CCP package

seispy.rfcorrect module

class seispy.rfcorrect.RFStation(data_path, only_r=False, prime_comp='R')[source]

Bases: object

Attributes:
stel

Methods

bin_stack([key, lim, val])

Stack RFs by bins of key with interval of val

harmonic([tb, te, is_stack])

Harmonic decomposition for extracting anisotropic and isotropic features from the radial and transverse RFs

jointani(tb, te[, tlen, stack_baz_val, ...])

Eastimate crustal anisotropy with a joint method.

moveoutcorrect([ref_rayp, dep_range, ...])

Moveout correction with specified reference ray-parameter and depth

normalize([method])

Normalize amplitude of each RFs.

plotr(**kwargs)

Plot radial RFs :param kwargs: Parameters for plot, see seispy.plotR.plotr() for detail

plotrt(**kwargs)

Plot radial and transverse RFs :param kwargs: Parameters for plot, see seispy.plotRT.plotrt() for detail

psrf2depth([dep_range])

Time-to-depth conversion with specified depth series.

psrf_1D_raytracing([dep_range])

1D back ray tracing to obtained Ps conversion points at discret depths

psrf_3D_moveoutcorrect(mod3dpath, **kwargs)

3D moveout correction with 3D velocity model

psrf_3D_raytracing(mod3dpath[, dep_range, srayp])

3D back ray tracing to obtained Ps conversion points at discret depths

psrf_3D_timecorrect(mod3dpath[, dep_range, ...])

3D time-to-depth conversion with 3D velocity model

read_stream(stream, rayp, baz[, prime_comp, ...])

Create RFStation instance from obspy.Stream

resample(dt)

Resample RFs with specified dt

slantstack([ref_dis, rayp_range, tau_range])

Slant stack for receiver function

sort([key])

Sort RFs by keys in given event, evla, evlo, evdp, dis, bazi, rayp, mag, f0
bin_stack(key='bazi', lim=[0, 360], val=10)[source]

Stack RFs by bins of key with interval of val

Parameters:

  • key (str, optional) – Key to stack, valid in [‘bazi’, ‘rayp’ (in s/km)], defaults to ‘bazi’

  • val (int, optional) – Interval of bins, defaults to 10 degree for bazi

Returns:

Stacked RFs

Return type:

dict with keys of data_prime, datat and count

harmonic(tb=-5, te=10, is_stack=True)[source]

Harmonic decomposition for extracting anisotropic and isotropic features from the radial and transverse RFs

Parameters:

  • tb (float, optional) – Start time relative to P, defaults to -5

  • te (float, optional) – End time relative to P, defaults to 10

  • is_stack (bool, optional) – Wether stack the result, defaults to True

Returns:

Harmonic components and unmodel components

Return type:

numpy.ndarray, numpy.ndarray

jointani(tb, te, tlen=3.0, stack_baz_val=10, rayp=0.06, velmodel='iasp91', weight=[0.4, 0.4, 0.2])[source]

Eastimate crustal anisotropy with a joint method. See Liu and Niu (2012, doi: 10.1111/j.1365-246X.2011.05249.x) in detail.

Parameters:

  • tb (float) – Time before Pms for search Ps peak

  • te (float) – Time after Pms for search Ps peak

  • tlen (float, optional) – Half time length for cut out Ps phase, defaults to 3.0

  • stack_baz_val (float, optional) – The interval for stacking binned by back-azimuth, defaults to 10

  • rayp (float, optional) – Reference ray-parameter for moveout correction, defaults to 0.06

  • velmodel (str, optional) – velocity model for moveout correction. ‘iasp91’, ‘prem’ and ‘ak135’ is valid for internal model. Specify path to velocity model for the customized model. The format is the same as in Taup, but the depth should be monotonically increasing, defaults to ‘iasp91’

  • weight (list, optional) – Weight for three different method, defaults to [0.4, 0.4, 0.2]

Returns:

Dominant fast velocity direction and time delay

Return type:

List, List

moveoutcorrect(ref_rayp=0.06, dep_range=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), velmod='iasp91', replace=False, **kwargs)[source]

Moveout correction with specified reference ray-parameter and depth

Parameters:

  • ref_rayp (float, optional) – reference ray-parameter in s/km, defaults to 0.06

  • dep_range (np.ndarray, optional) – Depth range used for extracting velocity in velocity model, defaults to np.arange(0, 150)

  • velmod (str, optional) – Velocity model for moveout correction. ‘iasp91’, ‘prem’ and ‘ak135’ is valid for internal model. Specify path to velocity model for the customized model. The format is the same as in Taup, but the depth should be monotonically increasing, defaults to ‘iasp91’

  • replace (bool, optional) – whether replace original data, False to return new array, defaults to False

Returns:
rf_corr: np.ndarray

Corrected RFs with component of RFStation.comp

t_corr: np.ndarray or None

Corrected RFs in transverse component. If only_r is True, this variable is None

normalize(method='single')[source]

Normalize amplitude of each RFs.

