template.yml

# Random seed for pytorch and all shuffle functions
seed: 20230503

name: coding_fwi

# Data type to use for tensors, either float32 or float64
dtype: float32

equation: acoustic

training:

  # Smooth the gradients
  smooth:
    counts: 
    radius:
      x:
      z:
    sigma:
      x:
      z:

  implicit:
    use: false
    pretrained:

  minibatch: true

  batch_size: 20

  # Number of training epochs
  N_epochs: 100

  # Optimizer learning rate
  lr: 5

  # Decay the learning rate by each of multi-scale
  scale_decay: 0.85

  # decay the lr by each of epochs
  lr_decay: 0.995

  # Filter order
  filter_ord: 3

geom:

  # The observer data
  obsPath: /public1/home/wangsw/FWI/NO_LOWFREQ/data/marmousi_acoustic_obn_p.npy

  # Wave speed model from disk_file for forward modeling
  truePath:
    vp: /home/les_01/wangsw/models/marmousi/10m/velocity/true_vp.npy
    vs:
    rho:
    Q: 
    

  obsmask:

  synmask:

  sources: /home/les_01/wangsw/fwi2d/geometry/marmousi_obn_10m/sources.pkl

  receivers: /home/les_01/wangsw/fwi2d/geometry/marmousi_obn_10m/receivers.pkl

  seabed: /home/les_01/wangsw/fwi2d/geometry/marmousi_obn_10m/seabed.npy

  wavelet: /public1/home/wangsw/FWI/NO_LOWFREQ/wavelet/nolow5hz.npy

  multiple: false

  boundary_saving: true

  wavelet_delay: 500

  wavelet_inverse: false
  #./geometry/ricker_lowcut5hz.npy

  source_type:
    - h1

  receiver_type:
    - h1

  # Inital Wave speed model from disk_file
  initPath: 
    vp: /public1/home/wangsw/FWI/EFWI/Marmousi/marmousi_10m/velocity/inverted_p_vp.npy
    vs:
    rho:
    Q:

  invlist:
    vp: true
    vs: true
    rho: false
    q: false

  # Save the results of FWI
  inv_savePath: /mnt/data/wangsw/inversion/marmousi_10m/Test

  multiscale:
    - - 1.0
      - 3.0
    - 5.0
    - all

  # Time Sampling step
  dt: 0.001

  # Time samples
  nt: 7000

  # Main frequency of ricker wave
  fm: 15.0

  # Spatial grid step size
  h: 10.0

  # Total Shots
  Nshots: 999

  boundary:
    type:
    width: