DOI: 10.5061/dryad.cjsxksndw
title: Identification of transient seismo-acoustic signals from crashing ocean waves: Template matching and location of discrete surf events

description: Crashing ocean waves, or surf, have previously been identified as
 persistent generators of coherent infrasound signals from 0.5 to 20 Hz.
 Here, we demonstrate that infrasonic and
 seismic (seismo-acoustic) signals from surf are composed of
 repetitive transient events which can be detected and
 characterized using template matching. Using data collected from a series
 of field experiments designed to study seismo-acoustic surf
 signals in Santa Barbara, California, we show that source regions
 of these events can be constrained primarily to just offshore of a
 local coastal headland using a reverse-time-migration
 implementation on a small spatial scale (&lt;5 km2). Our data
 include one continuously running infrasound sensor (September
 2022–July 2023) to examine temporal signal evolution,
 complemented by several short-duration campaigns involving various
 infrasound arrays, co-located seismometers, and video recordings.
 Throughout varied oceanographic and atmospheric conditions, we detect up
 to tens of thousands of independent surf repeaters per day over the course
 of a year. The amplitudes of detected infrasound signals are correlated
 with offshore significant wave height and local wind speed. We identify
 coincident arrivals of seismic and infrasound signals with similar
 spectral characteristics, suggesting a linked source mechanism
 locally producing both the seismic and acoustic transient
 signals. Source regions estimated from array- and network-based
 methods correspond to the surf zone as seen in video footage, and
 the directions of selected transient signals align with the
 location of a rocky reef shelf nearshore. This work showcases the ability
 to extract near-real-time information about the coastal sea state from
 seismic and acoustic signal features.
description: The majority of this dataset is composed of infrasound data
 recorded with a principal station (Hyperion 3111-series set to 400 Hz
 sampling frequency), which recorded near-continuous data from September 6,
 2022 to July 17, 2023 via DiGOS DATA-CUBE3. Data collection was performed
 (in part) at the University of California Natural Reserve System Coal Oil
 Point Reserve DOI: 10.21973/N3Z07N. Roughly every 2 weeks, the battery was
 swapped with a new one to keep the sensor running. Sensor was placed
 within a bush, with no other physical noise filters.
 The principal infrasound station was supplemented by four
 temporary array deployments; for each deployment data collection period,
 there is also data available from the principal station. Deployments are
 described in more detail below. Deployment 1: January
 11, 2023 (local PST). Involved 4 Gem 1.0 sensors [Anderson et al., 2017]
 with sampling frequency 100 Hz, placed along the base of the cliff at
 Sands Beach (wrapped in a towel, no other physical noise
 filter). Deployment 2: January 12–19, 2023 (local PST).
 Involved 4 Gem 1.0 sensors with sampling frequency 100 Hz (1 inactive for
 majority of data collection period), placed along the top of the cliff at
 Sands Beach (directly within bushes, no other physical noise
 filters).  Deployment 3: July 10, 2023 (local
 PST). <br>-    Three broadband Trillium Compact 120s 3-component
 seismometers with sampling frequency 400 Hz, named TCA1, TCA2, TCA3.
 Unfortunately, TCA2 did not record viable data; blank miniseed file is
 excluded from the converted dataset. TCA1 was buried directly in soil,
 composed of a top layer of loose sand overlying uplifted marine terrace of
 Sisquoc formation shale. TCA3 was buried directly in a sand
 dune. <br>-    Same four Gem 1.0 sensors as in deployments 1 and 2,
 sampling at 100 Hz. Placed again in bushes with no other physical noise
 filters.<br>-    Six Chaparral Physics C60 sensors with sampling
 frequency 400 Hz. Three of the six C60’s recorded data on one DiGOS
 DATA-CUBE3, other three recorded on another DiGOS DATA-CUBE3. Stations
 placed directly in bushes, with no other physical noise filters.
 Deployment 4: October 17–23, 2023.<br>-    One broadband
 Trillium Compact 120s 3-component seismometers with sampling frequency 400
 Hz, named TCA1. Data housed in “TC_mseed” subfolder. Co-located with
 principal infrasound station.<br>-    Same four Gem 1.0 sensors as
 in deployments 1 and 2, sampling at 100 Hz. Placed again within
 bushes.<br>-    Three Chaparral Physics C60 sensors with sampling
 frequency 400 Hz. Data recorded on one DiGOS DATA-CUBE3. Sensors were
 placed in bushes with no physical noise filters.<br>Sub-deployment
 4: October 20, 2023.<br>-    Three Chaparral Physics C60 sensors
 with sampling frequency 400 Hz. Data recorded on one DiGOS DATA-CUBE3.