Parameters:

method (str, optional) – Method of normalization with single and average avaliable. - single for normalization with max amplitude of current RF. - average for normalization with average amplitude of current station.

plotr(**kwargs)[source]

Plot radial RFs :param kwargs: Parameters for plot, see seispy.plotR.plotr() for detail

plotrt(**kwargs)[source]

Plot radial and transverse RFs :param kwargs: Parameters for plot, see seispy.plotRT.plotrt() for detail

psrf2depth(dep_range=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), **kwargs)[source]

Time-to-depth conversion with specified depth series.

Parameters:

  • dep_range (np.ndarray() , optional) – Discret conversion depth, defaults to np.arange(0, 150)

  • velmod (str, optional) – Velocity model for time-to-depth conversion. ‘iasp91’, ‘prem’ and ‘ak135’ is valid for internal model. Specify path to velocity model for the customized model. The format is the same as in Taup, but the depth should be monotonically increasing, defaults to ‘iasp91’

  • srayp (numpy.lib.npyio.NpzFile, optional) – Ray-parameter lib for Ps phases, If set up to None the rayp of direct is used, defaults to None

Returns:

2D array of RFs in depth

Return type:

np.ndarray()

psrf_1D_raytracing(dep_range=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), **kwargs)[source]

1D back ray tracing to obtained Ps conversion points at discret depths

Parameters:

  • dep_range (numpy.ndarray, optional) – Discret conversion depth, defaults to np.arange(0, 150)

  • velmod (str, optional) – Velocity model for time-to-depth conversion. ‘iasp91’, ‘prem’ and ‘ak135’ is valid for internal model. Specify path to velocity model for the customized model. The format is the same as in Taup, but the depth should be monotonically increasing, defaults to ‘iasp91’

  • srayp (numpy.lib.npyio.NpzFile, optional) – Ray-parameter lib for Ps phases, If set up to None the rayp of direct is used, defaults to None

Return pplat_s:

Latitude of conversion points

Return pplon_s:

Longitude of conversion points

Return tps:

Time difference of Ps at each depth

Return type:

list

psrf_3D_moveoutcorrect(mod3dpath, **kwargs)[source]

3D moveout correction with 3D velocity model

Parameters:

  • mod3dpath (str) – Path to 3D velocity model

  • dep_range (numpy.ndarray, optional) – Discret conversion depth, defaults to np.arange(0, 150)

  • srayp (numpy.lib.npyio.NpzFile, optional) – Ray-parameter lib for Ps phases, If set up to None the rayp of direct is used, defaults to None

Returns:

2D array of RFs in depth

Return type:

numpy.ndarray

psrf_3D_raytracing(mod3dpath, dep_range=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), srayp=None)[source]

3D back ray tracing to obtained Ps conversion points at discret depths

Parameters:

  • mod3dpath (str) – Path to 3D velocity model

  • dep_range (numpy.ndarray, optional) – Discret conversion depth, defaults to np.arange(0, 150)

  • srayp (numpy.lib.npyio.NpzFile, optional) – Ray-parameter lib for Ps phases, If set up to None the rayp of direct is used, defaults to None

Return pplat_s:

Latitude of conversion points

Return pplon_s:

Longitude of conversion points

Return tps:

Time difference of Ps at each depth

Return type:

list

psrf_3D_timecorrect(mod3dpath, dep_range=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149]), normalize='single', **kwargs)[source]

3D time-to-depth conversion with 3D velocity model

Parameters:

  • mod3dpath (str) – Path to 3D velocity model

  • dep_range (numpy.ndarray, optional) – Discret conversion depth, defaults to np.arange(0, 150)

  • normalize (str, optional) – Normalization method, defaults to ‘single’, see RFStation.normalize for detail

Returns:

2D array of RFs in depth

Return type:

numpy.ndarray

classmethod read_stream(stream, rayp, baz, prime_comp='R', stream_t=None)[source]

Create RFStation instance from obspy.Stream

Parameters:

