# A fine-tuning workflow for automatic first-break picking with deep learning

Amir Mardan
Geostack
Polytechnique Montréal
QC, Canada
<EMAIL_ADDRESS>
&Martin Blouin
Geostack
Québec, QC, Canada
<EMAIL_ADDRESS>
& Gabriel Fabien-Ouellet
Polytechnique Montréal
Montréal, QC, Canada
<EMAIL_ADDRESS>
& Bernard Giroux
INRS-ETE
Québec, QC, Canada
<EMAIL_ADDRESS>
&Christophe Vergniault
Électricité de France
France
<EMAIL_ADDRESS>
&Jeremy Gendreau
Polytechnique Montréal
Montréal, QC, Canada
<EMAIL_ADDRESS>Corresponding author<EMAIL_ADDRESS>
###### Abstract

First-break picking is an essential step in seismic data processing. For
reliable results, first arrivals should be picked by an expert. This is a
time-consuming procedure and subjective to a certain degree, leading to
different results for different operators. In this study, we have used a U-Net
architecture with residual blocks to perform automatic first-break picking
based on deep learning. Focusing on the effects of weight initialization on
first-break picking, we conduct this research by using the weights of a
pretrained network that is used for object detection on the ImageNet dataset.
The efficiency of the proposed method is tested on two real datasets. For both
datasets, we pick manually the first breaks for less than 10% of the seismic
shots. The pretrained network is fine-tuned on the picked shots and the rest
of the shots are automatically picked by the neural network. It is shown that
this strategy allows to reduce the size of the training set, requiring fine
tuning with only a few picked shots per survey. Using random weights and more
training epochs can lead to a lower training loss, but such a strategy leads
to overfitting as the test error is higher than the one of the pretrained
network. We also assess the possibility of using a general dataset by training
a network with data from three different projects that are acquired with
different equipment and at different locations. This study shows that if the
general dataset is created carefully it can lead to more accurate first-break
picking, otherwise the general dataset can decrease the accuracy. Focusing on
near-surface geophysics, we perform traveltime tomography and compare the
inverted velocity models based on different first-break picking methodologies.
The results of the inversion show that the first breaks obtained by the
pretrained network lead to a velocity model that is closer to the one obtained
from the inversion of expert-picked first breaks.

_Keywords_ First-break picking, Near-surface seismic, Deep learning,
Segmentation, Transfer learning

## Introduction

In a seismic shot record, the first arrival is usually the direct wave from
the source followed by refractions at the base of the weathering layer. The
first break (FB) is the time taken by a seismic wave to travel from a source
to a geophone. First breaks are picked to estimate static corrections
(Coppens,, 1985; Marsden,, 1993), near-surface velocity estimation (Azwin et
al.,, 2013; Fabien-Ouellet and Fortier,, 2014), muting parameters for
processing reflected data, and microseismic event location (Dip et al.,, 2021;
Nasr et al.,, 2022). In marine studies, first breaks can be studied to also
estimate the location of hydrophones Walia and Hannay, (1999) as well as
modeling the water velocity variation, which is highly important for time-
lapse seismic studies (Mardan et al.,, 2023).

First breaks are conventionally picked manually. Manual picking is a labor-
intensive and time-consuming processing step which can take up to 30$\%$ of
the processing time Sabbione and Velis, (2010). This is also a subjective task
that leads to different results for different operators. To mitigate this
problem, numerous methods have been introduced with the aim of automating the
process. Zwartjes and Yoo, (2022) classified these methods into three classes;
cross-correlation based, energy-based, and neural-network (NN) based. Peraldi
and Clement, (1972) proposed a method to automate the first-break picking
based on the cross-correlation of adjacent traces in a seismic shot. Finding
the maximum cross-correlation between traces, they can estimate the time delay
between first breaks in different traces. This method has difficulty to detect
first breaks efficiently in a seismic shot with bad or dead traces (Figure
1a). Coppens, (1985) proposed a method to automate first-break picking based
on the energy ratio of the signal in two windows with different sizes. This
method is called short-term-average over long-term average (STA-LTA) and its
efficiency decreases in noisy data (Figure 1b). Although other methods such as
modified Coppens’ method (Sabbione and Velis,, 2010), entropy method (Sabbione
and Velis,, 2010), and statistical methods (Hatherly,, 1982) have been
proposed to improve the accuracy of automatic first-break picking, these
methods usually require an expert to select parameters such as window length
and threshold among others, which increases human bias in this processing
step.