 Sensors were placed in bushes with no physical noise filters.<br>-  
  One Chaparral Physics C60 sensor with sampling frequency 400 Hz placed on
 the sand. Sensor had no physical noise filters.<br>-    One GoPro
 Hero10 mounted on a tripod. Data is stored in miniSEED
 format, which can be loaded with several programming languages including
 the ObsPy library in Python. Infrasound data have been calibrated such
 that amplitudes are in Pascals [Pa]. Seismic data have been calibrated
 such that amplitudes are in meters per second [m/s]. Sensor locations are
 listed in metadata folder. 
 Anderson, J. F., Johnson, J. B., Bowman, D. C., and
 Ronan, T. J. (2017). The gem infrasound logger and custom-built
 instrumentation. Seismological Research Letters, 89(1):153–164.
description: # Identification of transient seismo-acoustic signals from crashing ocean
 waves: Template matching and location of discrete surf events This readme
 file was generated on [2024-03-22] and updated on [2025-08-11] ## GENERAL
 INFORMATION Title of Dataset: Seismo-acoustic observations of crashing
 ocean waves: Investigating surf monitoring at Coal Oil Point Reserve,
 Santa Barbara, California Date of data collection: 2022-09-06 to
 2023-07-13, and 2023-10-17 to 2023-10-23 Geographic location of data
 collection: Data collection was performed at the University of California
 Natural Reserve System Coal Oil Point Reserve (DOI: 10.21973/N3Z07N). Coal
 Oil Point Reserve, Santa Barbara, California: (34.40831 N, 119.87868 W).
 Information about funding sources that supported the collection of the
 data: Supported by a University of California, Santa Barbara faculty
 research grant awarded to Matoza. ## SHARING/ACCESS INFORMATION
 Licenses/restrictions placed on the data: Creative Commons Zero (CC0)
 Links/relationships to ancillary data sets: NOAA buoy 46053:
 [https://www.ndbc.noaa.gov/station_realtime.php?station=46053](https://www.ndbc.noaa.gov/station_realtime.php?station=46053) USCRN weather station Santa Barbara 11 W: [https://erddap.cencoos.org/erddap/tabledap/ncei-uscrn-ca_santa_barbara_11_w.html](https://erddap.cencoos.org/erddap/tabledap/ncei-uscrn-ca_santa_barbara_11_w.html) NOAA tide station 9411340: [https://tidesandcurrents.noaa.gov/waterlevels.html?id=9411340](https://tidesandcurrents.noaa.gov/waterlevels.html?id=9411340) DEM (3DEP)—USGS one meter x23y382 CA SoCal Wildfires B4 2018: [https://www.sciencebase.gov/catalog/item/5eaa4a0182cefae35a21ee6e](https://www.sciencebase.gov/catalog/item/5eaa4a0182cefae35a21ee6e) Topobathy—1930 - 2014 USGS CoNED Topobathy DEM (Compiled 2016): Southern Coast of CA & Channel Islands: [https://www.fisheries.noaa.gov/inport/item/55359](https://www.fisheries.noaa.gov/inport/item/55359) ## DATA & FILE OVERVIEW ### File List: example_load_mseed.py Data-COPR-1.zip ``` |- metadata/ | |- Master_station_locations.csv | |- 3111_gps | | |- JF.COPR.01.CDF-2.png | | |- JF.COPR.01.CDF-3.png | | |- JF.COPR.01.CDF.png | | |- JF.COPR.AUTO.CDF.png | | |- JF.COPR.01.CDF-2.json | | |- JF.COPR.01.CDF-3.json | | |- JF.COPR.01.CDF.json | | |- JF.COPR.AUTO.CDF.json | |- deploy1 | | |- SNXmetadata_000.txt | | |- gps/ | | | |- SNXgps_000.txt | |- deploy2 | | |- SNXmetadata_000.txt | | |- gps/ | | | |- SNXgps_000.txt | |- deploy3 | | |- C60_gps/ | | | |- CR.COP1.AUTO.CDF.png | | | |- CR.COP2.AUTO.CDF.png | | | |- CR.COP1.AUTO.CDF.json | | | |- CR.COP2.AUTO.CDF.json | | |- GEM_metadata/ | | | |- SNXmetadata_000.txt | | | |- SNXmetadata_001.txt | | | |- SNXmetadata_002.txt | | | |- SNXmetadata_003.txt | | | |- gps/ | | | | |- SNXgps_000.txt | | |- COP_array_conversion_notes.docx | | |- COPR_deploy3_plan.docx | |- deploy4 | | |- C60_gps/ | | | |- CR.COP1.AUTO.CDF.png | | | |- CR.COP2.AUTO.CDF.png | | | |- CR.COP3.AUTO.CDF.png | | | |- CR.COP1.AUTO.CDF.json | | | |- CR.COP2.AUTO.CDF.json | | | |- CR.COP3.AUTO.CDF.json | | |- GEM_metadata/ | | | |- SNXmetadata_000.txt | | | |- gps/ | | | | |- SNXgps_000.txt |- processed_data/ | |- 3111_mseed/ | | |- JF.COPR.01.CDF.YYYY.JD.HH.mseed | |- deploy1/ | | |- GEM_mseed/ | | | |- JF.SNX.YYYY.JD.mseed | |- deploy2/ | | |- GEM_mseed/ | | | |- JF.SNX.YYYY.JD.mseed | |- deploy3/ | | |- C60_mseed/ | | | |- CR.COPX.XX.CDF.YYYY.JD.HH.mseed | | |- GEM_mseed/ | | | |- JF.SNX.YYYY.JD1-JD2.mseed | | |- TC_mseed/ | | | |- JF.TCAX.YYYY.JD.mseed | |- deploy4/ | | |- C60_mseed/ | | | |- JF.COPX.XX.CDF.YYYY.JD.HH.mseed | | |- GEM_mseed/ | | | |- JF.SNX.YYYY.JD.mseed | | |- gopro_deploy4/ | | | |-GX010040.MP4 | | | |-GX010044.MP4 | | | |-GX010045.MP4 | | |- TC_mseed/ | | | |- JF.TCA1.YYYY.JD.mseed |- raw_data/ | |- deploy1_raw.zip | |- deploy2_raw.zip | |- deploy3_raw.zip | |- deploy4_raw.zip | |- 3111_raw.zip |- README.txt ``` ### Relationship between files: There are four primary folders in this zip: 1) aux_data; 2) metadata; 3) processed_data; and 4) raw_data. 1. Aux_data: contains auxiliary data, including relevant buoy data, tide data, and weather station data. Buoy data taken from NOAA buoy 46053: [https://www.ndbc.noaa.gov/station_realtime.php?station=46053](https://www.ndbc.noaa.gov/station_realtime.php?station=46053). Tide data taken from NOAA tide station 9411340: [https://tidesandcurrents.noaa.gov/waterlevels.html?id=9411340](https://tidesandcurrents.noaa.gov/waterlevels.html?id=9411340). Weather data taken from USCRN weather station Santa Barbara 11 W: [https://erddap.cencoos.org/erddap/tabledap/ncei-uscrn-ca_santa_barbara_11_w.html](https://erddap.cencoos.org/erddap/tabledap/ncei-uscrn-ca_santa_barbara_11_w.html) 1. Buoy data and wind data are included as raw text files with headers and units included in the first two rows. Tide data is included as a csv file with water level measured in feet in the column with header "verified," and predicted water level is given in feet in the column with header "predicted." 2. Metadata: contains five subfolders and a csv file with all station locations from each temporary array deployment, as well as the locations of buoy, tide, and weather stations. Locations are given as latitude and longitude in the respective columns. 1. In each subfolder are txt, png, and json files. Txt files from gem sensors have timestamps in UTC, as well as sensor temperature in Celsius, battery and other information described in the gem manual ([https://github.com/ajakef/gemlog](https://github.com/ajakef/gemlog)). Gem sensors also produce txt files with gps information, housed in the "gps" subfolders. Png files show spatial distributions of gps returns during sensor operation. Json files show the best locations for each sensor during each deployment. 3. Processed_data: contains processed infrasound data (amplitudes given in [Pa]), seismic data (amplitudes given in [m/s]), and video data (MP4 files). Infrasound and seismic data are stored in miniSEED format. Video files are time-synced to infrasound and seismic data via the clock on screen during the first few frames of each video and utilizing the file creation times given in "gopro*_*times.txt". 