  • stream (obspy.Stream()) – Stream of RFs

  • rayp (float or np.ndarray()) – Ray-parameter of RFs

  • baz (float or np.ndarray()) – Back-azimuth of RFs

  • prime_comp (str, optional) – Prime component in RF filename. R or Q for PRF and L or Z for SRF, defaults to ‘R’

  • stream_t (obspy.Stream(), optional) – Stream of transverse RFs, defaults to None

Returns:

RFStation instance

Return type:

RFStation

resample(dt)[source]

Resample RFs with specified dt

Parameters:

dt (float) – New sampling rate

slantstack(ref_dis=None, rayp_range=None, tau_range=None)[source]

Slant stack for receiver function

Parameters:

  • ref_dis (int or float, optional) – reference distance, by default None

  • rayp_range (numpy.ndarray, optional) – range of ray parameter, by default None

  • tau_range (numpy.ndarray, optional) – range of tau, by default None

sort(key='bazi')[source]

Sort RFs by keys in given event, evla, evlo, evdp, dis, bazi, rayp, mag, f0

Parameters:

key (str, optional) – key to sort, defaults to bazi

property stel
class seispy.rfcorrect.SACStation(data_path, only_r=False)[source]

Bases: RFStation

Attributes:
stel

Methods

bin_stack([key, lim, val])

Stack RFs by bins of key with interval of val

harmonic([tb, te, is_stack])

Harmonic decomposition for extracting anisotropic and isotropic features from the radial and transverse RFs

jointani(tb, te[, tlen, stack_baz_val, ...])

Eastimate crustal anisotropy with a joint method.

moveoutcorrect([ref_rayp, dep_range, ...])

Moveout correction with specified reference ray-parameter and depth

normalize([method])

Normalize amplitude of each RFs.

plotr(**kwargs)

Plot radial RFs :param kwargs: Parameters for plot, see seispy.plotR.plotr() for detail

plotrt(**kwargs)

Plot radial and transverse RFs :param kwargs: Parameters for plot, see seispy.plotRT.plotrt() for detail

psrf2depth([dep_range])

Time-to-depth conversion with specified depth series.

psrf_1D_raytracing([dep_range])

1D back ray tracing to obtained Ps conversion points at discret depths

psrf_3D_moveoutcorrect(mod3dpath, **kwargs)

3D moveout correction with 3D velocity model

psrf_3D_raytracing(mod3dpath[, dep_range, srayp])

3D back ray tracing to obtained Ps conversion points at discret depths

psrf_3D_timecorrect(mod3dpath[, dep_range, ...])

3D time-to-depth conversion with 3D velocity model

read_stream(stream, rayp, baz[, prime_comp, ...])

Create RFStation instance from obspy.Stream

resample(dt)

Resample RFs with specified dt

slantstack([ref_dis, rayp_range, tau_range])

Slant stack for receiver function

sort([key])

Sort RFs by keys in given event, evla, evlo, evdp, dis, bazi, rayp, mag, f0
seispy.rfcorrect.moveoutcorrect_ref(stadatar, raypref, YAxisRange, chan='', velmod='iasp91', sphere=True, phase=1)[source]

Moveout correction refer to a specified ray-parameter

Parameters:

  • stadatar – data class of RFStation

  • raypref – referred ray parameter in rad

  • YAxisRange – Depth range in nd.array type

  • velmod – Path to velocity model

  • chan – channel name for correction, ‘r’, ‘t’…

Returns:

Newdatar, EndIndex

seispy.rfcorrect.psrf2depth(stadatar, YAxisRange, velmod='iasp91', srayp=None, normalize='single', sphere=True, phase=1)[source]

Time-to-depth conversion with S-wave backprojection.

Parameters:

  • stadatar (RFStation()) – Data class of RFStation

  • YAxisRange (numpy.ndarray) – Depth range for conversion

  • velmod (str, optional) – Velocity for conversion, whcih can be a path to velocity file, defaults to ‘iasp91’

  • srayp (str or seispy.psrayp.PsRayp(), optional) – ray-parameter library of conversion phases. See seispy.psrayp() in detail, defaults to None

  • normalize (str, optional) – method of normalization, defaults to ‘single’. Please refer to RFStation.normalize()

  • sphere (bool, optional) – Wether do earth-flattening transformation, defaults to True

Returns:
ps_rfdepth: 2-D numpy.ndarray, float

RFs in depth with shape of (stadatar.ev_num, YAxisRange.size), stadatar.ev_num is the number of RFs in current station. YAxisRange.size is the size of depth axis.

