (14 events): High Rate Global Navigation Satellite System (HR-GNSS) displacement data (1-5Hz). Raw observations have been processed with a precise point positioning algorithm [2] to obtain displacement time series in geodetic coordinates. Undifferenced GNSS ambiguities were fixed to integers to improve accuracy, especially over the low frequency band of tens of seconds [3]. Then, coordinates have been rotated to a local east-west, north-south and up-down system. > Seismometers (14 events): seismometers strong motion data (1-10Hz). Channel files are specifying the units, sample rates, and gains of each channel. - Normal Activity / Medium Earthquakes: > GPS stations (255 events: 255 normal activity): High Rate Global Navigation Satellite System (HR-GNSS) normal activity displacement data (1Hz). GPS data outside of large earthquake periods can be considered as normal activity (noise). Data is downloaded from [4], an archive maintained by the University of Oregon which stores a representative extract of GPS noise. It is an archive of real-time three component positions for 240 stations in the western U.S. from California to Alaska and spanning from October 2018 to the present day. The raw GPS data (observations of phase and range to visible satellites) are processed with an algorithm called FastLane [5] and converted to 1 Hz sampled positions. Normal activity MTS are randomly sampled from the archive to match the number of seismometers events and to keep a ratio above 30% between the number of large earthquakes MTS and normal activity in order not to encounter a class imbalance issue.> Seismometers (255 events: 170 normal activity, 85 medium earthquakes): seismometers strong motion data (1-10Hz). Time series data collected from the international Federation of Digital Seismograph Networks (FDSN) client available in Python package ObsPy [6]. Channel information is specifying the units, sample rates, and gains of each channel. The number of medium earthquakes is calculated by the ratio of medium over large earthquakes during the past 10 years in the region. A ratio above 30% is kept between the number of 60 seconds MTS corresponding to earthquakes (medium + large) and total (earthquakes + normal activity) number of MTS to prevent a class imbalance issue. The number of GPS stations and seismometers for each event varies (tens to thousands). Preprocessing:- Conversion (seismometers): data are available as digital signal, which is specific for each sensor. Therefore, each instrument digital signal is converted to its physical signal (acceleration) to obtain comparable seismometers data- Aggregation (GPS stations and seismometers): data aggregation by second (mean)Variables:- event_id: unique ID of an event. Dataset is composed of 269 events.- event_time: timestamp of the event occurence - event_magnitude: magnitude of the earthquake (Richter scale)- event_latitude: latitude of the event recorded (degrees)- event_longitude: longitude of the event recorded (degrees)- event_depth: distance below Earth's surface where earthquake happened (km)- mts_id: unique multivariate time series ID. Dataset is composed of 2,072 MTS from GPS stations and 13,265 MTS from seismometers.- station: sensor name (GPS station or seismometer)- station_latitude: sensor (GPS station or seismometer) latitude (degrees)- station_longitude: sensor (GPS station or seismometer) longitude (degrees)- timestamp: timestamp of the multivariate time series- dimension_E: East-West component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_N: North-South component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_Z: Up-Down component of the sensor (GPS station or seismometer) signal (cm/s/s)- label: label associated with the event. There are 3 labels: normal activity (GPS stations: 255 events, seismometers: 170 events) / medium earthquake (GPS stations: 0 event, seismometers: 85 events) / large earthquake (GPS stations: 14 events, seismometers: 14 events). EEW relies on the detection of the primary wave (P-wave) before the secondary wave (damaging wave) arrive. P-waves follow a propagation model (IASP91 [7]). Therefore, each MTS is labeled based on the P-wave arrival time on each sensor (seismometers, GPS stations) calculated with the propagation model.[1] Ruhl, C. J., Melgar, D., Chung, A. I., Grapenthin, R. and Allen, R. M. 2019. Quantifying the value of real‐time geodetic constraints for earthquake early warning using a global seismic and geodetic data set. Journal of Geophysical Research: Solid Earth 124:3819-3837.[2] Geng, J., Bock, Y., Melgar, D, Crowell, B. W., and Haase, J. S. 2013. A new seismogeodetic approach applied to GPS and accelerometer observations of the 2012 Brawley seismic swarm: Implications for earthquake early warning. Geochemistry, Geophysics, Geosystems 14:2124-2142.[3] Geng, J., Jiang, P., and Liu, J. 2017. Integrating GPS with GLONASS for high‐rate seismogeodesy. Geophysical Research Letters 44:3139-3146.[4] http://tunguska.uoregon.edu/rtgnss/data/cwu/mseed/[5] Melgar, D., Melbourne, T., Crowell, B., Geng, J, Szeliga, W., Scrivner, C., Santillan, M. and Goldberg, D. 2019. Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes (Version 1.0) [Data set]. Zenodo.[6] https://docs.obspy.org/packages/obspy.clients.fdsn.html[7] Kennet, B. L. N. 1991. Iaspei 1991 Seismological Tables. Terra Nova 3:122–122.

Description of Earthquakes Dataset (1990-2023)

The earthquakes dataset is an extensive collection of data containing information about all the earthquakes recorded worldwide from 1990 to 2023. The dataset comprises approximately three million rows, with each row representing a specific earthquake event. Each entry in the dataset contains a set of relevant attributes related to the earthquake, such as the date and time of the event, the geographical location (latitude and longitude), the magnitude of the earthquake, the depth of the epicenter, the type of magnitude used for measurement, the affected region, and other pertinent information.

Features - time in millisecconds - place - status
- tsunami (boolean value) - significance - data_type - magnitudo - state - longitude - latitude
- depth - date

Importance and Utility of the Dataset:

Earthquake Analysis and Prediction: The dataset provides a valuable data source for scientists and researchers interested in analyzing spatial and temporal distribution patterns of earthquakes. By studying historical data, trends, and patterns, it becomes possible to identify high-risk seismic zones and develop predictive models to forecast future seismic events more accurately.

Safety and Prevention: Understanding factors contributing to earthquake frequency and severity can assist authorities and safety experts in implementing preventive measures at both local and global levels. These data can enhance the design and construction of earthquake-resistant infrastructures, reducing material damage and safeguarding human lives.

Seismological Science: The dataset offers a critical resource for seismologists and geologists studying the dynamics of the Earth's crust and various geological faults. Analyzing details of recorded earthquakes allows for a deeper comprehension of geological processes leading to seismic activity.

Study of Tectonic Movements: The dataset can be utilized to analyze patterns of tectonic movements in specific areas over the years. This may help identify seasonal or long-term seismic activity, providing additional insights into plate tectonic behavior.

Public Information and Awareness: Earthquake data can be made accessible to the public through portals and applications, enabling individuals to monitor seismic activity in their regions of interest and promoting awareness and preparedness for earthquakes.

In summary, the earthquakes dataset represents a fundamental information source for scientific research, public safety, and community awareness. By analyzing historical data and building predictive models, this dataset can significantly contribute to mitigating seismic risks and protecting people and infrastructure from the consequences of earthquakes.

Dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. Continuous data was acquired from February 2023 to the end of June 2023. The continuous data was then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy, during the acquisition period. A total of 67 events were selected from the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The waveform data was then further analyzed and annotated by analysts from INGV. Annotations include a pick time for the S and P wave, and an uncertainty level for the annotations.

The data consists of two datasets, one containing earthquake traces, the other containing noise-only traces. There are two folders: the dataset_earthquakes folder contains seismic traces; the dataset_noise folder contains noise-only traces.

The earthquake dataset consists of 328 3x25001 arrays, each related to a seismic event and with its own metadata. The dataset follows the Seisbench format, in which each trace follows the convention 'bucket0$trace_number;:n_dimensions;:n_samples', where 'bucket0' indicates the block to which the trace belongs; 'trace_number' indicates the trace' index within the block; 'n_dimensions' denotes the number of measurement axes; and 'n_samples' represents the number of samples in the trace. The waveforms are included in the the waveforms.hdf5 file of the earthquake_dataset folder, while the metadata is in the metadata.csv file in the folder. For each trace in the waveforms.hdf5 file there is an associated row in the metadata.csv file at the same index (indicated by 'trace_number' in the trace name).