4. Raw_data: contains raw sensor data written by either Gem sensors or DiGOS DATA-CUBE3 digitizers. Further description of these sensors is given below. We also include an example script entitled "example_load_mseed.py" which allows a user to load and plot the infrasound and seismic time series data. ### Data and equipment description: The majority of this dataset is composed of infrasound data recorded with a principal station (Hyperion 3111-series set to 400 Hz sampling frequency), which recorded near-continuous data from September 6, 2022 to July 17, 2023 via DiGOS DATA-CUBE3. We dub this station “COPR-1”. Data from this sensor is housed in “3111_mseed” subfolder. The principal infrasound station was supplemented by four array deployments; for each deployment data collection period, there is also data available from COPR-1 (within the afore-mentioned 3111_mseed folder). The data from supplementary stations of each deployment are housed in “deployX” subfolders, where X represents field deployment number (1–4). Deployments are described in more detail below. **Deployment 1:** January 11, 2023 (local PST). Involved 4 Gem 1.0 sensors (Anderson et al., 2017) with sampling frequency 100 Hz, placed along the base of the cliff at Sands Beach. Stations are named by Gem serial number (192–195). Data from all Gems are housed in “GEM_mseed” subfolder of “deploy1”. **Deployment 2:** January 12–19, 2023 (local PST). Involved 4 Gem 1.0 sensors with sampling frequency 100 Hz (1 inactive for majority of data collection period), placed along the top of the cliff at Sands Beach. Stations are named by Gem serial number (192–195). Data from all Gems are housed in “GEM_mseed” subfolder of “deploy2”. **Deployment 3:** July 10, 2023 (local PST). * Three broadband Trillium Compact 120s 3-component seismometers with sampling frequency 400 Hz, named TCA1, TCA2, TCA3. Unfortunately, TCA2 did not record viable data; blank miniseed file is excluded from the converted dataset. TCA1 buried directly in soil, composed of a top layer of loose sand overlying uplifted marine terrace of Sisquoc formation shale. TCA3 buried directly in a sand dune. Data housed in “TC_mseed” subfolder. * Same four Gem 1.0 sensors as in deployments 1 and 2, sampling at 100 Hz. Data housed in “GEM_mseed” subfolder. * Six Chaparral Physics C60 sensors with sampling frequency 400 Hz. Three of the six C60’s recorded data on one DiGOS DATA-CUBE3, other three recorded on another DiGOS DATA-CUBE3. Stations named COP1 and COP2, each with 3 channels corresponding to the 3 sensors. Data housed in “C60_mseed”. COP2-02 did not produce viable data. **Deployment 4:** October 17–23, 2023. * One broadband Trillium Compact 120s 3-component seismometers with sampling frequency 400 Hz, named TCA1. Data housed in “TC_mseed” subfolder. Co-located with COPR-1 principal infrasound station. * Same four Gem 1.0 sensors as in deployments 1 and 2, sampling at 100 Hz. Data housed in “GEM_mseed” subfolder. * Three Chaparral Physics C60 sensors with sampling frequency 400 Hz. Data recorded on one DiGOS DATA-CUBE3. Station named COP1, with 3 channels corresponding to each of the 3 sensors. Data housed in “C60_mseed”. * Sub-deployment 4: October 20, 2023. * Three Chaparral Physics C60 sensors with sampling frequency 400 Hz. Data recorded on one DiGOS DATA-CUBE3. Station named COP2, with 3 channels corresponding to each of the 3 sensors. Data housed in “C60_mseed”. * One Chaparral Physics C60 sensor with sampling frequency 400 Hz placed on the sand. Data recorded on a DiGOS DATA-CUBE3. Station named COP3, with 1 channel. Data housed in “C60_mseed”. * One GoPro Hero10. Three clips are included within "gopro_deploy4" subfolder. Infrasound and seismic miniSEED files are named according to the year, Julian day, and (for Hyperion 3111 and Chaparral C60 sensors ONLY) hour for which the data was recorded. All times are UTC. Data are separated in folders according to the field deployment number. ### Additional related data collected that was not included in the current data package: For each supplementary deployment, video data was recorded by a GoPro Hero10. Only the clips used in analysis by primary authors are included; many other clips show human