101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
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import os
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import numpy as np
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import pandas as pd
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import scipy.io
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import pyocto
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class PhaseAssociation:
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"""
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Class for phase association using PyOcto.
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"""
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def __init__(self, inventory, config):
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"""
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Initialize the PhaseAssociation class.
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Args:
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inventory: ObsPy inventory object.
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config (dict): Configuration parameters.
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"""
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self._inv = inventory
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self._config = config
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self._setup_velocity_model()
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self._setup_associator()
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def _setup_velocity_model(self):
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"""
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Set up the velocity model based on configuration.
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"""
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velocity_config = self._config["velocity"]
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if self._config["velocity"]["path"] is None:
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self.velocity_model = pyocto.VelocityModel0D(
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p_velocity=self._config["velocity"]["0D_P_vel"],
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s_velocity=self._config["velocity"]["0D_S_vel"],
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tolerance=self._config["params"]["tolerance"],
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association_cutoff_distance=self._config["params"]["association_cutoff_distance"])
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else:
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if not os.path.exists(velocity_config["path_out"]):
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self._create_1d_vel_model()
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self.velocity_model = pyocto.VelocityModel1D(velocity_config["path_out"],
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tolerance=self._config["params"]["tolerance"])
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def _mat2df(self):
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# Load the .mat file
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mat_contents = scipy.io.loadmat(self._config["velocity"]["path"])
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data = mat_contents["d"][0][0]
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depth = data[0].T[0]
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depth = np.append(depth[::2], depth[-1])
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vel_p = data[1].T[0]
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vel_p = np.append(vel_p[0], vel_p[::2])
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vel_s = data[2].T[0]
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vel_s = np.append(vel_s[0], vel_s[::2])
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# Create DataFrame from the lists
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data = {
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'depth': depth,
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'vp': vel_p,
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'vs': vel_s
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}
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layers = pd.DataFrame(data)
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return layers
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def _create_1d_vel_model(self):
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layers = self._mat2df()
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pyocto.VelocityModel1D.create_model(
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layers,
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self._config["velocity"]["grid_spacing"],
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self._config["velocity"]["grid_hor_extent"],
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self._config["velocity"]["grid_ver_extent"],
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self._config["velocity"]["path_out"]
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)
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def _setup_associator(self):
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"""
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Set up the associator based on configuration.
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"""
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grid_config = self._config["grid"]
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associator_params = self._config["params"]
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self.associator = pyocto.OctoAssociator.from_area(
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lat=(grid_config["lat_min"], grid_config["lat_max"]),
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lon=(grid_config["lon_min"], grid_config["lon_max"]),
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zlim=(grid_config["z_min"], grid_config["z_max"]),
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time_before=associator_params["time_before"],
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velocity_model=self.velocity_model,
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n_picks=associator_params["n_picks"],
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n_p_picks=associator_params["n_p_picks"],
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n_s_picks=associator_params["n_s_picks"],
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n_p_and_s_picks=associator_params["n_p_and_s_picks"],
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pick_match_tolerance=associator_params["pick_match_tolerance"]
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)
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def associate_phases(self, picks):
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stations = self.associator.inventory_to_df(self._inv)
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events, assignments = self.associator.associate_seisbench(picks, stations)
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events = self.associator.transform_events(events)
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return events, assignments
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