The original miniSEED files that were analyzed by the INGV analysts are made available. They are contained in the miniseed_files folder. Each file name follows the format '_eventID_originTime_WS.POZA.Sx.DNy.MSEED' where eventID is the ID of the event that is recorded in the trace, originTime is the origin of the event in UTC time (expressed with the YYYY-MM-DDThh:mm:ss.ssssss format), x is a number that is used to identify the sensor that recorded the trace, y indicates the measurement direction of that trace, named '1', '2', 'Z'. For each trace in the waveforms.hdf5 file, the name of the miniSEED files that comprise the trace are in the metadata row for that trace, under the ‘trace_name_original_1’, ‘trace_name_original_2’, and ‘trace_name_original_Z’ fields in the metadata.csv file.

The dataset_noise folder follows the same convention. It contains a waveforms.hdf5 file with waveforms without seismic activity. The metadata_csv file has the metadata associated to each noise trace. The miniSEED_files_noise folder contains the original miniSEED files of the noise traces.

The objective of the study was to examine if there are detectable localized increases in geostationary satellite-derived Land Surface Temperatures (LST) prior to twenty large (Mw>5.5) and shallow (<35km) land-based earthquakes. Two one-year-long datasets are constructed for every study area: one in a year with earthquakes and one in a year without. LST data are normalized based on the methodology described in Pavlidou et al., 2016. Anomalies are detected when normalized values exceed a threshold. Numbers of anomalies are counted in four spatial zones laying at different distances from the earthquakes and in five temporal periods before, during and after the earthquake. Anomaly densities (number of anomalies per zone and per period) are statistically evaluated to see if there exist significant differences between years, periods and locations relative to the earthquakes. The assumption is that a link between earthquakes and anomalies can be established only if significantly more anomalies appear prior to, or during, an earthquake; closer to the earthquake; and only in the year of the earthquake. The calculations and the comparisons are repeated for two different anomaly detection thresholds and for two different definitions of the length of a co-seismic period. .dta and .por versions of SPSS-output files provided by DANS.

These processed data and provisional codes were created to investigate seismic velocity changes associated with the collapse of Kīlauea caldera during its 2018 eruption. Primary data (i.e., seismic waveforms) are hosted at the Incorporated Research Institutions for Seismology (IRIS; https://www.iris.edu/) and are ingested by the codes included here to reproduce the data analyzed in Hotovec-Ellis et al., 'Earthquake-derived seismic velocity changes during the 2018 caldera collapse of Kīlauea volcano.' The included code ('cwire' short for Coda Wave Interferometry with Repeating Earthquakes) takes a catalog of earthquakes clustered by waveform similarity (e.g., REDPy, https://github.com/ahotovec/REDPy/) and processes the coda for each pair of earthquakes within each cluster for changes in seismic velocity. Specifically, it finds the optimal time stretching factor to either expand or condense one earthquake's coda waveform to match the other, with that stretching factor equal to the relative change in seismic velocity (dv/v). It then takes those pairwise measurements and uses damped least-squares inversion to find the continuous time series that fits the pairwise measurements and writes them to the 'out' directory; results from runs described by the included configuration files are included in this directory for validation as part of this data release. The resulting time series can then be compared across the local seismic network and with other time series such as deformation. All data are stored in HDF5 database files (*.h5) created in the working directory. See README.txt for further documentation.

Seismic data collected with broadband seismometers at rock outcrops as part of AAS 4318. Seven new sites were established (sites BHIL, CAD1, CAD2, CAD3, CAD4, CAD5 and CAD6) and measurements at a previously established site (DAVI) was recommenced. The sites were deployed continuously, although measurements were intermittent based on power restrictions and hardware performance issues.