Nowadays, numerous seismic data acquisitions are carried out every day around
the globe using efficient techniques such as distributed-acoustic sensing
(DAS) or landstreamers, with the consequence that the volume of seismic data
that must be processed is significantly increasing. In this regard, artificial
intelligence (AI) has been used to improve the seismic data processing and
interpretation workflow (Zhao and Mendel,, 1988; Veezhinathan and Wagner,,
1990; Röth and Tarantola,, 1994; Lim,, 2005; Leite and Vidal,, 2011; Mardan et
al.,, 2017; Fabien-Ouellet and Sarkar,, 2020; Alali et al.,, 2022; Mardan and
Fabien-Ouellet,, 2024). With the focus on automatic first-break picking,
researchers started employing NN in the early 1990s Veezhinathan and Wagner,
(1990); Murat and Rudman, (1992); McCormack et al., (1993). With subsequent
development in computational power and NN techniques, convolutional neural
network (CNN) has been employed to improve the accuracy of automatic picking
by taking the spatial coherency of data into account. Yuan et al., (2018)
employed a CNN to classify the data on a seismic shot into two groups: FB and
non-FB. They showed the potential of CNN for first-break picking. However,
preparing the training data for this strategy is challenging and time
consuming (if not more than manual first-break picking). To mitigate this
problem, first-break picking has been considered as a semantic segmentation
problem. Hu et al., (2019) employed a U-Net (Ronneberger et al.,, 2015)
architecture to train a network for first-break picking based on segmenting a
seismic section into FB and non-FB. They widened the FB segment to three
pixels around picked first-break to increase the accuracy. Their workflow does
not require picking non-FB for data preparation. Nevertheless, it suffers from
data imbalance which is a common challenge for image segmentation (Hossain et
al.,, 2021).

As is shown in Figure 1c, a seismic shot can be segmented into before and
after FB. In this way, FB can be automatically picked as the interface between
two segments. Considering first-break picking as a segmentation problem, Wu et
al., (2019) and Yuan et al., (2022) employed SegNet (Badrinarayanan et al.,,
2017) to automate the procedure. Wu et al., (2019) trained SegNet on traces of
microseismic sections to predict first-arrivals. They assessed the efficiency
of SegNet for detecting first arrivals in synthetic data with different
signal-to-noise ratio (SNR) and in field data. They showed the superiority of
CNN-based method over STA-LTA method. Yuan et al., (2022) employed a SegNet
architecture to autopick first breaks on real seismic shots with sparsely
distributed traces. They showed that the CNN-based methods can give good
performance for processing sparse data.

U-Net is another network architecture that has been used to pick first breaks
accurately by performing segmentation. Zhu and Beroza, (2019) introduced
PhaseNet by modifying U-Net to process 1D time-series data. For an accurate
first-arrival picking of $P$\- and $S$-waves, they picked the first arrivals
manually in 700,920 traces that were used in the training process. Focusing on
earthquake data, Zhu and Beroza, (2019) showed that the trained PhaseNet was
able to provide higher accuracy than STA-LTA. Ma et al., (2020) used a U-Net
to automate first-break picking in microseismic and borehole studies. They
investigated the effects of noise level in data and shape of the input data
(1D or 2D seismic data) on the performance of the network. Zwartjes and Yoo,
(2022) compared different architectures and training parameters aiming to find
crucial parameters in CNN-based automatic first-break picking. Comparing auto-
encoder, U-Net, and U-Net with residual blocks, Zwartjes and Yoo, (2022)
showed that standard U-Net is computationally less demanding, while U-Net with
residual blocks can lead to a better generalization and requires fewer epochs
to reach a specific loss.

Figure 1: An example of automatic first-break picking, its challenges, and
seismic shot segmentation. (a) An example of seismic shot with corresponding
FB picked by an expert (blue line). Red rectangle shows some dead traces whose
corresponding geophones have not been active during the acquisition. (b) A
shot gather with first breaks picked by an expert and STA-LTA method (orange
dot). (c) In data segmentation a shot gather is divided into two segments as
before FB (blue) and after FB (red) whose interface is the predicted FB as
shown with black line. Bad segmentation is shown with red rectangle.

The aforementioned methods have different degree of accuracy in the presence
of noise. Effects of noise on results are more pronounced when receivers
record strong noise before the arrival of direct and refracted waves (Figure
1c). Although DL has shown the highest accuracy to automate the first-break
picking compared to cross-correlation and energy-based methods, it can be seen
in the mentioned studies that the presence of noise before FBs remains
challenging. Performance of DL-based methods also relies drastically on the
size of the dataset. The motivation of this study is to increase the accuracy
of automatic first-break picking while decreasing the required size of the
training dataset. This makes the DL-based FB picking applicable to near
surface surveys, because training from scratch and building a training set
with usual strategy is presently too costly for near surface studies. To
achieve this goal, we employ transfer learning (Bozinovski and Fulgosi,, 1976;
Goodfellow et al.,, 2016). Transfer learning is a machine learning technique
where the knowledge that a model has obtained to perform a task can be used to
improve the learning of a new task (Goodfellow et al.,, 2016). This technique
has shown its potential to mitigate the challenges of using AI in geophysical
problems (Cunha et al.,, 2020; Park and Sacchi,, 2020; Simon et al.,, 2023).
In these studies, authors first train a network on a simple or synthetic
dataset and then fine tune the parameters of the network with more complex or
field data.

