Working with SEG-Y headers

Headers in SEG-Y data are additional meta information associated with each trace. In SEG-Y these are not pooled in a common data block but interleaved with the seismic trace data so we need to do some work to extract it. segysak has two helper methods for extracting information from a SEG-Y file. These are segy_header_scan and segy_header_scrape. Both of these functions return pandas.DataFrame objects containing header or header related information which can be used in QC, analysis and plotting.

Scanning the headers

segy_header_scan is primarily designed to help quickly asscertain the byte locations of key header information for loading or converting the full SEG-Y file. It does this by just looking at the first N traces (1000 by default) and returns the byte location and statistics related to the file.

[2]:

from segysak.segy import segy_header_scan

# default just needs the file name
scan = segy_header_scan("data/volve10r12-full-twt-sub3d.sgy")
scan

100%|██████████| 1.00k/1.00k [00:00<00:00, 11.8k traces/s]

[2]:

	byte_loc	count	mean	std	min	25%	50%	75%	max
TRACE_SEQUENCE_LINE	1	1000.0	100.54	57.831072	1.0	50.75	100.5	150.25	202.0
TRACE_SEQUENCE_FILE	5	1000.0	10091.98	1.407687	10090.0	10091.00	10092.0	10093.00	10094.0
FieldRecord	9	1000.0	10091.98	1.407687	10090.0	10091.00	10092.0	10093.00	10094.0
TraceNumber	13	1000.0	100.54	57.831072	1.0	50.75	100.5	150.25	202.0
EnergySourcePoint	17	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0
...	...	...	...	...	...	...	...	...	...
SourceEnergyDirectionMantissa	219	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceEnergyDirectionExponent	223	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementMantissa	225	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementExponent	229	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0
SourceMeasurementUnit	231	1000.0	0.00	0.000000	0.0	0.00	0.0	0.00	0.0

89 rows × 9 columns

If you want to see the full DataFrame in a notebook, use the pandas options context manager.

[3]:

import pandas as pd
from IPython.display import display

with pd.option_context("display.max_rows", 89):
    display(scan)

