A probabilistic earthquake source inversion framework. Designed and crafted in Mordor. https://pyrocko.org/grond/
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problem.py 3.8KB

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  1. import numpy as num
  2. import logging
  3. from pyrocko import gf, util
  4. from pyrocko.guts import String, Float, Dict, Int
  5. from grond.meta import expand_template, Parameter, has_get_plot_classes, \
  6. GrondError
  7. from ..base import Problem, ProblemConfig
  8. guts_prefix = 'grond'
  9. logger = logging.getLogger('grond.problems.rectangular.problem')
  10. km = 1e3
  11. as_km = dict(scale_factor=km, scale_unit='km')
  12. class RectangularProblemConfig(ProblemConfig):
  13. ranges = Dict.T(String.T(), gf.Range.T())
  14. decimation_factor = Int.T(default=1)
  15. distance_min = Float.T(default=0.)
  16. nthreads = Int.T(default=4)
  17. def get_problem(self, event, target_groups, targets):
  18. if self.decimation_factor != 1:
  19. logger.warn(
  20. 'Decimation factor for rectangular source set to %i. Results '
  21. 'may be inaccurate.' % self.decimation_factor)
  22. base_source = gf.RectangularSource.from_pyrocko_event(
  23. event,
  24. anchor='top',
  25. decimation_factor=self.decimation_factor)
  26. subs = dict(
  27. event_name=event.name,
  28. event_time=util.time_to_str(event.time))
  29. problem = RectangularProblem(
  30. name=expand_template(self.name_template, subs),
  31. base_source=base_source,
  32. distance_min=self.distance_min,
  33. target_groups=target_groups,
  34. targets=targets,
  35. ranges=self.ranges,
  36. norm_exponent=self.norm_exponent,
  37. nthreads=self.nthreads)
  38. return problem
  39. @has_get_plot_classes
  40. class RectangularProblem(Problem):
  41. # nucleation_x
  42. # nucleation_y
  43. # time
  44. # stf
  45. problem_parameters = [
  46. Parameter('east_shift', 'm', label='Easting', **as_km),
  47. Parameter('north_shift', 'm', label='Northing', **as_km),
  48. Parameter('depth', 'm', label='Depth', **as_km),
  49. Parameter('length', 'm', label='Length', **as_km),
  50. Parameter('width', 'm', label='Width', **as_km),
  51. Parameter('slip', 'm', label='Slip'),
  52. Parameter('strike', 'deg', label='Strike'),
  53. Parameter('dip', 'deg', label='Dip'),
  54. Parameter('rake', 'deg', label='Rake')
  55. ]
  56. problem_waveform_parameters = [
  57. Parameter('nucleation_x', 'offset', label='Nucleation X'),
  58. Parameter('nucleation_y', 'offset', label='Nucleation Y'),
  59. Parameter('time', 's', label='Time'),
  60. ]
  61. dependants = []
  62. distance_min = Float.T(default=0.0)
  63. def pack(self, source):
  64. arr = self.get_parameter_array(source)
  65. for ip, p in enumerate(self.parameters):
  66. if p.name == 'time':
  67. arr[ip] -= self.base_source.time
  68. return arr
  69. def get_source(self, x):
  70. d = self.get_parameter_dict(x)
  71. p = {}
  72. for k in self.base_source.keys():
  73. if k in d:
  74. p[k] = float(
  75. self.ranges[k].make_relative(self.base_source[k], d[k]))
  76. source = self.base_source.clone(**p)
  77. return source
  78. def random_uniform(self, xbounds, rstate, fixed_magnitude=None):
  79. if fixed_magnitude is not None:
  80. raise GrondError(
  81. 'Setting fixed magnitude in random model generation not '
  82. 'supported for this type of problem.')
  83. x = num.zeros(self.nparameters)
  84. for i in range(self.nparameters):
  85. x[i] = rstate.uniform(xbounds[i, 0], xbounds[i, 1])
  86. return x
  87. def preconstrain(self, x):
  88. # source = self.get_source(x)
  89. # if any(self.distance_min > source.distance_to(t)
  90. # for t in self.targets):
  91. # raise Forbidden()
  92. return x
  93. @classmethod
  94. def get_plot_classes(cls):
  95. plots = super(RectangularProblem, cls).get_plot_classes()
  96. return plots
  97. __all__ = '''
  98. RectangularProblem
  99. RectangularProblemConfig
  100. '''.split()