Approximate site locations are given below in units of decimal degrees (WGS84) as (latitude longitude) pairs: CAD1 approximate coordinates only - Carey Nunatak - as no data yet downloaded CAD2 -68.566510 86.100627 Mt Brown, new site established under this project CAD3 -66.521103 96.363667 Gillies Island, new site established under this project CAD4 -67.419656 99.143785 Mt Strathcona, new site established under this project CAD5 -66.552475 107.764024 Snyder Rocks, new site established under this project CAD6 -66.789282 120.990964 Chick Island, new site established under this project BHIL -66.251024 100.599006 Bunger Hills site, new site established under this project

Site changes

Instruments were deployed continuously but did not operate continuously due to solar powering of the instruments with a small battery array.

Due to instrument failures, the recorders at CAD5 and BHIL were doubled in some periods. The recorder at DAVI was also doubled.

Data sets:

Seismic data is stored in miniSEED format, one stream for each channel. One file for each day with data recorded. If the recording is interrupted, additional files are generated for that day.

The file name contains station name and starting time information, e.g.

CAD3_2016_12_09T19_10_06_840000Z.BHE

SSSS_YYYY_MM_DDTHH_mm_ss_mmmmmmZ.BHC

Where S is a four-letter station name YYYY is year MM is month DD is the day (in the month) HH is the hour (0-24) mm is minute ss is second mmmmmm is milliseconds, padded with 0’s C (last in file extension) is the orientation of the channel. N = North-South E – East-West Z – Up-Down

MiniSEED is the subset of the SEED standard that is used for time series data. Very limited metadata for the time series is included in miniSEED beyond time series identification and simple state-of-health flags. In particular, geographic coordinates, response/scaling information and other information needed to interpret the data values are not included. Time series are stored as generally independent, fixed-length data records which each contain a small segment of contiguous series values.

Alongside the time series files, are two spreadsheets containing teleseismic event data, that could be of utility in a reconnaissance of the time series data. 1) Earthquake list, Casey-Davis seismic deployment, global teleseismic earthquakes, greater than or equal to magnitude 6.5. 2) Earthquake list, Casey-Davis seismic deployment, regional teleseismic earthquakes, greater than or equal to magnitude 6.0.

Anyone working with seismic data is likely to be familiar with the abbreviations in the earthquake list files, otherwise, refer to the IRIS Data Management Centre, https://ds.iris.edu/ds/nodes/dmc/tools/#data_types=events.

Details of different datasets downloaded from IRIS.

Introduction

This dataset records seismic events (i.e., earthquakes) in California and the Southwestern United States recorded by the IRIS Seismic Network between 1960 and 2024. IRIS (Incorporated Research Institutions for Seismology) is a consortium of over 120 U.S. universities dedicated to the operation of facilities for the acquisition, management, and distribution of seismological data.

Data Feature Definitions

datetime: timestamp of the recorded seismic event.

lat: latitude of the recorded seismic event.

lon: longitude of the recorded seismic event.

depth: depth of of the recorded seismic event.

mag: magnitude of the recorded seismic event.

type: type of magnitude scale used for the recorded measurement (e.g., Ml is Local Magnitude, Md is Duration Magnitude, etc.)

Magnetotellurics (MT) is an electromagnetic geophysical method that is sensitive to variations in subsurface electrical resistivity. Measurements of natural electric and magnetic fields are done in the time domain, where instruments can record for a couple of hours up to mulitple months resulting in data sets on the order of gigabytes. The principles of findability, accessibility, interoperability, and reuse of digital assets (FAIR) requires standardized metadata. Unfortunately, the MT community has never had a metadata standard for time series data. In 2019, the Working Group for Magnetotelluric Data Handling and Software (https://www.iris.edu/hq/about_iris/governance/mt_soft) was assembled by the Incorporated Research Institutions for Seismology (IRIS) to develop a metadata standard for time series data. This product describes the metadata definitions. Metadata Hierarchy: Survey -> Station -> Run -> Channel The hierarchy and structure of the MT metadata logically follows how MT time series data are collected. The highest level is "survey" which contains metadata for data collected over a certain time interval in a given geographic region. This may include multiple principle investigators or multiple data collection episodes but should be confined to a specific project. Next, a "station" which contains metadata for a single location over a certain time interval. If the location changes during a run, then a new station should be created and subsequently a new run under the new station. If the sensors, cables, data logger, battery, etc. are replaced during a run but the station remains in the same location, then this can be recorded in the "run" metadata but does not require a new station entry. A "run" contains metadata for continuous data collected at a single sample rate. If channel parameters are changed between runs, this would require creating a new run. If the station is relocated then a new station should be created. If a run has channels that drop out, the start and end period will be the minimum time and maximum time for all channels recorded. Finally, a "channel" contains metadata for a single channel during a single run, where "electric", "magnetic", and "auxiliary" channels have some different metadata to uniquely describe the physical measurement.