endindex: numpy.ndarray, int

End index of each RF in depth

x_s: 2-D numpy.ndarray, float

Horizontal distance between station and S-wave conversion points with shape of (stadatar.ev_num, YAxisRange.size)

x_p: 2-D numpy.ndarray, float

Horizontal distance between station and P-wave conversion points with shape of (stadatar.ev_num, YAxisRange.size)

seispy.rfcorrect.psrf_1D_raytracing(stadatar, YAxisRange, velmod='iasp91', srayp=None, sphere=True, phase=1)[source]
seispy.rfcorrect.psrf_3D_migration(pplat_s, pplon_s, pplat_p, pplon_p, raylength_s, raylength_p, Tpds, dep_range, mod3d)[source]
3D time difference correction with specified ray path and 3D velocity model.

The input parameters can be generated with psrf_1D_raytracing().

Parameters:
pplat_snp.ndarray()

2D array of latitude of S-wave in dep_range, (RFStation.ev_num(), dep_range.size)

pplon_snp.ndarray()

2D array of longitude of S-wave in dep_range, (RFStation.ev_num(), dep_range.size)

pplat_pnp.ndarray()

2D array of latitude of P-wave in dep_range, (RFStation.ev_num(), dep_range.size)

pplon_pnp.ndarray()

2D array of longitude of P-wave in dep_range, (RFStation.ev_num(), dep_range.size)

raylength_snp.ndarray()

2D array of ray path length of S-wave in dep_range, (RFStation.ev_num(), dep_range.size)

raylength_pnp.ndarray()

2D array of ray path length of P-wave in dep_range, (RFStation.ev_num(), dep_range.size)

Tpdsnp.ndarray()

1D array of time difference in dep_range (dep_range.size)

dep_rangenp.ndarray()

1D array of depths in km, (dep_range.size)

mod3dnp.lib.npyio.NpzFile()

3D velocity loaded from a .npz file

Returns:
np.ndarray()

Corrected time difference in dep_range

seispy.rfcorrect.psrf_3D_raytracing(stadatar, YAxisRange, mod3d, srayp=None, elevation=0, sphere=True)[source]

Back ray trace the S wavs with a assumed ray parameter of P.

Parameters:

  • stadatar (object RFStation) – The data class including PRFs and more parameters

  • YAxisRange (numpy.ndarray) – The depth array with the same intervals

  • mod3d ('Mod3DPerturbation' object) – The 3D velocity model with fields of dep, lat, lon, vp and vs.

  • elevation (float) – Elevation of this station relative to sea level

Returns:

pplat_s, pplon_s, pplat_p, pplon_p, tps

Type:

numpy.ndarray * 5

seispy.rfcorrect.time2depth(stadatar, dep_range, Tpds, normalize='single')[source]

Interpolate RF amplitude with specified time difference and depth range

Parameters:
stadatarRFStation()

Data class of RFStation()

dep_rangenp.ndarray()

1D array of depths in km, (dep_range.size)

Tpdsnp.ndarray()

1D array of time difference in dep_range (dep_range.size)

normalizestr, optional

Normlization option, 'sinlge' and 'average' are available , by default ‘single’See RFStation.normalize() in detail.

Returns:
PS_RFdepth: np.ndarray()

2D array of RFs in depth, (RFStation.ev_num(), dep_range.size)

end_index: np.ndarray()

1D array of the last digit of effective depth (RFStation.ev_num())

seispy.rfcorrect.xps_tps_map(dep_mod: DepModel, srayp, prayp, is_raylen=False, sphere=True, phase=1)[source]

Calculate horizontal distance and time difference at depths

Parameters:

  • dep_mod (seispy.util.DepModel()) – 1D velocity model class

  • srayp (float) – conversion phase ray-parameters

  • prayp (float) – S-wave ray-parameters

  • is_raylen (bool, optional) – Wether calculate ray length at depths, defaults to False

  • sphere (bool, optional) – Wether do earth-flattening transformation, defaults to True, defaults to True

  • phase (int, optional) – Phases to calculate 1 for Ps, 2 for PpPs, 3 for PsPs+PpSs, defaults to 1

Returns:
If is_raylen = False
tps: 2-D numpy.ndarray, float

RFs in depth with shape of (stadatar.ev_num, YAxisRange.size), stadatar.ev_num is the number of RFs in current station. YAxisRange.size is the size of depth axis.