All previous studies on DL-based first-break picking use random
initialization. In this work, we investigate if the knowledge that a network
has obtained from solving a non-geoscientific problem can be helpful for
solving first-break picking. In this regard, we design an architecture based
on U-Net (Ronneberger et al.,, 2015) with residual blocks (He et al.,, 2016)
as its encoder, to perform automatic first-break picking. We show how the
weights used for solving the ImageNet challenge (Deng et al.,, 2009) can
increase the segmentation accuracy and lead to a more accurate first-break
picking and reduce the size of the required training set to attain a fixed
accuracy. The small size of the required dataset for training allows to fine
tune the network on a per survey basis which makes our method applicable to
most near surface surveys. We propose a workflow in which a small fraction
(around 10$\%$) of the survey is picked by a human expert, then the NN is fine
tuned and picks automatically the rest of the survey. In the current study, we
analyze this strategy by applying the methodology to two refraction surveys
used for near surface investigation.

This paper is organized as follows. First, we present the methodology
including data preparation, architecture of the neural network, and loss
function. Next, we study the efficiency of the proposed method to pick FBs
using two different datasets (with high and low SNR). We then show how
creating a general dataset can affect first-break picking by comparing the
accuracy after training using a project-based dataset and using a general
dataset. At the end, we assess the quality of the automatically picked first
breaks by comparing velocity models obtained with travel time tomography.

## Methodology

### Data preparation

The key to have an efficient network that can predict with high accuracy is
training with a suitable dataset. A network should be trained with a large
amount of data and the data should represent all possible scenarios that might
exist in the real-world problem that is tackled. Considering first-break
picking as a segmentation problem, we need pairs of seismic shots and their
corresponding FBs to train the network.

A common challenge in segmentation is class imbalance which reduces the
accuracy of the network to predict the right segment for the minority-segment
region (Hossain et al.,, 2021). Class imbalance means that the number of
pixels in one segment is significantly higher than the number of pixels in
other segments. Figure 2a shows a raw seismic shot (bottom) that is used in
this study for training the network. The relative abundance of data from each
segment is shown in the top part of Figure 2a. As can be seen, almost all data
samples belong to after FB ($99.0\%$). Due to the fact that we are only
interested in finding the first breaks, we can crop the seismic shot to
improve the balance between segments (Figure 2b). This reduces the relative
abundance of after FB to $84.7\%$.

In the next step, we scale each trace by its maximum amplitude (Figure 2c) to
facilitate learning in the training phase. In the final step and based on
number of traces in each seismic shot, shot gathers are divided into smaller
sections with a specific overlap (Figure 2d and 2e). This data augmentation
process can improve accuracy of the network by diversifying the training data.
At the end of processing, pairs of image-label are obtained from a raw seismic
shot (Figure 2a) as shown in Figure 2d and 2e.

Figure 2: The data preparation strategy employed for this study. (a) A raw
seismic shot is (b) cropped, (c) scaled, and (d-e) divided to subimages. The
relative abundance of data in each segment is shown as bar plots above each
seismic shot where blue indicates before FB and red demonstrates after FB.
Yellow line shows the manually picked FB.

### Network architecture

The neural network employed in this study is based on U-Net (Ronneberger et
al.,, 2015) as presented in Figure 3. U-Net is a fully convolutional encoder-
decoder network with long skip-connections between encoder and decoder that
helps to have a smoother loss leading to faster training (Li et al.,, 2018).
The input of the network is a one-channel patch of upsampled subimages
$x\in\mathbb{R}^{512\times 256\times 1}$. This input size allows to have the
desired depth for the employed network. Through the network, we successively
downsample the spatial resolution of the input to catch the higher-level
features. We use residual blocks (He et al.,, 2016) to build the encoder which
can helps to prevent the gradient vanishing problem (Zwartjes and Yoo,, 2022).

At the lowest level of the network, the output of the encoder is a patch of
$x\in\mathbb{R}^{32\times 16\times 256}$. The decoder acts as an expanding
path which builds a segmentation map from the encoded features. The decoder
includes four blocks. Each block duplicates the spatial resolution by
performing a bilinear upsampling. Using skip connection, the result of
upsampling is concatenated with the output of its symmetric block in the
encoder. At each step, the concatenated tensor is passed into two convolution
operators in addition to batch normalization and rectified linear unit (ReLU)
operators to halve the number of channels with respect to the output of the
previous decoding block. At the end, the output of the network is a
probability image $P(x)\in\left[0,1\right]^{512\times 256\times 2}$ which
contains two channels corresponding to the predicted probability of each class
(before FB and after FB).

