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Phase Arrival Image Function

For image functions this version of Qseek relies heavily on machine learning phase-arrival pickers delivered by SeisBench.

SeisBench Image Function

SeisBench offers access to a variety of machine learning phase pickers pre-trained on various data sets.

Citation PhaseNet

Zhu, Weiqiang, and Gregory C. Beroza. "PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method." arXiv preprint arXiv:1803.03211 (2018).

SeisBench Module

PhaseNet image function. For more details see SeisBench documentation.

model: PhaseNet | EQTransformer | OBSTransformer | LFEDetect | GPD

The model to use for the image function. Currently supported models are PhaseNet, EQTransformer, GPD, OBSTransformer, LFEDetect.

pretrained

SeisBench pre-trained model to use. Choose from the available pre-trained models or provide a path to a custom model .json file. For more details see SeisBench documentation.

Available models are:cascadia, cms, diting, dummy, ethz, geofon, instance, iquique, jcms, jcs, jms, lendb, mexico, nankai, neic, obs, obst2024, original, original_nonconservative, san_andreas, scedc, stead, volpick

**`window_overlap_samples`: `1500`**

:   Window overlap in samples.

**`torch_use_cuda`**

:   Use CUDA for inference. If `True` use default device, if `int` use the specified device.

**`torch_cpu_threads`: `4`**

:   Number of CPU threads to use if only CPU is used.

**`batch_size`: `128`**

:   Batch size for inference, larger values can improve performance.

**`stack_method`: `avg | max`**

:   Method to stack the overlaping blocks internally. Choose from `avg` and `max`.

**`sampling_rate`: `100.0`**

:   Upscale input by factor. This augments the input data from e.g. 100 Hz to 50 Hz (factor: `2`). Can be useful for high-frequency microseismic events.

**`phase_map`**

:   Phase mapping from SeisBench PhaseNet to Qseek travel time phases.

**`weights`**

:   Weights for each phase.
JSON for SeisBench
{
  "image": "SeisBench",
  "model": "PhaseNet",
  "pretrained": "original",
  "window_overlap_samples": 1500,
  "torch_use_cuda": true,
  "torch_cpu_threads": 4,
  "batch_size": 128,
  "stack_method": "avg",
  "sampling_rate": 100.0,
  "phase_map": {
    "P": "cake:P",
    "S": "cake:S"
  },
  "weights": {
    "P": 1.0,
    "S": 1.0
  }
}