	byte_loc	count	mean	std	min	25%	50%	75%	max
TRACE_SEQUENCE_LINE	1	1000.0	1.005400e+02	57.831072	1.0	5.075000e+01	100.5	1.502500e+02	202.0
TRACE_SEQUENCE_FILE	5	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
FieldRecord	9	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
TraceNumber	13	1000.0	1.005400e+02	57.831072	1.0	5.075000e+01	100.5	1.502500e+02	202.0
EnergySourcePoint	17	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
CDP	21	1000.0	2.249540e+03	57.831072	2150.0	2.199750e+03	2249.5	2.299250e+03	2351.0
CDP_TRACE	25	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
TraceIdentificationCode	29	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
NSummedTraces	31	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
NStackedTraces	33	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
DataUse	35	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
offset	37	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
ReceiverGroupElevation	41	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceSurfaceElevation	45	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceDepth	49	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
ReceiverDatumElevation	53	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceDatumElevation	57	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceWaterDepth	61	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GroupWaterDepth	65	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
ElevationScalar	69	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
SourceGroupScalar	71	1000.0	-1.000000e+02	0.000000	-100.0	-1.000000e+02	-100.0	-1.000000e+02	-100.0
SourceX	73	1000.0	4.351992e+07	70152.496037	43396267.0	4.345933e+07	43519976.5	4.358062e+07	43641261.0
SourceY	77	1000.0	6.477772e+08	17532.885301	647744704.0	6.477622e+08	647777222.0	6.477923e+08	647809133.0
GroupX	81	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GroupY	85	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
CoordinateUnits	89	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
WeatheringVelocity	91	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SubWeatheringVelocity	93	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceUpholeTime	95	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GroupUpholeTime	97	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceStaticCorrection	99	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GroupStaticCorrection	101	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TotalStaticApplied	103	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
LagTimeA	105	1000.0	4.000000e+00	0.000000	4.0	4.000000e+00	4.0	4.000000e+00	4.0
LagTimeB	107	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
DelayRecordingTime	109	1000.0	4.000000e+00	0.000000	4.0	4.000000e+00	4.0	4.000000e+00	4.0
MuteTimeStart	111	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
MuteTimeEND	113	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TRACE_SAMPLE_COUNT	115	1000.0	8.500000e+02	0.000000	850.0	8.500000e+02	850.0	8.500000e+02	850.0
TRACE_SAMPLE_INTERVAL	117	1000.0	4.000000e+03	0.000000	4000.0	4.000000e+03	4000.0	4.000000e+03	4000.0
GainType	119	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
InstrumentGainConstant	121	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
InstrumentInitialGain	123	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
Correlated	125	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
SweepFrequencyStart	127	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SweepFrequencyEnd	129	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SweepLength	131	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SweepType	133	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
SweepTraceTaperLengthStart	135	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SweepTraceTaperLengthEnd	137	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TaperType	139	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
AliasFilterFrequency	141	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
AliasFilterSlope	143	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
NotchFilterFrequency	145	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
NotchFilterSlope	147	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
LowCutFrequency	149	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
HighCutFrequency	151	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
LowCutSlope	153	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
HighCutSlope	155	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
YearDataRecorded	157	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
DayOfYear	159	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
HourOfDay	161	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
MinuteOfHour	163	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SecondOfMinute	165	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TimeBaseCode	167	1000.0	1.000000e+00	0.000000	1.0	1.000000e+00	1.0	1.000000e+00	1.0
TraceWeightingFactor	169	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GeophoneGroupNumberRoll1	171	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GeophoneGroupNumberFirstTraceOrigField	173	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GeophoneGroupNumberLastTraceOrigField	175	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
GapSize	177	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
OverTravel	179	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
CDP_X	181	1000.0	4.351992e+07	70152.496037	43396267.0	4.345933e+07	43519976.5	4.358062e+07	43641261.0
CDP_Y	185	1000.0	6.477772e+08	17532.885301	647744704.0	6.477622e+08	647777222.0	6.477923e+08	647809133.0
INLINE_3D	189	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
CROSSLINE_3D	193	1000.0	2.249540e+03	57.831072	2150.0	2.199750e+03	2249.5	2.299250e+03	2351.0
ShotPoint	197	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
ShotPointScalar	201	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TraceValueMeasurementUnit	203	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TransductionConstantMantissa	205	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TransductionConstantPower	209	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TransductionUnit	211	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
TraceIdentifier	213	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
ScalarTraceHeader	215	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceType	217	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceEnergyDirectionMantissa	219	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceEnergyDirectionExponent	223	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceMeasurementMantissa	225	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceMeasurementExponent	229	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0
SourceMeasurementUnit	231	1000.0	0.000000e+00	0.000000	0.0	0.000000e+00	0.0	0.000000e+00	0.0

Often lots of header fields don’t get filled, so lets filter by the standard deviation column std. In fact, there are so few here we don’t need the context manager. As you can see, for segy_loader or segy_converter we will need to tell those functions that the byte location for iline and xline are 189 and 193 respectively, and the byte locations for cdpx and cdpy are either 73 and 77 or 181 and 185 which are identical pairs.

[4]:

# NIIIICCCEEEE...
scan[scan["std"] > 0]

[4]:

	byte_loc	count	mean	std	min	25%	50%	75%	max
TRACE_SEQUENCE_LINE	1	1000.0	1.005400e+02	57.831072	1.0	5.075000e+01	100.5	1.502500e+02	202.0
TRACE_SEQUENCE_FILE	5	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
FieldRecord	9	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
TraceNumber	13	1000.0	1.005400e+02	57.831072	1.0	5.075000e+01	100.5	1.502500e+02	202.0
CDP	21	1000.0	2.249540e+03	57.831072	2150.0	2.199750e+03	2249.5	2.299250e+03	2351.0
SourceX	73	1000.0	4.351992e+07	70152.496037	43396267.0	4.345933e+07	43519976.5	4.358062e+07	43641261.0
SourceY	77	1000.0	6.477772e+08	17532.885301	647744704.0	6.477622e+08	647777222.0	6.477923e+08	647809133.0
CDP_X	181	1000.0	4.351992e+07	70152.496037	43396267.0	4.345933e+07	43519976.5	4.358062e+07	43641261.0
CDP_Y	185	1000.0	6.477772e+08	17532.885301	647744704.0	6.477622e+08	647777222.0	6.477923e+08	647809133.0
INLINE_3D	189	1000.0	1.009198e+04	1.407687	10090.0	1.009100e+04	10092.0	1.009300e+04	10094.0
CROSSLINE_3D	193	1000.0	2.249540e+03	57.831072	2150.0	2.199750e+03	2249.5	2.299250e+03	2351.0

Scraping Headers

Scraping the header works like a scan but instead of statistics we get a DataFrame of actual trace header values. You can reduce the size of the scan by using the partial_scan keyword if required. The index of the DataFrame is the trace index and the columns are the header fields.