Figure 3: U-Net architecture with ResNet encoder where the purple, blue, and
light red arrows in the encoder show the residual blocks.

### Loss function and optimization

In this study, the cross-entropy loss function is used to measure the accuracy
of the segmentation. This loss function for one sample (subimage) can be
written as

$E=-\sum_{i}y_{i}log\left(\hat{y}_{i}\right),$ (1)

where $y_{i}$ and $\hat{y}_{i}$ are the given and predicted segments for pixel
$i\in\Omega$ with $\Omega\subset\mathcal{Z}^{2}$.

The output of the network is the probability of each pixel to fall in one of
the two segments. The final segmentation can be obtained by passing this
probability tensor into an argmax operator which can be defined as equation 2
for function $f$,

$\text{argmax}f:=\\{x:f(s)\leq f(x)\text{ for all }s\in X\\}.$ (2)

Equation 2 allows to build the segmentation map,
$\Phi(x)\in\left[0,1\right]^{512\times 256\times 1}$, from the probability
image, $P(x)$.

The cross-entropy loss function (equation 1) can be used to perform an
accurate segmentation for first-break picking (Wu et al.,, 2019; Zhu and
Beroza,, 2019; Ma et al.,, 2020; Zwartjes and Yoo,, 2022). While equation 1 is
used to train and evaluate the network for segmentation, we also used the
root-mean-squared error (RMSE) to measure the error for predicting the FBs in
a dataset as,

$\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}\left(x_{i}-\hat{x}_{i}\right)^{2}}{N}},$
(3)

where $x_{i}$ and $\hat{x}_{i}$ are the given and predicted FB for trace $i$
in a shot with $N$ traces. It should be mentioned that equation 3 is not
involved in training and it is only employed to measure the accuracy of the
network for first-break picking.

Employing the Adam optimization method (Kingma and Ba,, 2014), the network
parameters are optimized to reduce the loss based on equation 1. In all case
studies, we use a learning rate of $1\times 10^{-4}$ and each batch of
training data includes 15 pairs of subimages and their corresponding labels.

### Parameter initialization

To train a network, loss is decreased through an iterative method. Thereby,
initial weights are required to start the training and the performance of most
algorithms are affected by the initial weights of the network (Goodfellow et
al.,, 2016). Even with a very large training dataset, the effects of an
efficient initialization are considerable (Glorot and Bengio,, 2010) and an
ineffective initialization can prevent the training to converge (Goodfellow et
al.,, 2016). Besides, preparing a large and comprehensive dataset for training
is expensive and time-consuming. Transfer learning is a technique that allows
us to mitigate these problems. Using this technique, we can transfer the
knowledge that a network has learned in one setting to improve generalization
in another setting (Goodfellow et al.,, 2016). Thereby, a network can be
initialized using pretrained weights of another network and fine-tuned to pick
FBs accurately.

In this study, we assess the effects of two different initializations
strategies to improve the accuracy of automatic first-break picking. In
addition to random initialization, we use the weights of a pretrained
resnet-34 to initialize the encoder presented in Figure 3. The pretrained
resnet-34 is trained to solve the ImageNet classification problem (Deng et
al.,, 2009). The ImageNet is a database of more than 14 million images and in
the next section, we assess the effects of initializing the encoder using the
parameters of the pretrained resnet-34. For this purpose, we have performed
three tests and have trained three networks for each test:

  * •

training using ImageNet weights with 20 epochs (ImageNet-20),

  * •

training using random initialization with 20 epochs (Random-20),

  * •

training using random initialization with 100 epochs (Random-100).

All networks are trained using Adam optimization with a learning rate of
$1\times 10^{-4}$.

## Results

In this study, we use datasets from three different land surveys to perform
three experiments (named Experiment 1 - 3). Dataset characteristics of each
experiment are presented in Table 1. There are 244 and 291 seismic shots in
Dataset 1 and Dataset 2, respectively. Seismic source in Dataset 1 is an
Isotta energizer while seismic data in Dataset 2 are obtained using a sledge
hammer.

In Experiment 1, we use 21 seismic shots from Dataset 1 (less that $9\%$ of
the shots) to train and evaluate the networks. Then, the trained networks are
used to perform the first-break picking on the remaining 223 seismic shots.
This experiment is conducted to study the efficiency of different
initialization strategies for analyzing clean seismic data (Dataset 1).

Name |  Total number of shots |  Number of shots for training and testing |  Number of traces per shot |  Number of subimages per shot |  Number of subimages for training
---|---|---|---|---|---
Dataset 1 | 244 | 21 | 101 to 201 | 4 to 8 | 133
Dataset 2 | 291 | 20 | 48 | 3 | 54
Dataset 3 (general) | 585 | 91 | 48 to 201 | 2 to 8 | 277
Table 1: The used datasets in this study and their characteristics.