x_s: 2-D numpy.ndarray, float

Horizontal distance between station and S-wave conversion points with shape of (stadatar.ev_num, YAxisRange.size)

x_p: 2-D numpy.ndarray, float

Horizontal distance between station and P-wave conversion points with shape of (stadatar.ev_num, YAxisRange.size)

otherwise, two more variables will be returned
raylength_s: 2-D numpy.ndarray, float
raylength_p: 2-D numpy.ndarray, float

seispy.rf2depth_makedata module

class RF2depth : process cal RF2depth class sta_part, sta_full, _RFInd

class seispy.rf2depth_makedata.RFDepth(cpara: ~seispy.ccppara.CCPPara, log: ~logging.Logger = <Logger RF2depth (INFO)>, raytracing3d=False, velmod3d=None, modfolder1d=None)[source]

Bases: object

Convert receiver function to depth axis

Methods

makedata([psphase])

Convert receiver function to depth axis
makedata(psphase=1)[source]

Convert receiver function to depth axis

Parameters:

psphase (int) – 1 for Ps, 2 for PpPs, 3 for PpSs

class seispy.rf2depth_makedata.Station(sta_lst: str)[source]

Bases: object

seispy.rf2depth_makedata.rf2depth()[source]

CLI for Convert receiver function to depth axis There’s 4 branch provided to do RF 2 depth conversion

  1. only -d :do moveout correction

  2. only -r : do raytracing but no moveout correction

  3. -d and -r : do moveout correction and raytracing

  4. -m : use {staname}.vel file for RF2depth conversion

class seispy.rf2depth_makedata.sta_full(station, stla, stlo, stel)

Bases: tuple

Methods

count(value, /)

Return number of occurrences of value.

index(value[, start, stop])

Return first index of value.
station

Alias for field number 0

stel

Alias for field number 3

stla

Alias for field number 1

stlo

Alias for field number 2

class seispy.rf2depth_makedata.sta_part(station, stla, stlo)

Bases: tuple

Methods

count(value, /)

Return number of occurrences of value.

index(value[, start, stop])

Return first index of value.
station

Alias for field number 0

stla

Alias for field number 1

stlo

Alias for field number 2

seispy.ccpprofile module

class seispy.ccpprofile.CCPProfile(cfg_file=None, log: ~seispy.setuplog.SetupLog = <seispy.setuplog.SetupLog object>)[source]

Bases: object

Methods

initial_profile()

Initialize bins of profile

read_rfdep()

Read RFdepth file

save_stack_data([format])

If format is npz, saving stacked data and parameters to local as a npz file.

stack()

Stack RFs in bins
initial_profile()[source]

Initialize bins of profile

read_rfdep()[source]

Read RFdepth file

Raises:

FileNotFoundError – Not Found RFdepth file

save_stack_data(format='npz')[source]

If format is npz, saving stacked data and parameters to local as a npz file. To load the file, please use data = np.load(fname, allow_pickle=True). data['cpara'] is the parameters when CCP stacking. data['stack_data'] is the result of stacked data.

If format is dat the stacked data will be save into a txt file with 8 columns, including bin_lat, bin_lon, profile_dis, depth, amp, ci_low, ci_high and count.

  • bin_lat and bin_lon represent the position of each bin;

  • profile_dis represents the distance in km between each bin and the start point of the profile;

  • depth represents depth of each bin;

  • amp means the stacked amplitude;

  • ci_low and ci_high mean confidence interval with bootstrap method;

  • count represents stacking number of each bin.

Parameters:

format (str) – Format for stacked data

stack()[source]

Stack RFs in bins

seispy.ccpprofile.create_center_bin_profile(stations, val=5, method='linear')[source]

Create bins along a profile with given stations

Parameters:

  • stations (seispy.rf2depth_makedata.Station) – Stations along a profile

  • val (float) – The interval between two points in km

  • method (str) – Method for interpolation

Returns:

The location of bins (bin_loca), and length between each bin and the start point (profile_range)

Return type:

(numpy.array, numpy.array)

seispy.ccpprofile.init_profile(lat1, lon1, lat2, lon2, val)[source]

Initial bins along a profile with given position of two points.

Parameters:

  • lat1 (float) – The latitude of the start point

  • lon1 (float) – The lontitude of the start point

  • lat2 (float) – The latitude of the end point

  • lon2 (float) – The lontitude of the end point

  • val (float) – The interval between two points in km

Returns:

The location of bins (bin_loca), and length between each bin and the start point (profile_range)

The bin_loca is positions of bins with a numpy.array with two column. The profile_range is distance between bin center and the start point with an 1D numpy.array. :rtype: (numpy.array, numpy.array)

seispy.cc3d module

class seispy.ccp3d.CCP3D(cfg_file=None, log=None)[source]

Bases: object

Class for 3-D CCP stacking, Usually used to study mantle transition zone structure.

