# A workflow to generate DAS based earthquake catalog, applied to an offshore telecommunication cable in central Chile

Marie Baillet, Alister Trabattoni, Martijn van den Ende, Clara Vernet, Diane Rivet

► To cite this version:

Marie Baillet, Alister Trabattoni, Martijn van den Ende, Clara Vernet, Diane Rivet. A workflow to generate DAS based earthquake catalog, applied to an offshore telecommunication cable in central Chile. EGU, Apr 2023, Vienna (Austria), Austria. 2023, 10.5194/egusphere-egu23-11782. hal-04920422

HAL Id: hal-04920422

https://hal.science/hal-04920422v1

Submitted on 30 Jan 2025

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## Automatic DAS based phase picking

## The ABYSS project

Fibre-optic **Distributed Acoustic Sensing (DAS)** is of critical value for the expansion of seismological networks, particularly in regions that are hard to instrument. The work presented here is part of the **5-year ERC ABYSS project**. It aims at building a permanent seafloor observatory **to increase our ability to capture low magnitude seismic signals** from the subduction fault zone in the DAS data recorded by offshore telecommunication cables along the central coast of Chile.

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### The POST experiment:

* This first experiment was conducted from **October to December 2021**.

* With a GTD Submarine fiber-optic cable connecting the city of Concón to La Serena.

* Continuous DAS data recorded for **38 days** over a **distance of 150 km** from Concón.

Date range : 2021-10-27 to 2021-12-03 (38 days)
Dataset size : 72 TB
File format : HDF5 (Hierarchical Data Format 5)
DAS interrogator : ASN (Alcatel Submarine Networks) OptoDAS
Sampling frequency : 125 Hz
Spatial sampling : 4.08 m
Number of virtual sensors : 36718 (over 150 km)

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## Towards an earthquake catalogue

### A Phases separation

* Recursive **STA/LTA** for all the channels

* **Cluster** the detection points: define the cluster with the highest number of points as the earthquake

* **Clean** the STA/LTA point cloud (density of neighbours > 5)

* **Cluster** the earthquake detection points with adapted parameters (to separate phases)

* **Merge** the closest clusters

A

starttime = 2021-11-05T22:28:30

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### B Earthquake localisation

* From the separated phases as above, construct the **'first arrivals' envelope** of each wave fronts

* Find with these envelopes the **best solution for P and S arrivals** according to a simple velocity model

* **Re-locate** earthquakes with DAS data and onland seismic stations data

B

DAS
CSN*

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### C Template matching

* For each earthquake in the catalogue, **stack** all the traces of the virtual sensors

* **Cross-correlate** all the stacked earthquakes

* Define a **correlation coefficient threshold** to create families of similar events

* So far, we have more than **170** families mostly composed of 2 to 4 events (62 earthquakes in the biggest one)

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### First results for the earthquake classification

### Family of 3 similar events

## What's next?

* We need to manage noise that is unique to DAS

* This workflow needs to be integrated into a real time pipeline