The seismic data in Dataset 1 were acquired in the eastern bank of the Rhône
river in France (Figure 4a). The profile (yellow line) has a South-East to
North-West direction, on the top of Cuijanet syncline crossing the Marsanne
fault (black line). In the northwestern part, the Barremian limestone is
almost outcropping. In its southeastern part, the fluvial alluvium covers the
Albian and Cenomanian conglomerates, themselves covering Albian sandstone. For
this acquisition, the seismic sources were placed on the profile with a
spacing of 2 m.

Figure 4: Acquisition lines for the data studied in (a) Experiment 1 and (b)
Experiment 2. Yellow lines show the acquisition profiles and black line shows
the Marsanne fault in the study area. White circle denotes an exploration well
in the study area.

In Experiment 2, we analyze the network for performing first-break picking on
noisy data (Dataset 2). Only 20 seismic shots out of 291 ($\sim 7\%$) are
labeled manually and used to train the network in this experiment. Dataset 2
is highly noisy and first-break picking is very challenging even for a human
operator.

This dataset is from a near-surface seismic survey acquired with a 48-channel
system in Saint-Liboire in Quebec, Canada (Figure 4b). In this study, the
spacing between seismic sources is 2.5 m and the receivers are placed at every
5 m. In this project, seismic refraction tomography was used in combination
with electrical resistivity tomography for characterizing the aquifer with the
ultimate goal of finding an appropriate location for a water well. To increase
the SNR, seismic shots in Dataset 2 are a stack of 3 to 5 recordings at each
location. Nevertheless, these data have a poor SNR. Hence, this dataset is a
good candidate to analyze the efficiency of the proposed automatic FB picking
method with noisy data.

After studying the efficiency of different initialization strategies for
picking FBs on clean and noisy seismic data, we focus on creating a general
dataset (Dataset 3) through Experiment 3. This can help us to understand the
efficiency of the DL-based first-break picking if we augment the training
dataset by combining the data obtained under different circumstances (i.e.
various equipment, acquisition parameters, and geology of the study areas).
Dataset 3 is built by combining Dataset 1, Dataset 2, and 50 seismic shots
(with picked FB) from a third project where sledge hammer is used as seismic
source. The data in these three datasets are acquired by different companies
and different equipment at different locations. It is better to have a single
specialist to prepare the training dataset to reduce the subjectivity of the
results. However, the first breaks in Dataset 3 were picked by three different
experts in the present case. Although this is not ideal, it affects all three
networks equally and it should not impact the results of this study, because
we compare all the employed networks under the same circumstances and data
preparation is the same for all these networks.

As presented in Figure 2 and Table 1, the seismic shots are converted to the
different numbers of subimages (based on the number of available traces) for
each project. For each experiment, we use $90\%$ of the labeled data to train
the network and the other $10\%$ as a testing set.

### Data with high signal-to-noise ratio (Experiment 1)

For Experiment 1, a dataset with good SNR (Dataset 1) has been used. The
training dataset for this project includes 21 seismic shots with variable
number of traces per shot. These seismic shots are divided into subimages of
30 traces with $15\%$ overlap. Figure 5a-5c shows three examples of seismic
shots that have been used to train the network. The manually picked FBs (solid
blue line) are presented in addition to the FBs picked using DL and STA-LTA.
As is shown, with the same number of epochs, the network that is initialized
using ImageNet (dashed red line) is more accurate than the network that is
initialized using random weights (dotted purple line). The final training
losses for Random-100, Random-20, and ImageNet-20 are presented in Table 2.
Using 100 epochs, the network with random initialization leads to the same
accuracy as ImageNet-20 for segmentation of the training dataset. However,
more epochs causes overfitting as the test loss of Random-100 is higher than
that of ImageNet-20 (Table 2). In Figure 5, we can also see the effects of
noise on the STA-LTA results (orange dots). In traces with larger offset, the
efficiency of STA-LTA decreases, so does the efficiency of Random-20 and
Random-100.

Figure 5: Results of FB picking on (a-c) labeled and (d-f) unlabeled datasets for the data with high SNR. The solid blue line shows the manually picked FB while the results of STA-LTA are shown with orange dots. The dash-dotted green line shows the results using random initialization with 100 epochs. The dotted purple and the dashed red lines show the picked FB using the same number of epochs with different initialization (random and ImageNet respectively).  | Experiment 1 (High SNR) | Experiment 2 (Low SNR) | Experiment 3 (General data)
---|---|---|---
| Training | Test | FB RMSE | Training | Test | FB RMSE | Training | Test | FB RMSE
ImageNet-20 | 0.026 | 0.039 | 0.425 | 0.073 | 0.126 | 0.941 | 0.018 | 0.030 | 0.394
Random-20 | 0.029 | 0.072 | 0.614 | 0.070 | 0.156 | 0.990 | 0.020 | 0.051 | 1.0
Random-100 | 0.026 | 0.067 | 1.0 | 0.068 | 0.159 | 1.0 | 0.017 | 0.052 | 0.969
Table 2: The segmentation loss and normalized first-break picking loss for
different tests and networks.