This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The earthquake classification problem involves predicting whether a major event is about to occur based on the most recent readings in the surrounding area. The data is taken from Northern California Earthquake Data Center and each data is an averaged reading for one hour, with the first reading taken on Dec 1st 1967, the last in 2003. We transform this single time series into a classification problem by first defining a major event as any reading of over 5 on the Rictor scale. Major events are often followed by aftershocks. The physics of these are well understood and their detection is not the objective of this exercise. Hence we consider a positive case to be one where a major event is not preceded by another major event for at least 512 hours. To construct a negative case, we consider instances where there is a reading below 4 (to avoid blurring of the boundaries between major and non major events) that is preceded by at least 20 readings in the previous 512 hours that are non-zero (to avoid trivial negative cases). None of the cases overlap in time (i.e. we perform a segmentation rather than use a sliding window). Of the 86,066 hourly readings, we produce 368 negative cases and 93 positive.

Donator: A. Bagnall

Earthquakes (USGS: Magnitude, Location, and Freq)

Magnitude, Location, and Frequency

By [source]

About this dataset

Earthquakes form an integral part of our planet’s geology. It is crucial to gain an understanding of the frequency and strength of these seismic activities, as this information is essential in both the cause and preventions of damaging earthquakes. Fortunately for us, the United States Geological Survey (USGS) captures comprehensive data on Earthquakes magnitude and location across the United States and its surrounding areas.

This dataset contains information such as time, latitude, longitude, depth, magnitude, type, gap between azimuthal gaps (gap), dmin which is minimum distance to nearest station (dmin), root mean square travel time residual (rms), Network which reported raised an incident report (net), updated date that was last updated or modified(updated) place horizonation uncertainty error - absolute value serves as 95% confidence interval radius(horizontalError)depth Horizonation uncertainty error - absolute value serve as 95% confidence interval radius(depthError)magHorizonation uncertainty error - absolute value serve as 95% confidence numberof seismic stations used to measure magnitude(magNst )Number statuses ie reviewed/reviewed_manual/automatic etc..status). This data can be a useful tool in building a more contextual picture around potential dangers posed by seismic activity

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset can be incredibly useful in uncovering geophysical insights about earthquakes. It contains comprehensive data about the magnitude and location of seismic activity, which can help to better understand the cause and prevention of damaging quakes.

Research Ideas

• Generating earthquake hazard maps to indicate seismic activity and risk levels in different areas.
• Developing predictive models of earthquake magnitude and probability of occurrence on the basis of geographic characteristics, previous seismic history and observed patterns of activity.
• Conducting analysis to determine correlations between geological features, human activities, and seismic events in order to better understand the causes and effects of potentially damaging earthquakes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: usgs_current.csv | Column name | Description | |:--------------------|:--------------------------------------------------------------------------------| | time | The time of the earthquake. (DateTime) | | latitude | The latitude of the earthquake. (Float) | | longitude | The longitude of the earthquake. (Float) | | depth | The depth of the earthquake. (Float) | | mag | The magnitude of the earthquake. (Float) | | magType | The type of magnitude measurement used. (String) | | nst | The number of seismic stations used to calculate the magnitude. (Integer) | | gap | The maximum angular distance between azimuthal gaps. (Float) | | dmin | The distance to the nearest station. (Float) | | rms | The root-mean-square travel time residual. (Float) | | net | The network detected. (String) | | updated | The time the earthquake was last updated. (DateTime) | | place | The location of the earthquake. (String) | | horizontalError | The horizontal error of the earthquake. (Float) | | depthError | The depth error of the earthquake. (Float) | | magError ...