[5]:

from segysak.segy import segy_header_scrape

scrape = segy_header_scrape("data/volve10r12-full-twt-sub3d.sgy", partial_scan=10000)
scrape

100%|██████████| 10.0k/10.0k [00:00<00:00, 11.4k traces/s]

[5]:

	TRACE_SEQUENCE_LINE	TRACE_SEQUENCE_FILE	FieldRecord	TraceNumber	EnergySourcePoint	CDP	CDP_TRACE	TraceIdentificationCode	NSummedTraces	NStackedTraces	...	TransductionConstantPower	TransductionUnit	TraceIdentifier	ScalarTraceHeader	SourceType	SourceEnergyDirectionMantissa	SourceEnergyDirectionExponent	SourceMeasurementMantissa	SourceMeasurementExponent	SourceMeasurementUnit
0	1	10090	10090	1	0	2150	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
1	2	10090	10090	2	0	2151	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
2	3	10090	10090	3	0	2152	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
3	4	10090	10090	4	0	2153	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
4	5	10090	10090	5	0	2154	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9995	98	10139	10139	98	0	2247	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
9996	99	10139	10139	99	0	2248	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
9997	100	10139	10139	100	0	2249	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
9998	101	10139	10139	101	0	2250	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0
9999	102	10139	10139	102	0	2251	1	1	0	0	...	0	0	0	0	0	0	0	0	0	0

10000 rows × 89 columns

We know from the scan that many of these fields were empty so lets go ahead and filter our scrape by using the standard deviation again and passing the index which is the same as our column names.

[6]:

scrape = scrape[scan[scan["std"] > 0].index]
scrape

[6]:

	TRACE_SEQUENCE_LINE	TRACE_SEQUENCE_FILE	FieldRecord	TraceNumber	CDP	SourceX	SourceY	CDP_X	CDP_Y	INLINE_3D	CROSSLINE_3D
0	1	10090	10090	1	2150	43640052	647744704	43640052	647744704	10090	2150
1	2	10090	10090	2	2151	43638839	647745006	43638839	647745006	10090	2151
2	3	10090	10090	3	2152	43637626	647745309	43637626	647745309	10090	2152
3	4	10090	10090	4	2153	43636413	647745611	43636413	647745611	10090	2153
4	5	10090	10090	5	2154	43635200	647745914	43635200	647745914	10090	2154
...	...	...	...	...	...	...	...	...	...	...	...
9995	98	10139	10139	98	2247	43537224	647833471	43537224	647833471	10139	2247
9996	99	10139	10139	99	2248	43536011	647833773	43536011	647833773	10139	2248
9997	100	10139	10139	100	2249	43534798	647834076	43534798	647834076	10139	2249
9998	101	10139	10139	101	2250	43533585	647834378	43533585	647834378	10139	2250
9999	102	10139	10139	102	2251	43532372	647834681	43532372	647834681	10139	2251

10000 rows × 11 columns

We know from the scan that many of these fields were empty so lets go ahead and filter our scrape by using the standard deviation again and passing the index which is the same as our column names.

[7]:

import matplotlib.pyplot as plt

%matplotlib inline

[8]:

plot = scrape.hist(bins=25, figsize=(20, 10))

../_images/examples_example_segy_headers_14_0.png

We can also just plot up the geometry to check that everything looks ok, here the line numbering and coordinates seem to match up, great!

[9]:

fig, axs = plt.subplots(nrows=2, figsize=(12, 10), sharex=True, sharey=True)

scrape.plot(
    kind="scatter", x="CDP_X", y="CDP_Y", c="INLINE_3D", ax=axs[0], cmap="gist_ncar"
)
scrape.plot(
    kind="scatter", x="CDP_X", y="CDP_Y", c="CROSSLINE_3D", ax=axs[1], cmap="gist_ncar"
)
for aa in axs:
    aa.set_aspect("equal", "box")

../_images/examples_example_segy_headers_16_0.png