In the next step, the trained networks are employed to detect the FBs in the
unlabeled data. The results are shown in Figure 5d-5f. As for the test set,
these shot gathers have not been seen before by the networks. However, in this
case, no first breaks were manually picked. Initializing the network using
ImageNet leads to the most accurate result, while increasing number of epochs
for training randomly initialized networks does not improve the results
significantly (Table 2).

To train the networks, the cross-entropy loss function (equation 1) is used to
evaluate the accuracy of the results, numerically. Although the loss value
shows the accuracy of each network for segmentation, this parameter is not
really representative for first-break picking quality. This is due to the fact
that bad segmentation of even one pixel before the FB, can lead to erroneous
picked FB (Figure 1c). On the other hand, bad-segmentation after the FB, does
not affect the accuracy of picked FB while it still affects the value of the
segmentation loss. The final accuracy of segmentation and the normalized
first-break picking errors (equation 3) for Random-100, Random-20, and
ImageNet-20 are presented in Table 2. This experiment shows that initializing
a network with weights of a pretrained network leads to more accurate first-
break picking for seismic data with high SNR.

### Data with low signal-to-noise ratio (Experiment 2)

For the second experiment, we focus on picking FBs in seismic data with low
SNR (Dataset 2). We have picked the FBs on 20 seismic shots (out of 291) used
18 as training data and 2 as test data. We divided each shot into three
subimages. Each subimage is created using 22 traces from the corresponding
seismic shot with $15\%$ overlap with other subimages.

Figure 6a-6c shows the results of automatic first-break picking for three
examples from the training dataset. While the performance of all three
networks is comparable in most seismic shots, random initialization of the
network with 100 epochs leads to higher training accuracy. Results of STA-LTA
for this dataset show that this method is completely unreliable to pick the
FBs on such a noisy dataset.

Figure 6: Results of FB picking on (a-c) labeled and (d-f) unlabeled datasets
for the data with low SNR. The solid blue line shows the manually picked FB
while the results of STA-LTA are shown with orange dots. The dash-dotted green
line shows the results using random initialization with 100 epochs. The dotted
purple and the dashed red lines show the picked FB using the same number of
epochs with different initialization (random and ImageNet respectively).

The training and test losses for Experiment 2 are presented in Table 2. The
final training losses for Random-100, Random-20, and ImageNet-20 are
respectively $0.068$, $0.070$, and $0.073$. While Random-100 provides the
highest training accuracy, the goal is to train a network that can have the
highest test accuracy. This is the case for ImageNet-20 where the network is
pre-trained. Based on the test loss, ImageNet-20 is more powerful for
performing the segmentation of test dataset, with a loss of $0.126$ in
comparison to Random-20 and Random-100 with losses of $0.156$ and $0.159$. The
first-break picking errors for the networks also demonstrates higher accuracy
of ImageNet-20.

Applying the trained networks on the unlabeled data, we can again see the
advantage of using ImageNet-20 over Random-20 and Random-100. Using pretrained
weights, the network is better at picking FBs. Nevertheless, these data are
highly noisy and even the ImageNet-20 has problem in some seismic shots (e.g.
Figure 6a in training set and Figure 6e from unlabeled dataset). It should be
pointed out that picking the right FBs in these seismic shots can be also a
very challenging task for an expert. For data with this level of noise,
supervision of an expert is unavoidable. However, for the sake of making a
comparison between the studied networks, we can see that ImageNet-20 is more
accurate for first-break picking and segmenting the test dataset.

### General dataset (Experiment 3)

For the third experiment, we mixed the labeled datasets of the first two case
studies with another 50 seismic shots that have acquisition geometry similar
to the seismic shots in Dataset 2. As shown in Table 1, Dataset 3 includes 244
seismic shots from Dataset 1 and 291 seismic shots from Dataset 2. The 50
seismic shots from the other study have 48 traces, and 30 traces are used to
create the corresponding subimages. This allows us to add 100 examples to the
training and test datasets. In total, the labeled dataset for this experiment
includes 308 shots from which $90\%$ are used as training set (277 subimages)
and $10\%$ as test set (31 subimages).

The segmentation and FB picking losses for the three networks are presented in
Table 2. The test loss shows the better generalizability of ImageNet-20 with
loss of $0.030$ in comparison with $0.051$ and $0.052$ for Random-20 and
Random-100. The first-break picking error of this experiment also shows the
superiority of the ImageNet-20.