In this study ( The paper ) we present a large dataset of 1,249,411 3-component seismograms, recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide, including 631,105 3-component seismograms generated by 304,878 local earthquakes and labeled as earthquakes (EQ), and 618,306 ones labeled as noise (AN). The choice of collecting only local earthquake-data is motivated by the fact that small-magnitude events, which generate relatively small amplitudes and are easily attenuated, are often problematic to detect but provide valuable information about earthquake processes. The labeled data are split into HDF5-Groups: EQ and AN. Each of these groups contains as many HDF5-Datasets as the number of 3-component seismograms; these are labeled in accordance to the format net_sta_starttime, where net, sta, and starttime represent the seismic network, station, and start time of the seismograms. Each HDF5-Dataset (i.e. each triplet of seismograms) has an attribute, which allows accessing the respective metadata. In addition, the HDF5-Group Stations allows accessing stations’ metadata through as many HDF5-Datasets (which are labeled in accordance to the format net_sta) as the number of receivers employed for collecting the waveforms.

This global dataset is intended to be used for carrying out a multitude of seismological and signal processing tasks on single-station recordings, and its size particularly suits machine learning (ML) applications.. Application of ML to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings with high accuracy (93.2%), even if applied in regions not investigated by the training set. We make the dataset publicly available as a unique file in HDF5 data format, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in seismology and signal processing.

Seismic data recorded at Base Esperanza in the period 1992 - 1994 are currently available in PDAS (DADISP) format. The seismic signals have been recorded on three primary channels at a rate of 2 samples per second and three secondary channels at a rate of 0.2 samples per sec. The timing is provided through the PDAS internal clock. The sensor is the BB-13 Teledyne Geotech seismometer which shows a flat response to acceleration between dc to 20 Hz. The site is a wooden building located in the Argentinian Base Esperanza in the interior of which the seismometers are installed on acement pillar anchored to the underlying rocky ground.

This data collection contains time series of RMS amplitude, computed on the vertical component of the seismic signal acquired by ECPN and EMFO seismic stations, belonging to the permanent seismic network run by INGV, Osservatorio Etneo. These stations are equipped with broadband (40s cut-off period) 3C Trillium seismometers, acquiring at a ssampling rate of 100 Hz. The signals were band-pass filtered in the range 0.5-5.5 Hz. The windowlength is equal to 5 min and 5 h for ECPN and EMFO, respectively. The data collection is linked to the paper "Changing magma recharge/discharge dynamics during the 2020-22 lava fountaining activity at Mt. Etna revealed by tilt deformation and volcanic tremor" by Massimiliano Cardone, Andrea Cannata, Adriana Iozzia, Vittorio Minio, Marco Viccaro, Salvatore Gambino.

SeisMIC (Seismological Monitoring using Interferometric Concepts) is a python software that emerged from the miic library. SeisMIC provides functionality to apply some concepts of seismic interferometry to different data of elastic waves. Its main use case is the monitoring of temporal changes in a mediums Green's Function (i.e., monitoring of temporal velocity changes). SeisMIC will handle the whole workflow to create velocity-change time-series including: Downloading raw data, Adaptable preprocessing of the waveform data, Computating cross- and/or autocorrelation, Plotting tools for correlations, Database management of ambient seismic noise correlations, Adaptable postprocessing of correlations, Computation of velocity change (dv/v) time series, postprocessing of dv/v time series, plotting of dv/v time-series

Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are daily geodetic displacement time series (compressed). They are combined, cleaned and filtered, GIPSY-GAMIT long-term time series of Continuous Global Navigation Satellite System (CGNSS) station positions (global and regional) in the latest version of ITRF