first-breaks-picking 0.1.0

Tool for picking first breaks in seismic gather

First breaks picking

This project is devoted to pick waves that are the first to be detected on a seismogram (first breaks, first arrivals). Traditionally, this procedure is performed manually. When processing field data, the number of picks reaches hundreds of thousands. Existing analytical methods allow you to automate picking only on high-quality data with a high signal / noise ratio.

As a more robust algorithm, it is proposed to use a neural network to pick the first breaks. Since the data on adjacent seismic traces have similarities in the features of the wave field, we pick first breaks on 2D seismic gather, not individual traces.

Examples

In the drop-down menus below you will find examples of our model picking on good quality data as well as noisy ones.

It is difficult to validate the quality of the model on noisy data, but they show the robustness of the model to various types of noise.

Good quality data

Noisy data

Installation

Library is available in PyPI:

pip install -U first-breaks-picking

Extra data

• To pick first breaks you need to download model.

• If you have no seismic data, you can also download small SGY file.

It's also possible to download them with Python using the following snippet:

from first_breaks.utils.utils import (download_demo_sgy,
                                      download_model_onnx)

sgy_filename = 'data.sgy'
model_filename = 'model.onnx'

download_demo_sgy(sgy_filename)
download_model_onnx(model_filename)

How to use it

The library can be used in Python, or you can use the desktop application.

Python

Programmatic way has more flexibility for building your own picking scenario and processing multiple files.

Minimal example

The following snippet implements the picking process of the demo file. As a result, you can get an image from the project preview.

from first_breaks.utils.utils import download_demo_sgy
from first_breaks.sgy.reader import SGY
from first_breaks.picking.task import Task
from first_breaks.picking.picker import PickerONNX
from first_breaks.desktop.graph import export_image

sgy_filename = 'data.sgy'
download_demo_sgy(fname=sgy_filename)
sgy = SGY(sgy_filename)

task = Task(sgy_filename,
            traces_per_gather=12,
            maximum_time=100,
            gain=2)
picker = PickerONNX()
task = picker.process_task(task)

# create an image with default parameters
image_filename = 'default_view.png'
export_image(task, image_filename)

# create an image from the project preview
image_filename = 'project_preview.png'
export_image(task, image_filename,
             time_window=(0, 60),
             traces_window=(79.5, 90.5),
             show_processing_region=False,
             headers_total_pixels=80,
             height=500,
             width=700,
             hide_traces_axis=True)

For a better understanding of the steps taken, expand and read the next section.

Detailed examples

Show examples

Create SGY

We provide several ways to create SGY object: from file, bytes or numpy array.

From file:

from first_breaks.sgy.reader import SGY

sgy_filename = ...  # put path to your file.
sgy = SGY(sgy_filename)

From bytes:

from first_breaks.sgy.reader import SGY

sgy_filename = ...  # put path to your file.

with open(sgy_filename, 'rb') as fin:
    sgy_bytes = fin.read()

sgy = SGY(sgy_bytes)

If you want to create from numpy array, extra argument dt_mcs is required:

import numpy as np
from first_breaks.sgy.reader import SGY

num_samples = 1000
num_traces = 48
dt_mcs = 1e3

traces = np.random.random((num_samples, num_traces))
sgy = SGY(traces, dt_mcs=dt_mcs)

Content of SGY

Created SGY allows you to read traces, get observation parameters and view headers (empty if created from numpy)

from first_breaks.sgy.reader import SGY

sgy: SGY = ...  # put here previously created SGY

# get all traces or specific traces limited by time
all_traces = sgy.read()
block_of_data = sgy.read_traces_by_ids(ids=[1, 2, 3, 10],
                                       min_sample=100,
                                       max_sample=500)

# number of traces, values are the same
print(sgy.num_traces, sgy.ntr)
# number of time samples, values are the same
print(sgy.num_samples, sgy.ns)
# = (ns, ntr)
print(sgy.shape)
# time discretization, in mcs, in mcs, in ms
print(sgy.dt, sgy.dt_mcs, sgy.dt_ms)