Parameters:

  • cfg_file (str, optional) – Path to configure file. If not defined a instance of CCP3D.cpara will be initialed, defaults to None

  • log (seispy.sutuplog.logger , optional) – A logger instance. If not defined, seispy.sutuplog.logger will be initialed, defaults to None

Methods

initial_grid()

Initial grid points and search stations within a distance.

load_para(cfg_file)

Load parameters from configure file.

read_rfdep()

Read RFdepth data from npz file.

read_stack_data(stack_data_path[, cfg_file, ...])

Read stacked data from local.

save_good_410_660(fname)

Save good 410 and 660 km discontinuities to local.

save_stack_data(fname)

Save stacked data and parameters to local as a npz file.

search_good_410_660([peak_410_min, ...])

Search good 410 and 660 km discontinuities from stacked data.

stack()

Search conversion points falling within a bin and stack them with bootstrap method.

get_depth_err
get_depth_err(type='std')[source]
initial_grid()[source]

Initial grid points and search stations within a distance.

load_para(cfg_file)[source]

Load parameters from configure file.

Parameters:

cfg_file (str) – Path to configure file.

read_rfdep()[source]

Read RFdepth data from npz file.

classmethod read_stack_data(stack_data_path, cfg_file=None, good_depth_path=None, ismtz=False)[source]

Read stacked data from local.

Parameters:

  • stack_data_path (str) – Path to stacked data.

  • cfg_file (str, optional) – Path to configure file, defaults to None

  • good_depth_path (str, optional) – Path to good depth file, defaults to None

  • ismtz (bool, optional) – Whether the good depth file is in mtz format, defaults to False

Returns:

A instance of CCP3D.

Return type:

CCP3D

save_good_410_660(fname)[source]

Save good 410 and 660 km discontinuities to local.

Parameters:

fname (str) – file name of good 410 and 660 km discontinuities

save_stack_data(fname)[source]

Save stacked data and parameters to local as a npz file. To load the file, please use data = np.load(fname, allow_pickle=True). data[‘cpara’] is the parameters when CCP stacking. data[‘stack_data’] is the result of stacked data.

Parameters:

fname (str) – file name of stacked data

search_good_410_660(peak_410_min=380, peak_410_max=440, peak_660_min=630, peak_660_max=690)[source]

Search good 410 and 660 km discontinuities from stacked data.

Parameters:

  • peak_410_min (float, optional) – Minimum depth of 410 km discontinuity, defaults to 380

  • peak_410_max (float, optional) – Maximum depth of 410 km discontinuity, defaults to 440

  • peak_660_min (float, optional) – Minimum depth of 660 km discontinuity, defaults to 630

  • peak_660_max (float, optional) – Maximum depth of 660 km discontinuity, defaults to 690

stack()[source]

Search conversion points falling within a bin and stack them with bootstrap method.

seispy.ccp3d.bin_shape(cpara)[source]

Compute the radius of bins in degree.

Parameters:

cpara (CCPPara) – Parameters of CCP stacking.

Returns:

Radius of bins in degree.

Return type:

1-D ndarray of floats with shape (n, ), where n is the number of bins.

seispy.ccp3d.boot_bin_stack(data_bin, n_samples=3000)[source]

Stack data with bootstrap method.

Parameters:

  • data_bin (1-D ndarray of floats) – Data falling within a bin.

  • n_samples (int, optional) – Number of bootstrap samples, defaults to 3000

Returns:

Mean, confidence interval and number of data falling within a bin.

Return type:

tuple of floats and int

seispy.ccp3d.gen_center_bin(center_lat, center_lon, len_lat, len_lon, val)[source]

Create spaced grid point with coordinates of the center point in the area in spherical coordinates.

Parameters:

  • center_lat (float) – Latitude of the center point.

  • center_lon (float) – Longitude of the center point.

  • len_lat (float) – Half length in degree along latitude axis.

  • len_lon (float) – Half length in degree along longitude axis.

  • val (float) – Interval in degree between adjacent grid point.

Returns:

Coordinates of Grid points.

Return type:

2-D ndarray of floats with shape (n, 2), where n is the number of grid points.