The results of applying the trained networks on four examples are shown in
Figure 7. Figure 7a and 7b belong to the training dataset while Figure 7c and
7d are part of the unlabeled dataset. Comparing Figure 7a with Figure 5c shows
that the strategy of adding training data from different projects cannot
guarantee better accuracy. This is shown in Table 3 where project-based
training dataset led to higher accuracy for Dataset 1 in comparison to using
the general dataset. On the contrary, Table 3 shows that the general dataset
increased the accuracy for first-break picking of shots in Dataset 2
(comparing Figure 7b with Figure 6a). This can be due to the fact that in
addition to 20 seismic shots that are common between Dataset 2 and Dataset 3,
another 50 shots have similar seismic source and acquisition geometry to
Dataset 2 (Table 1). It means 78$\%$ of the shots in Dataset 3 have similar
acquisition geometry and seismic source. Thereby, training a network using
this general dataset leads to higher accuracy for seismic shots in Dataset 2
while less accuracy for seismic shots in Dataset 1.

| Shots from Dataset 1 | Shots from Dataset 2
---|---|---
| Project-based Dataset | General Dataset | Project-based Dataset | General Dataset
ImageNet-20 | 0.0011 | 0.0014 | 0.0014 | 0.0002
Random-20 | 0.0016 | 0.0030 | 0.0015 | 0.0013
Random-100 | 0.0026 | 0.0029 | 0.0015 | 0.0013
Table 3: The first-break prediction loss based on equation 3 for comparing the
accuracy of results in case of using project-based or general datasets.

Figures 7c and 7d show two seismic shots from Dataset 1 and Dataset 2,
respectively. The FBs are picked using the networks that are trained with
general dataset. The results can be compared with Figures 5d and 6e,
respectively where project-based datasets are employed to train the networks.

Figure 7: Results for training the network on data from different projects.
The solid blue line shows the manually picked FB while the dotted purple and
the dashed red lines show the picked FB using the same network with different
initialization (random and ImageNet respectively). The dash-dotted green line
shows the result of using random initialization with 100 epochs. These
examples are (a and b) training (c and d) unlabeled samples of (a and c)
Dataset 1 and (b and d) Dataset 2.

## Refraction traveltime tomography

In near surface studies, FBs are usually used to estimate a $P$-wave velocity
($V_{P}$) model of the subsurface. In this section, we inverted the picked FBs
of Experiment 1 and Experiment 2 to compare the estimated velocities. This
inversion is done using PyGIMLi (Rücker et al.,, 2017) and based on the
shortest path algorithm (Moser,, 1991). It allows us to analyze the accuracy
of picked FBs in terms of the final outcome of a near surface seismic survey:
a $P$-wave velocity model. Our assessment criterion is that the inversion
result of the output of an efficient automatic FB picking method should have
the minimum discrepancy with the inversion result obtained from manually
picked FBs.

Estimates are shown in Figure 8. The first column shows the results for
Experiment 1 and the second column presents the results obtained from
Experiment 2. Figure 8a - 8h denotes the estimated velocity from the picks in
the labeled dataset (training and test set). $V_{P}$ estimated from manually
picked FBs are shown in Figure 8a and 8b. As indicated by normalized root-
mean-squared error (NRMSE) of velocity, the estimates obtained from FBs picked
by ImageNet-20 (Figure 8c - 8d) have smaller discrepancy with the ones
obtained from manually picked FBs in comparison to estimates from Random-20
(Figure 8e - 8f) and Random-100 (Figure 8g - 8h). Employing Random-20 and
Random-100 to detect FBs in Experiment 1 leads to some non-geological
structures in the velocity model that are due to inaccurate picks. These
anomalies are shown with red arrows.

Based on our workflow, the trained network is used to obtain FBs in all
seismic shots. We then verify the obtained FBs and modify them in case of
inaccuracy. Using more information and better ray path coverage decrease the
uncertainty in traveltime inversion and leads to higher accuracy. Hence, as
ImageNet-20 has provided the most accurate FBs (Table 2), we have used this
network to predict the FBs in all seismic shots in Dataset 1 and Dataset 2.
These FBs are then verified and inverted to obtain velocity models. The
velocity models are shown in Figure 8i and 8j. These models show meaningful
images of the subsurface. While Figure 8i does not have anomalies such as the
ones denoted with red arrows in other estimates of Experiment 1, Figure 8j
shows using more FBs can improve the accuracy of estimate. Picking more FB in
Experiment 2 led to a better model of the deeper part of our study area. This
can also be seen with comparing the 1D estimations close to the well location
(red triangle) which is shown in the right side of the second column (Figure
8). The lithology of the well is color coded based of the drilling report and
it can be seen that utilizing more FBs allows better constraining the
inversion and leads to higher accuracy in the deeper parts.