# dict with headers in the first 3600 bytes of the file
print(sgy.general_headers)
# pandas DataFrame with headers for each trace
print(sgy.traces_headers.head())

Create task for picking

Next, we create a task for picking and pass the picking parameters to it. They have default values, but for the best quality, they must be matched to specific data. You can use the desktop application to evaluate the parameters. A detailed description of the parameters can be found in the Picking process chapter.

from first_breaks.sgy.reader import SGY
from first_breaks.picking.task import Task

sgy: SGY = ...  # put here previously created SGY
task = Task(sgy,
            traces_per_gather=24,
            maximum_time=200)

Pick first breaks

In this step, we use the neural network for picking. If you downloaded the model according to the installation section, then pass the path to it. Or leave the path to the model empty so that we can download it automatically.

from first_breaks.picking.task import Task
from first_breaks.picking.picker import PickerONNX

task: Task = ...  # put here previously created task
picker = PickerONNX()
task = picker.process_task(task)

# you can see results of picking
print(task.picks_in_samples)
print(task.picks_in_ms)
print(task.confidence)

# you can export picks to file as plain text
task.export_result('result.txt', as_plain=True)
# or save as json file
task.export_result('result.json', as_plain=False)

Visualizations

You can save the seismogram and picks as an image. We use Qt backend for visualizations. Here we describe some usage scenarios.

We've added named arguments to various scenarios for demonstration purposes, but in practice you can use them all. See the function arguments for more visualization options.

Plot SGY only:

from first_breaks.sgy.reader import SGY
from first_breaks.desktop.graph import export_image

sgy_filename = 'data.sgy'
image_filename = 'image.png'

sgy = SGY(sgy_filename)
export_image(sgy, image_filename,
             normalize=False,
             traces_window=(5, 10),
             time_window=(0, 200),
             height=300,
             width_per_trace=30)

Plot numpy traces:

import numpy as np
from first_breaks.sgy.reader import SGY
from first_breaks.desktop.graph import export_image

image_filename = 'image.png'
num_traces = 48
num_samples = 1000
dt_mcs = 1e3

traces = np.random.random((num_samples, num_traces))
export_image(traces, image_filename,
             dt_mcs=dt_mcs,
             clip=0.5)

# or create SGY as discussed before
sgy = SGY(traces, dt_mcs=dt_mcs)
export_image(sgy, image_filename,
             gain=2)

Plot SGY with custom picks:

import numpy as np
from first_breaks.sgy.reader import SGY
from first_breaks.desktop.graph import export_image

sgy_filename = 'data.sgy'
image_filename = 'image.png'

sgy = SGY(sgy_filename)
picks_ms = np.random.uniform(low=0,
                             high=sgy.ns * sgy.dt_ms,
                             size=sgy.ntr)
export_image(sgy, image_filename,
             picks_ms=picks_ms,
             picks_color=(0, 100, 100))

Plot result of picking:

from first_breaks.picking.task import Task
from first_breaks.desktop.graph import export_image

image_filename = 'image.png'

task: Task = ...  # put here previously created task
# if the task was not finished, then picks will not be drawn

export_image(task, image_filename,
             show_processing_region=False,
             fill_black_left=False,
             width=1000)

*Limit processing region

Unfortunately, processing of a part of a file is not currently supported natively. We will add this functionality soon!

However, you can use the following workaround to do this:

from first_breaks.sgy.reader import SGY

sgy_filename = 'data.sgy'

sgy = SGY(sgy_filename)

interesting_traces = sgy.read_traces_by_ids(ids=list(range(20, 40)),
                                            min_sample=100,
                                            max_sample=200)

# we create new SGY based on region of interests
sgy = SGY(interesting_traces, dt_mcs=sgy.dt_mcs)

Desktop application

Application under development

Desktop application allows you to work interactively with only one file and has better performance in visualization. You can use application as SGY viewer, as well as visually evaluate the optimal values of the picking parameters for your data.

