Awesome

SEGFAST

<a href="#installation">Installation</a> • <a href="#benchmarks">Benchmarks</a> • <a href="#getting-started">Getting Started</a>

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segfast is a library for interacting with SEG-Y seismic data. Main features are:

Faster access to read data: both traces headers and values
Optional bufferization, where user can provide a preallocated memory to load the data into
Convenient API that relies on numpy.memmap for most operations, while providing segyio as a fallback engine

Installation

# pip / pip3
pip3 install segfast

# developer version (add `--depth 1` if needed)
git clone https://github.com/analysiscenter/segfast.git

Benchmarks

Timings for reading data along various projections:

	slide_i	slide_x	slide_d	crop<br/>(256, 256, 500)	batch<br/>(20, 256, 256, 500)
segyio	2.58254	7.16672	3041.3	941.285	16104.4
segfast	1.48056	3.37418	50.1355	82.0574	2761.94
segfast<br/>segyio engine	2.92379	5.69101	225.13	117.571	3968.81
seismiqb	1.46763	3.45154	50.3333	151.877	2738.86
seismiqb+HDF5	1.04213	1.93414	1.80567	81.3581	2616.83
segfast <br/>quantized	0.252452	0.518485	56.6672	7.71151	1212.74

SlideBenchmarks

Getting started

After installation just import segfast into your code. A quick demo of our primitives and methods:

import segfast

# Open file and read some meta info. Engine can be `segyio` or `memmap`
segfast_file = segfast.open('/path/to/cube.sgy', engine='memmap')

# Load requested headers as dataframe
segfast_file.load_headers(['INLINE_3D', 'CROSSLINE_3D', ...])

# Data access. All methods support optional buffer as target memory
segfast_file.load_traces([123, 333, 777], buffer=None)
segfast_file.load_depth_slices([5, 10, 15], buffer=None)

# Convert data format to IEEE float32: speeds up operations by a lot
segfast_file.convert(format=5)

You can get more familiar with the library, its functional and timings by reading examples.