Figure 8: Results of traveltime inversion for Experiment 1 (left column) and
Experiment 2 (right column). Results of inversion for first breaks that are
involved in training procedure picked by (a and b) an expert, (c and d)
ImageNet-20, (e and f) Random-20, and (g and h) Random-100. (i and j)
Estimated $V_{P}$ using FBs of all seismic shots picked by ImageNet-20. Red
arrows show the main differences between the estimates from automatically
picked FBs and manually-picked ones. Red triangle shows the well location
(white circle in Figure 4b). The 1D plots show the estimated velocity at the
well location.

## Discussion

The effects of weight initialization for DL-based automatic first-break
picking is analyzed in this study. Considering a common network and
hyperparameters in three different scenarios, it is shown that using
pretrained weights can improve the accuracy of picked FBs. To make a fair
comparison, the network with random initialization is trained also with more
epochs. Considering first-break picking as a segmentation problem, the network
with pretrained weights leads to more accurate results for test set while it
can lead to a lower training accuracy compared to the randomly initialized
networks. This shows that this network is better generalizable. Table 2
presents the segmentation error for the three tests conducted in this study.
For these tests, Random-100 provides the lowest training loss while
ImageNet-20 provides the highest accuracy for test set. This shows that using
random initialization and higher number of epochs can cause overfitting,
especially with small training sets as employed in this study. Although the
ImageNet challenge is an object recognition problem, the weights of the
trained network can be employed in a segmentation problem and lead to a lower
test loss than using random values for initializing a network.

As the goal of this study is first-break picking, Table 2 shows the loss
between manually picked FBs and predicted FBs using the different networks. As
is shown, the ImageNet-20 provides the highest accuracy and the networks with
random initialization provide the lowest accuracy.

This study shows that a general training dataset can decrease the accuracy of
the network in comparison with project-based training dataset if the dataset
is not large enough to represent appropriately different situations such as
various types of sources, noise level, geology of the study area (depth of the
bedrock), and other acquisition parameters. A better understanding of this
fact can allow to create multiple general training datasets for a specific
situation (e.g. a general dataset for acquisitions with a sledgehammer as the
source or acquisitions with similar geometry).

Besides, it is important to conduct more studies to analyze the effects of
data preparation parameters such as the width of subimages and overlap between
subimages on the efficiency of DL-based automatic first-break picking.
Although this will not affect the fact that initializing the network with
pretrained weights leads to more accurate results, it will help one to develop
a more effective workflow for automatic first-break picking.

The method we propose is readily applicable on field data. A workflow based on
NN fine tuning comprises the following steps:

  1. 1.

pick the first breaks of a small fraction of shots (between 5 to 10$\%$),

  2. 2.

prepare the training and testing set as described in the section "data
preparation",

  3. 3.

fine-tune the network on the training set, and monitor overfitting with the
test set,

  4. 4.

apply the fine-tuned network on the remaining shots.

This workflow can lead to a significant reduction in the time required for
first-break picking. Indeed, the data preparation can be fully automated and
is a rather straightforward task. The network training is also fast and cheap,
as the training set can be small using fine-tuning. For instance, training in
this study was conducted on a laptop with an Apple M1 Max chip and it took
86.76 seconds and 26.74 seconds for Experiment 1 and Experiment 2,
respectively.

## Conclusions

An efficient automatic first-break (FB) picking based on artificial
intelligence relies significantly on the size of training dataset whose
preparation and processing are costly and time consuming. This study focuses
on reducing the size of required dataset for training a network while not
compromising the accuracy. In this regard, we have proposed performing
automatic first-break picking by fine-tuning a neural-network pretrainined on
ImageNet. A U-Net architecture with residual encoder has been used for this
study. Using random initialization, we have shown that the network has lower
accuracy than in the case of using pretrained weights (ImageNet challenge in
this study). This strategy allows to use only 5 to 10$\%$ of seismic data from
one project to train a network and perform automatic picking with high
accuracy on the remaining shots. We have shown that using first breaks
obtained by random initialization can lead to non-geological features in the
velocity model estimated using the FBs. Comparing with short-term-average over
long-term-average method, we have also shown that the proposed deep learning
based autopicking provides more accurate results especially in the presence of
noise and in seismic shots with low signal-to-noise ratio. Finally, we have
demonstrated that using a general training dataset for various projects might
cause a decrease in the accuracy of picked FBs if the dataset is biased toward
a specific acquisition geometry.

## Acknowledgement

This work was supported by Mitacs through the Mitacs Elevate Program.

## Data and code availability statement

Python package for performing first-break picking based on the presented
workflow is available at https://github.com/geo-stack/first_break_picking.
Data associated with this research are confidential and cannot be released.

## Conflict of interest

All authors declare that they have no known financial interests or personal
relationships that could have influenced the work presented in this paper.

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