Launch app

Enter command to launch the application

first-breaks-picking app

or

first-breaks-picking desktop

Select and view SGY file

Click on 2 button to select SGY. After successful reading you can analyze SGY file.

The following mouse interactions are available:

• Left button drag / Middle button drag: Pan the scene.
• Right button drag: Scales the scene. Dragging left/right scales horizontally; dragging up/down scales vertically.
• Right button click: Open dialog with extra options, such as limit by X/Y axes and export.
• Wheel spin: Zooms the scene in and out.

You can also use slider in toolbar to change gain of traces. The gain value for the slider is only used for visualization, it is not used in picking process.

Load model

To use picker in desktop app you have to download model. See the Installation section for instructions on how to download the model.

Click on 1 button and select file with model. After successfully loading the model, access to the pick will open.

Run picking

Click on 3 button to open window with picking parameters. A detailed description of the parameters can be found in the Picking process chapter. Then run picking process. After some time, a line will appear connecting the first arrivals.

Run again with different parameters to achieve optimal values of the picking parameters for your data.

Processing grid

Click on 4 button to toggle the display of the processing grid on or off. Horizontal line shows Maximum time and vertical lines are drawn at intervals equal to Traces per gather. The neural network processes blocks independently, as separate images.

Save results

Click on 5 button to save picks, picking parameters and info about SGY file into plain .txt file.

Picking process

Neural network process file as series of images. There is why the traces should not be random, since we are using information about adjacent traces.

To obtain the first breaks we do the following steps:

1. Read all traces in the file.
2. Limit time range by Maximum time.
3. Split the sequence of traces into independent gathers of lengths Traces per gather each.
4. Apply trace modification on the gathers level if necessary (Gain, Clip, etc).
5. Calculate first breaks for individual gathers independently.
6. Join the first breaks of individual gathers.

To achieve the best result, you need to modify the picking parameters.

Traces per gather

Traces per gather is the most important parameter for picking. The parameter defines how we split the sequence of traces into individual gathers.

Suppose we need to process a file with 96 traces. Depending on the value of Traces per gather parameter, we will process it as follows:

• Traces per gather = 24. We will process 4 gathers with 24 traces each.
• Traces per gather = 48. We will process 2 gathers with 48 traces each.
• Traces per gather = 40. We will process 2 gathers with 40 traces each and 1 gather with the remaining 16 traces. The last gather will be interpolated from 16 to 40 traces.

Maximum time

You can localize the area for finding first breaks. Specify Maximum time if you have long records but the first breaks located at the start of the traces. Keep it 0 if you don't want to limit traces.

List of traces to inverse

Some receivers may have the wrong polarity, so you can specify which traces should be inversed. Note, that inversion will be applied on the gathers level. For example, if you have 96 traces, Traces per gather = 48 and List of traces to inverse = (2, 30, 48), then traces (2, 3, 48, 50, 78, 96) will be inversed.

Notes:

• Trace indexing starts at 1.
• Option is not available on desktop app.

Recommendations

You can receive predictions for any file with any parameters, but to get a better result, you should comply with the following guidelines:

• Your file should contain one or more gathers. By a gather, we mean that traces within a single gather can be geophysically interpreted. The traces within the same gather should not be random, since we are using information about adjacent traces.
• All gathers in the file must contain the same number of traces.
• The number of traces in gather must be equal to Traces per gather or divisible by it without a remainder. For example, if you have CSP gathers and the number of receivers is 48, then you can set the parameter value to 48, 24, or 12.
• We don't sort your file (CMP, CRP, CSP, etc), so you should send us files with traces sorted by yourself.
• You can process a file with independent seismograms obtained from different polygons, under different conditions, etc., but the requirements listed above must be met.

Acknowledgments

We would like to thank GEODEVICE for providing field data from land and borehole seismic surveys with annotated first breaks for model training.