subjects and cannot be published openly under University guidelines. Clips of a given video are automatically separated into ~4 GB clips. File naming conventions are GX{2-digit number for clip number}00{2-digit number for unique video number}. E.g., GX020003 would be the second clip of the third video captured on the GoPro’s SD card. Are there multiple versions of the dataset? No ## METHODOLOGICAL INFORMATION ### Methods for loading the data: Processed data are in miniSEED format (see e.g., [https://ds.iris.edu/ds/nodes/dmc/data/formats/miniseed/](https://ds.iris.edu/ds/nodes/dmc/data/formats/miniseed/)\ or [https://epic.earthscope.org/content/all-about-seed-format](https://epic.earthscope.org/content/all-about-seed-format) for more information) and can be read with e.g., the ObsPy library in Python ([https://github.com/obspy/obspy)](https://github.com/obspy/obspy). These data are found in the 'processed_data' folder. Seismic amplitudes are velocity in [m/s], and infrasound amplitudes are pressure in [Pa]. ### Methods for re-processing raw data: For data from Gem sensors, conversion is done with gemlog ([https://github.com/ajakef/gemlog](https://github.com/ajakef/gemlog)). We specify: calib_gem = 0.003501200 # constant to multiply raw amplitude by For data from C60 and 3111 series, conversion is done with cube_conversion ([https://github.com/uafgeotools/cube_conversion](https://github.com/uafgeotools/cube_conversion)). We specify: BOB_FACTOR = 9.7 # constant to divide sensitivity by BITWEIGHT = 1.5258789e-8 # [V / ct] Fixed value always applied (does not include BOB factor) For data from TC-120s, conversion is done with cube2mseed ([https://www.gfz-potsdam.de/en/section/geophysical-imaging/infrastructure/geophysical-instrument-pool-potsdam-gipp/software/cube2mseed/](https://www.gfz-potsdam.de/en/section/geophysical-imaging/infrastructure/geophysical-instrument-pool-potsdam-gipp/software/cube2mseed/)). We specify: BITWEIGHT = 1.5258789e-8 # [V / ct], per CUBE manual SENSOR_SENSITIVITY = 754.3 # [V / m/s], per TC120 manual BOB_FACTOR = 9.7 # constant to divide sensitivity by Below are sensor sensitivities and sensor-CUBE pairs for data conversion. \------------------------------------------------------------------------------------------------------------------------------- Deploy3: CUBE: SENSOR ID’S AL3: TC3888 AL1: TC3876 ATB: TC3871 ALA: COPR-1 (SN007) AKV: C60-31, C60-32, C60-36 AKZ: C60-33, C60-34, C60-35 For conversion with cube_convert from uafgeotools: \########## \# sensor-digitizer-pairs: { "AKV" : "SN31", "AKV" : "SN32", "AKV" : "SN36", "AKZ" : "SN33", "AKZ" : "SN34", "AKZ" : "SN35", “ALA” : “SN007”, “AL1” : “SN3876”, “ATB” : “SN3871”, “AL3” : “SN3888” } \######### \# sensor_sensitivities: { "SN31" : 0.00905, "SN32" : 0.00901, "SN33" : 0.00908, "SN34" : 0.00910, "SN35" : 0.00903, "SN36" : 0.00904, "SN007" : 0.02904, "SN3876" : 753.1, "SN3871" : 753.1, "SN3888" : 753.1 } \------------------------------------------------------------------------------------------------------------------------------- Deploy4: CUBE: SENSOR ID’S AL3: C60-30, C60-31, C60-32 AKV: TC3876 AL4: C60 33, 34, 35 ATB: C60-36 ALA: COPR-1 (SN007) \########## \# sensor-digitizer-pairs: “AL3” : “SN30”, “AL3” : “SN31”, “AL3” : “SN32”, “AL4” : “SN33”, “AL4” : “SN34”, “AL4” : “SN35”, “ATB” : “SN36”, “ALA” : “SN007” \######### \# sensor_sensitivities: { "SN007" : 0.02904, "SN30" : 0.00906, "SN31" : 0.00905, "SN32" : 0.00901, "SN33" : 0.00908, "SN34" : 0.00910, "SN35" : 0.00903, "SN36" : 0.00904, } \------------------------------------------------------------------------------------------------------------------------------- Describe any quality-assurance procedures performed on the data: None beyond basic plotting.